Reviewerly/exHarmony huggingface dataset:
Point-mass Filter with Functional Decomposition of Transient Density and Two-level Convolution

The paper deals with Bayesian state estimation using the point-mass filter with a particular focus on the prediction step involving the convolution of two grids of points. To reduce the computational costs of the step, a functional decomposition-based convolution was proposed by Tichavský et al. (2022), which approximates the transient probability density function over an approximation region. This paper addresses the problem of having spacious grids of points due to state uncertainty while the approximation region is kept small to preserve low computational complexity. A two-level convolution is proposed based on splitting the grids into subgrids and processing the convolution in the upper level for the subgrids and in the lower level for their points. An example demonstrates the proposed technique efficiency.
Multi-Agent Exploration-Based Search for an Unknown Number of Targets

This paper presents an active sensor fusion technique for multiple mobile agents (robots) to detect an unknown number of static targets at unknown positions. To process and fuse sensor measurements from the agents, we use a random finite set formulation with an iterated-corrector probability hypothesis density filter. Our main contribution is to introduce two different multi-agent planners to quickly find the targets. The planners make greedy decisions for the next state of each agent by maximizing an objective function consisting of target refinement and exploration components. We demonstrate the performance of our approach through a series of simulations using homogeneous and heterogeneous agents. The results show that our framework works better than a lawnmower baseline, and that a centralized version of the planner works best.
Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers*

Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off between exploration and exploitation. However, the use of BO as a tool for coordinated decision-making in multi-agent systems with unknown structure has not been widely studied. This paper investigates a black-box optimization problem over a multi-agent network coupled via shared variables or constraints, where each subproblem is formulated as a BO that uses only its local data. The proposed multi-agent BO (MABO) framework adds a penalty term to traditional BO acquisition functions to account for coupling between the subsystems without data sharing. We derive a suitable form for this penalty term using alternating directions method of multipliers (ADMM), which enables the local decision-making problems to be solved in parallel (and potentially asynchronously). The effectiveness of the proposed MABO method is demonstrated on an intelligent transport system for fuel efficient vehicle platooning.
LSTM-OBE based Interval Prediction of Effluent BOD for Wastewater Treatment

In this paper, a new interval prediction method of effluent BOD for wastewater treatment is proposed. Firstly, a point prediction method of effluent BOD based on a long short term memory (LSTM) neural network model is generated. Then, an improved LSTM-MTOBE algorithm is designed to predict the interval of effluent BOD, in which the minimum trace optimal boundary ellipsoid (MTOBE) algorithm is combined with LSTM to identify the real parameters of its output layer weights. Next, based on the properties of ellipsoids, the determination interval of the model uncertainty is obtained and analyzed. Finally, compared with some existing algorithms, the proposed method is illustrated to have better prediction accuracy and interval performance.
Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters

According to UNESCO's Atlas of the World's Languages in Danger, 40% of the languages today are counted as endangered in the future. Indigenous languages are endangered because of the less availability of interactive learning mediums for those languages. Thus this paper proposes an interactive deep learning method for Handwritten Character Recognition of the indigenous language “Chakma.” The method comprises dataset creation using a mobile app named “EthnicData.” It reports the first “Handwriting Character Dataset” of Chakma containing 47,000 images of 47 characters of Chakma language using the app. A novel SelfONN-based deep learning model, Self-ChakmaNet, is proposed in this research for Chakma Handwritten character recognition. The Self-ChakmaNet achieved 99.84% for overall accuracy, precision, recall, F1 score, and sensitivity. The proposed model with high accuracy can be implemented in mobile devices for handwritten character recognition as the model has less number of parameters and a faster processing speed.
An approach to improve the accuracy of rating prediction for recommender systems

Sentiment analysis is critical for classifying users on social media and reviewing products through comments and reviews. At the same time, rating prediction is a popular and valuable topic in research on recommendation systems. This study improves the accuracy of ratings in recommendation systems through the combination of rating prediction and sentiment analysis from customer reviews. New ratings have been generated based on original ratings and sentiment analysis. Experimental results show that in almost all cases, revised ratings using a deep learning-based algorithm called LightGCN on 7 various real-life datasets improve rating prediction. In particular, rating prediction metrics (RMSE and MAE, R2, and explained variance) of the proposed approach (with revised ratings) are better than those of the typical approach (with unrevised ratings). Furthermore, evaluating ranking metrics (also top-k item recommendation metrics) for this model also shows that our proposed approach (with revised ratings) is much more effective than the original approach (with unrevised ratings). Our significant contribution to this research is to propose a better rating prediction model that uses a supplement factor sentiment score to enhance the accuracy of rating prediction.
A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization

Abstract Over the last decade, particle swarm optimization has become increasingly sophisticated because well-balanced exploration and exploitation mechanisms have been proposed. The sequential quadratic programming method, which is widely used for real-parameter optimization problems, demonstrates its outstanding local search capability. In this study, two mechanisms are proposed and integrated into particle swarm optimization for single-objective numerical optimization. A novel ratio adaptation scheme is utilized for calculating the proportion of subpopulations and intermittently invoking the sequential quadratic programming for local search start from the best particle to seek a better solution. The novel particle swarm optimization variant was validated on CEC2013, CEC2014, and CEC2017 benchmark functions. The experimental results demonstrate impressive performance compared with the state-of-the-art particle swarm optimization-based algorithms. Furthermore, the results also illustrate the effectiveness of the two mechanisms when cooperating to achieve significant improvement.
Safeguarding Data through Homomorphic Cryptosystems in Cloud Storage

In this chapter, we have given implementation and performance details of two homomorphic encryption schemes, Rivest Shamir Adleman (RSA) and Paillier. The RSA is multiplicative homomorphic scheme where the Paillier is additive homomorphic scheme. Cloud computing is a revolutionary technology that enables users to access and utilize computing resources, including servers, storage, databases, and software applications, over the internet. It operates on a vast network of remote servers, allowing seamless scalability, cost-efficiency, and flexible data management. By allowing users to use remote resources, cloud computing lightens the load on local storage. But using these services creates a whole new set of issues. Since users have no control over the data they save on those storage devices, it is crucial to accomplish data authentication and secrecy. Since not every user possesses that level of knowledge, they can ask Trusted Verifier (TV), an authorized third party, to do data verification on their behalf in order to ensure that the outsourced data is intact. Since the data owner stores the data on the cloud in an encrypted format, it becomes difficult to check the integrity of the data without decrypting. But by using homomorphic encryption schemes the integrity checking can be made possible without original copy. Using the homomorphic properties of RSA or Paillier cryptosystems, we can check the intactness of remotely stored data without having the original copy, thus can achieve public auditability. Due to their partial homomorphism, both techniques have limited functionality. Owing to these methods' homomorphic nature, the original contents will remain hidden during the verification process. By preserving secrecy, this architecture will enable data authentication.
Active eavesdropping detection: a novel physical layer security in wireless IoT

Abstract Considering the variety of Internet of Things (IoT) device types and access methods, it remains necessary to address the security challenges we currently encounter. Physical layer security (PLS) can offer streamlined security solutions for the next generation of IoT networks. Presently, we are witnessing the application of intelligent technologies including machine learning (ML) and artificial intelligence (AI) for precise prevention or detection of security breaches. Active eavesdropping detection is a physical layer security-based method that can differentiate wireless signals between wireless devices through feature classification. However, the operation of numerous IoT devices operate in environments characterized by low signal-to-noise ratios (SNR), and active eavesdropping attack detection during communication is rarely studied. We assume that the wireless system comprising an access point (AP), K authorized users and a proactive eavesdropper (E), following the framework of transforming wireless signals at AP into organized datasets that this article proposes a BP neural network model based on deep learning as a classifier to distinguish eavesdropping and non-eavesdropping attack signals. By conducting experiments under SNRs, the numerical results show that the proposed model has stronger robustness and detection accuracy can significantly improve the up to 19.58% compared with the reference approach, which show the superiority of our proposed method.
Three-component Seismic Data Acquisition System Base on STM32 and ADS1285

In the three-component seismic VSP seismic data acquisition, high-resolution and low-power seismic wave acquisition is an important research content of seismic exploration methods. In view of the problems of insufficient resolution and signal attenuation of existing geophones, which lead to distortion of seismic signals collected in complex strata. A digital downhole digital three-component detector based on ADS1285 is proposed. The design uses STM32F405 as the microcontroller, ADM2582E as the RS485 transmission chip, and uses the PPP communication protocol to achieve high-speed communication with the host computer. Experimental results show that the dynamic range of the data acquisition system based on ADS1285 is 135dB, and the average analog-to-digital conversion equivalent noise level is below 0.5μV, which can effectively improve the acquisition accuracy of field seismic exploration.
Real-time Monitoring System for Driver Phone Usage Based on Improved YOLOv5s

