Reviewerly/exHarmony huggingface dataset:
A Novel Sensor Fusion Approach for Precise Hand Tracking in Virtual Reality-Based Human—Computer Interaction

The rapidly evolving field of Virtual Reality (VR)-based Human–Computer Interaction (HCI) presents a significant demand for robust and accurate hand tracking solutions. Current technologies, predominantly based on single-sensing modalities, fall short in providing comprehensive information capture due to susceptibility to occlusions and environmental factors. In this paper, we introduce a novel sensor fusion approach combined with a Long Short-Term Memory (LSTM)-based algorithm for enhanced hand tracking in VR-based HCI. Our system employs six Leap Motion controllers, two RealSense depth cameras, and two Myo armbands to yield a multi-modal data capture. This rich data set is then processed using LSTM, ensuring the accurate real-time tracking of complex hand movements. The proposed system provides a powerful tool for intuitive and immersive interactions in VR environments.
FCDedup: A Two-Level Deduplication System for Encrypted Data in Fog Computing

Distributed fog computing has received increasing attention recently and fog-assisted cloud storage can provide a real-time service to collect and manage large-scale data for the applications of Internet of Things. Encrypted data deduplication over cloud storage can significantly save storage space of the cloud server while protecting the confidentiality of the outsourced data. Previous encrypted data deduplication schemes are mostly designed for traditional cloud storage with a two-layer architecture and cannot be applied to the emerging fog-assisted cloud storage that has a more complex three-layer architecture (i.e., cloud server, fog node and endpoint device). In this paper, we design, analyze and implement FCDedup, a new encrypted data deduplication scheme for fog-assisted cloud storage. FCDedup is a two-level deduplication system that enables each fog node to detect duplicated encrypted data uploaded by different endpoint devices, as well as enables cloud server to detect duplicated encrypted data from different fog nodes. By doing so, FCDedup can achieve both intra-deduplication within a single data owner and inter-deduplication across different data owners. FCDedup is also designed to prevent cloud server and fog nodes launching the brute-force attacks, and to guarantee the reliability of files downloaded from the cloud. Formal analysis is provided to justify its deduplication correctness and security. Besides, we implement a prototype of FCDedup using Alibaba Cloud as backend storage. Our evaluations demonstrate that FCDedup is completely compatible with existing cloud storage systems and achieves modest performance overhead.
HPKA: A High-Performance CRYSTALS-Kyber Accelerator Exploring Efficient Pipelining

CRYSTALS-Kyber (Kyber) was recently chosen as the first quantum resistant Key Encapsulation Mechanism (KEM) scheme for standardisation, after three rounds of the National Institute of Standards and Technology (NIST) initiated PQC competition which begin in 2016 and search of the best quantum resistant KEMs and digital signatures. Kyber is based on the Module-Learning with Errors (M-LWE) class of Lattice-based Cryptography, that is known to manifest efficiently on FPGAs. This work explores several architectural optimizations and proposes a high-performance and area-time (AT) product efficient hardware accelerator for Kyber. The proposed architectural optimizations include inter-module and intra-module pipelining, that are designed and balanced via FIFO based buffering to ensure maximum parallelisation. The implementation results show that compared to state-of-the-art designs, the proposed architecture delivers 25–51% speedups for Kyber's three different security levels on Artix-7 and Zynq UltraScale+ devices, and a 50–75% reduction in DSPs at comparable security level. Consequently, the proposed design achieve higher AT product efficiencies of 19–33%.
Machine Learning Based Image Forgery Detection Using Natural Scene Statistics

A copy-move image forgery is the most common type of image tampering. It can be done by copying a part of an image and paste on another part of the same image. Therefore, it can be one of the challenging tasks to find that forgery. This paper suggested a different approach to detect the copy move image forgery by the natural scene statistic features. These features are extracted from both original and forged images of MICC-F2000 dataset. Natural scene statistics are the statistical properties of any natural image captured by any camera, so an attempt of forging an image makes these properties un-natural. By this method, an original and forged images can be easily classified by state-of-the-art machine learning models trained on these features. The performance of this method is quantitatively assessed using the famous evaluation metrics i-e accuracy, TPR, FPR, TNR, Recall and F1-score. A comparison with other state-of-the-art techniques has shown that the proposed technique has shown better results in comparison with the other techniques.
EFL teachers’ digital literacy: the role of contextual factors in their literacy development

