Reviewerly/exHarmony huggingface dataset:
Towards a GDPR-Compliant Blockchain-Based COVID Vaccination Passport

The COVID-19 pandemic has shaken the world and limited work/personal life activities. Besides the loss of human lives and agony faced by humankind, the pandemic has badly hit different sectors economically, including the travel industry. Special arrangements, including COVID test before departure and on arrival, and voluntary quarantine, were enforced to limit the risk of transmission. However, the hope for returning to a normal (pre-COVID) routine relies on the success of the current COVID vaccination drives administered by different countries. To open for tourism and other necessary travel, a need is realized for a universally accessible proof of COVID vaccination, allowing travelers to cross the borders without any hindrance. This paper presents an architectural framework for a GDPR-compliant blockchain-based COVID vaccination passport (VacciFi), whilst considering the relevant developments, especially in the European Union region.
The dominant eigenvector of a noisy quantum state

Abstract Although near-term quantum devices have no comprehensive solution for correcting errors, numerous techniques have been proposed for achieving practical value. Two works have recently introduced the very promising error suppression by derangements (ESD) and virtual distillation (VD) techniques. The approach exponentially suppresses errors and ultimately allows one to measure expectation values in the pure state as the dominant eigenvector of the noisy quantum state. Interestingly this dominant eigenvector is, however, different than the ideal computational state and it is the aim of the present work to comprehensively explore the following fundamental question: how significantly different are these two pure states? The motivation for this work is two-fold. First, comprehensively understanding the effect of this coherent mismatch is of fundamental importance for the successful exploitation of noisy quantum devices. As such, the present work rigorously establishes that in practically relevant scenarios the coherent mismatch is exponentially less severe than the incoherent decay of the fidelity—where the latter can be suppressed exponentially via the ESD/VD technique. Second, the above question is closely related to central problems in mathematics, such as bounding eigenvalues of a sum of two matrices (Weyl inequalities)—solving of which was a major breakthrough. The present work can be viewed as a first step towards extending the Weyl inequalities to eigenvectors of a sum of two matrices—and completely resolves this problem for the special case of the considered density matrices.
Toward Human Digital Twins for Cybersecurity Simulations on the Metaverse: Ontological and Network Science Approach

Background Cyber defense is reactive and slow. On average, the time-to-remedy is hundreds of times larger than the time-to-compromise. In response, Human Digital Twins (HDTs) offer the capability of running massive simulations across multiple domains on the Metaverse. Simulated results may predict adversaries' behaviors and tactics, leading to more proactive cyber defense strategies. However, current HDTs’ cognitive architectures are underdeveloped for such use. Objective This paper aims to make a case for extending the current digital cognitive architectures as the first step toward more robust HDTs that are suitable for realistic Metaverse cybersecurity simulations. Methods This study formally documented 108 psychology constructs and thousands of related paths based on 20 time-tested psychology theories, all of which were packaged as Cybonto—a novel ontology. Then, this study applied 20 network science centrality algorithms in ranking the Cybonto psychology constructs by their influences. Results Out of 108 psychology constructs, the top 10 are Behavior, Arousal, Goals, Perception, Self-efficacy, Circumstances, Evaluating, Behavior-Controllability, Knowledge, and Intentional Modality. In this list, only Behaviors, Goals, Perception, Evaluating, and Knowledge are parts of existing digital cognitive architectures. Notably, some of the constructs are not explicitly implemented. Early usability tests demonstrate that Cybonto can also be useful for immediate uses such as manual analysis of hackers’ behaviors and automatic analysis of behavioral cybersecurity knowledge texts. Conclusions The results call for specific extensions of current digital cognitive architectures such as explicitly implementing more refined structures of Long-term Memory and Perception, placing a stronger focus on noncognitive yet influential constructs such as Arousal, and creating new capabilities for simulating, reasoning about, and selecting circumstances.
Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

