Reviewerly/exHarmony huggingface dataset:
Evaluation of Information Security Awareness among Palestinian Learners

Most of cyber security threats originate from users' unconcerned behaviors, especially when those users are less aware of security threats and consequences. Attackers are aware of this attitude and work to exploit it the way they can, by breaking up the security chain. The dramatic increase of Internet usage by people of different ages and backgrounds places them under serious cyberthreats and attacks. The most popular and straightforward motives of these attacks is basically the lack of awareness and knowledge around cybersecurity measures and procedures which dramatically increases security breaches.This research is motivated by the facts mentioned above, and it aims to assess the level of Internet users' security awareness among Palestinian learners. The study focuses on five fundamental security issues involving passwords, social media usage, email usage, security of mobile devices, and social engineering. A quantitative approach is employed in the study, where data was collected by means of pre-designed and tested questionnaires from a random sample of 200 participants. Data were collected, encoded, preprocessed and then analyzed using SPSS. Results emphasized the overall carelessness of Internet users in relation to security measures, knowledge and practices. Most of respondents did not try to gain any knowledge whatsoever by, for instance, attending an awareness course. The study found that users with higher level of knowledge in security awareness acted in a more professional way toward cyberthreats than those who did not have that knowledge.
Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation

Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and obtain labeled data. Obtaining labeled data is especially difficult in biomedical applications, where only trained domain experts can provide labels, which often come in large diversity and complexity. Recently presented approaches for unsupervised detection of visual anomalies approaches omit the need for labeled data and demonstrate promising results in domains, where anomalous samples significantly deviate from the normal appearance. Despite promising results, the performance of such approaches still lags behind supervised approaches and does not provide a one-fits-all solution. In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.
Improved (Related-key) Differential Cryptanalysis on GIFT

In this paper, we reevaluate the security of GIFT against differential cryptanalysis under both single-key scenario and related-key scenario. Firstly, we apply Matsui's algorithm to search related-key differential trails of GIFT. We add three constraints to limit the search space and search the optimal related-key differential trails on the limited search space. We obtain related-key differential trails of GIFT-64/128 for up to 15/14 rounds, which are the best results on related-key differential trails of GIFT so far. Secondly, we propose an automatic algorithm to increase the probability of the related-key boomerang distinguisher of GIFT by searching the clustering of the related-key differential trails utilized in the boomerang distinguisher. We find a 20-round related-key boomerang distinguisher of GIFT-64 with probability $$ 2^{-58.557} $$ . The 25-round related-key rectangle attack on GIFT-64 is constructed based on it. This is the longest attack on GIFT-64. We also find a 19-round related-key boomerang distinguisher of GIFT-128 with probability $$ 2^{-109.626} $$ . We propose a 23-round related-key rectangle attack on GIFT-128 utilizing the 19-round distinguisher, which is the longest related-key attack on GIFT-128. The 24-round related-key rectangle attack on GIFT-64 and 22-round related-key boomerang attack on GIFT-128 are also presented. Thirdly, we search the clustering of the single-key differential trails. We increase the probability of a 20-round single-key differential distinguisher of GIFT-128 from $$ 2^{-121.415} $$ to $$ 2^{-120.245} $$ . The time complexity of the 26-round single-key differential attack on GIFT-128 is improved from $$ 2^{124.415} $$ to $$ 2^{123.245} $$ .
Integration model for collaborative learning in Covid-19 times : Validation of the Constructivist On-Line Learning Environment Survey (COLLES)

Collaborative learning is an essential part of a new educational approach, characterized by social isolation and virtual learning experiences because of Covid-19. The objective of this work was to validate an integration model for collaborative learning mediated by the LMS - blackboard collaborate, from the application of the Constructivist On-Line Learning Environment Survey (COLLES), an instrument with 24 items and 6 factors, which assesses specifically online learning. An exploratory analysis was used to establish the reliability and adequacy of the factors with the items of the questionnaire, then a confirmatory analysis was carried out using the Modeling of Structural Equations with Partial Least Squares PLS-SEM. The study was carried out with 347 university students, 57.8% being men and 42.0% women. The results through R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> show that the influence of the support of the tutor/professor in the interpretation of the contents is 52.6%; followed by relevance with 22.1%; the influence on the development of reflective thinking with 20.4%; the influence of the Tutor/professor in supporting peers to promote a favorable integration environment represents 12.8% of the variance explained by the model. It is concluded that the Constructivist On-Line Learning Environment Survey (COLLES) allows the validation of an integration model for collaborative online learning in times of Covid-19.
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints

