Publications

Mathieson, L & Moscato, P 2019, 'An Introduction to Proximity Graphs' in Moscato, P & de, VRIESNJ (eds), Business and Consumer Analytics: New Ideas, Springer International Publishing, Germany, pp. 213-233.
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Proximity graphs are one of the combinatorial data-miner’s frontline tools. They allow expression of complex proximity relationships and are the basis of many other algorithms. Here we introduce the concept of proximity graphs, present basic definitions and discuss some of the most common types of proximity graphs.

Mathieson, L & Moscato, P 2019, 'An Introduction to Proximity Graphs' in Business and Consumer Analytics: New Ideas, pp. 213-233.
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Proximity graphs are one of the combinatorial data-miner’s frontline tools. They allow expression of complex proximity relationships and are the basis of many other algorithms. Here we introduce the concept of proximity graphs, present basic definitions and discuss some of the most common types of proximity graphs.

Mathieson, L, de Vries, NJ & Moscato, P 2019, 'Using Network Alignment to Identify Conserved Consumer Behaviour Modelling Constructs' in Moscato, P & de, VRIESNJ (eds), Business and Consumer Analytics: New Ideas, Springer International Publishing, pp. 513-541.
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Extracting topological information from networks is a central problem in many fields including business analytics. With the increase in large-scale datasets, effectively comparing similarities and differences between networks is impossible without automation. In some cases, computational search of simple subgraphs is used to understand the structure of a network. These approaches, however, miss the “global picture” of network similarity. Here we examine the Network Alignment problem, in which we look for a mapping between vertex sets of two networks preserving topological information. Elsewhere, we showed that data analytics problems are often of varied computational complexity. We prove that this problem is W[1]-complete for several parameterizations. Since we expect large instances in the data analytics field, our result indicates that this problem is a prime candidate for metaheuristic approaches as it will be hard in practice to solve exact methods. We develop a memetic algorithm and demonstrate the effectiveness of the Network Alignment problem as a tool for discovering structural information through an application in the area of consumer behaviour modelling. We believe this to be the first demonstration of such an approach in the social sciences and in particular a consumer analytics application.

Moscato, P & Mathieson, L 2019, 'Memetic Algorithms for Business Analytics and Data Science: A Brief Survey' in Moscato, P & de, VRIESNJ (eds), Business and Consumer Analytics: New Ideas, Springer International Publishing, Switzerland, pp. 545-608.
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This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018.

Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 2019, 'Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis', IEEE Transactions on Information Forensics and Security, 2020, vol. 15, pp. 3567-3579.
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In this paper, we work on intra-variable handwriting, where the writingsamples of an individual can vary significantly. Such within-writer variationthrows a challenge for automatic writer inspection, where the state-of-the-artmethods do not perform well. To deal with intra-variability, we analyze theidiosyncrasy in individual handwriting. We identify/verify the writer fromhighly idiosyncratic text-patches. Such patches are detected using a deeprecurrent reinforcement learning-based architecture. An idiosyncratic score isassigned to every patch, which is predicted by employing deep regressionanalysis. For writer identification, we propose a deep neural architecture,which makes the final decision by the idiosyncratic score-induced weightedaverage of patch-based decisions. For writer verification, we propose twoalgorithms for patch-fed deep feature aggregation, which assist inauthentication using a triplet network. The experiments were performed on twodatabases, where we obtained encouraging results.

Alaei, F, Alaei, A, Pal, U & Blumenstein, M 2019, 'A comparative study of different texture features for document image retrieval', Expert Systems with Applications, vol. 121, pp. 97-114.
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© 2018 Elsevier Ltd Due to the rapid increase of different digitised documents, there has been significant attention dedicated to document image retrieval over the past two decades. Finding discriminative and effective features is a fundamental task for providing a fast and more accurate retrieval system. Texture features are generally fast to compute and are suitable for large volume data. Thus, in this study, the effectiveness of texture features widely used in the literature of content-based image retrieval is investigated on document images. Twenty-six different texture feature extraction methods from four main categories of texture features, statistical, transform, model, and structural-based approaches, are considered in this research work to compare their performance on the problem of document image retrieval. Three document image datasets, MTDB, ITESOFT, and CLEF_IP with various content and page layouts are used to evaluate the twenty-six texture-based features on document image retrieval systems. The retrieval results are computed in terms of precision, recall and F-score, and a comparative analysis of the results is also provided. Feature dimensions and time complexity of the texture-based feature methods are further compared. Finally, some conclusions are drawn and suggestions are made about future research directions.

Anshu, A, Berta, M, Jain, R & Tomamichel, M 2019, 'A minimax approach to one-shot entropy inequalities', Journal of Mathematical Physics, vol. 60, no. 12, pp. 122201-122201.
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One-shot information theory entertains a plethora of entropic quantities,such as the smooth max-divergence, hypothesis testing divergence andinformation spectrum divergence, that characterize various operational tasksand are used to prove the asymptotic behavior of various tasks in quantuminformation theory. Tight inequalities between these quantities are thus ofimmediate interest. In this note we use a minimax approach (appearingpreviously for example in the proofs of the quantum substate theorem), tosimplify the quantum problem to a commutative one, which allows us to derivesuch inequalities. Our derivations are conceptually different from previousarguments and in some cases lead to tighter relations. We hope that theapproach discussed here can lead to progress in open problems in quantumShannon theory, and exemplify this by applying it to a simple case of the jointsmoothing problem.

Buscemi, F, Sutter, D & Tomamichel, M 2019, 'An information-theoretic treatment of quantum dichotomies', Quantum, vol. 3, p. 209.
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Given two pairs of quantum states, we want to decide if there exists aquantum channel that transforms one pair into the other. The theory of quantumstatistical comparison and quantum relative majorization provides necessary andsufficient conditions for such a transformation to exist, but such conditionsare typically difficult to check in practice. Here, by building upon work byMatsumoto, we relax the problem by allowing for small errors in one of thetransformations. In this way, a simple sufficient condition can be formulatedin terms of one-shot relative entropies of the two pairs. In the asymptoticsetting where we consider sequences of state pairs, under some mild convergenceconditions, this implies that the quantum relative entropy is the only relevantquantity deciding when a pairwise state transformation is possible. Moreprecisely, if the relative entropy of the initial state pair is strictly largercompared to the relative entropy of the target state pair, then atransformation with exponentially vanishing error is possible. On the otherhand, if the relative entropy of the target state is strictly larger, then anysuch transformation will have an error converging exponentially to one. As animmediate consequence, we show that the rate at which pairs of states can betransformed into each other is given by the ratio of their relative entropies.We discuss applications to the resource theories of athermality and coherence.

Cheng, H-C & Hsieh, M-H 2019, 'Matrix Poincaré, Φ-Sobolev inequalities, and quantum ensembles', Journal of Mathematical Physics, vol. 60, no. 3, pp. 032201-032201.
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Sobolev-type inequalities have been extensively studied in the frameworks of real-valued functions and non-commutative Lp spaces, and have proven useful in bounding the time evolution of classical/quantum Markov processes, among many other applications. In this paper, we consider yet another fundamental setting—matrix-valued functions—and prove new Sobolev-type inequalities for them. Our technical contributions are two-fold: (i) we establish a series of matrix Poincaré inequalities for separably convex functions and general functions with Gaussian unitary ensembles inputs; and (ii) we derive Φ-Sobolev inequalities for matrix-valued functions defined on Boolean hypercubes and for those with Gaussian distributions. Our results recover the corresponding classical inequalities (i.e., real-valued functions) when the matrix has one dimension. Finally, as an application of our technical outcomes, we derive the upper bounds for a fundamental entropic quantity—the Holevo quantity—in quantum information science since classical-quantum channels are a special instance of matrix-valued functions. This is obtained through the equivalence between the constants in the strong data processing inequality and the Φ-Sobolev inequality.

Halasi, Z, Maróti, A, Pyber, L & Qiao, Y 2019, 'An improved diameter bound for finite simple groups of Lie type', Bulletin of the London Mathematical Society, vol. 51, no. 4, pp. 645-657.
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© 2019 London Mathematical Society For a finite group (Formula presented.), let (Formula presented.) denote the maximum diameter of a connected Cayley graph of (Formula presented.). A well-known conjecture of Babai states that (Formula presented.) is bounded by (Formula presented.) in case (Formula presented.) is a non-abelian finite simple group. Let (Formula presented.) be a finite simple group of Lie type of Lie rank (Formula presented.) over the field (Formula presented.). Babai's conjecture has been verified in case (Formula presented.) is bounded, but it is wide open in case (Formula presented.) is unbounded. Recently, Biswas and Yang proved that (Formula presented.) is bounded by (Formula presented.). We show that in fact (Formula presented.) holds. Note that our bound is significantly smaller than the order of (Formula presented.) for (Formula presented.) large, even if (Formula presented.) is large. As an application, we show that more generally (Formula presented.) holds for any subgroup (Formula presented.) of (Formula presented.), where (Formula presented.) is a vector space of dimension (Formula presented.) defined over the field (Formula presented.).

