Reviewerly/exHarmony huggingface dataset:
Cross-channel spatial attention network for few-shot classification

Few-shot image classification learns from the limited samples of known categories, with the purpose of identifying new categories of samples. However, due to the under-representation of the class prototype generated by support samples for few-shot image classification, it leads to large classification errors. Therefore, we propose the multi-loss dual prototype network. First, the class prototype of the support samples and the class prototype of the query samples are constructed by the support samples or query samples, respectively. Second, the local loss of self-calibration (SC) is obtained by calculating the difference between the class prototype features of the support samples and the support sample features. The local loss of mutual-calibration (MC) is obtained by computing the difference between the class prototype features of the query sample and the support sample features, as well as the difference between the class prototype features of the support samples and the query sample features. Then, the global loss of the support samples and the query sample classification is calculated, and the local and global losses are weighted as the total loss to train the network. The total loss includes both SC and MC, which enables the network to make full use of the sample feature information. To adaptively enhance the features of the target region, we propose a cross-channel spatial attention model that generates class prototype features that are more consistent with the feature distribution of the class. Finally, we improve the transductive inference algorithm by assigning weights to the unlabeled query samples using cosine similarity and expanding the weighted query samples into the support set through iteration to further enrich the class prototype features. Experimental results show that the proposed method is effective.
Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms

Today, intelligent drone technology is rapidly expanding, particularly in the defense industry. A swarm of drones can communicate, share data, and make the best decisions on their own. Drone swarms can swiftly and effectively carry out missions like surveillance, reconnaissance, and rescue operations, without exposing military troops to hostile conditions. However, there are still significant problems that need to be resolved. One of them is to protect communications on these systems from threat actors. In this paper, we use blockchain technology as a defense mechanism against such issues. Drones can communicate data safely, without the need for a centralized authority (ground station), when using a blockchain to facilitate communication between them in a leader–follower hierarchy structure. Solidity has been used to create a compact, lightweight, and effective smart contract that automates the process of choosing a position in a certain swarm formation structure. Additionally, a mechanism for electing a new leader is proposed. The effectiveness of the presented model is assessed through a simulation that makes use of a DApp we created and Gazebo software. The purpose of this work is to develop a reliable and secure UAV swarm communication system that will enable widespread global adoption by numerous sectors.
Blockchain-Based Crop Recommendation System for Precision Farming in IoT Environment

In agriculture, soil is a vital element that decides the quality and yield of agricultural produce. Soil consists of various nutrients such as nitrogen (N), phosphorous (P), potassium (K), the potential of hydrogen (pH), and water content. Nitrogen is responsible for building chlorophyll, which helps produce proteins and thus directly contributes to plant growth and development. Phosphorous is needed to develop root systems and flowers, whereas potassium helps increase disease resistance. Each of these play a role in crop cultivation. Thus, in this research paper, considering the fact that soil health will provide farmers with the best selection of crops that are compatible with their farm’s soil nutrients, we propose an algorithm for recommending a set of suitable crops based on various soil attributes. These soil nutrients can be collected in real-time using soil sensors, such as N, P, K, and pH, and humidity sensors. They can be deployed in farms where the cultivation takes place. These sensor readings would then be transferred to the blockchain layer, thereby validating the data and ensuring it is tamper-proof and evident. The crop recommendation model uses data from these sensors in real-time, increasing the results’ accuracy. The last stage leads us to display these results via a user dashboard, which helps the farmers to keep in check with their farm’s practices, and their sensor states from remote locations.
Examination of the relationship between information and communication technology competencies and communication skills

This study aimed to explore the impact of different dimensions of communication skills, namely competence (COMP), self-esteem (SELF), social communication challenge (SCC), and listening-speaking (LI-S) on the utilization of information and communication technology (ICT) for student support (SS) and instructional design (ID). This study implemented inferential statistical methods to explore the impact of different dimensions of communication skills on the utilization of ICT among pre-service teachers. A sample of 324 pre-service teachers from Kazan Federal University was examined, utilizing validated scales adapted to the Russian context, with data analyzed using structural equation modeling to identify complex relationships between variables and to ascertain potential differences according to gender and department. The results demonstrated a significant positive relationship between participants’ perceived COMP in communication skills and their information and communication technology competencies (ICTCs), both in SS and ID. This suggests the need for targeted training to enhance educators’ communication competencies for optimal ICT utilization. Conversely, SELF and LI-S communication skills did not significantly influence ICTCs, indicating that these elements do not necessarily result in more effective ICT use in educational contexts. However, handling SCCs was positively associated with ICTCs in ID but not for SS, indicating a nuanced relationship between different communication skills and areas of ICT utilization. The study findings provide implications for professional development programs and underline the importance of certain dimensions of communication skills in leveraging ICT in education. Further research is recommended to verify these findings across different populations and educational settings.
How Do Background and Remote User Representations Affect Social Telepresence in Remote Collaboration?: A Study with Portal Display, a Head Pose-Responsive Video Teleconferencing System