In response to the impact of driver's violations, such as using a mobile phone, on vehicle safety during the driving process, we propose an improved real-time monitoring algorithm based on YOLOv5s with lightweight optimization. Firstly, we replace the C3 module (CSP Bottleneck with 3 convolutions) in the backbone network of YOLOv5s with a lightweight Ghost Module to reduce the model's parameter count, enhance detection speed, and maintain inference accuracy unaffected, thus meeting the requirements of real-time monitoring. Secondly, we introduce the RepConv (Receptive Field Block) module into the Feature Extraction Network (PANet) structure to increase the neural network's receptive field for input images and further reduce the model's computational load. Experimental results show that the improved network achieves an mAP@0.5 of 95.7%, a detection speed of 140 FPS, and a model size reduction to 10.6MB, meeting the demand for real-time and reliable detection on embedded devices.
The Relationship between a Company’s Cryptocurrency Holdings and Its Sustainable Performance—With a Focus on External and Internal Financial Issues and Cash

This study explores the relationship between a company’s cryptocurrency holdings and its sustainable performance. The study also looks into how factors such as external financial crises, internal financial conditions, and cash shortages affect the link between possession of cryptocurrencies and company sustainable performance. The empirical findings showed that while holdings of cryptocurrencies may generally have a negative impact on a company’s performance, cryptocurrency holdings by businesses during an external financial crisis such as COVID-19 may have a positive relationship with the sustainable performance of the business. The findings support earlier research that suggested cryptocurrency ownership can have both positive and negative effects on a company, but that it can also boost firm performance in times of external financial hardship. By demonstrating a higher favorable connection for larger amounts of cryptocurrency holdings, these results can be further supported. The implications of holding cryptocurrencies on internal and external financial strain vary. Regarding internal financial issues, it was discovered that keeping cryptocurrencies had a favorable impact on sustainable performance for financially healthy businesses. It was also demonstrated that the company’s cryptocurrency holdings, which it keeps despite its cash shortage, had a detrimental impact on performance. Even in such a case, it was confirmed that holding cryptocurrencies has a favorable impact on a company’s sustainable performance when it is in good financial standing. The findings imply that, despite the unavoidable external financial challenges, the internal financial condition must be healthily maintained if a business engages in cryptocurrency.
Unlocking the Potential of Explainable Artificial Intelligence in Remote Sensing Big Data

In the ever-evolving landscape of artificial intelligence and big data, the concept of explainable artificial intelligence (XAI) [...]
An exploration of human and platform intra-actions in a digital teaching and learning environment

This study uses a sociomaterial critical posthuman paradigm to examine possibilities, constraints, and tensions that come into view in the ‘intra-action’ between teachers and a digital environment. It considers how teacher subjectivities may be reimagined in new digitised visuospatial environments. This qualitative study utilises reflective interviews with things to illuminate general issues of subjectivities in human/digital contexts in a newly adopted Virtual Learning Environment (VLE) at a university in the Middle East. The interviews reveal several diffractions from the new entanglement created by introducing the new VLE. This highlights what it means to be a higher education educator in the digital age. This study contributes to critical research on digital assemblages to counteract the wholly positivist and non-problematized discourse of instrumentalization of technology use in education that often accompanies them. It also creates more openings for discussing posthuman subjectivities in sociomaterial contexts.
High-speed train timetable optimization based on space–time network model and quantum simulator

Abstract Timetable scheduling is a combinatorial optimization problem that presents formidable challenges for classical computers. This paper introduces a pioneering methodology for addressing the high-speed train timetabling problem through quantum computing. Initially, a comprehensive binary integer programming model, grounded in the space–time network, is proposed (M1). To manage the intricacy of model M1, a knapsack problem reformulation is employed to establish a simplified binary integer programming model (M2). Both M1 and M2 are subsequently converted into quadratic unconstrained binary optimization (QUBO) models to harness the potential of quantum computing. Several techniques, including the Gurobi solver, simulated annealing, and the coherent Ising machine (CIM) quantum simulator, are deployed to solve the model across four distinct scenarios of varying complexity. The findings indicate that CIM quantum simulator outperforms the simulated annealing method in terms of solution quality for medium-scale problems.
Performance Analysis of Virtual Machine Monitoring System