Hou, Z, Tang, J-F, Ferrie, C, Xiang, G-Y, Li, C-F & Guo, G-C 2019, 'Experimental realization of self-guided quantum process tomography', Phys. Rev. A, vol. 101, no. 2, p. 022317.
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Characterization of quantum processes is a preliminary step necessary in thedevelopment of quantum technology. The conventional method uses standardquantum process tomography, which requires $d^2$ input states and $d^4$ quantummeasurements for a $d$-dimensional Hilbert space. These experimentalrequirements are compounded by the complexity of processing the collected data,which can take several orders of magnitude longer than the experiment itself.In this paper we propose an alternative self-guided algorithm for quantumprocess tomography, tuned for the task of finding an unknown unitary process.Our algorithm is a fully automated and adaptive process characterizationtechnique. The advantages of our algorithm are: inherent robustness to bothstatistical and technical noise; requires less space and time since there is nopost-processing of the data; requires only a single input state andmeasurement; and, provides on-the-fly diagnostic information while theexperiment is running. Numerical results show our algorithm achieves the same$1/n$ scaling as standard quantum process tomography when $n$ uses of theunknown process are used. We also present experimental results wherein thealgorithm, and its advantages, are realized for the task of finding an elementof $SU(2)$.

Ivanyos, G & Qiao, Y 2019, 'Algorithms Based on *-Algebras, and Their Applications to Isomorphism of Polynomials with One Secret, Group Isomorphism, and Polynomial Identity Testing', SIAM Journal on Computing, vol. 48, no. 3, pp. 926-963.
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© 2019 Society for Industrial and Applied Mathematics. We consider two basic algorithmic problems concerning tuples of (skew-)symmetric matrices. The first problem asks us to decide, given two tuples of (skew-)symmetric matrices (B1,..., Bm) and (C1,..., Cm), whether there exists an invertible matrix A such that for every i ∈ {1,..., m}, AtBiA = Ci. We show that this problem can be solved in randomized polynomial time over finite fields of odd size, the reals, and the complex numbers. The second problem asks us to decide, given a tuple of square matrices (B1,..., Bm), whether there exist invertible matrices A and D, such that for every i ∈ {1,..., m}, ABiD is (skew-)symmetric. We show that this problem can be solved in deterministic polynomial time over fields of characteristic not 2. For both problems we exploit the structure of the underlying ∗-algebras (algebras with an involutive antiautomorphism) and utilize results and methods from the module isomorphism problem. Applications of our results range from multivariate cryptography to group isomorphism and to polynomial identity testing. Specifically, these results imply efficient algorithms for the following problems. (1) Test isomorphism of quadratic forms with one secret over a finite field of odd size. This problem belongs to a family of problems that serves as the security basis of certain authentication schemes proposed by Patarin [J. Patarin, in Advances in Cryptology, EUROCRYPT'96, Springer, Berlin, 1996, pp. 33-48]. (2) Test isomorphism of p-groups of class 2 and exponent p (p odd) with order p in time polynomial in the group order, when the commutator subgroup is of order pO(). (3) Deterministically reveal two families of singularity witnesses caused by the skew-symmetric structure. This represents a natural next step for the polynomial identity testing problem, in the direction set up by the recent resolution of the noncommutative rank problem [A. Garg et al., in Proceedings of the 57th Annu...

Ji, Z 2019, 'Classical verification of quantum proofs', Theory of Computing, vol. 15, no. 1, pp. 1-42.
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© 2019 Zhengfeng Ji. We present a classical interactive protocol that checks the validity of a quantum witness state for the local Hamiltonian problem. It follows from this protocol that approximating the nonlocal value of a multi-player one-round game to inverse polynomial precision is QMA-hard. Our result makes a connection between the theory of QMA-completeness and Hamiltonian complexity on one hand and the study of nonlocal games and Bell inequalities on the other.

Ji, Z 2019, 'The complexity-theoretic Bell inequality', National Science Review, vol. 6, no. 1, pp. 21-21.
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Kaljahi, MA, Palaiahnakote, S, Anisi, MH, Idris, MYI, Blumenstein, M & Khan, MK 2019, 'A scene image classification technique for a ubiquitous visual surveillance system', Multimedia Tools and Applications, vol. 78, no. 5, pp. 5791-5818.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature The concept of smart cities has quickly evolved to improve the quality of life and provide public safety. Smart cities mitigate harmful environmental impacts and offences and bring energy-efficiency, cost saving and mechanisms for better use of resources based on ubiquitous monitoring systems. However, existing visual ubiquitous monitoring systems have only been developed for a specific purpose. As a result, they cannot be used for different scenarios. To overcome this challenge, this paper presents a new ubiquitous visual surveillance mechanism based on classification of scene images. The proposed mechanism supports different applications including Soil, Flood, Air, Plant growth and Garbage monitoring. To classify the scene images of the monitoring systems, we introduce a new technique, which combines edge strength and sharpness to detect focused edge components for Canny and Sobel edges of the input images. For each focused edge component, a patch that merges nearest neighbor components in Canny and Sobel edge images is defined. For each patch, the contribution of the pixels in a cluster given by k-means clustering on edge strength and sharpness is estimated in terms of the percentage of pixels. The same percentage values are considered as a feature vector for classification with the help of a Support Vector Machine (SVM) classifier. Experimental results show that the proposed technique outperforms the state-of-the-art scene categorization methods. Our experimental results demonstrate that the SVM classifier performs better than rule and template-based methods.

Kaljahi, MA, Shivakumara, P, Hu, T, Jalab, HA, Ibrahim, RW, Blumenstein, M, Lu, T & Ayub, MNB 2019, 'A geometric and fractional entropy-based method for family photo classification', Expert Systems with Applications: X, vol. 3, pp. 100008-100008.
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© 2019 Due to the power and impact of social media, unsolved practical issues such as human trafficking, kinship recognition, and clustering family photos from large collections have recently received special attention from researchers. In this paper, we present a new idea for family and non-family photo classification. Unlike existing methods that explore face recognition and biometric features, the proposed method explores the strengths of facial geometric features and texture given by a new fractional-entropy approach for classification. The geometric features include spatial and angle information of facial key points, which give spatial and directional coherence. The texture features extract regular patterns in images. The proposed method then combines the above properties in a new way for classifying family and non-family photos with the help of Convolutional Neural Networks (CNNs). Experimental results on our own as well as benchmark datasets show that the proposed approach outperforms the state-of-the-art methods in terms of classification rate.

Kaljahi, MA, Shivakumara, P, Idris, MYI, Anisi, MH & Blumenstein, M 2019, 'A new image size reduction model for an efficient visual sensor network', Journal of Visual Communication and Image Representation, vol. 63, pp. 102573-102573.
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© 2019 Elsevier Inc. Image size reduction for energy-efficient transmission without losing quality is critical in Visual Sensor Networks (VSNs). The proposed method finds overlapping regions using camera locations, which eliminate unfocussed regions from the input images. The sharpness for the overlapped regions is estimated to find the Dominant Overlapping Region (DOR). The proposed model partitions further the DOR into sub-DORs according to capacity of the cameras. To reduce noise effects from the sub-DOR, we propose to perform a Median operation, which results in a Compressed Significant Region (CSR). For non-DOR, we obtain Sobel edges, which reduces the size of the images down to ambinary form. The CSR and Sobel edges of the non-DORs are sent by a VSN. Experimental results and a comparative study with the state-of-the-art methods shows that the proposed model outperforms the existing methods in terms of quality, energy consumption and network lifetime.

Kaljahi, MA, Shivakumara, P, Idris, MYI, Anisi, MH, Lu, T, Blumenstein, M & Noor, NM 2019, 'An automatic zone detection system for safe landing of UAVs', Expert Systems with Applications, vol. 122, pp. 319-333.
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© 2019 Elsevier Ltd As the demand increases for the use Unmanned Aerial Vehicles (UAVs) to monitor natural disasters, protecting territories, spraying, vigilance in urban areas, etc., detecting safe landing zones becomes a new area that has gained interest. This paper presents an intelligent system for detecting regions to navigate a UAV when it requires an emergency landing due to technical causes. The proposed system explores the fact that safe regions in images have flat surfaces, which are extracted using the Gabor Transform. This results in images of different orientations. The proposed system then performs histogram operations on different Gabor-oriented images to select pixels that contribute to the highest peak, as Candidate Pixels (CP), for the respective Gabor-oriented images. Next, to group candidate pixels as one region, we explore Markov Chain Codes (MCCs), which estimate the probability of pixels being classified as candidates with neighboring pixels. This process results in Candidate Regions (CRs) detection. For each image of the respective Gabor orientation, including CRs, the proposed system finds a candidate region that has the highest area and considers it as a reference. We then estimate the degree of similarity between the reference CR with corresponding CRs in the respective Gabor-oriented images using a Chi square distance measure. Furthermore, the proposed system chooses the CR which gives the highest similarity to the reference CR to fuse with that reference, which results in the establishment of safe landing zones for the UAV. Experimental results on images from different situations for safe landing detection show that the proposed system outperforms the existing systems. Furthermore, experimental results on relative success rates for different emergency conditions of UAVs show that the proposed intelligent system is effective and useful compared to the existing UAV safe landing systems.