Introduction Digital technology can have significant effects on language education. This effect makes the English language teachers teach the subjects better to the students and also improves the quality of teachers’ education. Methods The purpose of this research is to evaluate the digital literacy of English language teachers and to investigate the difference between digital literacy and their gender, education level, and teaching experience. To conduct the study, the researcher invited 2,110 EFL teachers to fill out the Teachers’ Digital Literacy questionnaire. The researchers used SPPS and AMOS in analyzing the obtained data. Results The results of the study indicated that teachers’ contextual factors do not influence their digital literacy skills. In addition, the study showed that teachers’ attitudes toward technology, their skill to use technology, and their access to technology can significantly affect teachers’ digital literacy. Discussion Implications of the study are further discussed in this paper.
Physical and mental exhaustion of the caregiver in pediatric patients with leukemia

Introduction: Pediatric patients with leukemia require direct care from the nursing staff in order to solve the health needs of the child, always taking care of their integrity and forming a therapeutic relationship of trust that allows dignified treatment and provides care timely manner with the support of the patient and their families. Objective: To analyze the nursing process to be used in the physical and mental exhaustion of the caregiver in pediatric patients with leukemia. Methodology: This research is qualitative with a descriptive design, which is characterized by a bibliographic review through articles, publications, reviews and documents from scientific societies. Results: In the review, 19 full-text articles were analyzed, finding that analgesia, emotional support and coverage of basic needs are the main requirements of the patient. Conclusion: The exhaustion of the caregivers of a child with leukemia manifests itself through physical and emotional exhaustion, which include various symptoms such as depression, anxiety, migraines, joint pain, sleep disorders, irritability, etc.
Evaluation of Octo Mobile User Experience using the System Usability Scale Method

In order to improve the quality of the Octo Mobile by Bank Cimb Niaga application, it is necessary to carry out usability testing of the application using the System Usability Scale (SUS) with a Likert scale for the answer. The purpose of this study is to evaluate usability and analyze user experience on the Octo Mobile application, so that it is expected to be used as a reference for application development by Bank CIMB Niaga. Respondents consisted of 39 Octo Mobile application users. Testing was carried out by directly interviewing respondents and filling out a questionnaire containing 10 statements. The results of testing with the SUS method obtained a result of 58.5%. Based on these results, the acceptability of the Octo Mobile application is in the marginal low range, the adjective rating is in the OK level, and the grade scale is in class F. Based on these results it can be concluded that the Octo Mobile application has usability that is acceptable to users, but still needs to be improved and further upgrades.
Sequential Recommendation with User Causal Behavior Discovery

The key of sequential recommendation lies in the accurate item correlation modeling. Previous models infer such information based on item co-occurrences, which may fail to capture the real causal relations, and impact the recommendation performance and explainability. In this paper, we equip sequential recommendation with a novel causal discovery module to capture causalities among user behaviors. Our general idea is firstly assuming a causal graph underlying item correlations, and then we learn the causal graph jointly with the sequential recommender model by fitting the real user behavior data. More specifically, in order to satisfy the causality requirement, the causal graph is regularized by a differentiable directed acyclic constraint. Considering that the number of items in recommender systems can be very large, we represent different items with a unified set of latent clusters, and the causal graph is defined on the cluster level, which enhances the model scalability and robustness. In addition, we provide theoretical analysis on the identifiability of the learned causal graph. To the best of our knowledge, this paper makes a first step towards combining sequential recommendation with causal discovery. For evaluating the recommendation performance, we implement our framework with different neural sequential architectures, and compare them with many state-of-the-art methods based on real-world datasets. Empirical studies manifest that our model can on average improve the performance by about 6.1% and 11.3% on F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and NDCG, respectively. To evaluate the model explainability, we build a new dataset with human labeled explanations for both quantitative and qualitative analysis.
Few-Shot Learning for Issue Report Classification

We describe our participation in the tool competition in the scope of the 2nd International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on SETFIT, a framework for few-shot learning and sentence-BERT (SBERT), a variant of BERT for effective sentence embedding. We experimented with different settings, achieving the best performance by training and testing the SETFIT-based model on a subset of data with manually verified labels (Fl-micro <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=.8321$</tex> ). For the sake of the challenge, we evaluate the SETFIT model on the challenge test set, achieving Fl-micro <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=.7767$</tex> .
3D Model Encryption Algorithm by Parallel Bidirectional Diffusion and 1D Map with Sin and Logistic Coupling

