Reviewerly/exHarmony huggingface dataset:
Evolutionary Population Dynamic Mechanisms for the Harmony Search Algorithm

Evolutionary algorithms have been widely adopted in science and industry for optimizing challenging problems mainly due to their black box nature and high local optima avoidance. As popular soft computing techniques, they benefit from several stochastic operators, including but not limited to, selection, recombination, mutation, elitism, population diversity, and population dynamics. Among such operators, some have been extensively used and analyzed in different algorithms, while others are yet to be explored in different algorithms. This motivated our attempts to integrate Evolutionary Population Dynamics (EPD) in the Harmony Search (HS) algorithm. EPD is an evolutionary mechanism that excludes and/or replaces a set of the poor solutions in each generation and prevents them from reducing the quality of other solutions. EPD has been used in three different ways in HS to impact 10%, 30%, or 50% of the population to see its impact on the performance of this algorithm. It was observed that 10% is a reasonable portion of the population in HS to improve its performance on IEEE Congress of Evolutionary computation (CEC) test functions, which effectively mimic challenging real-world optimization problems.
Implementation of Revocable Keyed-Verification Anonymous Credentials on Java Card

Java Card stands out as a good choice for the development of smart card applications due to the high interoperability between different manufacturers, its security, and wide support of cryptographic algorithms. Despite extensive cryptographic support, current Java Cards do not support non-standard cryptographic algorithms such as post-quantum, secure-multiparty computations, and privacy-enhancing cryptographic schemes. Moreover, Java Card is restricted by the Application Programming Interface (API) in algebraic operations, which are the foundation of modern cryptographic schemes. This paper addresses the issue of developing these modern schemes by exploiting the limited cryptographic API provided by these types of cards. We show how to (ab)use the Java Card’s API to perform modular arithmetic operations, as well as basic operations on elliptic curves. Furthermore, we implement an attribute-based privacy-enhancing scheme on an off-the-shelf Java Card. To do so, we use our cryptographic API and several optimization techniques to make the scheme as efficient as possible. To demonstrate the practicality of our solution, we present the implementation results and benchmark tests.
Towards Neural Network-Based Communication System: Attack and Defense

Recent progress has witnessed the excellent success of neural networks in many emerging applications, such as image recognition, text classification, and speech analysis. In order to achieve secure communication, the utilization of neural networks has been realized yet has not raised sufficient research attention. In addition, the existing neural network-based communication system falls short due to its critical security flaws. In this article, we investigate the security vulnerabilities of the existing neural communication system. Based on our analysis, we design two kinds of attack models, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target man-in-the-middle attack</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target fraud attack</i> . After that, to improve the security performance of neural communication systems, we develop a new defense mechanism to facilitate two-way secure communication by separating secret key from plaintext and incorporating defensive loss into the training process. Moreover, we show the effectiveness of our proposed neural communication system via theoretical proof. Finally, we implement comprehensive real data experiments to evaluate the performance of our attack and defense methods from the aspects of classification accuracy, communication efficiency and communication qualify, which confirms the advantages of our proposed neural communication system compared with the state-of-the-art.
A Grid-Based Density Peaks Clustering Algorithm

This article focuses on the improvement of density peaks clustering (DPC, also known as clustering by fast search and find of density peaks) by the introduction of the grid clustering. A grid division is used to divide the data space into grid cells. The global characteristic of grid cell replaces the characteristic of the data points. The local density of DPC is replaced by the density of the grid cell. The cut-off distance of DPC is no longer needed. Then, the cluster centers are determined adaptively based on the quantities. Finally, a two-stage allocation strategy (neighborhood searching and border grid cell assigning) is introduced to obtain the final clustering results. Six datasets are used to experimentally verify the effectiveness and performance of the proposed algorithm. Experimental results show that the improvement of DPC is successful. Compared with DPC, the proposed algorithm is more efficient with less manual intervention.
The Effects of Spatial Complexity on Narrative Experience in Space-Adaptive AR Storytelling

A critical yet unresolved challenge in designing space-adaptive narratives for Augmented Reality (AR) is to provide consistently immersive user experiences anywhere, regardless of physical features specific to a space. For this, we present a comprehensive analysis on a series of user studies investigating how the size, density, and layout of real indoor spaces affect users playing Fragments, a space-adaptive AR detective game. Based on the studies, we assert that moderate levels of traversability and visual complexity afforded in counteracting combinations of size and complexity are beneficial for narrative experience. To confirm our argument, we combined the experimental data of the studies (n=112) to compare how five different spatial complexity conditions impact narrative experience when applied to contrasting room sizes. Results show that whereas factors of narrative experience are rated significantly higher in relatively simple settings for a small space, they are less affected by complexity in a large space. Ultimately, we establish guidelines on the design and placement of space-adaptive augmentations in location-independent AR narratives to compensate for the lack or excess of affordances in various real spaces and enhance user experiences therein.
From Pixels to Semantics: Self-Supervised Video Object Segmentation With Multiperspective Feature Mining

