Reviewerly/exHarmony huggingface dataset:
The Distributed Ledger Technology as Development Platform for Distributed Information Systems

Distributed Ledger Technology (DLT) has emerged as a technology enabler for developing trusted and decentralized solutions for various distributed systems worldwide. Because of their sophisticated architectural patterns, current Information System (IS) architectural frameworks are primarily designed for centralized information systems and can no longer ensure the requisite degree of availability and dependability for Distributed Information Systems (DIS). In the current study, I am the first to declare and define the term “DLT-Native”. A “DLT-Native” DIS is built on the DLT platform and uses DLT design patterns to grow internationally, support thousands of distributed nodes, and withstand operational system failures and cyber-attacks. This research creates a reference architecture for Permissioned DLTNS organizing dispersed information system components that may share information, issue and service requests, and conduct outcome-focused activities. The proposed DLTNS architecture combines prominent security, privacy, and trust domain aspects. DLT overcomes conventional DIS solutions with an inherently centralized governance approach and a lack of transparency, data traceability, and trust.
Performance of a Novel Enhanced Sparrow Search Algorithm for Engineering Design Process: Coverage Optimization in Wireless Sensor Network

Burgeoning swarm intelligence techniques have been creating a feasible theoretical computational method for the modeling, simulation, and optimization of complex systems. This study aims to increase the coverage of a wireless sensor network (WSN) and puts forward an enhanced version of the sparrow search algorithm (SSA) as a processing tool to achieve this optimization. The enhancement of the algorithm covers three aspects. Firstly, the Latin hypercube sampling technique is utilized to generate the initial population to obtain a more uniform distribution in the search space. Secondly, a sine cosine algorithm with adaptive adjustment and the Lévy flight strategy are introduced as new optimization equations to enhance the convergence efficiency of the algorithm. Finally, to optimize the individuals with poor fitness in the population, a novel mutation disturbance mechanism is introduced at the end of each iteration. Through numerical tests of 13 benchmark functions, the experimental results show that the proposed enhanced algorithm can converge to the optimum faster and has a more stable average value, reflecting its advantages in convergence speed, robustness, and anti-local extremum ability. For the WSN coverage problem, this paper established a current optimization framework based on the swarm intelligence algorithms, and further investigated the performance of nine algorithms applied to the process. The simulation results indicate that the proposed method achieves the highest coverage rate of 97.66% (on average) among the nine algorithms in the calculation cases, which is increased by 13.00% compared with the original sparrow search algorithm and outperforms other methods by 1.47% to 15.34%.
Compression of Text in Selected Languages—Efficiency, Volume, and Time Comparison

The goal of the research was to study the possibility of using the planned language Esperanto for text compression, and to compare the results of the text compression in Esperanto with the compression in natural languages, represented by Polish and English. The authors performed text compression in the created program in Python using four compression algorithms: zlib, lzma, bz2, and zl4 in four versions of the text: in Polish, English, Esperanto, and Esperanto in x notation (without characters outside ASCII encoding). After creating the compression program, and compressing the proper texts, authors conducted an analysis on the comparison of compression time and the volume of the text before and after compression. The results of the study confirmed the hypothesis, based on which the planned language, Esperanto, gives better text compression results than the natural languages represented by Polish and English. The confirmation by scientific methods that Esperanto is more optimal for text compression is the scientific added value of the paper.
Distributed Ledger Technology and Fully Homomorphic Encryption: Next-Generation Information-Sharing for Supply Chain Efficiency

Information-sharing facilitates the coordination of supply chains and can be essential for enterprises' survival. Studies have shown that supply chains benefit from the availability of aggregated information (e.g., inventory levels), as that would facilitate planning, inform decisions, and limit the emergence of operational distortions that result from the bullwhip effect and inefficient inventory allocation. Despite this, the degree of supply chain transparency within and across companies remains deficient at best, even as advancements in information and communication technology have facilitated secure information-sharing. What is more important is that companies have a direct incentive not to share certain operational attributes (e.g., inventory levels or sales, order sizes, wholesale prices). The issues resulting from technology limitations and irrational ordering- and allocation processes were brought into acute focus during the 2020 COVID-19 (C-19) pandemic, manifesting as persistent and global disruptions in supply operations and delivery delays. This chapter outlines promising approaches and methodologies for resolving these chronic shortcomings by leveraging both distributed ledger ("Blockchain") technology (DLT) and the newly emerging capabilities of fully homomorphic encryption (FHE). By employing zero-trust information-sharing, we present an approach that combines DLT with FHE to bridge information-sharing limitations in hyperconnected supply chains without compromising data control. The chapter also introduces real-world applications of both DLT and FHE to illustrate their transformative potential. We conclude by highlighting the practical benefits of implementing both technologies, as well as the implications for broader adoption.
Urdu Sentiment Analysis