3D models are essential in virtual reality, game development, architecture design, engineering drawing, medicine, and more. Compared to digital images, 3D models can provide more realistic visual effects. In recent years, significant progress has been made in the field of digital image encryption, and researchers have developed new algorithms that are more secure and efficient. However, there needs to be more research on 3D model encryption. This paper proposes a new 3D model encryption algorithm, called the 1D map with sin and logistic coupling (1D-MWSLC), because existing digital image encryption algorithms cannot be directly applied to 3D models. Firstly, this paper introduce 1D-MWSLC, which has a wider range of parameters compared to traditional 1D chaotic systems. When the parameter exceeds a specific range, the chaotic phenomenon does not weaken. Additionally, 1D-MWSLC has two control parameters, which increases the cryptosystem’s parameter space. Next, 1D-MWSLC generates keystreams for confusion and diffusion. In the confusion stage, this paper use random confusion, and the keystream generates an index matrix that confuses the integer and decimal parts of the 3D model simultaneously. In the diffusion stage, this paper use parallel bidirectional diffusion to simultaneously diffuse the integer parts of the three coordinates of the 3D model. Finally, this paper verify the proposed algorithm through statistical analysis, and experimental results demonstrate that the proposed 3D model encryption algorithm has robust security.
Security challenges in civil registration: safeguarding vital information in an evolving landscape

Civil registration is a fundamental process that captures vital events such as births, deaths, marriages, and divorces, enabling governments to create accurate demographic databases and deliver essential services to their citizens. However, in today's digital age, civil registration systems face numerous security challenges that jeopardize the integrity and confidentiality of vital information. This paper highlights some of the key security challenges encountered in civil registration systems and outlines potential strategies to address them. Firstly, the digitization of civil registration processes has opened new avenues for cyber threats. Malicious actors may attempt to compromise the security of databases, manipulate or steal vital records, or disrupt services through cyberattacks. Robust cybersecurity measures, including encryption, firewalls, intrusion detection systems, and regular security audits, are essential to safeguard sensitive data and ensure the continuity of civil registration operations. Secondly, the issue of identity theft poses a significant challenge to civil registration security. Fraudulent practices, such as the creation of fake identities or the alteration of existing records, can undermine the trustworthiness of the system and lead to the misallocation of resources. The implementation of identity verification mechanisms, such as biometrics or unique identifiers, can enhance the accuracy and integrity of civil registration data while reducing the risk of identity fraud. Thirdly, ensuring the privacy and confidentiality of individuals' personal information is critical in civil registration systems. With the increasing digitization and interconnectedness of data, there is a heightened risk of unauthorized access or data breaches. Strong data protection regulations, robust access controls, and encryption techniques can help mitigate these risks, fostering public trust and confidence in civil registration processes. Moreover, the challenge of inclusivity must be addressed to ensure the effectiveness and reliability of civil registration systems. Marginalized populations, including refugees, migrants, and those residing in remote areas, may face barriers in accessing civil registration services, leaving them vulnerable to identity-related challenges. Deploying mobile registration units, leveraging innovative technologies, and promoting community engagement are strategies that can improve inclusivity and extend the benefits of civil registration to all individuals.
Generative Art via Grammatical Evolution

Generative art produces artistic output via algorithmic design. Common examples include flow fields, particle motion, and mathematical formula visualization. Typically an art piece is generated with the artist/programmer acting as a domain expert to create the final output. A large amount of effort is often spent manipulating and/or refining parameters or algorithms and observing the resulting changes in produced images. Small changes to parameters of the various techniques can substantially alter the final product. We present GenerativeGI, a proof of concept evolutionary framework for creating generative art based on an input suite of artistic techniques and desired aesthetic preferences for outputs. GenerativeGI encodes artistic techniques in a grammar, thereby enabling multiple techniques to be combined and optimized via a many-objective evolutionary algorithm. Specific combinations of evolutionary objectives can help refine outputs reflecting the aesthetic preferences of the designer. Experimental results indicate that GenerativeGI can successfully produce more visually complex outputs than those found by random search.
Enhanced Federated Learning with Adaptive Block-wise Regularization and Knowledge Distillation

Federated Learning (FL) has emerged as an efficient distributed model training framework that enables multiple clients cooperatively to train a global model without exposing their local data in edge computing (EC). However, FL usually faces statistical heterogeneity (e.g., non-IID data) and system heterogeneity (e.g., computing and communication capabilities), resulting in poor model training performance. To deal with the above two challenges, we propose an efficient FL framework, named FedBR, which integrates the idea of block-wise regularization and knowledge distillation (KD) into the pioneer FL algorithm FedAvg, for resource-constrained edge computing. Besides, we design a heuristic algorithm (GMBS) to determine the appropriate number of model blocks for clients according to their varied data distributions, computing, and communication capabilities. Extensive experimental results show that FedBR can reduce the time cost by 19.5% and the communication cost by 27% on average compared with the other three baselines when achieving the target testing accuracy under heterogeneous settings.
Systematic Review: Approaches in Improving English Literature Teaching and Learning in ESL Classrooms