Khare, V, Shivakumara, P, Chan, CS, Lu, T, Meng, LK, Woon, HH & Blumenstein, M 2019, 'A novel character segmentation-reconstruction approach for license plate recognition', Expert Systems with Applications, vol. 131, pp. 219-239.
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© 2019 Elsevier Ltd Developing an automatic license plate recognition system that can cope with multiple factors is challenging and interesting in the current scenario. In this paper, we introduce a new concept called partial character reconstruction to segment characters of license plates to enhance the performance of license plate recognition systems. Partial character reconstruction is proposed based on the characteristics of stroke width in the Laplacian and gradient domain in a novel way. This results in character components with incomplete shapes. The angular information of character components determined by PCA and the major axis are then studied by considering regular spacing between characters and aspect ratios of character components in a new way for segmenting characters. Next, the same stroke width properties are used for reconstructing the complete shape of each character in the gray domain rather than in the gradient domain, which helps in improving the recognition rate. Experimental results on benchmark license plate databases, namely, MIMOS, Medialab, UCSD data, Uninsbria data Challenged data, as well as video databases, namely, ICDAR 2015, YVT video, and natural scene data, namely, ICDAR 2013, ICDAR 2015, SVT, MSRA, show that the proposed technique is effective and useful.

Kieferová, M, Scherer, A & Berry, DW 2019, 'Simulating the dynamics of time-dependent Hamiltonians with a truncated Dyson series', Physical Review A, vol. 99, no. 4.
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Korzekwa, K, Puchała, Z, Tomamichel, M & Życzkowski, K 2019, 'Encoding classical information into quantum resources', IEEE Trans. Inf. Theory, vol. 68, no. 7, pp. 4518-4530.
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We introduce and analyse the problem of encoding classical information intodifferent resources of a quantum state. More precisely, we consider a generalclass of communication scenarios characterised by encoding operations thatcommute with a unique resource destroying map and leave free states invariant.Our motivating example is given by encoding information into coherences of aquantum system with respect to a fixed basis (with unitaries diagonal in thatbasis as encodings and the decoherence channel as a resource destroying map),but the generality of the framework allows us to explore applications rangingfrom super-dense coding to thermodynamics. For any state, we find that thenumber of messages that can be encoded into it using such operations in aone-shot scenario is upper-bounded in terms of the information spectrumrelative entropy between the given state and its version with erased resources.Furthermore, if the resource destroying map is the twirling channel over someunitary group, we find matching one-shot lower-bounds as well. In theasymptotic setting where we encode into many copies of the resource state, ourbounds yield an operational interpretation of resource monotones such as therelative entropy of coherence and its corresponding relative entropy variance.

Li, Y, Lei, L & Li, S 2019, 'Computation tree logic model checking based on multi-valued possibility measures', Information Sciences, vol. 485, pp. 87-113.
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© 2019 Elsevier Inc. Multi-valued model checking has been studied extensively recently, but important uncertain information contained in systems of multi-valued logics has not been considered in previous work and, as a consequence, some serious deficiencies arise. To make up for these deficiencies, this paper considers the possibility information implied in multi-valued systems. Precisely, we investigate computation tree logic model checking based on multi-valued possibility measures. We model multi-valued logic systems by multi-valued Kripke structures (MvKSs) and specify their verification properties by multi-valued computation tree logic (MvCTL) formulae. Based on generalized possibility measures and generalized necessity measures, an MvCTL model checking method is proposed, the pseudocode of the corresponding model checking algorithm is presented, and its time complexity is analyzed in detail. Furthermore, after detailed comparisons with χCTL (introduced in Chechik et al. [10]) and the classical CTL, we show that MvCTL is more general than χCTL, but cannot be reduced to the classical CTL. The conditions on lattice and MvKS under which MvCTL is equivalent to χCTL are given. Finally, some examples and a case study are given to illustrate the MvCTL model-checking method.

Liu, S, He, K & Duan, R 2019, 'Implementing termination analysis on quantum programming', Science China Information Sciences, vol. 62, no. 12.
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© 2019, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature. Termination analysis is an essential part in programming. Especially quantum programming concerning measurement, entanglement and even superposition are the foundations of bizarre behaviours in quantum programs. In this paper, we analyse and extend the theoretical theorems on termination analysis proposed by Ying et al. into computational theorems and algorithms. The new algorithm without the Jordan decomposition process has a significant acceleration with polynomial complexity both on terminating and almost-surely terminating programs. Moreover, the least upper bound of termination programs steps is studied and utilized to output the substituted matrix representation of quantum programs. We also implement four groups of experiments to illustrate the advantages of the new algorithm in case of processing a simplified quantum walk example comparing with the original counterpart.

Liu, S, Li, Y & Duan, R 2019, 'Distinguishing unitary gates on the IBM quantum processor', Science China Information Sciences, vol. 62, no. 7.
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© 2019, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature. An unknown unitary gate, which is secretly chosen from several known ones, can always be distinguished perfectly. In this paper, we implement such a task on IBM’s quantum processor. More precisely, we experimentally demonstrate the discrimination of two qubit unitary gates, the identity gate and the 23π-phase shift gate, using two discrimination schemes — the parallel scheme and the sequential scheme. We program these two schemes on the ibmqx4, a 5-qubit superconducting quantum processor via IBM cloud, with the help of the QSI modules. We report that both discrimination schemes achieve success probabilities at least 85%.

Mandal, R, Roy, PP, Pal, U & Blumenstein, M 2019, 'Bag-of-visual-words for signature-based multi-script document retrieval', Neural Computing and Applications, vol. 31, no. 10, pp. 6223-6247.
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© 2018, The Natural Computing Applications Forum. An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample, and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a support vector machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e., signature strokes) and the background spatial information (i.e., background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The ‘Tobacco’ (The Legacy Tobacco Document Library (LTDL). University of California, San Francisco, 2007. http://legacy.library.ucsf.edu/) document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents, and the promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches.

McKinlay, A & Tomamichel, M 2019, 'Decomposition Rules for Quantum Rényi Mutual Information with an Application to Information Exclusion Relations', J. Math. Phys., vol. 61, no. 7, p. 072202.
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We prove decomposition rules for quantum R\'enyi mutual information,generalising the relation $I(A:B) = H(A) - H(A|B)$ to inequalities betweenR\'enyi mutual information and R\'enyi entropy of different orders. The proofuses Beigi's generalisation of Reisz-Thorin interpolation to operator norms,and a variation of the argument employed by Dupuis which was used to show chainrules for conditional R\'enyi entropies. The resulting decomposition rule isthen applied to establish an information exclusion relation for R\'enyi mutualinformation, generalising the original relation by Hall.

Mills, PW, Rundle, RP, Samson, JH, Devitt, SJ, Tilma, T, Dwyer, VM & Everitt, MJ 2019, 'Quantum invariants and the graph isomorphism problem', Physical Review A, vol. 100, no. 5.
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Paler, A, Herr, D & Devitt, SJ 2019, 'Really Small Shoe Boxes: On Realistic Quantum Resource Estimation', Computer, vol. 52, no. 6, pp. 27-37.
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© 2019 IEEE. The reliable resource estimation and benchmarking of quantum algorithms is a critical component of the development cycle of viable quantum applications for quantum computers of all sizes. Determining resource bottlenecks in algorithms, especially when resource intensive error correction protocols are required, is crucial to reduce the cost of implementing viable algorithms on actual quantum hardware.