This study presents Portal Display, a screen-based telepresence system that mediates the interaction between two distinct spaces, each using a single display system. The system synchronizes the users’ viewpoint with their head position and orientation to provide stereoscopic vision through this single monitor. This research evaluates the impact of graphically rendered and video-streamed backgrounds and remote user representations on social telepresence, usability, and concentration during conversations and collaborative tasks. Our results indicate that the type of background has a negligible impact on these metrics. However, point cloud streaming of remote users significantly improves social telepresence, usability, and concentration compared with graphical avatars. This study implies that Portal Display can operate more efficiently by substituting the background with graphical rendering and focusing on higher-resolution 3D point cloud streaming for narrower regions for remote user representations. This configuration may be especially advantageous for applications where the remote user’s background is not essential to the task, potentially enhancing social telepresence.
Managing Personal Information

There is an increasing awareness of the potential that our own self-gathered personal information has for our wellness and our health. This is partly because of our increasing awareness of what others – the major internet companies mainly – have been able to do with the personal information that they gather about us. The biggest hurdle to us using and usefully exploiting our own self-gathered personal data are the applications to support that. In this paper we highlight both the potential and the challenges associated with more widespread use of our own personal data by ourselves and we point at ways in which we believe this might happen. We use the work done in lifelogging and the annual Lifelog Search Challenge as an indicator of what we can do with our own data. We review the small number of existing systems which do allow aggregation of our own personal information and show how the use of large language models could make the management of our personal data more straightforward.
Understanding employees' responses to information security management practices: a person-environment fit perspective

ABSTRACTThis study tests the person–environment fit theory in information security (ISec), which describes situations in which the extent of ISec management is linked to positive or negative employee outcomes. We hypothesise that employees appraise their organisational ISec depending on the mismatch or match perception between their needs and organisational supplies. We define a mismatch as ISec misfit and a match as ISec fit and develop hypotheses framing the relationship between ISec fit (vs. misfit), employees’ emotional reactions (anxiety and attentiveness), and, subsequently, ISec behaviours (ISec in-role, extra-role, and violation behaviours). We conduct quadratic polynomial regressions and apply response surface methodology to test our hypotheses and obtain two findings. First, ISec management are proactively needed by employees instead of prior enforcement perspective. The results show that ISec management practices are appraised negatively when they either exceed or fail to meet employees’ preferences. Second, in expounding on a theoretical construction and empirical test of ISec fit and misfit and their relationships with employee outcomes, we find that attentiveness is greater with ISec fit, whereas anxiety is higher and attentiveness is lesser with ISec misfit. We also find that emotion mediates employees’ ISec behaviours, while only anxiety does not mediate extra-role behaviour.KEYWORDS: Person–environment fitAnxietyAttentivenessISec behavioursResponse surface methodsPolynomial regression Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by National Natural Science Foundation of China: [Grant Number 71872129, 72011530135, 72261160394].
Mixed Nondeterministic-Probabilistic Automata

Graphical models in probability and statistics are a core concept in the area of probabilistic reasoning and probabilistic programming—graphical models include Bayesian networks and factor graphs. For modeling and formal verification of probabilistic systems, probabilistic automata were introduced. This paper proposes a coherent suite of models consisting of Mixed Systems, Mixed Bayesian Networks, and Mixed Automata, which extend factor graphs, Bayesian networks, and probabilistic automata with the handling of nondeterminism. Each of these models comes with a parallel composition, and we establish clear relations between these three models. Also, we provide a detailed comparison between Mixed Automata and Probabilistic Automata
A High-Performance Federated Learning Aggregation Algorithm Based on Learning Rate Adjustment and Client Sampling

Federated learning is a distributed learning framework designed to protect user privacy, widely applied across various domains. However, existing federated learning algorithms face challenges, including slow convergence, significant loss fluctuations during aggregation, and imbalanced client sampling. To address these issues, this paper introduces a high-performance federated learning aggregation algorithm. This algorithm combines a cyclic adaptive learning rate adjustment strategy with client-weighted random sampling, addressing the aforementioned problems. Weighted random sampling assigns client weights based on their sampling frequency, balancing client sampling rates and contributions to enhance model aggregation. Additionally, it adapts the learning rate based on client loss variations and communication rounds, accelerating model convergence and reducing communication costs. To evaluate this high-performance algorithm, experiments are conducted using well-known datasets MNIST and CIFAR-10. The results demonstrate significant improvements in convergence speed and loss stability. Compared to traditional federated learning algorithms, our approach achieves faster and more stable convergence while effectively reducing training costs.
Is ChatGPT not Appropriate for Religious Use?