With the popularity of mobile devices and development of computationally intensive applications, researchers are focusing on offloading computation to Mobile Edge Computing (MEC) server due to its high computational efficiency and low communication delay. As the computing resources of an MEC server are limited, vehicles in the urban area who have abundant idle resources should be fully utilized. However, offloading computing tasks to vehicles faces many challenging issues. In this paper, we introduce a vehicular fog-edge computing paradigm and formulate it as a multi-stage Stackelberg game to deal with these issues. Specifically, vehicles are not obligated to share resources, let alone disclose their private information (e.g., stay time and the amount of resources). Therefore, in the first stage, we design a contract-based incentive mechanism to motivate vehicles to contribute their idle resources. Next, due to the complicated interactions among vehicles, road-side unit (RSU), MEC server and mobile device users, it is challenging to coordinate the resources of all parties and design a transaction mechanism to make all entities benefit. In the second and third stages, based on Stackelberg game, we develop pricing strategies that maximize the utilities of all parties. The analytical forms of optimal strategies for each stage are given. Simulation results demonstrate the effectiveness of our proposed incentive mechanism, reveal the trends of energy consumption and offloading decisions of users with various parameters, and present the performance comparison between our framework and existing MEC offloading paradigm in vehicular networks.
Quantum-Access-Secure Message Authentication via Blind-Unforgeability

Formulating and designing authentication of classical messages in the presence of adversaries with quantum query access has been a longstanding challenge, as the familiar classical notions of unforgeability do not directly translate into meaningful notions in the quantum setting. A particular difficulty is how to fairly capture the notion of "predicting an unqueried value" when the adversary can query in quantum superposition. We propose a natural definition of unforgeability against quantum adversaries called blind unforgeability. This notion defines a function to be predictable if there exists an adversary who can use "partially blinded" oracle access to predict values in the blinded region. We support the proposal with a number of technical results. We begin by establishing that the notion coincides with EUF-CMA in the classical setting and go on to demonstrate that the notion is satisfied by a number of simple guiding examples, such as random functions and quantum-query-secure pseudorandom functions. We then show the suitability of blind unforgeability for supporting canonical constructions and reductions. We prove that the "hash-and-MAC" paradigm and the Lamport one-time digital signature scheme are indeed unforgeable according to the definition. To support our analysis, we additionally define and study a new variety of quantum-secure hash functions called Bernoulli-preserving. Finally, we demonstrate that blind unforgeability is strictly stronger than a previous definition of Boneh and Zhandry [EUROCRYPT '13, CRYPTO '13] and resolve an open problem concerning this previous definition by constructing an explicit function family which is forgeable yet satisfies the definition.
Tournament selection based antlion optimization algorithm for solving quadratic assignment problem

We propose and develop an improved version of antlion optimizer (ALO), namely tournament selection based antlion optimization algorithm for quadratic assignment problem (QAP). ALO algorithm has some handicaps, such as long run time, local optima stagnation and premature convergence for some problems. The literature describes different methods that improve the performance of antlion optimizer, but most of these are about specific optimization problems. In this paper, we introduce the tournament selection method instead of the roulette wheel method on random walking mechanism of ALO and we update some equations used in ALO algorithm. To compare the proposed tournament selection based ALO (TALO) algorithm with classic ALO, we deal with ten benchmark functions from literature. The comparison results are evaluated according to the different metrics, such as mean best, standard deviation, optimality, accuracy, CPU time, number of function evaluations (NFE). The detailed analyzes of the proposed TALO algorithm are performed. These are the convergence analysis, statistical analysis, search history analysis, trajectory analysis, average distance analysis, computational complexity analysis. The proposed TALO algorithm is compared with the other ALO versions (binary ALO and chaotic ALO variants) for same ten benchmark functions. As last, TALO algorithm has been also implemented for the quadratic assignment problem (QAP). The QAP results has been compared with several well-known meta-heuristic algorithms. TALO’s performance has been evaluated with those of binary ALO and chaotic ALO variants for same QAP instance. Finally, the solution quality of proposed TALO algorithm has been analyzed for solving QAP using some instances presented in QAPLIB site. The results provide the proposed TALO algorithm has the best performance in comparison with those of the other meta-heuristic algorithms. Due to the performance of TALO algorithm, we expect this version to be applied for different optimization problems.
Robust Semantic Template Matching Using a Superpixel Region Binary Descriptor

