Reviewerly/exHarmony huggingface dataset:
Generating High-Fidelity Texture in RGB-D Reconstruction using Patches Density Regularization

We propose a texture-mapping-based online RGB-D reconstruction framework that can generate stable and high-fidelity texture results in near real-time. Different from previous texture generation methods that are prone to output blur results (point blending methods) or require huge computational costs (previous texture mapping methods), our approach achieves high-fidelity and fast texture generation by introducing a new patches density regularization and a series of lightweight optimization methods. The patches density regularization addresses a universal but widely neglected problem: the texture patches fragmentation, which causes discontinuous seams and worsens texture results. By adding this regularization to the optimization process, the fragmentation can be effectively alleviated. To maintain high performance, we design lightweight optimization methods to significantly reduce the computational cost during texture generation. An incremental texture update strategy removes redundant triangular mesh, and a hierarchical-aware seam leveling reduces the number of optimization objects by utilizing light-varying smooth features. Furthermore, the enhanced voxel representation ensures the continuity and stability of the global texture results. Extensive experiments demonstrate that our method achieves a state-of-the-art level, even compared to the mainstream online and offline RGB-D reconstruction frameworks.
Fostering Student Agency and Teacher Leadership Through Augmented Reality-Mediated Learning

There is a rise in the integration of augmented reality (AR) in education in recent years. This emerging medium is less explored in literacy education, yet the new textual form is necessary for adapting to the changes in the world of literacy. The chapter examines students' active use of bodily movement in AR-mediated literacy learning and addresses the issue of the relationships between literacy, body, space, and new media in a primary school classroom in Australia. The findings presented are based on the systemic functional-multimodal discourse analysis of classroom observations. Data includes the multimodal instructions provided by an AR app as well as students' work samples, specifically their AR compositions. These findings provide insights into students' agentive meaning making processes and teachers' leadership in expanding students' metalanguages in AR-mediated learning.
Fuzzy Networks for Modeling Shared Semantic Knowledge

Abstract Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description logic as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation C ( a ) for some concept C ” or “what are the roles R that connect the individuals a and b to degree of sharedness ε.”
‘Catfishes: How do they reel us in?’ Exploring adolescent vulnerabilities to online grooming

The practice of adopting a fake online persona (‘catfishing’) is commonly adopted by cyber criminals to facilitate contact with -victims, yet there is a dearth of research exploring what makes an individual vulnerable to being catfished. This study explored the risk factors and vulnerability of young people to catfishing. 42 participants aged 13–17 completed an online questionnaire exploring problematic internet use, self-esteem, social isolation, parental involvement and online safety awareness, and The Catfish Test. Participants were asked to identify each profile as real or fake. The results of the study are discussed alongside implications for practice.
Achieving Anonymous and Covert Reporting on Public Blockchain Networks

Reporting helps to combat illegal activities and deters lawbreakers and potential lawbreakers. From ancient times to the present, public authorities have usually rewarded effective reporting information to build harmonious societies. In this process, protecting the privacy of the whistleblower is a very important issue. Existing blockchain-based anonymous reporting solutions help solve the problem of insufficient anonymity in traditional reporting solutions, but they do not address the issue of hiding the reporting behavior. The disclosure of reporting behavior may alert offenders in advance and negatively impact case handling. This paper proposes an anonymous and covert reporting scheme and rewarding mechanism based on blockchain, which realizes the covertness of the reporting behavior while protecting the privacy of the whistleblower. The proposed scheme uses ring signature and derived address technology to ensure anonymity and achieves covertness by embedding information in the ring signature based on the idea of covert communication. Theoretical analysis proves that the proposed scheme has covertness, anonymity, and unforgeability properties. Experiments show that the proposed scheme takes only 0.08 s to upload data and 0.07 s to verify while achieving covertness.
Variational inference on a Bayesian adaptive lasso Tobit quantile regression model

The Tobit quantile regression model is a useful tool for quantifying the relationship between response variables with limited values and the explanatory variables. Under the Bayesian framework, the Tobit quantile regression model is often simulated by an asymmetric Laplacian distribution (ALD), which can be reformulated as a hierarchical structure model. An adaptive lasso prior is used to address the selection of active explanatory variables. A mean‐field variational family is adopted, where the variables are assumed to be mutually independent with each being governed by a different factor in the variational density. A coordinate ascent variational inference (CAVI) algorithm is developed to iteratively optimize each factor, and the evidence lower bound (ELBO) is obtained. Parameter estimation and variable selection are simultaneously produced by the optimal variational density. Several simulation studies and an example are presented to illustrate the proposed methodologies.
BayesKGR: Bayesian Few-Shot Learning for Knowledge Graph Reasoning