Pirandola, S, Andersen, UL, Banchi, L, Berta, M, Bunandar, D, Colbeck, R, Englund, D, Gehring, T, Lupo, C, Ottaviani, C, Pereira, J, Razavi, M, Shaari, JS, Tomamichel, M, Usenko, VC, Vallone, G, Villoresi, P & Wallden, P 2019, 'Advances in Quantum Cryptography', Adv. Opt. Photon., vol. 12, no. 4, pp. 1012-1236.
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Quantum cryptography is arguably the fastest growing area in quantuminformation science. Novel theoretical protocols are designed on a regularbasis, security proofs are constantly improving, and experiments are graduallymoving from proof-of-principle lab demonstrations to in-field implementationsand technological prototypes. In this review, we provide both a generalintroduction and a state of the art description of the recent advances in thefield, both theoretically and experimentally. We start by reviewing protocolsof quantum key distribution based on discrete variable systems. Next weconsider aspects of device independence, satellite challenges, and high rateprotocols based on continuous variable systems. We will then discuss theultimate limits of point-to-point private communications and how quantumrepeaters and networks may overcome these restrictions. Finally, we willdiscuss some aspects of quantum cryptography beyond standard quantum keydistribution, including quantum data locking and quantum digital signatures.

Raeisi, S, Kieferová, M & Mosca, M 2019, 'Novel Technique for Robust Optimal Algorithmic Cooling', Physical Review Letters, vol. 122, no. 22, pp. 220501-220501.
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Heat-bath algorithmic cooling provides algorithmic ways to improve the purity of quantum states. These techniques are complex iterative processes that change from each iteration to the next and this poses a significant challenge to implementing these algorithms. Here, we introduce a new technique that on a fundamental level, shows that it is possible to do algorithmic cooling and even reach the cooling limit without any knowledge of the state and using only a single fixed operation, and on a practical level, presents a more feasible and robust alternative for implementing heat-bath algorithmic cooling. We also show that our new technique converges to the asymptotic state of heat-bath algorithmic cooling and that the cooling algorithm can be efficiently implemented; however, the saturation could require exponentially many iterations and remains impractical. This brings heat-bath algorithmic cooling to the realm of feasibility and makes it a viable option for realistic application in quantum technologies.

Razzak, I, Blumenstein, M & Xu, G 2019, 'Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 6, pp. 1117-1127.
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© 2001-2011 IEEE. Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins. The objective function is a combination of binary hinge loss that works on C matrices and spectral elastic net penalty as regularization term. This regularization term is a combination of Frobenius and nuclear norm, which promotes structural sparsity and shares similar sparsity patterns across multiple predictors. It also maximizes the inter-class margin that helps to deal with complex high dimensional noisy data. The extensive experiment results supported by theoretical analysis and statistical tests show the effectiveness of the M-SMM for solving the problem of classifying EEG signals associated with motor imagery in brain-computer interface applications.

Sanders, YR, Low, GH, Scherer, A & Berry, DW 2019, 'Black-Box Quantum State Preparation without Arithmetic', Physical Review Letters, vol. 122, no. 2, p. 020502.
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Black-box quantum state preparation is an important subroutine in many quantum algorithms. The standard approach requires the quantum computer to do arithmetic, which is a key contributor to the complexity. Here we present a new algorithm that avoids arithmetic. We thereby reduce the number of gates by a factor of 286-374 over the best prior work for realistic precision; the improvement factor increases with the precision. As quantum state preparation is a crucial subroutine in many approaches to simulating physics on a quantum computer, our new method brings useful quantum simulation closer to reality.

Saqib, M, Khan, SD, Sharma, N & Blumenstein, M 2019, 'Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks', IEEE Access, vol. 7, pp. 35317-35329.
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© 2013 IEEE. Crowd counting and density estimation is an important and challenging problem in the visual analysis of the crowd. Most of the existing approaches use regression on density maps for the crowd count from a single image. However, these methods cannot localize individual pedestrian and therefore cannot estimate the actual distribution of pedestrians in the environment. On the other hand, detection-based methods detect and localize pedestrians in the scene, but the performance of these methods degrades when applied in high-density situations. To overcome the limitations of pedestrian detectors, we proposed a motion-guided filter (MGF) that exploits spatial and temporal information between consecutive frames of the video to recover missed detections. Our framework is based on the deep convolution neural network (DCNN) for crowd counting in the low-to-medium density videos. We employ various state-of-the-art network architectures, namely, Visual Geometry Group (VGG16), Zeiler and Fergus (ZF), and VGGM in the framework of a region-based DCNN for detecting pedestrians. After pedestrian detection, the proposed motion guided filter is employed. We evaluate the performance of our approach on three publicly available datasets. The experimental results demonstrate the effectiveness of our approach, which significantly improves the performance of the state-of-the-art detectors.

Vijayan, MK, Chitambar, E & Hsieh, M-H 2019, 'Simple Bounds for One-shot Pure-State Distillation in General Resource Theories', Phys. Rev. A, vol. 102, no. 5, p. 052403.
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We present bounds for distilling many copies of a pure state from anarbitrary initial state in a general quantum resource theory. Our bounds applyto operations that are able to generate no more than a $\delta$ amount ofresource, where $\delta \geq 0$ is a given parameter. To maximize applicabilityof our upper bound, we assume little structure on the set of free states underconsideration besides a weak form of superadditivity of the function$G_{min}(\rho)$, which measures the overlap between $\rho$ and the set of freestates. Our bounds are given in terms of this function and the robustness ofresource. Known results in coherence and entanglement theory are reproduced inthis more general framework.

Vijayan, MK, Lund, AP & Rohde, PP 2019, 'A robust W-state encoding for linear quantum optics', Quantum, vol. 4, p. 303.
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Error-detection and correction are necessary prerequisites for any scalablequantum computing architecture. Given the inevitability of unwanted physicalnoise in quantum systems and the propensity for errors to spread ascomputations proceed, computational outcomes can become substantiallycorrupted. This observation applies regardless of the choice of physicalimplementation. In the context of photonic quantum information processing,there has recently been much interest in passive linear optics quantumcomputing, which includes boson-sampling, as this model eliminates thehighly-challenging requirements for feed-forward via fast, active control. Thatis, these systems are passive by definition. In usual scenarios, errordetection and correction techniques are inherently active, making themincompatible with this model, arousing suspicion that physical error processesmay be an insurmountable obstacle. Here we explore a photonic error-detectiontechnique, based on W-state encoding of photonic qubits, which is entirelypassive, based on post-selection, and compatible with these near-term photonicarchitectures of interest. We show that this W-state redundant encodingtechniques enables the suppression of dephasing noise on photonic qubits viasimple fan-out style operations, implemented by optical Fourier transformnetworks, which can be readily realised today. The protocol effectively mapsdephasing noise into heralding failures, with zero failure probability in theideal no-noise limit. We present our scheme in the context of a single photonicqubit passing through a noisy communication or quantum memory channel, whichhas not been generalised to the more general context of full quantumcomputation.

Wang, B, Gao, Y, Sun, C, Blumenstein, M & La Salle, J 2019, 'Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves', IEEE Transactions on Image Processing, vol. 28, no. 12, pp. 5963-5976.
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Effectively describing and recognizing leaf shapes under arbitrary variations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing leaf shapes by walking and measuring along a bunch of chords that pass through the shape. A novel chord bunch walks (CBW) descriptor is developed through the chord walking behavior that effectively integrates the shape image function over the walked chord to reflect both the contour features and the inner properties of the shape. For each contour point, the chord bunch groups multiple pairs of chords to build a hierarchical framework for a coarse-to-fine description that can effectively characterize not only the subtle differences among leaf margin patterns but also the interior part of the shape contour formed inside a self-overlapped or compound leaf. Instead of using optimal correspondence based matching, a Log-Min distance that encourages one-to-one correspondences is proposed for efficient and effective CBW matching. The proposed CBW shape analysis method is invariant to rotation, scaling, translation, and mirror transforms. Five experiments, including image retrieval of compound leaves, image retrieval of naturally self-overlapped leaves, and retrieval of mixed leaves on three large scale datasets, are conducted. The proposed method achieved large accuracy increases with low computational costs over the state-of-the-art benchmarks, which indicates the research potential along this direction.

Wang, X, Ma, Y, Hsieh, M-H & Yung, M 2019, 'Quantum Speedup in Adaptive Boosting of Binary Classification', Science China: Physics, Mechanics and Astronomy, vol. 64, no. 2.
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In classical machine learning, a set of weak classifiers can be adaptivelycombined to form a strong classifier for improving the overall performance, atechnique called adaptive boosting (or AdaBoost). However, constructing thestrong classifier for a large data set is typically resource consuming. Here wepropose a quantum extension of AdaBoost, demonstrating a quantum algorithm thatcan output the optimal strong classifier with a quadratic speedup in the numberof queries of the weak classifiers. Our results also include a generalizationof the standard AdaBoost to the cases where the output of each classifier maybe probabilistic even for the same input. We prove that the update rules andthe query complexity of the non-deterministic classifiers are the same as thoseof deterministic classifiers, which may be of independent interest to theclassical machine-learning community. Furthermore, the AdaBoost algorithm canalso be applied to data encoded in the form of quantum states; we show how thetraining set can be simplified by using the tools of t-design. Our approachdescribes a model of quantum machine learning where quantum speedup is achievedin finding the optimal classifier, which can then be applied for classicalmachine-learning applications.