Five days after the launching of ChatGPT the number of users hit one million and the investment collected hit more than twenty billion US dollars in a couple of months. Nevertheless, the public reaction to the surprise launching of ChatGPT met with mixed feelings. Some paraded the release with positive reactions and considered it as the breakthrough of the year. Some consider it as faux science and an incompetent linguistic system. Thousands of public figures signed an open petition for immediate pause of AI experiments like ChatGPT. It is likened to the Manhattan Project in the sudden invention of the atomic bomb without much thought given on its impact. From the perspective of Islamic world, the Malaysian former minister of religious affairs has announced that ChatGPT is not appropriate to be used as reference for religious matters. Many have written and commented on the shortcomings of ChatGPT, such as, suffering from the hallucination and lack of traceable classical reasoning mechanism. It is not able to track and explain the sources of knowledge that are used to derive and support the conclusions made. This paper is to present the results obtained from ChatGPT on some WH questions and make assessment on their factual accuracy. A data set, obtained from previous research on Al-Quran knowledge base system, is used to evaluate the performance of ChatGPT. These experiments will provide some concrete technological reasons to support or reject the view made by the former Malaysian religious minister whether ChatGPT is not appropriate for use in religious matters.
Participatory Design Workshop to Create a Virtual Reality Musical Instrument Based on Tumbuk Kalang

This paper describes a participatory design workshop to design a virtual reality musical instrument based on Tumbuk Kalang. Originating as a performance for rice harvesting festivals, Tumbuk Kalang as a traditional musical instrument consists of a mortar and pestles. Several players, each holding a pestle will coordinate their movements to pound on the same mortar to create rhythmic sounds whilst singing. These days, Tumbuk Kalang is no longer associated with rice harvest but has been innovated in cultural performances that include songs, dances, and other musical instruments. As machines take over rice production, pestles and mortars are out of use and the tradition of Tumbuk Kalang may be lost. Thus, this research proposes to create a Virtual Reality (VR) Tumbuk Kalang so that it can be preserved and played by future musicians. However, instead of recreating Tumbuk Kalang in VR, we suggest that innovation should be introduced in the design of the new VR Tumbuk Kalang. Hence, we have conducted a participatory design workshop with 12 musicians to design the new VR Tumbuk Kalang. The findings from the workshop produces six new designs of VR musical instruments based on Tumbuk Kalang. The contributions of this paper include the new designs of VR Tumbuk Kalang and the participatory design method used which may be replicated by other researchers who are aiming to preserve traditional musical instruments in the form of VR.
Realizing Persistent Signals in JavaScript

Reactive programming enables declarative descriptions of dependencies between and computations throughout signals, an abstraction of time-varying values. Signals have been extended to persistent signals (an abstraction of time-varying values with their execution histories) to enable them to go back in time with any given time. Currently, this feature is only supported by SignalJ, an extension to Java with signals. This limits the use of persistent signals only within the Java-based applications. This is an undesirable restriction, because mechanisms used in realizing persistent signals are actually language-independent. To tackle this problem, we propose an implementation of persistent signals in JavaScript, which makes application areas of persistent signals broader, including Web-frontend. To realize persistent signals in JavaScript, seamless connections between JavaScript programs that run on restricted environments such as browsers and time-series databases that serve histories of persistent signals are required. It is also desirable to reuse existing JavaScript ecosystem. To address these problems, we design a relay-server-based architecture and realize persistent signals in JavaScript as a DSL library.
Open framework for analyzing public parliaments data

