Reviewerly/exHarmony huggingface dataset:
An improved PSO algorithm based on stream computing and multi-swarm

Due to its simplicity and fast convergence speed, the particle swarm optimization (PSO) algorithm has been applied in many fields to solve various optimization problems. An improved PSO algorithm based on stream computing and multiswarm is proposed to solve the problem of low efficiency and easy to fall into local optimal solution for the traditional PSO algorithm, and it is abbreviated as SCMS PSO. In order to solve the problem of premature convergence caused by multimodal functions, this algorithm adopts multi-swarm. In the initial stage, the swarm is divided into several swarms based on the particle characteristics, and each swarm evolves separately, improving the global search ability of particles. Based on stream computing technology, this algorithm executes particle swarm evolving in distributed parallel. The experimental results show that the proposed algorithm not only has a faster convergence rate and more effective global search capability under the unimodal function, but also achieves better experimental results under the complex and deceptive multimodal function, which confirms that stream computing and multi- swarm can effectively improve the performance of the PSO algorithm.
Application method of computer image processing technology in visual communication design

By utilizing computer graphics and image processing technology, we can effectively enhance the effectiveness of visual communication design. Specifically, using the principle of three primary colors to create a set of vector functions can more accurately calculate the partition model for visual communication. Firstly, we need to comprehensively consider all the characteristics related to the image, and then encode them in an orderly manner to meet our requirements. Secondly, the appropriate hyperplane is selected according to these characteristics to adjust the visual transmission mode. Ultimately, some creative image processing techniques are needed to improve their quality. After systematic research, it has been found that using visual communication design methods can achieve excellent results. It has strong stability, flexibility, and can exert the best communication ability in four types of complex images, greatly improving the level of precision in communication. In addition, this method further clarifies the application direction and methods of image processing technology, in order to provide theoretical reference for optimizing visual communication application solutions.
A Blockchain- and ABE-based scheme for data security and data sharing

Problems such as low trust, opaque data and data leakage are easily found in data sharing of evidential testing among different power engineering departments responsible for quality management. In this regard, this paper proposed several schemes for a secure data sharing in the power engineering. In the schemes, each department forms an allied blockchain, and the generated data for testing are stored on-chain and off-chain. To ensure data security and sharing, the testing data are encrypted into ciphertext, and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is used to realize cross-unit sharing of ciphertext data. The proposed schemes can effectively solve the problems existing in data provenance, data sharing and privacy protection in an engineering data testing and can promote the application of blockchain in this field.
An audio-video processing system design and implement based on CCSDS AOS protocol for manned spacecraft

In view of the development trend of manned spacecraft information system, especially the audio-video technology, combined with the requirements of manned spacecraft audio-video interaction, this paper proposes a system architecture and information processing flow of manned spacecraft audio-video processing system supporting CCSDS AOS protocol and the traditional digital information transmission protocol. Solutions are also given to some certain important issues such as system flexibility, robustness, and convenience. It lays a foundation for better support of space-to-earth communication for future manned spacecraft.
Study on distributed hydrogen block chain trading mechanism based on game strategy

The hydrogen market trading mechanism has problems such as high transaction costs and a small market scale, making it difficult to meet the demand for large-scale renewable energy hydrogen production. Blockchain smart contracts have the potential to solve these problems with their decentralized, distributed ledger, and transparent features. Firstly, this paper analyzes the interest relationship between trading subjects and introduces game theory to establish a game model and mechanism for hydrogen energy trading strategies. An improved ant colony algorithm is used to obtain the Nash equilibrium solution, providing the most optimal pricing strategy for trading parties. Secondly, by dynamically changing the stability coefficient of nodes and selecting new main node mechanisms, the probability of selecting malicious nodes is reduced, thus improving transaction consensus speed. Finally, through case analysis verification it was found that this proposed hydrogen energy trading mechanism can effectively reduce transaction costs and improve system operating efficiency. This research has certain significance in promoting the application of hydrogen energy in distributed energy.
Blockchain meets Internet of Things (IoT) forensics: A unified framework for IoT ecosystems