Yamasaki, H, Vijayan, MK & Hsieh, M-H 2019, 'Hierarchy of quantum operations in manipulating coherence and entanglement', Quantum, vol. 5, p. 480.
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Quantum resource theory under different classes of quantum operationsadvances multiperspective understandings of inherent quantum-mechanicalproperties, such as quantum coherence and quantum entanglement. We establishhierarchies of different operations for manipulating coherence and entanglementin distributed settings, where at least one of the two spatially separatedparties are restricted from generating coherence. In these settings, weintroduce new classes of operations and also characterize those maximal, i.e.,the resource-non-generating operations, progressing beyond existing studies onincoherent versions of local operations and classical communication and thoseof separable operations. The maximal operations admit asemidefinite-programming formulation useful for numerical algorithms, whereasthe existing operations not. To establish the hierarchies, we prove a sequenceof inclusion relations among the operations by clarifying tasks whereseparation of the operations appears. We also demonstrate an asymptoticallynon-surviving separation of the operations in the hierarchy in terms ofperformance of the task of assisted coherence distillation, where a separationin a one-shot scenario vanishes in the asymptotic limit. Our results serve asfundamental analytical and numerical tools to investigate interplay betweencoherence and entanglement under different operations in the resource theory.

Youssry, A, Chapman, RJ, Peruzzo, A, Ferrie, C & Tomamichel, M 2019, 'Modeling and Control of a Reconfigurable Photonic Circuit using Deep Learning', Quantum Science and Technology 5 (2), 025001 (2020), vol. 5, no. 2, pp. 025001-025001.
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The complexity of experimental quantum information processing devices isincreasing rapidly, requiring new approaches to control them. In this paper, weaddress the problems of practically modeling and controlling an integratedoptical waveguide array chip, a technology expected to have many applicationsin telecommunications and optical quantum information processing. This photoniccircuit can be electrically reconfigured, but only the output optical signalcan be monitored. As a result, the conventional control methods cannot benaively applied. Characterizing such a chip is challenging for three reasons.First, there are uncertainties associated with the Hamiltonian describing thechip. Second, we expect distortions of the control voltages caused by thechip's electrical response, which cannot be directly observed. Finally, thereare imperfections in the measurements caused by losses from coupling the chipexternally to optical fibers. We developed a deep neural network approach tosolve these problems. The architecture is designed specifically to overcome theaforementioned challenges using a Gated Recurrent Unit (GRU)-based network asthe central component. The Hamiltonian is estimated as a blackbox, while therules of quantum mechanics such as state evolution is embedded in the structureas a whitebox. The resulting overall graybox model of the chip shows goodperformance both quantitatively in terms of the mean square error andqualitatively in terms of the predicted waveforms. We use this neural networkto solve a classical and a quantum control problem. In the classicalapplication we find a control sequence to approximately realize atime-dependent output power distribution. For the quantum application we obtainthe control voltages to realize a target set of quantum gates. The proposedmethod is generic and can be applied to other systems that can only be probedindirectly.

Zhao, Y, Chen, J, Wu, D, Teng, J, Sharma, N, Sajjanhar, A & Blumenstein, M 2019, 'Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern', Information, vol. 10, no. 8, pp. 262-262.
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Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.

Zhu, EY, Zhuang, Q, Hsieh, M-H & Shor, PW 2019, 'Superadditivity in Trade-Off Capacities of Quantum Channels', IEEE Transactions on Information Theory, vol. 65, no. 6, pp. 3973-3989.
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© 1963-2012 IEEE. In this paper, we investigate the additivity phenomenon in the quantum dynamic capacity region of a quantum channel for trading the resources of classical communication, quantum communication, and entanglement. Understanding such an additivity property is important if we want to optimally use a quantum channel for general communication purposes. However, in a lot of cases, the channel one will be using only has an additive single or double resource capacity region, and it is largely unknown if this could lead to a strictly superadditive double or triple resource capacity region, respectively. For example, if a channel has additive classical and quantum capacities, can the classical-quantum capacity region be strictly superadditive? In this paper, we answer such questions affirmatively. We give proof-of-principle requirements for these channels to exist. In most cases, we can provide an explicit construction of these quantum channels. The existence of these superadditive phenomena is surprising in contrast to the result that the additivity of both classical-entanglement and classical-quantum capacity regions imply the additivity of the triple resource capacity region for a given channel.

Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 1970, 'Detecting Named Entities in Unstructured Bengali Manuscript Images', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 196-201.
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© 2019 IEEE. In this paper, we undertake a task to find named entities directly from unstructured handwritten document images without any intermediate text/character recognition. Here, we do not receive any assistance from natural language processing. Therefore, it becomes more challenging to detect the named entities. We work on Bengali script which brings some additional hurdles due to its own unique script characteristics. Here, we propose a new deep neural network-based architecture to extract the latent features from a text image. The embedding is then fed to a BLSTM (Bidirectional Long Short-Term Memory) layer. After that, the attention mechanism is adapted to an approach for named entity detection. We perform experimentation on two publicly-available offline handwriting repositories containing 420 Bengali handwritten pages in total. The experimental outcome of our system is quite impressive as it attains 95.43% balanced accuracy on overall named entity detection.

Ahadi, A & Mathieson, L 1970, 'A Comparison of Three Popular Source code Similarity Tools for Detecting Student Plagiarism', Proceedings of the Twenty-First Australasian Computing Education Conference, ACE'19: Twenty-First Australasian Computing Education Conference, ACM, Australia, pp. 112-117.
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© 2019 Association for Computing Machinery. This paper investigates automated code plagiarism detection in the context of an undergraduate level data structures and algorithms module. We compare three software tools which aim to detect plagiarism in the students' programming source code. We evaluate the performance of these tools on an individual basis and the degree of agreement between them. Based on this evaluation we show that the degree of agreement between these tools is relatively low. We also report the challenges faced during utilization of these methods and suggest possible future improvements for tools of this kind. The discrepancies in the results obtained by these detection techniques were used to devise guidelines for effectively detecting code plagiarism.

Ahadi, A, Lister, R & Mathieson, L 1970, 'ArAl', Proceedings of the Twenty-First Australasian Computing Education Conference, ACE'19: Twenty-First Australasian Computing Education Conference, ACM, Australia, pp. 118-125.
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© 2019 Association for Computing Machinery. Several systems that collect data from students' problem solving processes exist. Within computing education research, such data has been used for multiple purposes, ranging from assessing students' problem solving strategies to detecting struggling students. To date, however, the majority of the analysis has been conducted by individual researchers or research groups using case by case methodologies. Our belief is that with increasing possibilities for data collection from students' learning process, researchers and instructors will benefit from ready-made analysis tools. In this study, we present ArAl, an online machine learning based platform for analyzing programming source code snapshot data. The benefit of ArAl is two-fold. The computing education researcher can use ArAl to analyze the source code snapshot data collected from their own institute. Also, the website provides a collection of well-documented machine learning and statistics based tools to investigate possible correlation between different variables. The presented web-portal is available at online-analysisdemo. herokuapp.com. This tool could be applied in many different subject areas given appropriate performance data.

Alaei, F, Alaei, A, Pal, U & Blumenstein, M 1970, 'Document Image Retrieval Based on Visual Saliency Maps', 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), IEEE, pp. 7-12.
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There has been a massive growth in the production of various unstructured, complex and multi-lingual digitised documents in recent years. Storing and manipulating such digitised documents towards a paperless society has been the objective of emerging technology. As the human visual system can easily distinguish the global summary of images, extracting features based on human attention from images is desirable to achieve more accurate document image retrieval results. Thus, in this research work, an appearance-based document image retrieval system using image saliency maps depending on human visual attention is proposed. The saliency map obtained from the input document image is used to generate a weighted document image. Features are then extracted from the weighted document images using the Gist operator. Then, locality-sensitive hashing is considered to compute similarity distances between a query and the document images in the knowledge-based database. To evaluate the performance of the proposed document image retrieval system MTDB, ITESOFT, and CLEF-IP datasets of document images were used for experimentation. The proposed document image retrieval system provided promising retrieval results compared to the results reported in the literature.