Abstract The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users with each passing day. Each post and comment can give an insight into valuable information about a certain topic or issue, a product or a brand, etc. Similarly, the process to uncover the underlying information from the opinion that a person keeps about any entity is called a sentiment analysis. The analysis can be carried out through two main approaches, i.e., either lexicon-based or machine learning algorithms. A significant amount of work in the different domains has been done in numerous languages for sentiment analysis, but minimal research has been conducted on the national language of Pakistan, which is Urdu. Twitter users who are familiar with Urdu update the tweets in two different textual formats either in Urdu Script (Nastaleeq) or in Roman Urdu. Thus, the paper is an attempt to perform the sentiment analysis on the Urdu language by extracting the tweets (Nastaleeq and Roman Urdu both) from Twitter using Tweepy API. A machine learning-based approach has been adopted for this study and the tool opted for the purpose is WEKA. The best algorithm was identified based on evaluation metrics, which comprise the number of correctly and incorrectly classified instances, accuracy, precision, and recall. SMO was found to be the most suitable machine learning algorithm for performing the sentiment analysis on Urdu (Nastaleeq) tweets, while the Roman Urdu Random Forest algorithm was identified as the best one.
MUSH: Multi-Stimuli Hawkes Process Based Sybil Attacker Detector for User-Review Social Networks

User-Review Social Networks (URSNs) now become the targets of Sybil attacks, where fake reviews are posted by attackers to raise the reputation of listed services or products. Unlike previous fake accounts on Twitter or Weibo, Sybil attackers on URSNs are also genuine users in many cases, which presents a challenge for the existing fake review detection system. Hence, the main purpose of detecting Sybil attackers is to profile abnormal behaviors of users, and we propose a novel Sybil detection model named MUSH to extract long-term features of user behaviors on URSNs. First, aiming to measure the uncertainty of user behaviors, four entropy-based preference models are designed to quantify user preferences, including catering preferences, price preferences, word-of-mouth preferences, and rating preferences. Second, in order to extract temporal logic features, a novel multi-stimuli Hawkes process is introduced by combining external incentives and internal incentives, which can detect abnormal event sequences of posted reviews. This approach is quite different from previous solutions which mostly use direct/indirect graph models or user-related features for detection. Finally, by integrating entropy-based preferences with temporal logic features, a smart Sybil detection model is proposed based on a binary classification approach. Extensive experimental results indicate that MUSH can effectively detect Sybil attackers with a high detection accuracy. By comparing with other approaches, MUSH is quite suitable for adversarial network environments, which could provide better security services to social networks.
Communication-Efficient Federated Multitask Learning Over Wireless Networks

This article investigates the scheduling framework of the federated multitask learning (FMTL) problem with a hard-cooperation structure over wireless networks, in which the scheduling becomes more challenging due to the different convergence behaviors of different tasks. Based on the special model structure, we propose a dynamic user and task scheduling scheme with a block-wise incremental gradient aggregation algorithm, in which the neural network model is decomposed into a common feature-extraction module and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> task-specific modules. Different block gradients with respect to different modules can be scheduled separately. We further propose a Lyapunov-drift-based scheduling scheme that minimizes the overall communication latency by utilizing both the instantaneous data importance and the channel state information. We prove that the proposed scheme can converge almost surely to a KKT solution of the training problem such that the data-distortion issue is resolved. Simulation results illustrate that the proposed scheme significantly reduces the communication latency compared to the state-of-the-art baseline schemes.
Investigating the Current State of Security in Large-Scale Agile Development