With the continuously increasing number of security incidents targeting sensors, embedded devices, and other electronics appliances and systems that comprise the so-called Internet of things (IoT), the field of IoT forensics emerged as a branch of digital forensics focusing on the investigation of attacks in the IoT ecosystem. Digital evidence authenticity, integrity, confidentiality, and privacy are among the factors affecting the investigation process in IoT forensics, which can be addressed by employing the blockchain as the means not only for the preservation of digital evidence, but also for other steps of a digital forensics investigation process. The high-level goal of the paper is three-fold: to establish a holistic IoT forensics process as a reference point against which blockchain integration patterns and best practices will be identified in order to yield a robust, widely accepted and scalable architecture of the blockchain-enabled solution; to integrate the blockchain with the proposed IoT forensics process for addressing the above challenges; and to evaluate the overall solution highlighting improvements and potential performance bottlenecks. The proposed blockchain-enabled platform leverages multi-access edge computing and has been implemented with Hyperledger Fabric on an extensive virtualized testbed providing a realistic smart home environment. A thorough evaluation was conducted with real cyber-attacks generating digital evidence at high rates for testing the platform's behavior at high load. The experimental results showed that the proposed platform achieved high throughput, excessively low latency, and zero error rate in the operation of the blockchain network.
The effect of retrieval practice on fluently retrieving multiplication facts in an authentic elementary school setting

Abstract Fluently retrieving simple multiplication facts leads to an improvement of overall math scores. In the current study, we investigated how to best reach this fluency in an authentic elementary school setting. We compared the short‐term and long‐term effects of the learning strategies retrieval practice (using flashcards) to a restudy control condition (chanting multiplication facts out loud) on multiplication fact fluency. Forty‐eight second‐grade pupils received an instruction lesson and attended three spaced practice sessions per learning strategy. The learning strategies were counterbalanced across two different sets of multiplication tables in a within‐subjects design. A pre‐test, a five‐minute delay post‐test, and a one‐week delay post‐test were administered. Compared to restudy, retrieval practice led to a stronger short‐term and long‐term increase in the fluency of retrieving multiplication facts. This study demonstrates the effectiveness of retrieval practice on gaining multiplication fact fluency in an authentic classroom setting using original course materials.
Process-Oriented Model for Managing Software Development Projects Using the PRINCE2 Method

This chapter focuses on the process-oriented model for software development using the PRINCE2 method, and it is divided into two primary parts. The first part offers a theoretical overview of project management, encompassing project definition, the definition and classification of project management, including the characteristics of both traditional and agile project management. Additionally, within this part, the process of software development and the key characteristics of PRINCE2 are presented, including the description of the processes defined by the mentioned method. The second part of the chapter is dedicated to integrating the software development process with the PRINCE2 processes. The constructed process-oriented model includes the nine phases with both the PRINCE2 processes and software development processes intertwined. Based on a critical review of the usefulness of the integration model, the advantages and disadvantages are presented. This well-structured model can significantly contribute to the successful management of software projects.
A Study of GPU Memory Vulnerabilities

GPUs are a fundamental component of most high computing environments available nowadays, being a general solution to various purposes that range from scientific solutions designed as software applications to clusters that hold and process sensitive data. In this paper we focus on Buffer Overflow attacks and memory vulnerability analysis with the intention to prove that given special circumstances the stack and heap zones are susceptible to memory overflow due to a lack of protection mechanisms.
Awareness on Spear Phishing Attacks using Novel TLD names in Research and Academic Field

This paper conducts a study upon possibilities and dangers of fraudulent attacks that can be performed upon academic and research platforms, with the usage of the novel top level domain names issued by Google Registry in May 2023. In the following sections, we will focus on attempts using.zip TLD. The present contains a bidirectional perspective simulated attack -one in which the academic institution represents the target and another in which the said institution represents the impersonated entity. We aim to illustrate an attacker’s workflow in order to comprehend how.zip domains can be used and lessen attacks’ implementations in academic and research scenario. By understanding attackers’ methodology, this paper offers suggestions to mitigate risks in said attacks.
Overcoming Challenges in Migrating Modular Monolith from On-Premises to AWS Cloud

Cloud computing has gained popularity for efficient data storage, processing, and application access. This study focuses on migrating Hermit Portal’s modular monolithic web application from on-premises to AWS cloud. The aim is to identify migration patterns, optimize costs, and consider project management constraints (e.g., time, cost, and performance). Considering the Cloud Computing models perspective, the study analyzed Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Serverless models, with a focus on Amazon Web Services (AWS). Initial “lift and shift” using IaaS is recommended, followed by a gradual adoption of PaaS components like AWS Elastic Beanstalk or AWS Lambda for simplified management and reduced infrastructure responsibilities. Additionally, we explored various cost optimization strategies specific to AWS. Utilizing Reserved Instances, optimizing Windows Server and SQL Server licensing, considering PostgreSQL as an alternative to SQL Server, and leveraging Linux-based apps and web servers on AWS, were found to be effective in reducing costs. Also, we realized during the research that conducting a comprehensive analysis within the AWS ecosystem and understanding AWS-specific features before migration is crucial. This avoids unexpected cost increases compared with on premises hosting and accounts for refactoring and acquiring AWS-specific expertise. In conclusion, based on our research, a phased approach with thorough analysis and AWS-Specific strategies is the key to successfully and cost-effectively migrating monolith applications to AWS Cloud. Choosing the right cloud computing model and implementing cost optimization techniques ensures a smooth transition and maximizes the benefits of AWS.
Hybrid Quantum-Classical Networks characteristics and optimization for Error Correction and Noise Mitigation