Anshu, A, Berta, M, Jain, R & Tomamichel, M 1970, 'Second-Order Characterizations via Partial Smoothing', 2019 IEEE International Symposium on Information Theory (ISIT), 2019 IEEE International Symposium on Information Theory (ISIT), IEEE, pp. 937-941.
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© 2019 IEEE. Smooth entropies are a tool for quantifying resource trade-offs in information theory and cryptography. However, in typical multi-partite problems some of the sub-systems are often left unchanged and this is not reflected by the standard smoothing of information measures over a ball of close states. We propose to smooth instead only over a ball of close states which also have some of the reduced states on the relevant sub-systems fixed. This partial smoothing of information measures naturally allows to give more refined characterizations of various information-theoretic problems in the one-shot setting. As a consequence, we can derive asymptotic second-order characterizations for tasks such as privacy amplification against classical side information or classical state splitting. For quantum problems like state merging the general resource trade-off is tightly characterized by partially smoothed information measures as well.

Arunachalam, S, Chakraborty, S, Lee, T, Paraashar, M & De Wolf, R 1970, 'Two new results about quantum exact learning', Leibniz International Proceedings in Informatics, LIPIcs, International Colloquium on Automata, Languages, and Programming, Greece.
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We present two new results about exact learning by quantum computers. First, we show how to exactly learn a k-Fourier-sparse n-bit Boolean function from O(k1.5(log k)2) uniform quantum examples for that function. This improves over the bound of Θe (kn) uniformly random classical examples (Haviv and Regev, CCC'15). Our main tool is an improvement of Chang's lemma for sparse Boolean functions. Second, we show that if a concept class C can be exactly learned using Q quantum membership queries, then it can also be learned using O logQ2Q log |C| classical membership queries. This improves the previous-best simulation result (Servedio-Gortler, SICOMP'04) by a log Q-factor.

Bannink, T, Briet, J, Buhrman, H, Lee, T & Labib, F 1970, 'Bounding quantum-classical separations for classes of nonlocal games', Symposium on Theoretical Aspects of Computer Science, Germany.

Basavaraja, V, Shivakumara, P, Guru, DS, Pal, U, Lu, T & Blumenstein, M 1970, 'Age Estimation using Disconnectedness Features in Handwriting', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 1131-1136.
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© 2019 IEEE. Real-time applications of handwriting analysis have increased drastically in the fields of forensic and information security because of accurate cues. One of such applications is human age estimation based on handwriting for the purpose of immigrant checking. In this paper, we have proposed a new method for age estimation using handwriting analysis using Hu invariant moments and disconnectedness features. To make the proposed method robust to both ruled and un-ruled documents, we propose to explore intersection point detection in Canny edge images of each input document, which results in text components. For each text component pair, we propose Hu invariant moments for extracting disconnectedness features, which in fact measure multi-shape components based on distance, shape and mutual position analysis of components. Furthermore, iterative k-means clustering is proposed for the classification of different age groups. Experimental results on our dataset and some standard datasets, namely, IAM and KHATT, show that the proposed method is effective and outperforms the state-of-the-art methods.

Bravyi, S, Gosset, D, Koenig, R & Tomamichel, M 1970, 'Quantum advantage with noisy shallow circuits in 3D', Nature Physics (2020), Annual Symposium on Foundations of Computer Science, IEEE Computer Society Press, Baltimore, MD, USA, USA, pp. 995-999.
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Prior work has shown that there exists a relation problem which can be solvedwith certainty by a constant-depth quantum circuit composed of geometricallylocal gates in two dimensions, but cannot be solved with high probability byany classical constant depth circuit composed of bounded fan-in gates. Here weprovide two extensions of this result. Firstly, we show that a separation incomputational power persists even when the constant-depth quantum circuit isrestricted to geometrically local gates in one dimension. The correspondingquantum algorithm is the simplest we know of which achieves a quantum advantageof this type. It may also be more practical for future implementations. Oursecond, main result, is that a separation persists even if the shallow quantumcircuit is corrupted by noise. We construct a relation problem which can besolved with near certainty using a noisy constant-depth quantum circuitcomposed of geometrically local gates in three dimensions, provided the noiserate is below a certain constant threshold value. On the other hand, theproblem cannot be solved with high probability by a noise-free classicalcircuit of constant depth. A key component of the proof is a quantumerror-correcting code which admits constant-depth logical Clifford gates andsingle-shot logical state preparation. We show that the surface code meetsthese criteria. To this end, we provide a protocol for single-shot logicalstate preparation in the surface code which may be of independent interest.

Chubb, CT, Korzekwa, K & Tomamichel, M 1970, 'Moderate deviation analysis of majorisation-based resource interconversion', 2019 IEEE International Symposium on Information Theory (ISIT), 2019 IEEE International Symposium on Information Theory (ISIT), IEEE, France, pp. 3002-3006.
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© 2019 IEEE. We consider the problem of interconverting a finite amount of resources within all theories whose single-shot transformation rules are based on a majorisation relation, e.g. the resource theories of entanglement and coherence (for pure state transformations), as well as thermodynamics (for energy-incoherent transformations). When only finite resources are available we expect to see a non-trivial trade-off between the rate rn at which n copies of a resource state ρ can be transformed into nrn copies of another resource state σ, and the error level ϵn of the interconversion process, as a function of n. In this work we derive the optimal trade-off in the so-called moderate deviation regime, where the rate of interconversion rn approaches its optimum in the asymptotic limit of unbounded resources (n → ∞), while the error ϵn vanishes in the same limit. We find that the moderate deviation analysis exhibits a resonance behaviour which implies that certain pairs of resource states can be interconverted at the asymptotically optimal rate with negligible error, even in the finite n regime.

Coluccia, A, Fascista, A, Schumann, A, Sommer, L, Ghenescu, M, Piatrik, T, De Cubber, G, Nalamati, M, Kapoor, A, Saqib, M, Sharma, N, Blumenstein, M, Magoulianitis, V, Ataloglou, D, Dimou, A, Zarpalas, D, Daras, P, Craye, C, Ardjoune, S, De la Iglesia, D, Mendez, M, Dosil, R & Gonzalez, I 1970, 'Drone-vs-Bird Detection Challenge at IEEE AVSS2019', 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Taipei, Taiwan.
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© 2019 IEEE. This paper presents the second edition of the 'drone-vs-bird' detection challenge, launched within the activities of the 16-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The challenge's goal is to detect one or more drones appearing at some point in video sequences where birds may be also present, together with motion in background or foreground. Submitted algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. This paper reports on the challenge results on the 2019 dataset, which extends the first edition dataset provided by the SafeShore project with additional footage under different conditions.

Das, A, Pal, U, Blumenstein, M, Wang, C, He, Y, Zhu, Y & Sun, Z 1970, 'Sclera Segmentation Benchmarking Competition in Cross-resolution Environment', 2019 International Conference on Biometrics (ICB), 2019 International Conference on Biometrics (ICB), IEEE, Crete, Greece, pp. 1-7.
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© 2019 IEEE. This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this competition was to record the developments on sclera segmentation in the cross-resolution environment (sclera trait captured using multiple acquiring sensors with different image resolutions). Additionally, the competition also aimed to gain the attention of researchers on this subject of research.For the purpose of benchmarking, we have employed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1). The second dataset is the Mobile Sclera Dataset (MSD), and in this dataset, images were captured using.a mobile phone rear camera of 8-megapixels. Baseline manual segmentation masks of the sclera images from both the datasets were developed.Precision and recall-based measures were employed to evaluate the effectiveness and ranking of the submitted segmentation techniques. Four algorithms were submitted to address the segmentation task. In this paper we analyzed the results produced by these algorithms/systems, and we have defined a way forward for this problem. Both the datasets along with some of the accompanying ground truth/baseline masks will be freely available for research purposes.

Fitzsimons, J, Ji, Z, Vidick, T & Yuen, H 1970, 'Quantum proof systems for iterated exponential time, and beyond', Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC '19: 51st Annual ACM SIGACT Symposium on the Theory of Computing, ACM, Phoenix, AZ, USA, pp. 473-480.
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© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. We show that any language solvable in nondeterministic time exp(exp(· · · exp(n))), where the number of iterated exponentials is an arbitrary function R(n), can be decided by a multiprover interactive proof system with a classical polynomial-time verifier and a constant number of quantum entangled provers, with completeness 1 and soundness 1 − exp(−C exp(· · · exp(n))), where the number of iterated exponentials is R(n) − 1 and C > 0 is a universal constant. The result was previously known for R = 1 and R = 2; we obtain it for any time-constructible function R. The result is based on a compression technique for interactive proof systems with entangled provers that significantly simplifies and strengthens a protocol compression result of Ji (STOC’17). As a separate consequence of this technique we obtain a different proof of Slofstra’s recent result on the uncomputability of the entangled value of multiprover games (Forum of Mathematics, Pi 2019). Finally, we show that even minor improvements to our compression result would yield remarkable consequences in computational complexity theory and the foundations of quantum mechanics: first, it would imply that the class MIP∗ contains all computable languages; second, it would provide a negative resolution to a multipartite version of Tsirelson’s problem on the relation between the commuting operator and tensor product models for quantum correlations.