Existing self-supervised methods pose one-shot video object segmentation (O-VOS) as pixel-level matching to enable segmentation mask propagation across frames. However, the two tasks are not fully equivalent since O-VOS is more reliant on semantic correspondence rather than accurate pixel matching. To remedy this issue, we explore a new self-supervised framework that integrates pixel-level correspondence learning with semantic-level adaptation. The pixel-level correspondence learning is performed through photometric reconstruction of adjacent RGB frames during offline training, while semantic-level adaption operates at test-time by enforcing a bi-directional agreement of the predicted segmentation masks. In addition, we further propose a new network architecture with multi-perspective feature mining mechanism which can not only enhance reliable features but also suppress noisy ones to facilitate more robust image matching. By training the network using the proposed self-supervised framework, we achieve state-of-the-art performance on widely adopted datasets, further closing up the gap between self-supervised learning methods and their fully supervised counterparts.
Content-Methodological Aspects of Tutor and Mentor Support Implementation in the System of Continuing Additional Professional Education

The aim of the study is to identify the content and methodological aspects of the implementation of tutor and mentor support in institutions of additional professional education. The article presents the content and methodological aspects of tutor and mentor support organization in additional professional education, taking into account the provisions of acmeological, activity and personal approaches. The scientific novelty of the study lies in the determination of the pedagogical conditions for organizing tutor and mentor support, necessary for the implementation of an individual educational route for students of additional professional programs for advanced training and professional retraining. As a result, the forms of tutor and mentor support implementation in the system of additional professional education for adults are determined.
Covid-19 WhatsApp sticker memes in Oman

In this paper, the form and function of personalized Covid-19 WhatsApp sticker memes created and shared as social laments by citizens in Oman are examined. The compiled data set of 288 WhatsApp stickers was taken from a larger ethnographic project on Arabs and Covid-19. To collect and analyze the data, perspectives from visual semiotics were integrated with participatory and geosemiotic approaches to ground the stickers socially and globally. Six functions of Covid-19 WhatsApp stickers in Oman were identified: expressing political dissent, creating public signs, promoting religious agenda, indexing frustration, expressing levity, and constructing counter-discourse. Based on this analysis, it is suggested that by creating and using WhatsApp stickers during the 2020–2021 Covid-19 pandemic, Omani citizens positioned themselves as agentive participants in charge of their own lives, thus, solidifying a decade-long request for a new form of public-government relationship. The paper adds to research on Arabic digital communication and pandemic discourse.
Exposing digital image forgeries from statistical footprints

Photo attack and anti-forgery techniques have been on the rise in the internet age. Attackers conceal tampering by applying anti-forgery techniques, such as contrast enhancement. Digital image forensics concerns with detecting traces of these operations subjected upon the image. In this work, a novel Anti-Forensic Contrast Enhancement Detector (AFCED) scheme has been proposed to assess contrast changes from anti-forensic digital images. The proposed model is based on the underlying observation that, with repeated gamma correction, the rate of attaining contrast saturation in normal images is greater than already contrast-improved images. Thus, we employ skewness and kurtosis of the gamma-corrected second-order derivative histograms as features to distinguish normal images from contrast-enhanced images. These features are robustly classified on a Support Vector Machine. The proposed model achieved 97.40% F1-score and 98.18% sensitivity on the Dresden and Wikiart image databases on-par with state-of-the-art results.
Evoking the N400 Event-related Potential (ERP) Component Using a Publicly Available Novel Set of Sentences with Semantically Incongruent or Congruent Eggplants (Endings)

During speech comprehension, the ongoing context of a sentence is used to predict sentence outcome by limiting subsequent word likelihood. Neurophysiologically, violations of context-dependent predictions result in amplitude modulations of the N400 event-related potential (ERP) component. While the N400 is widely used to measure semantic processing and integration, no publicly-available auditory stimulus set is available to standardize approaches across the field. Here, we developed an auditory stimulus set of 442 sentences that utilized the semantic anomaly paradigm, measured cloze probability for all stimuli, and was made for both children and adults. With 20 neurotypical adults, we validated that this set elicits robust N400's, as well as two additional semantically-related ERP components: the recognition potential (∼ 250 ms) and the late positivity component (∼ 600 ms). This stimulus set (https://doi.org/10.5061/dryad.9ghx3ffkg) and the 20 high-density (128-channel) electrophysiological datasets (https://doi.org/10.5061/dryad.6wwpzgmx4) are made publicly available to promote data sharing and reuse. Future studies that use this stimulus set to investigate sentential semantic comprehension in both control and clinical populations may benefit from the increased comparability and reproducibility within this field of research.
Decentralized identifier based illegal digital content distribution tracking system using the format‐preserving encryption algorithm