Hybrid Quantum-Classical Networks (HQCNs) have emerged as a promising paradigm for harnessing the power of both classical and quantum computing. This paper explores the characteristics and optimizations associated with these networks. Firstly, the characteristics of HQCNs are discussed, including the integration of classical and quantum components, task allocation, parallel processing, and the exploitation of quantum speedup. The paper then delves into optimization techniques in hybrid networks, such as dynamic task allocation, workload distribution, and resource management. Optimizations for error correction and noise mitigation in HQCNs involve fault-tolerant protocols that enhance the resilience of quantum computations to errors and noise. The paper concludes by emphasizing the importance of understanding the characteristics and optimizations in HQCNs to fully leverage their potential for solving complex computational problems efficiently and effectively.
VXLAN - A practical approach to cloud computing scalability

In this paper, we present a simple practical approach to using network virtualization technology in an ecosystem that is mainly composed of open-source software and operating systems.
Practical Approach to Design and Implement a P2P and E2EE Instant Messaging System

As the internet became more and more globally accessible, so did messaging systems become more popular. Starting with mailing lists, IRC (Internet Relay Chat) channels and then evolving into instant messaging applications, the privacy aspect of the communication became an ever increasing concern when it comes to such applications. Many related applications and research studies tackle the problem of privacy with different approaches. The current article discusses the challenges in designing an open source, P2P (peer-to-peer) and E2EE (end-to-end encrypted) instant messaging system and offers technical details on how to implement and design a working proof of concept that allows message exchanges in real-time. The implemented proof of concept solution offers private conversations, while requiring no central servers or data silos, all the data is user hosted.
Improving Network Security Using DD-WRT as a Solution for SOHO Routers

Securing the IT infrastructure in today’s digital age against cyber-attacks has become vital, one of the most used robust solutions for cyber security being a DD-WRT solution. This article focuses on DD-WRT as a security solution for a TP-Link AC1750 router, model Archer C7. In this article, we will explore deploying DD-WRT on a network as a basic solution, followed by an analysis of port scanning and vulnerabilities. We will also examine the ways in which DD-WRT help increase network security by installing additional functionality such as VPN, firewall, and other applications. We will study how an implemented DD-WRT solution will prevent cyber-attacks and vulnerability scans, also referring to research studies conducted to date. This paper provides a starting point for understanding the role of DD-WRT in enhancing IT security against cyber threats.
Sliding Window Method for Imputing Missing Data

The rapid technological advancement has led to an increased amount of content and information which needs to be reliably analyzed and correlated in order to make accurate inferences. Data quality is a stringent issue when considering gaps or missing information and various imputation methods, algorithms or statistical techniques have been created to correct missing data. To this purpose we propose an imputation method using the Sliding Window Technique to help infer missing data from datasets, a method that is efficient in terms of accuracy and complexity. In order to illustrate the method, we present a case study that simulates a scenario in which a half-yearly dataset must be analyzed in relation to annual datasets. The obtained results are passed to a statistical descriptive analysis and the conclusions derived from the performed analysis suggest that the estimations are homogeneous enough.
Improved Speech Activity Detection Model Using Convolutional Neural Networks

In this study, the performance of four speech/voice activity detection (VAD) models, namely SpeechBrain, Picovoice, InaSpeechSegmenter and WebRTC, is compared using data collected from KAIST (Korea Advanced Institute of Science and Technology). The goal is to develop an improved VAD model based on the analysis of SpeechBrain’s performance and provide a detailed perspective on the differences and similarities between these models. The study focuses on the technologies and algorithms used by each model and utilizes experimental methods to evaluate their performance. The experimental results show that SpeechBrain performs the best, with an average recall value of 0.97 and a precision value of 0.96. Our research endeavors have led to the refinement of the existing VAD model, resulting in even more compelling performance metrics. The improved model achieves a recall value of 0.98 and precision value of 0.97, signifying its enhanced capability to accurately detect speech activity. These findings hold promise for the future advancement of VAD models and their application in various speech-processing domains. This research can be used to enhance future VAD models and develop more advanced speech processing applications.
Malicious Short URLs Detection Technique