In recent years, the computing world has shifted to cloud computing, which provides on-demand access to network and computing resources with cost-effectiveness and scalability. The flexibility and scalability of cloud computing is largely due to the powerful hypervisor for managing the physical platform and accessing resources. Since hypervisors are the most important part of the virtualized system, analyzing its performance is essential to optimize the performance of cloud applications. While providing a variety of features and services, hypervisors have proprietary licenses and may require specialized expertise to operate. In this paper, we studied hypervisors’ performance on virtual machine management under various workloads, and we focus on KVM which utilizes the standard Linux kernel and hardware-assisted virtualization. We conducted experiments to evaluate the performance merits of the hypervisor by varying workload configurations. Our experimental results show that KVM is effective for CPU with a performance overhead of 8% or less, while for memory-intensive workloads, the observed bandwidth of VM is greater than that of the native server. However, for network-intensive workloads, KVM is effective only when the socket size is sufficient and creates an overhead of around 18% when the socket size is smaller than the message size.
Preprocessing of Slang Words for Sentiment Analysis on Public Perceptions in Twitter

Nowadays, many people express their evaluations on certain issues via social media freely, which makes huge amounts of data generated every day on social media. On Twitter, public opinions are diverse, which makes them possible to be processed for sentiment analysis. However, many people conveniently use slang words in expressing their opinions on Twitter. These slang words in the text can sometimes lead to miscalculation of language processing due to the absence of the “real words.” This research aimed to investigate the effect of adding slang words as part of the preprocessing stage to the performance of the conducted sentiment analysis. The sentiment analysis was performed using Naïve Bayes Classifier as the classification algorithm with term frequency-inverse document frequency (TF-IDF) as the feature extraction. The research focused on comparing the performance of the conducted sentiment analysis on data that was preprocessed using slang dictionary and the ones that did not use slang dictionary. The case used in this research was texts related to COVID-19 pandemic in Indonesia, especially the ones related to the implementation of vaccines. The performance evaluation results indicate that sentiment analysis of data preprocessed using slang word dictionary has shown better accuracy than the ones preprocessed without it.
Self-Reporting of Teacher–Student Performance in Virtual Class Interactions in Biological Sciences during the SARS-CoV-2/COVID-19 Pandemic

We used an interbehavioral model of teacher–student didactic performance with six pairs of criteria, as follows. Exploration of competencies and precurrent for learning, explicitness of teacher criteria and identification of student criteria, teacher illustration and illustration—student participation, supervision of the practice by the teacher and pertinent student practice, teacher feedback and feedback—student improvement, and teacher evaluation and evaluation—student application. The objective was to determine the level of covariation, divergence and convergence between the constructs of teacher didactic performance and student didactic performance in virtual classes as a result of the SARS-CoV-2 pandemic, in November 2020. Three hundred-thirty undergraduate students of biological sciences from a public university in Peru completed two self-report scales in virtual modality through Google forms, one on the performance of their teachers and the other on their own performance. By means of confirmatory factor analysis and an analysis of the covariance of teacher–student performance constructs, three models were obtained with good fits for the convergent and divergent validities of six constructs of the two teaching performance scales. Adequate models of functional correspondence in each pair of the six teacher–student didactic performance criteria were obtained. Likewise, didactic performances related to the identification of teaching–learning criteria and to the evaluation—application criteria were the most frequent during teaching–learning, according to the students’ self-report.
Building a Blockchain-Based Decentralized Crowdfunding Platform for Social and Educational Causes in the Context of Sustainable Development

Blockchain technology contributes to achieving the Sustainable Development Goals. Education for sustainable development (ESD) is UNESCO’s education sector response to the urgent and dramatic challenges the planet faces. The traditional way of donating money to charitable causes, such as education, has been through centralized methods and organizations that lack transparency, and donors often do not have a clear understanding of how their contributions are being utilized. Blockchain technology, particularly, platforms like Ethereum and Polygon, has the potential to address the issues associated with traditional donation systems. This paper proposes a decentralized web3 application that utilizes blockchain technology to enhance transparency and efficiency in educational donations in the context of sustainable development. The platform leverages decentralized protocols and smart contracts to ensure secure and transparent transactions, enabling donors to track the utilization of their contributions and ensuring their funds reach their intended beneficiaries. This paper discusses the design and implementation of the platform, highlighting its features and potential for transforming the landscape of charitable donations. This software application can be used in education, and a demo plus some scenarios/work cases are presented/analyzed. The main results and contributions open other future research directions for not only authors.