Haque, MN, Mathieson, L & Moscato, P 1970, 'A memetic algorithm approach to network alignment', Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19: Genetic and Evolutionary Computation Conference, ACM, Prague, Czech Republic, pp. 258-265.
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© 2019 Association for Computing Machinery. Given two graphs modelling related, but possibly distinct, networks, the alignment of the networks can help identify signiicant structures and substructures which may relate to the functional purpose of the network components. The Network Alignment Problem is the NP-hard computational formalisation of this goal and is a useful technique in a variety of data mining and knowledge discovery domains. In this paper we develop a memetic algorithm to solve the Network Alignment Problem and demonstrate the efectiveness of the approach on a series of biological networks against the existing state of the art alignment tools. We also demonstrate the use of network alignment as a clustering and classiication tool on two mental health disorder diagnostic databases.

Hiroto, M, Keiichi, S, Devitt, S, Rui, W, Yukito, N & Jaw-Shen, T 1970, 'Packaging Large-scale Superconducting Quantum Computer with Airbridge', Bulletin of the American Physical Society, APS March Meeting 2019, Boston, Massachussetts.

Ji, Z, Qiao, Y, Song, F & Yun, A 1970, 'General Linear Group Action on Tensors: A Candidate for Post-quantum Cryptography', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Theory of Cryptography, Springer International Publishing, Nuremberg, pp. 251-281.
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© 2019, International Association for Cryptologic Research. Starting from the one-way group action framework of Brassard and Yung (Crypto’90), we revisit building cryptography based on group actions. Several previous candidates for one-way group actions no longer stand, due to progress both on classical algorithms (e.g., graph isomorphism) and quantum algorithms (e.g., discrete logarithm). We propose the general linear group action on tensors as a new candidate to build cryptography based on group actions. Recent works (Futorny–Grochow–Sergeichuk Lin. Alg. Appl., 2019) suggest that the underlying algorithmic problem, the tensor isomorphism problem, is the hardest one among several isomorphism testing problems arising from areas including coding theory, computational group theory, and multivariate cryptography. We present evidence to justify the viability of this proposal from comprehensive study of the state-of-art heuristic algorithms, theoretical algorithms, hardness results, as well as quantum algorithms. We then introduce a new notion called pseudorandom group actions to further develop group-action based cryptography. Briefly speaking, given a group G acting on a set S, we assume that it is hard to distinguish two distributions of (s, t) either uniformly chosen from S × S, or where s is randomly chosen from S and t is the result of applying a random group action of gεG on s. This subsumes the classical Decisional Diffie-Hellman assumption when specialized to a particular group action. We carefully analyze various attack strategies that support instantiating this assumption by the general linear group action on tensors. Finally, we construct several cryptographic primitives such as digital signatures and pseudorandom functions. We give quantum security proofs based on the one-way group action assumption and the pseudorandom group action assumption.

Lee, T, Bannink, T, Briët, J, Buhrman, H & Labib, F 1970, 'Bounding Quantum-Classical Separations for Classes of Nonlocal Games', LIPIcs : Leibniz International Proceedings in Informatics, International Symposium on Theoretical Aspects of Computer Science, Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik, Berlin, Germany, pp. 1-11.
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We bound separations between the entangled and classical values for several classes of nonlocal t-player games. Our motivating question is whether there is a family of t-player XOR games for which the entangled bias is 1 but for which the classical bias goes down to 0, for fixed t. Answering this question would have important consequences in the study of multi-party communication complexity, as a positive answer would imply an unbounded separation between randomized communication complexity with and without entanglement. Our contribution to answering the question is identifying several general classes of games for which the classical bias can not go to zero when the entangled bias stays above a constant threshold. This rules out the possibility of using these games to answer our motivating question. A previously studied set of XOR games, known not to give a positive answer to the question, are those for which there is a quantum strategy that attains value 1 using a so-called Schmidt state. We generalize this class to mod-m games and show that their classical value is always at least 1/m + (m-1)/m t^{1-t}. Secondly, for free XOR games, in which the input distribution is of product form, we show beta(G) >= beta^*(G)^{2^t} where beta(G) and beta^*(G) are the classical and entangled biases of the game respectively. We also introduce so-called line games, an example of which is a slight modification of the Magic Square game, and show that they can not give a positive answer to the question either. Finally we look at two-player unique games and show that if the entangled value is 1-epsilon then the classical value is at least 1-O(sqrt{epsilon log k}) where k is the number of outputs in the game. Our proofs use semidefinite-programming techniques, the Gowers inverse theorem and hypergraph norms.

Nalamati, M, Kapoor, A, Saqib, M, Sharma, N & Blumenstein, M 1970, 'Drone Detection in Long-Range Surveillance Videos', 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Taipei, Taiwan.
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© 2019 IEEE. The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.

Peng, X, Long, G, Shen, T, Wang, S, Jiang, J & Blumenstein, M 1970, 'Temporal Self-Attention Network for Medical Concept Embedding', Proceedings - IEEE International Conference on Data Mining, ICDM, International Conference on Data Mining, Beijing, China, pp. 498-507.
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In longitudinal electronic health records (EHRs), the event records of apatient are distributed over a long period of time and the temporal relationsbetween the events reflect sufficient domain knowledge to benefit predictiontasks such as the rate of inpatient mortality. Medical concept embedding as afeature extraction method that transforms a set of medical concepts with aspecific time stamp into a vector, which will be fed into a supervised learningalgorithm. The quality of the embedding significantly determines the learningperformance over the medical data. In this paper, we propose a medical conceptembedding method based on applying a self-attention mechanism to represent eachmedical concept. We propose a novel attention mechanism which captures thecontextual information and temporal relationships between medical concepts. Alight-weight neural net, 'Temporal Self-Attention Network (TeSAN)', is thenproposed to learn medical concept embedding based solely on the proposedattention mechanism. To test the effectiveness of our proposed methods, we haveconducted clustering and prediction tasks on two public EHRs datasets comparingTeSAN against five state-of-the-art embedding methods. The experimental resultsdemonstrate that the proposed TeSAN model is superior to all the comparedmethods. To the best of our knowledge, this work is the first to exploittemporal self-attentive relations between medical events.

Rahim, MS, Anh Nguyen, K, Stewart, RA, Giurco, D & Blumenstein, M 1970, 'Predicting Household Water Consumption Events: Towards a Personalised Recommender System to Encourage Water-conscious Behaviour', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary.
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© 2019 IEEE. Recommender systems assist customers to make decisions; however, the modest adoption of digital technology in the water industry means no such system exists for household water users. Such a system for the water industry would suggest to consumers the most effective ways to conserve water based on their historical data from smart water meters. The advantage for water utilities in metropolitan areas is in managing demand, such as low pressure during peak hours or water shortages during drought. For customers, effective recommendations could save them money. This paper presents a novel vision of a recommender system prototype and discusses the benefits both for the consumers and the water utility companies. The success of this type of system would depend on the ability to anticipate the time of the next major water use so as to make useful, timely recommendations. Hence, the prototype is based on a long short-term memory (LSTM) neural network that predicts significant water consumption events (i.e., showers, baths, irrigation, etc.) for 83 households. The preliminary results show that LSTM is a useful method of prediction with an average root mean square error (RMSE) of 0.403. The analysis also provides indications of the scope of further research required for developing a commercially successful recommender system.

Salek, F, Hsieh, M-H & Fonollosa, JR 1970, 'Publicness, Privacy and Confidentiality in the Single-Serving Quantum Broadcast Channel', IEEE International Symposium on Information Theory - Proceedings, IEEE International Symposium on Information Theory, IEEE, Paris, France, pp. 1712-1716.
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The 2-receiver broadcast channel is studied: a network with three partieswhere the transmitter and one of the receivers are the primarily involvedparties and the other receiver considered as third party. The messages that aredetermined to be communicated are classified into public, private andconfidential based on the information they convey. The public message containsinformation intended for both parties and is required to be decoded correctlyby both of them, the private message is intended for the primary party only,however, there is no secrecy requirement imposed upon it meaning that it canpossibly be exposed to the third party and finally the confidential messagecontaining information intended exclusively for the primary party such thatthis information must be kept completely secret from the other receiver. Atrade-off arises between the rates of the three messages, when one of the ratesis high, the other rates may need to be reduced to guarantee the reliabletransmission of all three messages. The encoder performs the necessaryequivocation by virtue of dummy random numbers whose rate is assumed to belimited and should be considered in the trade-off as well. We study thistrade-off in the one-shot regime of a quantum broadcast channel by providingachievability and (weak) converse regions. In the achievability, we prove anduse a conditional version of the convex-split lemma as well as position-baseddecoding. By studying the asymptotic behaviour of our bounds, we will recoverseveral well-known asymptotic results in the literature.