Abstract Open information about government organizations should interest all citizens who care about their governments’ functionality. Large-scale open governmental data open new opportunities for citizens and researchers to monitor their government’s activities and improve its transparency. Over the years, various projects and systems have processed and analyzed governmental data based on open government information. Here, we present the Collecting and Analyzing Parliament Data (CAPD) framework. This novel generic open framework enables collecting and analyzing large-scale public governmental data from multiple sources. This study utilized our framework to collect over 64,000 parliament protocols from over 90 committees from three countries and analyzed it to calculate structured features. Next, we utilized anomaly detection and time series analysis to achieve a number of insights into the committees’ activities. This study demonstrates that the CAPD framework can be utilized to effectively identify anomalous meetings and detect dates of events that affect the parliaments’ functionality and help to monitor their activities.
Automated library mapping approach based on cross‐platform for mobile development programming languages

Abstract Context The most popular mobile platforms, Android and iOS, are traditionally developed using native programming languages—Java and Kotlin for Android, and Objective‐C followed by Swift for iOS, respectively. Due to their popularity, there is always a demand to convert applications written for one of these two platforms to another. Cross‐platform mobile development is widely used as a solution where an application is written once and deployed on multiple platforms written in several other programming languages. One common cross‐platform approach that has been used recently by some research groups is the Trans‐Compilation approach. They focus on translating a program written in iOS into Android or vice versa. The main problem with their solutions is that library function mapping is not generalized and usually functions constitute most of the parts of any program. Objective This study aims to introduce an automatic library mapping approach for mobile programming languages. Method A library function of a source language will be automatically mapped to a corresponding function of the destination language by using the function structure for the two languages. The function structure includes the library to which the function belongs, the return type, parameter types, and the number of parameters. To test our approach, we map from Swift to Java. Results The results of our experiments show that our automatic library mapping approach achieves an average accuracy of 83.6% when tested on the most used libraries and outperforms current state‐of‐the‐art mapping techniques in terms of mapping accuracy. Conclusion These findings show that our automatic mapping approach is promising and can help to overcome the limitations of the trans‐compilation approaches.
Code Merging using Transformations and Member Identity

Conventionally, merging code files is performed using generic line-based merging algorithms (e.g., diff3) that are unaware of the syntax and semantics of the programming language, outputting conflicts that could be avoided. Structured and semistructured merging techniques are capable of reducing conflicts, but they still suffer from false positives (conflicts that could be avoided) and false negatives (conflicts that go undetected). We propose a merging technique that combines semistructured and transformation-based strategies, where conflict detection is aware of semantic aspects of the programming language. We extract transformations of two branches and apply a merging process that analyzes incompatible transformations, avoiding false positives and false negatives that occur in existing approaches. We developed Jaid, a prototype merging tool for Java based on the assumption that structural code elements evolve with attached UUIDs (representing identity). We performed an early experiment with 63 merge scenarios from two open-source projects to test the technique and assess its feasibility.
CloudJIT: A Just-in-Time FaaS Optimizer (Work in Progress)

Function-as-a-Service has emerged as a trending paradigm that provides attractive solutions to execute fine-grained and short-lived workloads referred to as functions. Functions are typically developed in a managed language such as Java and execute atop a language runtime. However, traditional language runtimes such as the HotSpot JVM are designed for peak performance as considerable time is spent profiling and Just-in-Time compiling code. As a consequence, warmup time and memory footprint are impacted. We observe that FaaS workloads, which are short-lived, do not fit this profile.
Schema matching based on energy domain pre-trained language model

Abstract Data integration in the energy sector, which refers to the process of combining and harmonizing data from multiple heterogeneous sources, is becoming increasingly difficult due to the growing volume of heterogeneous data. Schema matching plays a crucial role in this process by giving each representation a unique identity by matching raw energy data to a generic data model. This study uses an energy domain language model to automate schema matching, reducing manual effort in integrating heterogeneous data. We developed two energy domain language models, Energy BERT and Energy Sentence Bert, and trained them using an open-source scientific corpus. The comparison of the developed models with the baseline model using real-life energy domain data shows that Energy BERT and Energy Sentence Bert models significantly improve the accuracy of schema matching.
CloudJIT: A Just-in-Time FaaS Optimizer (Poster Abstract)

Function-as-a-Service provides attractive solutions to execute fine-grained and short-lived functions. Functions are typically developed in a managed language and execute atop a language runtime. However, traditional runtimes are designed for peak performance as considerable time is spent profiling and Just-in-Time compiling code. We observe that short-lived FaaS workloads do not fit this profile.
CHERI Performance Enhancement for a Bytecode Interpreter

During our port of the MicroPython bytecode interpreter to the CHERI-based Arm Morello platform, we encountered a number of serious performance degradations. This paper explores several of these performance issues in detail, in each case we characterize the cause of the problem, the fix, and the corresponding interpreter performance improvement over a set of standard Python benchmarks.