Abstract Agile methods have become the established way to successfully handle changing requirements and time-to-market pressure, even in large-scale environments. Simultaneously, security has become an increasingly important concern due to more frequent and impactful incidents, stricter regulations with growing fines, and reputational damages. Despite its importance, research on how to address security in large-scale agile development is scarce. Therefore, this paper provides an empirical investigation on tackling software product security in large-scale agile environments. Based on a literature review and preliminary interviews, we identified four essential categories that impact how to handle security: (i) the structure of the agile program, (ii) security governance, (iii) adaptions of security activities to agile processes, and (iv) tool-support and automation. We conducted semi-structured interviews with nine experts from nine companies in five industries based on these categories. We performed a content-structuring qualitative analysis to reveal recurring patterns of best practices and challenges in those categories and identify differences between organizations. Among the key findings is that the analyzed organizations introduce cross-team security-focused roles collaborating with agile teams and use automation where possible. Moreover, security governance is still driven top-down, which conflicts with team autonomy in agile settings.
Extracting Decision Model and Notation models from text using deep learning techniques

Companies and organizations often use manuals and guidelines to communicate and execute operational decisions. Decision Model and Notation (DMN) models can be used to model and automate these decisions. Modeling a decision from a textual source, however, is a time intensive and complex activity hence a need for shorter modeling times. This paper studies how NLP deep learning techniques can extract decision models from text faster. In this paper, we study and evaluate an automatic sentence classifier and a decision dependency extractor using NLP deep learning models (BERT and Bi-LSTM-CRF). A large labeled and tagged dataset was collected from real use cases to train these models. We conclude that BERT can be used for the (semi)-automatic extraction of decision models from text.
Text to Speech Conversion of Handwritten Kannada Words Using Various Machine Learning Models

Recognition of handwritten characters and words is challenging due to the presence of complex character sets and the complexity of the words. The machine learning models with feature extraction methods will help us to solve the problem of recognizing handwritten words. The various preprocessing techniques applied to the word are Bilateral filters, resizing the images to find the Region of Interest (ROI) by contour detection and cropping the images. After resizing the image, it is further deskewed for better results. The recognition of handwritten Kannada words by extracting histogram of oriented gradients (HOG) features from the word image using various Machine Learning (ML) techniques are presented in this paper. Then the recognized word is converted to speech using the Google Text-to-Speech (gTTS) API. The dataset consists of 54,742 handwritten word images. Various machine learning models like Support Vector Machine (SVM), k-nearest neighbors (KNN), and random forest were applied to the dataset. Average accuracy of 88% is obtained using the SVM classifier with Radial Basis Function (RBF) kernel.
Straight-Line Detection Within 1 Millisecond Per Frame for Ultrahigh-Speed Industrial Automation

Detecting straight lines in video plays a fundamental role in camera-based industrial automation. With the increasing demands on production efficiency, detection speed has become one of the bottlenecks for highly efficient industrial automation. Because of data dependence and hardware limitations, existing vision systems based on central processing unit/graphics processing unit are unable to detect straight lines at an ultrahigh speed. This article addresses this problem and proposes a hardware-friendly Hough transform that can be implemented in fully parallel for the ultrahigh-speed detection, because of the following two key features: it processes multiple pixels in parallel and directly calculates line parameters while capturing the current frame; and it simultaneously initializes the Hough parameter space and votes in the Hough parameter space without any delay. Based on the proposed hardware-friendly Hough transform, its chip-level implementation and system-level hardware design are presented. Experimental results show that the main benefits of the proposed architecture are in real-time performances at a high frame rate (784 frames/s) and an ultralow delay (0.7749 ms/frame).
Verification Grid and Map Slipping Based Graphical Password against Shoulder-Surfing Attacks

Graphical password systems have received significant attention as one potential solution to the need for more usable authentication, but graphical passwords are often considered prone to shoulder-surfing attacks. In this paper, in order to build a balance between usability and security for authentication, we propose a new graphical password scheme by combining a specific verification grid with map slipping strategy. In the proposed scheme, a set of fixed grids are pregenerated on a map. During the registration process, the user is not only asked to select several points in sequence on the map to form a password route, but also required to choose one of the pregenerated grids as a specific verification grid for the subsequent authentication. The password route and the specific verification grid together form the complete graphical password. During the authentication process, the user needs to slip the map to let each point on the password route sequentially fit inside the specific verification grid which is already remembered by the user but difficult to be detected by the attackers. With the specific verification grid and the map slipping strategy, the proposed scheme can effectively defend against shoulder-surfing attacks. Meanwhile, the password points are represented as coordinates on the map; therefore, the proposed scheme has a negligible storage burden. The comparative experiments show that, using the proposed scheme, the success rate of shoulder-surfing defense can be increased by 37% to 56% with different grid sizes and password point numbers, and the usability of passwords can also be improved by 3% to 6%. Therefore, the proposed scheme can achieve good shoulder-surfing defense and reasonable usability simultaneously.
Feature Transformation for Improved Software Bug Detection Models