This essay analyses a systematic assessment of methods for teaching and learning English literature in ESL classrooms based on earlier studies that have been published.A total of seventeen studies, spanning the years 2011 to 2021, were reviewed.One of the main goals of this systematic review is to demonstrate an empirical information formulation that has been found in previous academic studies on the teaching and learning of English literature in ESL classes.There are two research questions that need to be answered in this comprehensive study: i) How should ESL teachers improve the teaching and learning of English literature in ESL classrooms?ii) In order for the described strategy to be effectively implemented in ESL schools, how should it be used?The findings of the reviews showed that cultural approach, multi-modal approach, scaffold reading approach, readerresponse approach and literature circles are employed in ESL classrooms.Results indicated that all these five approaches can improving the English literature teaching and learning in ESL classrooms.And teachers can choose the method according to student's learning objectives, past knowledge and abilities, available teaching resources and facilities, teacher's teaching style and preferences and teaching objectives.
The BB84 Quantum Key Distribution Protocol and Potential Risks

Quantum computing is not only a revolution in information science, but it is going to cause colossal changes in almost all sciences and especially in telecommunications and cryptography. In this work, we present a quantum key distribution protocol, BB84, and also two possible attacks against this cryptographic scheme. Even though quantum cryptosystems are considered to be secure as they are based on the laws of quantum mechanics, always there are loopholes, and it is really urgent to prepare for the fast-approaching era of quantum computing.
Primary School Reading Diaries, Digital Enclosure and the Common Good: Exploring an Alternative Postdigital Commons Based on Data Cooperatives

This chapter looks at the issues, both practical and moral, that arise when a learning event is digitised. It analyses the act of replacing paper-based reading diaries in UK primary schools with a digitised version through Squirrel Learning’s GoRead initiative. It explores how the collection and amalgamation of children and families’ data on reading activities could be used within the digital economy. As GoRead is a relatively new initiative, its position in the digital economy is not yet fixed and as such is ideally placed to consider how its digitisation might be treated. Consequently, we consider how GoRead interacts with the data it creates and how those who create the data might be involved in its use. This requires an awareness of their position in the creation and use of their data so that this might be negotiated from a standpoint of personal and collective agency. The chapter considers the data ecosystems and business models that impact on legibility, agency, and negotiation within Human Data Interaction (HDI). It recounts current dominant data commodification practices while establishing a possible counter-narrative through the collective agency of practice groups to retain control of their data through cooperative self-organisation and negotiation with third parties. In this regard the chapter considers digital rights, and the creation of digital commons, collectives, and cooperatives as an alternative to click to accept - digital trespass.
Learning Hierarchical Policies by Iteratively Reducing the Width of Sketch Rules

Hierarchical policies are a key ingredient of intelligent behavior, expressing the different levels of abstraction involved in the solution of a problem. Learning hierarchical policies, however, remains a challenge, as no general learning principles have been identified for this purpose, despite the broad interest and vast literature in both model-free reinforcement learning and model-based planning. In this work, we introduce a principled method for learning hierarchical policies over classical planning domains, with no supervision from small instances. The method is based on learning to decompose problems into subproblems so that the subproblems have a lower complexity as measured by their width. Problems and subproblems are captured by means of sketch rules, and the scheme for reducing the width of sketch rules is applied iteratively until the final sketch rules have zero width and encode a general policy. We evaluate the learning method on a number of classical planning domains, analyze the resulting hierarchical policies, and prove their properties. We also show that learning hierarchical policies by learning and refining sketches iteratively is often more efficient than learning flat general policies in one shot.
Sentimental Analysis on IMDb Movies Review using BERT

The sentiment analysis is a system that is used to perform automated analysis processes on various services and product reviews. The Internet Movie Database (IMDb), which is categorized using Bidirectional Encoder Transformers (BERT), is included in this scheme. However, the sentiment analysis system is segregated into three stages: pre-processing, feature extraction, and sentiment classification. To extract features, Word Embedding using Word to Vector (Word2Vec) is employed. The data is then classified using a BERT-based test confusion matrix with settings for accuracy, recall, precision, and F1-Score. The test results showed that, the proposed scheme attain precision of 96.10%, recall of 88.40% and F1-Score of 92.10% for the negative class. Subsequently, precision of 89.20%, recall of 96.40% and F1-Score of 92.60% for the positive class using confusion matrix. Also overall 92.40% of accuracy is achieved to depict that the proposed scheme is an effective and reliable technique to detect sentiments for movie reviews.