Reasoning over knowledge graphs (KGs) has received increasing attention recently due to its promising applications in many areas, such as semantic search and recommendation systems. Subsequently, most reasoning models are inherently transductive and ignore uncertainties of KGs, making it difficult to generalize to unseen entities. Moreover, existing approaches usually require each entity in the KG to have sufficient training samples, which leads to the overfitting of the entity having few instances. In fact, long-tail distributions are quite widespread in KGs, and newly emerging entities will tend to have only a few related triples. In this work, we aim at studying knowledge graph reasoning under a challenging setting where only limited training samples are available. Specifically, we propose a Bayesian inductive reasoning method and incorporate meta-learning techniques in few-shot learning to solve data deficiency and uncertainties. We design a Bayesian graph neural network as a meta-learner to achieve Bayesian inference, which can extrapolate meta-knowledge from observed KG to emerging entities. We conduct extensive experiments on two large-scale benchmark datasets, and the results demonstrate considerable performance improvement with the proposed approach over other baselines.
Rhetoric Mining: A New Text-Analytics Approach for Quantifying Persuasion

Rhetoric mining is a novel text-analytics method for quantifying persuasion based on rhetorical analysis theory. Our mixed-methodology approach combines qualitative context analysis with automated tagging and quantification of rhetorical moves. Rhetorical moves are complex discursive patterns and, thus, require a sequence-based text-mining approach, rather than the simpler word-based frequency analyses. We apply a sequence-alignment method to detect semantically equivalent sequences with high precision and efficiency. We illustrate our method by analyzing arguments used to justify stock picks in an online investment community. For these data, we detect and quantify the rhetorical moves of ethos (personal versus cited expertise), hedging (confidence versus uncertainty), and evidence type (product-, company-, or stock-based evidence). We use rhetoric mining to identify argument styles of persuasiveness (pitches that receive community recommendations) and trustworthiness (pitches that are written by successful investors). Rhetoric mining provides a new analytic lens in Information Systems research to analyze the influence of persuasion in consumer decision making. History: W. Nick Street served as the senior editor for this article. Data Ethics &amp; Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/9373643/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.0024 ).
A Mixed Malay–English Language COVID-19 Twitter Dataset: A Sentiment Analysis

Social media has evolved into a platform for the dissemination of information, including fake news. There is a lot of false information about the current situation of the Coronavirus Disease 2019 (COVID-19) pandemic, such as false information regarding vaccination. In this paper, we focus on sentiment analysis for Malaysian COVID-19-related news on social media such as Twitter. Tweets in Malaysia are often a combination of Malay, English, and Chinese with plenty of short forms, symbols, emojis, and emoticons within the maximum length of a tweet. The contributions of this paper are twofold. Firstly, we built a multilingual COVID-19 Twitter dataset, comprising tweets written from 1 September 2021 to 12 December 2021. In particular, we collected 108,246 tweets, with over 67% in Malay language, 27% in English, 2% in Chinese, and 4% in other languages. We then manually annotated and assigned the sentiment of 11,568 tweets into three-class sentiments (positive, negative, and neutral) to develop a Malay-language sentiment analysis tool. For this purpose, we applied a data compression method using Byte-Pair Encoding (BPE) on the texts and used two deep learning approaches, i.e., the Multilingual Bidirectional Encoder Representation for Transformer (M-BERT) and convolutional neural network (CNN). BPE tokenization is used to encode rare and unknown words into smaller meaningful subwords. With the CNN, we converted the labeled tweets into image files. Our experiments explored different BPE vocabulary sizes with our BPE-Text-to-Image-CNN and BPE-M-BERT models. The results show that the optimal vocabulary size for BPE is 12,000; any values beyond that would not contribute much to the F1-score. Overall, our results show that BPE-M-BERT slightly outperforms the CNN model, thereby showing that the pre-trained M-BERT network has the advantage for our multilingual dataset.
Practical aspects on non-profiled deep-learning side-channel attacks against AES software implementation with two types of masking countermeasures including RSM