We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations. We propose augmenting the original deep restricted kernel machine formulation for kernel PCA by orthogonality constraints on the latent variables to promote disentanglement and to make it possible to carry out optimization without first defining a stabilized objective. After discussing a number of algorithms for end-to-end training, we quantitatively evaluate the proposed method’s effectiveness in disentangled feature learning. We demonstrate on four benchmark datasets that this approach performs similarly overall to β-VAE on several disentanglement metrics when few training points are available while being less sensitive to randomness and hyperparameter selection than β-VAE. We also present a deterministic initialization of Constr-DRKM’s training algorithm that significantly improves the reproducibility of the results. Finally, we empirically evaluate and discuss the role of the number of layers in the proposed methodology, examining the influence of each principal component in every layer and showing that components in lower layers act as local feature detectors capturing the broad trends of the data distribution, while components in deeper layers use the representation learned by previous layers and more accurately reproduce higher-level features.
SKFAC: Training Neural Networks with Faster Kronecker-Factored Approximate Curvature

The bottleneck of computation burden limits the widespread use of the 2nd order optimization algorithms for training deep neural networks. In this paper, we present a computationally efficient approximation for natural gradient descent, named Swift Kronecker-Factored Approximate Curvature (SKFAC), which combines Kronecker factorization and a fast low-rank matrix inversion technique. Our research aims at both fully connected and convolutional layers. For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy. For convolutional layers, we propose a way with two strategies to save computational efforts without affecting the empirical performance by reducing across the spatial dimension or receptive fields of feature maps. Specifically, we propose two effective dimension reduction methods for this purpose: Spatial Subsampling and Reduce Sum. Experimental results of training several deep neural networks on Cifar-10 and ImageNet-1k datasets demonstrate that SKFAC can capture the main curvature and yield comparative performance to K-FAC. The proposed method bridges the wall-clock time gap between the 1st and 2nd order algorithms.
UNBODY: A Poetry Escape Room in Augmented Reality

The integration of augmented reality (AR) technology into personal computing is happening fast, and augmented workplaces for professionals in areas such as Industry 4.0 or digital health can reasonably be expected to form liminal zones that push the boundary of what currently possible. The application potential in the creative industries, however, is vast and can target broad audiences, so with UNBODY, we set out to push boundaries of a different kind and depart from the graphic-centric worlds of AR to explore textual and aural dimensions of an extended reality, in which words haunt and re-create our physical selves. UNBODY is an AR installation for smart glasses that embeds poetry in the user’s surroundings. The augmented experience turns reality into a medium where holographic texts and film clips spill from dayglow billboards and totems. In this paper, we develop a blueprint for an AR escape room dedicated to the spoken and written word, with its open source code facilitating uptake by others into existing or new AR escape rooms. We outline the user-centered process of designing, building, and evaluating UNBODY. More specifically, we deployed a system usability scale (SUS) and a spatial interaction evaluation (SPINE) in order to validate its wider applicability. In this paper, we also describe the composition and concept of the experience, identifying several components (trigger posters, posters with video overlay, word dropper totem, floating object gallery, and a user trail visualization) as part of our first version before evaluation. UNBODY provides a sense of situational awareness and immersivity from inside an escape room. The recorded average mean for the SUS was 59.7, slightly under the recommended 68 average but still above ‘OK’ in the zone of low marginal acceptable. The findings for the SPINE were moderately positive, with the highest scores for output modalities and navigation support. This indicated that the proposed components and escape room concept work. Based on these results, we improved the experience, adding, among others, an interactive word composer component. We conclude that a poetry escape room is possible, outline our co-creation process, and deliver an open source technical framework as a blueprint for adding enhanced support for the spoken and written word to existing or coming AR escape room experiences. In an outlook, we discuss additional insight on timing, alignment, and the right level of personalization.
Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