Wang, Q, Jia, W, He, X, Lu, Y, Blumenstein, M, Huang, Y & Lyu, S 1970, 'DeepText: Detecting Text from the Wild with Multi-ASPP-Assembled DeepLab', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 208-213.
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© 2019 IEEE. In this paper, we address the issue of scene text detection in the way of direct regression and successfully adapt an effective semantic segmentation model, DeepLab v3+ [1], for this application. In order to handle texts with arbitrary orientations and sizes and improve the recall of small texts, we propose to extract features of multiple scales by inserting multiple Atrous Spatial Pyramid Pooling (ASPP) layers to the DeepLab after the feature maps with different resolutions. Then, we set multiple auxiliary IoU losses at the decoding stage and make auxiliary connections from the intermediate encoding layers to the decoder to assist network training and enhance the discrimination ability of lower encoding layers. Experiments conducted on the benchmark scene text dataset ICDAR2015 demonstrate the superior performance of our proposed network, named as DeepText, over the state-of-the-art approaches.

Wang, Q, Jia, W, He, X, Lu, Y, Blumenstein, M, Huang, Y & Lyu, S 1970, 'ReELFA: A Scene Text Recognizer with Encoded Location and Focused Attention', 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), IEEE, Australia.
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Wu, D, Chen, J, Sharma, N, Pan, S, Long, G & Blumenstein, M 1970, 'Adversarial Action Data Augmentation for Similar Gesture Action Recognition', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary.
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Human gestures are unique for recognizing and describing human actions, and video-based human action recognition techniques are effective solutions to varies real-world applications, such as surveillance, video indexing, and human-computer interaction. Most existing video human action recognition approaches either using handcraft features from the frames or deep learning models such as convolutional neural networks (CNN) and recurrent neural networks (RNN); however, they have mostly overlooked the similar gestures between different actions when processing the frames into the models. The classifiers suffer from similar features extracted from similar gestures, which are unable to classify the actions in the video streams. In this paper, we propose a novel framework with generative adversarial networks (GAN) to generate the data augmentation for similar gesture action recognition. The contribution of our work is tri-fold: 1) we proposed a novel action data augmentation framework (ADAF) to enlarge the differences between the actions with very similar gestures; 2) the framework can boost the classification performance either on similar gesture action pairs or the whole dataset; 3) experiments conducted on both KTH and UCF101 datasets show that our data augmentation framework boost the performance on both similar gestures actions as well as the whole dataset compared with baseline methods such as 2DCNN and 3DCNN.

Wu, D, Hu, R, Zheng, Y, Jiang, J, Sharma, N & Blumenstein, M 1970, 'Feature-Dependent Graph Convolutional Autoencoders with Adversarial Training Methods', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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© 2019 IEEE. Graphs are ubiquitous for describing and modeling complicated data structures, and graph embedding is an effective solution to learn a mapping from a graph to a low-dimensional vector space while preserving relevant graph characteristics. Most existing graph embedding approaches either embed the topological information and node features separately or learn one regularized embedding with both sources of information, however, they mostly overlook the interdependency between structural characteristics and node features when processing the graph data into the models. Moreover, existing methods only reconstruct the structural characteristics, which are unable to fully leverage the interaction between the topology and the features associated with its nodes during the encoding-decoding procedure. To address the problem, we propose a framework using autoencoder for graph embedding (GED) and its variational version (VEGD). The contribution of our work is two-fold: 1) the proposed frameworks exploit a feature-dependent graph matrix (FGM) to naturally merge the structural characteristics and node features according to their interdependency; and 2) the Graph Convolutional Network (GCN) decoder of the proposed framework reconstructs both structural characteristics and node features, which naturally possesses the interaction between these two sources of information while learning the embedding. We conducted the experiments on three real-world graph datasets such as Cora, Citeseer and PubMed to evaluate our framework and algorithms, and the results outperform baseline methods on both link prediction and graph clustering tasks.

Zhang, M, Gao, Y, Sun, C & Blumenstein, M 1970, 'Kernel Mean P Power Error Loss for Robust Two-Dimensional Singular Value Decomposition', 2019 IEEE International Conference on Image Processing (ICIP), 2019 IEEE International Conference on Image Processing (ICIP), IEEE, Taipei, Taiwan, pp. 3432-3436.
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© 2019 IEEE. Traditional matrix-based dimensional reduction methods, e.g., two-dimensional principal component analysis (2DPCA) and two-dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE), which is sensitive to outliers. To overcome this problem, in this paper we propose a new robust 2DSVD method based on the kernel mean p power error loss (KMPE-2DSVD). Different from the MSE and the correntropy based ones which are second order statistics based measurements, the KMPE-2DSVD is based on the non-second order statistics in the kernel space, and thus is more flexible in controlling the representation error. Experimental results show that the proposed method significantly improves the accuracy of facial image clustering.

Zhang, M, Gao, Y, Sun, C & Blumenstein, M 1970, 'Robust Sparse Learning Based on Kernel Non-Second Order Minimization', 2019 IEEE International Conference on Image Processing (ICIP), 2019 IEEE International Conference on Image Processing (ICIP), IEEE, Taipei, Taiwan, pp. 2045-2049.
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© 2019 IEEE. Partial occlusions in face images pose a great problem for most face recognition algorithms due to the fact that most of these algorithms mainly focus on solving a second order loss function, e.g., mean square error (MSE), which will magnify the effect from occlusion parts. In this paper, we proposed a kernel non-second order loss function for sparse representation (KNS-SR) to recognize or restore partially occluded facial images, which both take the advantages of the correntropy and the non-second order statistics measurement. The resulted framework is more accurate than the MSE-based ones in locating and eliminating outliers information. Experimental results from image reconstruction and recognition tasks on publicly available databases show that the proposed method achieves better performances compared with existing methods.

Zhang, X, Zhang, X, Verma, S, Liu, Y, Blumenstein, M & Li, J 1970, 'Detection of Anomalous Traffic Patterns and Insight Analysis from Bus Trajectory Data', PRICAI 2019: Trends in Artificial Intelligence, The 16th Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Cuvu, Fiji, pp. 307-321.
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Zhou, L, Yu, N & Ying, M 1970, 'An applied quantum Hoare logic.', PLDI, the 40th ACM SIGPLAN Conference, ACM, Phoenix, AZ, pp. 1149-1162.
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© 2019 Association for Computing Machinery. We derive a variant of quantum Hoare logic (QHL), called applied quantum Hoare logic (aQHL for short), by: (1) restricting QHL to a special class of preconditions and postconditions, namely projections, which can significantly simplify verification of quantum programs and are much more convenient when used in debugging and testing; and (2) adding several rules for reasoning about robustness of quantum programs, i.e. error bounds of outputs. The effectiveness of aQHL is shown by its applications to verify two sophisticated quantum algorithms: HHL (Harrow-Hassidim-Lloyd) for solving systems of linear equations and qPCA (quantum Principal Component Analysis).

Du, Y, Hsieh, M-H & Tao, D 2019, 'Efficient Online Quantum Generative Adversarial Learning Algorithms with Applications'.
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The exploration of quantum algorithms that possess quantum advantages is a central topic in quantum computation and quantum information processing. One potential candidate in this area is quantum generative adversarial learning (QuGAL), which conceptually has exponential advantages over classical adversarial networks. However, the corresponding learning algorithm remains obscured. In this paper, we propose the first quantum generative adversarial learning algorithm-- the quantum multiplicative matrix weight algorithm (QMMW)-- which enables the efficient processing of fundamental tasks. The computational complexity of QMMW is polynomially proportional to the number of training rounds and logarithmically proportional to the input size. The core concept of the proposed algorithm combines QuGAL with online learning. We exploit the implementation of QuGAL with parameterized quantum circuits, and numerical experiments for the task of entanglement test for pure state are provided to support our claims.

Guff, T, McMahon, NA, Sanders, YR & Gilchrist, A 2019, 'A Resource Theory of Quantum Measurements'.

Guff, T, Sanders, YR, McMahon, NA & Gilchrist, A 2019, 'Quantum State Discrimination as Bayesian Experimental Design'.

Jafarzadeh, M, Wu, Y-D, Sanders, YR & Sanders, BC 2019, 'Randomized benchmarking for qudit Clifford gates'.

Lipinska, V, Thinh, LP, Ribeiro, J & Wehner, S 2019, 'Certification of a functionality in a quantum network stage'.

Thinh, LP 2019, 'Computing quantum Bell inequalities'.

Thinh, LP, Dall'Arno, M & Scarani, V 2019, 'Worst-case Quantum Hypothesis Testing with Separable Measurements'.

Zhou, X, Li, S & Feng, Y 2019, 'Quantum Circuit Transformation Based on Simulated Annealing and Heuristic Search'.