Abstract Deep-learning side-channel attacks, applying deep neural networks to side-channel attacks, are known that can easily attack some existing side-channel attack countermeasures such as masking and random jitter. While there have been many studies on profiled deep-learning side-channel attacks, a new approach that involves applying deep learning to non-profiled attacks was proposed in 2018. In our study, we investigate the structure of multi-layer perceptrons and points of interest for non-profiled deep-learning side-channel attacks using the ANSSI database with a masking countermeasure. The results of investigations indicate that it is better to use a simple network model, apply regularization to prevent over-fitting, and select a wide range of power traces that contain side-channel information as the points of interest. We also implemented AES-128 software implementation protected with the Rotating Sboxes Masking countermeasure, which has never been attacked by non-profiled deep-learning side-channel attacks, on the Xmega128 microcontroller and carried out non-profiled deep-learning side-channel attacks against it. Non-profiled deep-learning side-channel attacks successfully recovered all partial keys while the conventional power analysis could not. The attack results also showed that the least significant bit is the adequate selection for successful non-profiled deep-learning side-channel attacks, but the best labeling method may vary depending on the implementation of the countermeasure algorithm. We conducted two experimental analyses to clarify that deep-learning side-channel attacks learn mask values used in the masking countermeasure. One is the gradient visualization used in previous studies, and the other is a new analysis method using partial removal of power traces.
The extent of emerging commercial banks commitment to cybersecurity governance: An empirical study

The interest of business organizations and banks in cybersecurity has become extremely important, and this is evident through many studies such as (Abu-Shanab et al., 2013; Al-Muhtadi, 2020) which emphasized the importance of cybersecurity in business. The study aims to identify the extent of the commitment of Jordanian commercial banks to the governance of cybersecurity, from the point of view of a certified public accountant. However, the population consisted of Jordanian commercial banks, and a sample of 83 respondents was taken from auditors who hold a chartered accountant certificate and work in the field of auditing the accounts of Jordanian commercial banks. The findings indicate that Jordanian commercial banks are committed to cybersecurity governance with regard to cybersecurity governance strategy, cybersecurity related to human resources, and cybersecurity risk management from the point of view of a certified public accountant. Nevertheless, this paper contributes to providing useful results for financial managers and accountants working in Jordanian commercial banks by introducing them to the importance of cybersecurity governance. Through its theoretical literature, prior investigations, and research methodologies whose validity and reliability have been proven and may be employed and used in future studies, it is hoped that this study would motivate many researchers to conduct more new research on this subject
A Marine Organism Detection Framework Based on Dataset Augmentation and CNN-ViT Fusion

Underwater vision-based detection plays an important role in marine resources exploration, marine ecological protection and other fields. Due to the restricted carrier movement and the clustering effect of some marine organisms, the size of some marine organisms in the underwater image is very small, and the samples in the dataset are very unbalanced, which aggravate the difficulty of vision detection of marine organisms. To solve these problems, this study proposes a marine organism detection framework with a dataset augmentation strategy and Convolutional Neural Networks (CNN)-Vision Transformer (ViT) fusion model. The proposed framework adopts two data augmentation methods, namely, random expansion of small objects and non-overlapping filling of scarce samples, to significantly improve the data quality of the dataset. At the same time, the framework takes YOLOv5 as the baseline model, introduces ViT, deformable convolution and trident block in the feature extraction network, and extracts richer features of marine organisms through multi-scale receptive fields with the help of the fusion of CNN and ViT. The experimental results show that, compared with various one-stage detection models, the mean average precision (mAP) of the proposed framework can be improved by 27%. At the same time, it gives consideration to both performance and real-time, so as to achieve high-precision real-time detection of the marine organisms on the underwater mobile platform.
A new Shamir’s three pass random matrix ciphering mechanism

A new Shamir's three pass random matrix ciphering mechanism. This new random matrix ciphering mechanism uses a three-pass protocol with encryption operators that are random commutative matrices. The three-pass protocol, at the same time, allows the authentication of the participants in the transaction (Alice and Bob as usual). The novelty of this mechanism is that it uses two encryption matrices for Alice [AC1] and [AC2] and for Bob [BC1] and [BC2], which "encapsulate" the message matrix [M]. For example, the data flow from Bob to Alice is the product of five matrices: [BC1][AC1][M][AC2][BC2] in which the possibility for Alice to delete her encryption matrices [AC1] and [AC2], implies that the matrices: [BC1] and [AC1] and also [AC2] and [BC2] be commutative, therefore randomly generated in the same eigenvector spaces. This is made possible because a non-singular matrix [G] can be put in the following form: [G] = [C] [D] [C]-1 in which the matrix [C] is related to the eigenvector space of the matrix [G], the diagonal matrix [D] being its eigenvalues. This equation then makes it possible to write an infinity of encryption commutative matrices [G] belonging to this eigenvector space, each having different eigenvalues [D] generated randomly by the mechanism. The proposed mechanism is based on this equation. The authentication process consists of a pseudo-random permutation module, embedded in the mechanism, generating permutation matrices which are unique identifiers of Alice and Bob. These permutation matrices also "encapsulate" the data stream exchanged between Alice and Bob.
A Decision-Making Model for Selecting Product Suppliers in Crop Protection Retail Sector