This paper introduces a new fundamental characteristic, i.e., the dynamic range, from real-world metric tools to deep visual recognition. In metrology, the dynamic range is a basic quality of a metric tool, indicating its flexibility to accommodate various scales. Larger dynamic range offers higher flexibility. In visual recognition, the multiple scale problem also exist. Different visual concepts may have different semantic scales. For example, "Animal" and "Plants" have a large semantic scale while "Elk" has a much smaller one. Under a small semantic scale, two different elks may look quite different to each other . However, under a large semantic scale (e.g., animals and plants), these two elks should be measured as being similar.Introducing the dynamic range to deep metric learning, we get a novel computer vision task, i.e., the Dynamic Metric Learning. It aims to learn a scalable metric space to accommodate visual concepts across multiple semantic scales. Based on three types of images, i.e., vehicle, animal and online products, we construct three datasets for Dynamic Metric Learning. We benchmark these datasets with popular deep metric learning methods and find Dynamic Metric Learning to be very challenging. The major difficulty lies in a conflict between different scales: the discriminative ability under a small scale usually compromises the discriminative ability under a large one, and vice versa. As a minor contribution, we propose Cross-Scale Learning (CSL) to alleviate such conflict. We show that CSL consistently improves the baseline on all the three datasets. The datasets and the code will be publicly available at https://github.com/SupetZYK/DynamicMetricLearning.
Learning Discriminative Speaker Embedding by Improving Aggregation Strategy and Loss Function for Speaker Verification

The embedding-based speaker verification (SV) technology has witnessed significant progress due to the advances of deep convolutional neural networks (DCNN). However, how to improve the discrimination of speaker embedding in the open world SV task is still the focus of current research in the community. In this paper, we improve the discriminative power of speaker embedding from three-fold: (1) NeXtVLAD is introduced to aggregate frame-level features, which decomposes the high-dimensional frame-level features into a group of low-dimensional vectors before applying VLAD aggregation. (2) A multi-scale aggregation strategy (MSA) assembled with NeXtVLAD is designed with the purpose of fully extract speaker information from the frame-level feature in different hidden layers of DCNN. (3) A mutually complementary assembling loss function is proposed to train the model, which consists of a prototypical loss and a marginal-based softmax loss. Extensive experiments have been conducted on the VoxCeleb-1 dataset, and the experimental results show that our proposed system can obtain significant performance improvements compared with the baseline, and obtains new state-of-the-art results. The source code of this paper is available at https://github.com/LCF2764/Discriminative-Speaker-Embedding.
Analysis and tuning of hierarchical topic models based on Renyi entropy approach

Hierarchical topic modeling is a potentially powerful instrument for determining topical structures of text collections that additionally allows constructing a hierarchy representing the levels of topic abstractness. However, parameter optimization in hierarchical models, which includes finding an appropriate number of topics at each level of hierarchy, remains a challenging task. In this paper, we propose an approach based on Renyi entropy as a partial solution to the above problem. First, we introduce a Renyi entropy-based metric of quality for hierarchical models. Second, we propose a practical approach to obtaining the "correct" number of topics in hierarchical topic models and show how model hyperparameters should be tuned for that purpose. We test this approach on the datasets with the known number of topics, as determined by the human mark-up, three of these datasets being in the English language and one in Russian. In the numerical experiments, we consider three different hierarchical models: hierarchical latent Dirichlet allocation model (hLDA), hierarchical Pachinko allocation model (hPAM), and hierarchical additive regularization of topic models (hARTM). We demonstrate that the hLDA model possesses a significant level of instability and, moreover, the derived numbers of topics are far from the true numbers for the labeled datasets. For the hPAM model, the Renyi entropy approach allows determining only one level of the data structure. For hARTM model, the proposed approach allows us to estimate the number of topics for two levels of hierarchy.
BMT: Behavior Driven Development-based Metamorphic Testing for Autonomous Driving Models

Deep Neural Network based models are widely used for perception and control in autonomous driving. Recent work leverages metamorphic testing to improve defect detection but is limited to using only an equality-based metamorphic relation. Thus, it does not provide sufficient expressiveness for users to define custom metamorphic relations nor means to automatically generate meaningful inputs based on such expressive metamorphic relations that reflect real-world traffic behaviors. In this paper, we preliminarily design and evaluate a declarative Behaviour-Driven Development (BDD)-based metamorphic testing framework BMT, which enables domain experts to specify custom traffic behaviors—a car shall decelerate by x% when a bicycle is in front, etc. It then automatically translates a human-written behavior to a corresponding metamorphic relation and synthesizes meaningful test inputs using a variety of image and graphics processing techniques. Our preliminary evaluation shows that BMT can detect a significant number of erroneous predictions of three driving models for speed predictions. These detected erroneous predictions are manually examined and confirmed by six human judges as meaningful traffic violations. By automating test generation from custom behaviors, BMT enables experts to easily express domain-specific constraints and finds violations of such constraints.
Towards compliance checking in reified I/O logic via SHACL