Abstract The coronavirus pandemic, which first began in December 2019, has completely changed our daily lives. In particular, daily online digital content consumption over the world has soared since the start of the coronavirus pandemic. For preventing illegal digital content distribution, various technologies have been applied, but current copyright protection technologies are still lacking to provide secure digital content distribution while preserving privacy. In this article, we introduce a new digital content distribution system, which utilizes blockchain‐based decentralized identifier and format‐preserving encryption to enable user privacy preserved digital content tracking. We present the system architecture with detailed message flows of the proposal and provide its analysis result in terms of security and overhead.
On Improving the Students’ Combinatorial Thinking Skill in Solving Rainbow Antimagic Colouring Problem on Cryptography for E-Commerce Security Systems under the Implementation of Research-Based Learning with STEM Approach

Combinatorial thinking is the process of obtaining multiple solutions for discrete problem-solving. Combinatorial thinking skills have several indicators: identifying several cases, recognizing patterns from all cases, generalizing all cases, proving mathematically, and considering other combinatorial problems. The learning approach in schools is growing along with the development of science and technology, one of the approaches used in STEM. STEM education focuses students on solving problems in everyday life with the help of science, technology, engineering, and mathematics. This paper aimed to analyze the students’ combinatorial thinking skills after using a research-based learning worksheet with a STEM approach to solving the rainbow antimagic colouring problem. This study uses a mixed-method where this research combines qualitative and quantitative methods. The subjects of this research are undergraduate students of Mathematics Education, University of Jember. This study involved two classes, namely the experimental and control classes, where the researcher gave the two classes different treatments. The t-test compares students’ combinatorial thinking skills between the experiment and control classes after using research-based learning tools with a STEM approach for solving the rainbow antimagic problem. The homogeneity test results on the pre-test items indicated a significance value of .789 &gt; .05, which indicates that the two classes are homogenous. The independent sample t-test score is .020 &lt; .05, which means the difference is significant. It concludes that implementation of the research-based learning materials with a STEM approach can improve the students’ combinatorial thinking skills in solving the rainbow antimagic colouring.
Pull–push: a measure of over- or underpersonalization in recommendation

Abstract A recommender system imposes differences between users, by presenting to them different recommendation lists, which they respond to, resulting in different “reaction” lists. Comparison of the differences in the recommendation and reaction lists can indicate different user states. Users can approve the imposed difference, end up narrowing the difference between them (pulling each other closer) by consuming more of the items in common or enlarge the difference between them (pushing each other further apart) by consuming the items not in common. When users do not approve the differences, they are either in a push state (implicitly disapproving under-personalization) or in a pull state (implicitly disapproving over-personalization). We offer the pull–push metric to quantify the magnitude of pull or push—measures of disapproval by the users of, respectively, over-personalization and under-personalization. Application on simulated datasets shows that users can push each other away up to disjoint sets or pull each other closer up to identical sets. On real-world datasets, we find that the particular recommender system was under-personalizing its recommendations. We show how the pull–push metric can be merged with another metric of personalization to come up with a measure of the potential for improvement in a recommender system and discuss its relationship to popularity bias.
Fully-weighted HGNN: Learning efficient non-local relations with hypergraph in aerial imagery

Interpretation of large-scale aerial imagery is an essential challenge due to the dense distribution of objects and complex intra-class backgrounds. Non-local relational modeling has been adopted as the mainstream solution to better interpret such large-scale scenes. Existing non-local relations modeling methods are based on building deep convolution neural networks, attention mechanisms, or deep graph neural networks for all positions of the whole image. However, the existing methods are computationally expensive and inefficient. In this paper, we dig into the main causes of such inefficiency and find two main disadvantages of existing methods, i.e., (1) semantic relations are blurry and homogeneous, (2) insufficient construction of high-order relations. To overcome the above issues, we analyze the inadequacy of traditional hypergraph definition and propose a lightweight hypergraph construction strategy to learn the non-local relations. The proposed method can model high-order relations more effectively and lead to an explicit representation of semantic information. We apply this strategy in the spatial dimension and construct a fully-weighted hypergraph neural network (HGNN) to capture short- and long-range dependencies in such a large-scale aerial image. Furthermore, we design a hypergraph convolutional feature pyramid network (Hyper-FPN), which learns the non-local relations in multi-scale features and then aggregates hierarchical global contexts. Extensive experiments on geospatial visual recognition demonstrate that Hyper-FPN significantly improves the performance under our strategy. Moreover, our approach can be easily embedded into state-of-the-art (SOTA) architectures to achieve higher performance.
Comparing and extending the use of defeasible argumentation with quantitative data in real-world contexts

Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity. A non-monotonic formalism is one that allows the retraction of previous conclusions or claims, from premises, in light of new evidence, offering some desirable flexibility when dealing with uncertainty. Among possible options, knowledge-base, non-monotonic reasoning approaches have seen their use being increased in practice. Nonetheless, only a limited number of works and researchers have performed any sort of comparison among them. This research article focuses on evaluating the inferential capacity of defeasible argumentation, a formalism particularly envisioned for modelling non-monotonic reasoning. In addition to this, fuzzy reasoning and expert systems, extended for handling non-monotonicity of reasoning, are selected and employed as baselines, due to their vast and accepted use within the AI community. Computational trust was selected as the domain of application of such models. Trust is an ill-defined construct, hence, reasoning applied to the inference of trust can be seen as non-monotonic. Inference models were designed to assign trust scalars to editors of the Wikipedia project. Scalars assigned to recognised trustworthy editors provided the basis for the analysis of the models’ inferential capacity according to evaluation metrics from the domain of computational trust. In particular, argument-based models demonstrated more robustness than those built upon the baselines despite the knowledge bases or datasets employed. This study contributes to the body of knowledge through the exploitation of defeasible argumentation and its comparison to similar approaches. It provides publicly implementations for the designed models of inference, which might be a useful aid to scholars interested in performing non-monotonic reasoning activities. It adds to previous works, empirically enhancing the generalisability of defeasible argumentation as a compelling approach to reason with quantitative data and uncertain knowledge.
Category-learning attention mechanism for short text filtering

• A novel category-learning attention mechanism is proposed. • It includes category-learning scaled-dot-product attention mechanism. • It includes category-learning multi-head attention mechanism (CL-MHA). • The attention mechanisms build category differentiation feature matrices dynamically. • The CL-MHA based bidirectional gate recurrent unit achieves the best performance. In machine translation, the attention mechanism highlights relevant words according to the distances between the source and target vectors dynamically. However, its ability to optimize text classification is limited because this mechanism only calculates weights inside the same text. There is an uneven distribution of words between ham (not spam) and spam categories, and this category-level feature has not previously been utilized in the attention mechanism for filtering short texts. In addition, short text filtering is uniquely challenging due to the length, sparsity and informal writing of texts, as well as a need for rapid processing. We propose a novel category-level attention mechanism called “category-learning attention,” which highlights words intensely distributed in the same category by dynamically calculating a category differentiation matrix for each short text. The category-learning attention mechanism is extended to the category-learning scaled-dot-product attention and the category-learning multi-head attention (CL-MHA) mechanisms. The CL-MHA mechanism is then applied to a bidirectional gate recurrent unit (Bi-GRU) model for performance evaluation by using the SMS spam collection dataset hosted at the University of California, Irvine. Performance metrics including the accuracy, precision, recall, and F1 score demonstrate that the CL-MHA mechanism significantly improves the performance of Bi-GRU for short text filtering with an accuracy of 99.35%, higher than any previously reported machine learning models. In addition, experiments conducted on three datasets - a Chinese SMS spam dataset, a benchmark movie review dataset, and a benchmark customer review dataset - further validate the effectiveness of the proposed model. The proposed CL-MHA Bi-GRU model has an accuracy of 99.46% when evaluated on the Chinese SMS spam dataset.
DeepMask: face mask detection using GAN algorithm

COVID-19 pandemic is the main reason people must wear face masks in public places. Traditionally, officers monitor the use of face masks in the public area manually. However, monitoring masks using manual techniques is challenging in a crowded spot. Thus, we propose a face mask detection based on Generative Adversarial Networks (GAN) through the learning model to accelerate mask detection accurately and quickly. To construct our detection model, we collect the dataset, conduct pre-processing, and train the model by tuning multiple parameters to obtain the highest accuracy and tiny loss. The experimental results can produce D_Loss = 0.0032 and G_Loss = 7.3296. Therefore, the proposed model can be a promising solution for mask detection issues.