The struggle between attackers and defenders is similar to a cat-and-mouse game. The former always try to find new ways to evade detections and remain undiscovered. One of the easiest tricks they employ to compromise victims is the use of malicious URLs that once accessed can lead to malware download or information steal. To hide the features of these URLs so that it becomes impossible to recognize them as being suspicious, attackers use URL shortening services. In this paper, we propose an exhaustive system to detect malicious short URLs by leveraging threat intelligence data from popular platforms like VirusTotal, and PhishTank, and by employing various Machine Learning (ML) algorithms. Our system works for every URL, no matter the shortening service used either public or custom. Moreover, concerning the ML classifier, we took a publicly available balanced dataset for training, improved its feature set, and obtained an accuracy of approximately 97%. The dataset contains 90 features that belong to three categories: the URL’s lexical properties, external specifications, and website content. We have tested our proposed ML model against JRip, PART, J48, and Random Forest algorithms, the last one being the most accurate. To showcase the effectiveness of our solution, we have implemented a system from scratch that detects malicious short URLs and notify the user concerning their legitimacy.
Understanding the Challenges and Opportunities of Pose-based Anomaly Detection

Pose-based anomaly detection is a video-analysis technique for detecting anomalous events or behaviors by examining human pose extracted from the video frames. Human anomaly detection plays a crucial role in various applications, such as smart cities and intelligent surveillance systems, for the safety of public environments. Utilizing pose data alleviates the privacy and ethical issues while reducing computational complexity compared to pixel-based approaches. However, it introduces more challenges, such as noisy skeleton data, losing important pixel information, and not having enriched enough features. These problems are exacerbated by the scarcity of high-quality anomaly detection datasets that are good enough representatives of real-world scenarios. In this work, we analyze and quantify the characteristics of two video anomaly datasets to better understand the difficulties of pose-based anomaly detection. We take a step forward, exploring the discriminating power of pose and trajectory for video anomaly detection and their effectiveness based on context. Consequently, our findings will provide valuable insights into the benefits and limitations of pose-based approaches for anomaly detection.
Exploring the Benefits of Time Series Data Augmentation for Wearable Human Activity Recognition.

Wearable Human Activity Recognition (HAR) is an important field of research in smart assistive technologies. Collecting the data needed to train reliable HAR classifiers is complex and expensive. As a way to mitigate data scarcity, Time Series Data Augmentation (TSDA) techniques have emerged as a promising approach for generating synthetic HAR data. TSDA is not as trivial as image augmentation and has been relatively less investigated. In this paper, a comparative study of various state-of-the-art TSDA techniques is applied in the context of wearable HAR. More specifically, we investigate the classification of human activities on the OPPORTUNITY dataset [26] using a deep CNN-LSTM architecture trained on raw and synthetic data. Our study highlights the importance of TSDA on performance enhancement for multivariate multi-class datasets. Interestingly very simple time domain-based TSDA techniques notably outperform complex ones based on Generative Adversarial Networks. We provide practical advice on how to apply TSDA for imbalanced datasets in practice for generating the ideal amount of synthetic data to achieve optimal classification accuracy. Our TSDA-based approach outperforms the previous state-of-the-art [24] on the OPPORTUNITY dataset by and in average and weighted F1-scores, respectively.
QGP-VPN: QKD enhanced VPN solution for general-purpose encrypted communications

Quantum-resistant encrypted communications is of particular importance in the context of recent advancements in quantum computing. Existing technologies such as Quantum Key Distribution achieve this, but are not suitable for general-purpose communication due to limited key rates. In this work, we propose a VPN solution enhanced by QKD for quantum-resistant encryption, we implement it using IDQ Cerberis XG QKD devices, and we test its general-purpose capabilities by performing an encrypted video-conference between two endpoints.
TongueMendous: IR-Based Tongue-Gesture Interface with Tiny Machine Learning

This paper presents TongueMendous, an non-intrusive, pervasive tongue-gesture recognition interface for the general population and use cases. It uses an infrared sensor to detect tongue gestures when the tongue sticks in different directions. The collected data is recognized by a tiny machine learning (TinyML) model, allowing TongueMendous to classify tongue gestures on a microcontroller. Evaluations on the initial prototype reported a 91.7% cross-validation accuracy and 89.4% leave-one-person-out accuracy. We also conduct a study to explore the user experience and future design space. These results suggest that the proposed system can be accurate and work well across different users.