This study aims to determine the importance of factors affecting supplier selection in the pesticide distribution sector as a global emerging market and present a decision-making model for the corporate marketing strategy. Specifically, a comparative study between suppliers and retail distribution experts was conducted to compare differences in the perception of supplier selection factors according to organizational characteristics. Based on previous studies, a decision-making model based on the AHP methodology was constructed with a total of 20 factors in five areas: product quality, price, flexibility, promotion support, and brand. Then, 42 Korean experts were surveyed to measure the importance of these factors. The results showed that product quality is the most critical factor in supplier selection, followed by price, brand, promotional support, and flexibility, in that order. Manufacturers consider product quality as the most important factor, while retailers consider price as the most important factor. Among the 20 factors, ‘quality excellence’, ‘expected return’, and ‘technological competitiveness’ were found to be the most important factors. In addition, while manufacturers considered factors such as ‘corporate reputation’ and ‘corporate trust’ as more important, retailers considered factors related to product characteristics, such as ‘product awareness’ and ‘brand reputation’ as more important.
A Multi-Task Knowledge-Tracking Model with a Novel Representative Approach to Problem Difficulty and Student Ability

Question difficulty and student ability are important factors that affect students’ correct answers. Because existing knowledge-tracking models fail to consider these factors, they cannot accurately predict the results of students’ answers. In order to explore question difficulty and student ability information more accurately and to improve the accuracy of model prediction, this paper proposes a multi-task knowledge-tracking model (MTLKT) with a novel representative approach to question difficulty and student ability. The model first used the idea of multi-task learning to share underlying information and parameters, to jointly train, and to obtain an information difficulty representation vector consisting of skill difficulty and question difficulty. Then, combined with student learning process, a performance bias function was introduced to improve the attention mechanism and obtain a vector for student current knowledge state and a vector for question-solving performance, thus obtaining a vector for student ability information representation. Finally, the above vectors were concatenated and input into the model as a new representative embedding vector. The experimental results of three real-world data sets showed that our model had great improvement in the evaluation criteria of AUC and ACC and had a better predictive performance than the existing advanced knowledge-tracking models.
U-DiVE - design and evaluation of a distributed photorealistic virtual reality environment

Mobile devices such as smartphones are increasingly being used for immersive content consumption, mostly involving 360o̱ video and 3D audio media delivery. However these smartphones, especially low-cost ones, are still not able to provide the processing and battery power needed for a real-time rendering, visualization and interaction with photorealistic virtual reality scenes. In this context, this paper proposes and evaluates U-DiVE (Unity-based Distributed Virtual Reality Environment), a framework that decouples the processing and rendering processes from the delivery, visualization and interaction with realistic VR models. The U-DiVE framework produces a photorealistic scene using a general ray-tracing algorithm and a virtual reality camera configured to use barrel shaders to correct the lens distortion, allowing the visualization through inexpensive smartphone-based head-mount displays. The framework also includes a method to obtain the smartphone’s spatial orientation to control the user’s field of view, which is delivered via real-time WebRTC streaming. The analysis show U-DiVE allows for the real-time visualization and manipulation of realistic, immersive scenes via smartphone-based, low-cost head-mounted displays, with low end-to-end latency, considering the required continuous data processing and delivery.
Comparing the Performance of Flutter and ReactJS for Web Development

This research paper aims to assess the performance of Flutter, a cross-platform development framework, as compared to other widely-used web development frameworks like ReactJS for developing web applications. The study will analyse the advantages and limitations of each framework with respect to cross-platform compatibility, development time, and community support. The research aims to provide significant insights to web developers seeking to build efficient and user-friendly cross-platform web applications. By comparing the frameworks' performance, this research will offer valuable guidance for selecting the most suitable framework for developing high-performing cross-platform web applications