Reified Input/Output logic [29] has been recently proposed to handle natural language meaning in Input/Output logic [17]. So far, the research in reified I/O logic has focused only on KR issues, specifically on how to use the formalism for representing contextual meaning of norms (see [28]). This paper is the first attempt to investigate reasoning in reified I/O logic, specifically compliance checking. This paper investigates how to model reified I/O logic formulae in Shapes Constraint Language (SHACL) [2], a recent W3C recommendation for validating and reasoning with RDFs/OWL.
A Cross-Border E-Commerce Approach Based on Blockchain Technology

In the current cross-border electronic commerce (e-commerce) system, various document recording and authorization processes are cumbersome, record sharing efficiency is low, and identity verification is difficult. A method of asymmetric encryption technology combining Blockchain technology and cryptography is proposed. The advantages of asymmetric encrypted communications include high security and ease of multiparties communication collaboration, being applied to a peer-to-peer network formed by Blockchain technology, and making cross-border e-commerce record cross-domain sharing traceable, data immutable, and identity verification simplified. First of all, based on the immutable modification of Blockchain technology and asymmetric encryption technology, file synchronization contracts and authorization contracts are designed. Its distributed storage advantages ensure the privacy of users’ cross-border e-commerce information. Second, the design of the cross-domain acquisition contract can effectively verify the identity and transmission efficiency of both parties to the data sharing, so that illegal users can be safely filtered without a third-party notary institution. The simulation experiment results show that the solution proposed in this paper has obvious advantages in data antitheft, multiparty authentication, and saving system overhead compared with traditional cloud computing methods to solve the problem of sharing medical records. It provides a reference for solving the security problems in the process of data sharing by using the advantages of Blockchain’s decentralization and auditability and provides reference ideas for solving the problems of data sharing and cross-domain authentication.
Algorithm mediated early detection of oral cancer from image analysis

To develop Automatic Oral Cancer Detection algorithm for identification and differentiation of premalignant lesions from buccal cavity images for early detection of oral cancer, which may reduce related fatalities in developing countries.The oral cavity images of normal, erythroplakia, and leukoplakia (20 images of each) were collected and processed using MATLAB image processing tools. First, maximum red value was used to differentiate between normal and abnormal. Second, mean red value was used for the selection of a processing path through YCbCr. Third, gray-level co-occurrence matrix (GLCM) based features were used to make final decisions. Images have been randomly divided and shuffled between training and test set to rigorously train the algorithm.With 100% efficiency, normal images were separated from abnormal images in the first step by applying R value distribution with a cutoff R value, 11,900. Further, images with a mean R value >200 and <200 were processed by segmentation of Y plane and Cr plane, respectively. For the final decision, abnormal images were analyzed through the GLCM using the entropy feature as one of the key indicators, which can apply to the differentiation decision with 89% efficiency.The developed algorithm can successfully differentiate premalignant lesions from normal. A graphic user interface was developed, which displays outcomes with reasonable accuracy.
Puck localization and multi-task event recognition in broadcast hockey videos

Puck localization is an important problem in ice hockey video analytics useful for analyzing the game, determining play location, and assessing puck possession. The problem is challenging due to the small size of the puck, excessive motion blur due to high puck velocity and occlusions due to players and boards. In this paper, we introduce and implement a network for puck localization in broadcast hockey video. The network leverages expert NHL play-by-play annotations and uses temporal context to locate the puck. Player locations are incorporated into the network through an attention mechanism by encoding player positions with a Gaussian-based spatial heatmap drawn at player positions. Since event occurrence on the rink and puck location are related, we also perform event recognition by augmenting the puck localization network with an event recognition head and training the network through multi-task learning. Experimental results demonstrate that the network is able to localize the puck with an AUC of 73.1% on the test set. The puck location can be inferred in 720p broadcast videos at 5 frames per second. It is also demonstrated that multitask learning with puck location improves event recognition accuracy.