Almost all conventional template-matching methods employ low-level image features to measure the similarity between a template image and a scene image using similarity measures, such as pixel intensity and pixel gradient. Although these methods have been widely used in many applications, they cannot simultaneously address all types of robustness challenges. In this paper, with the goal of simultaneously addressing the various challenges, we present a robust semantic template-matching (RSTM) approach. Inspired by the local binary descriptor, we propose a novel superpixel region binary descriptor (SRBD) to construct a multilevel semantic fusion feature vector for RSTM. SRBD uses a new kernel-distance-based simple linear iterative clustering method to extract the stable superpixels from the template image. Then, based on the average intensity difference between each superpixel region and its neighbors, the dominant gradient orientation of each superpixel can be obtained, and the semantic features of each superpixel can be described as the dominant orientation difference vector, which is coded as the rotation-invariant SRBD. In the offline matching phase, the fusion semantic feature vector of RSTM combines the multilevel SRBD features with different numbers of superpixels. In the online matching phase, to cope with rotation invariance, a marginal probability model is proposed and applied to locate the positions of template images in the scene image. Moreover, to accelerate computation, an image pyramid is employed. We conduct a series of experiments on a large dataset randomly selected from the MS COCO dataset to fully analyze the robustness of this approach. The experimental results show that RSTM simultaneously addresses rotation changes, scale changes, noise, occlusions, blur, nonlinear illumination changes, and deformation with high time efficiency while also outperforming the previous state-of-the-art template-matching methods.
Self-attentive Model for Headline Generation

Headline generation is a special type of text summarization task. While the amount of available training data for this task is almost unlimited, it still remains challenging, as learning to generate headlines for news articles implies that the model has strong reasoning about natural language. To overcome this issue, we applied recent Universal Transformer architecture paired with byte-pair encoding technique and achieved new state-of-the-art results on the New York Times Annotated corpus with ROUGE-L F1-score 24.84 and ROUGE-2 F1-score 13.48. We also present the new RIA corpus and reach ROUGE-L F1-score 36.81 and ROUGE-2 F1-score 22.15 on it.
ROSA: Robust Salient Object Detection Against Adversarial Attacks

Recently, salient object detection has witnessed remarkable improvement owing to the deep convolutional neural networks which can harvest powerful features for images. In particular, the state-of-the-art salient object detection methods enjoy high accuracy and efficiency from fully convolutional network (FCN)-based frameworks which are trained from end to end and predict pixel-wise labels. However, such framework suffers from adversarial attacks which confuse neural networks via adding quasi-imperceptible noises to input images without changing the ground truth annotated by human subjects. To our knowledge, this paper is the first one that mounts successful adversarial attacks on salient object detection models and verifies that adversarial samples are effective on a wide range of existing methods. Furthermore, this paper proposes a novel end-to-end trainable framework to enhance the robustness for arbitrary FCN-based salient object detection models against adversarial attacks. The proposed framework adopts a novel idea that first introduces some new generic noise to destroy adversarial perturbations, and then learns to predict saliency maps for input images with the introduced noise. Specifically, our proposed method consists of a segment-wise shielding component, which preserves boundaries and destroys delicate adversarial noise patterns and a context-aware restoration component, which refines saliency maps through global contrast modeling. The experimental results suggest that our proposed framework improves the performance significantly for state-of-the-art models on a series of datasets.
Entropy in Image Analysis

Image analysis is playing a very essential role in numerous research areas in the fields of science and technology, ranging from medical imaging to the computer science of automatic vision [...].
Fault-tolerant quantum gates with defects in topological stabilizer codes

Universal quantum computing by braiding defects in topological stabilizer codes of any dimension is proven to be impossible. Notwithstanding this no-go theorem, it is shown how braiding defects can yield all Clifford gates in three or more dimensions, and that universal quantum computing in three-dimensional surface codes is possible by supplementing braiding with adaptive gates.
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages

Rudra Murthy, Anoop Kunchukuttan, Pushpak Bhattacharyya. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A distributed approximate nearest neighbors algorithm for efficient large scale mean shift clustering

Mean Shift clustering, as a generalization of the well-known k-means clustering, computes arbitrarily shaped clusters as defined as the basins of attraction to the local modes created by the density gradient ascent paths. Despite its potential for improved clustering accuracy, the Mean Shift approach is a computationally expensive method for unsupervised learning. We introduce two contributions aiming to provide approximate Mean Shift clustering, based on scalable procedures to compute the density gradient ascent and cluster labeling, with a linear time complexity, as opposed to the quadratic time complexity for the exact clustering. Both propositions are based on Locality Sensitive Hashing (LSH) to approximate nearest neighbors. When implemented on a serial system, these approximate methods can be used for moderate sized datasets. To facilitate the analysis of Big Data, a distributed implementation, written for the Spark/Scala ecosystem is proposed. An added benefit is that our proposed approximations of the density gradient ascent, when used as a pre-processing step in other clustering methods, can also improve the clustering accuracy of the latter. We present experimental results illustrating the effect of tuning parameters on cluster labeling accuracy and execution times, as well as the potential to solve concrete problems in Big Clustering.
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence

Deep neural networks have received dramatic success based on the optimization method of stochastic gradient descent (SGD). However, it is still not clear how to tune hyper-parameters, especially batch size and learning rate, to ensure good generalization. This paper reports both theoretical and empirical evidence of a training strategy that we should control the ratio of batch size to learning rate not too large to achieve a good generalization ability. Specifically, we prove a PAC-Bayes generalization bound for neural networks trained by SGD, which has a positive correlation with the ratio of batch size to learning rate. This correlation builds the theoretical foundation of the training strategy. Furthermore, we conduct a large-scale experiment to verify the correlation and training strategy. We trained 1,600 models based on architectures ResNet-110, and VGG-19 with datasets CIFAR-10 and CIFAR-100 while strictly control unrelated variables. Accuracies on the test sets are collected for the evaluation. Spearman's rank-order correlation coefficients and the corresponding $p$ values on 164 groups of the collected data demonstrate that the correlation is statistically significant, which fully supports the training strategy.
Multi-task Learning of a Deep K-Nearest Neighbour Network for Histopathological Image Classification and Retrieval

Deep neural networks have achieved tremendous success in image recognition, classification and object detection. However, deep learning is often criticised for its lack of transparency and general inability to rationalise its predictions. The issue of poor model interpretability becomes critical in medical applications: a model that is not understood and trusted by physicians is unlikely to be used in daily clinical practice. In this work, we develop a novel multi-task deep learning framework for simultaneous histopathology image classification and retrieval, leveraging on the classic concept of k-nearest neighbours to improve model interpretability. For a test image, we retrieve the most similar images from our training databases. These retrieved nearest neighbours can be used to classify the test image with a confidence score, and provide a human-interpretable explanation of our classification. Our original framework can be built on top of any existing classification network (and therefore benefit from pretrained models), by (i) combining a triplet loss function with a novel triplet sampling strategy to compare distances between samples and (ii) adding a Cauchy hashing loss function to accelerate neighbour searching. We evaluate our method on colorectal cancer histology slides and show that the confidence estimates are strongly correlated with model performance. Nearest neighbours are intuitive and useful for expert evaluation. They give insights into understanding possible model failures, and can support clinical decision making by comparing archived images and patient records with the actual case.
Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method

Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints’ muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem with several numerical features. Three ensemble algorithms such as bagging, Adaboost, and random subspace were used in the study. These ensemble classifiers use k-NN (K-nearest neighbours) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. The assessment was based on set of performance measures such as precision, recall, F-measure, and receiver operating characteristic (ROC) curve. The performance was also measured based on the comparison of the overall classification accuracy rate between different ensembles classifiers and the base learners. Overall, it was found that for Dataset 1, random subspace classifier with k-NN shows the best results in terms of overall accuracy rate of 97.50% and for Dataset 2, bagging-RF shows the highest overall accuracy rate of 94.84% over different ensemble classifiers. The findings indicate that the efficiency of the base classifiers with ensemble classifier have substantially improved.
Multi-task Generative Adversarial Network for Detecting Small Objects in the Wild

Object detection results have been rapidly improved over a short period of time with the development of deep convolutional neural networks. Although impressive results have been achieved on large/medium sized objects, the performance on small objects is far from satisfactory and one of remaining open challenges is detecting small object in unconstrained conditions (e.g. COCO and WIDER FACE benchmarks). The reason is that small objects usually lack sufficient detailed appearance information, which can distinguish them from the backgrounds or similar objects. To deal with the small object detection problem, in this paper, we propose an end-to-end multi-task generative adversarial network (MTGAN), which is a general framework. In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection. The discriminator is a multi-task network, which describes each inputted image patch with a real/fake score, object category scores, and bounding box regression offsets. Furthermore, to make the generator recover more details for easier detection, the classification and regression losses in the discriminator are back-propagated into the generator during training process. Extensive experiments on the challenging COCO and WIDER FACE datasets demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a blurred small one, and show that the detection performance, especially for small sized objects, improves over state-of-the-art methods by a large margin.