Testing software is considered to be one of the most crucial phases in software development life cycle. Software bug fixing requires a significant amount of time and effort. A rich body of recent research explored ways to predict bugs in software artifacts using machine learning based techniques. For a reliable and trustworthy prediction, it is crucial to also consider the explainability aspects of such machine learning models. In this paper, we show how the feature transformation techniques can significantly improve the prediction accuracy and build confidence in building bug prediction models. We propose a novel approach for improved bug prediction that first extracts the features, then finds a weighted transformation of these features using a genetic algorithm that best separates bugs from non-bugs when plotted in a low-dimensional space, and finally, trains the machine learning model using the transformed dataset. In our experiment with real-life bug datasets, the random forest and k-nearest neighbor classifier models that leveraged feature transformation showed 4.25% improvement in recall values on an average of over 8 software systems when compared to the models built on original data.
Bitcoin as a Safe-Haven Asset and a Medium of Exchange

A monetary model is established to introduce that bitcoin does have the characteristics of a price rise when the economic situation is terrible under high risk aversion. At this time, Bitcoin has the property of a safe-haven asset, and when economic conditions are good and risk aversion is low, Bitcoin has a pro-cyclical nature. At this time, Bitcoin has a stronger property as a medium of exchange. To show the movement law of the bitcoin price, we make a logarithmic linearization of the model and simulation, and the result is consistent with the theoretical analysis. To better understand the role of bitcoin in the real economy, several standard portfolio models are used to measure the similarities and differences between gold and bitcoin in an investment portfolio. After the outbreak of COVID-19, bitcoin has shown stronger safe-haven asset properties.
Multi-colony ant optimization with dynamic collaborative mechanism and cooperative game

Abstract Ant Colony Optimization easily falls into premature stagnation when solving large-scale Travelling Salesmen Problems. To address this problem, a multi-colony ant optimization with dynamic collaborative mechanism and cooperative game is proposed. Firstly, Ant Colony System and Max–Min Ant System form heterogeneous colonies. Secondly, to diversify the solutions of the algorithm, the Shapley value in the cooperative game is applied to share the information by distributing the pheromone payoff of the sub-colonies. In addition, the dynamic collaborative mechanism that contains two methods is designed to enhance the co-evolution of the heterogeneous populations. One, called public path recommendation strategy, is proposed to improve the astringency of Max–Min Ant System . The other is the pheromone fusion mechanism to regulate the pheromone distribution of Ant Colony System when the algorithm falls into stagnation, which can help the algorithm jump out of the local extremum effectively. Finally, the results demonstrate that the proposed methodology can improve the accuracy of solution effectively in solving large-scale TSP instances and has strong competitiveness with other swarm intelligent algorithms.
Xonar: Profiling-based Job Orderer for Distributed Deep Learning

Deep learning models have a wide spectrum of GPU execution time and memory size. When running distributed training jobs, however, their GPU execution time and memory size have not been taken into account, which leads to the high variance of job completion time (JCT). Moreover, the jobs often run into the GPU out-of-memory (OoM) problem so that the unlucky job has to restart all over. To address the problems, we propose Xonar to profile the deep learning jobs and order them in the queue. The experiments show that Xonar with TensorFlow v1.6 reduces the tail JCT by 44% with the OoM problem eliminated.
Adaptive Edge-Cloud Environments for Rural AI

Cloud computing provides on-demand access to computational resources while outsourcing infrastructure and service maintenance. Edge computing could extend cloud computing capability to areas with limited computing resources, such as rural areas, by utilizing low-cost hardware, such as single-board computers. Cloud data centre hosted machine learning algorithms may violate user privacy and data confidentiality requirements. Federated learning (FL) trains models without sending data to a central server and ensures data privacy. Using FL, multiple actors can collaborate on a single machine learning model without sharing data. However, rural network outages can happen at any time, and the quality of a wireless network varies depending on location, which can affect the performance of the Federated Learning application. Therefore there is a need to have a platform that maintains service quality independent of infrastructure status. We propose a self-adaptive system for rural FL, which employs the Greedy Nominator Heuristic (GNH) based optimisation to orchestrate application workflows across multiple resources that make up a rural computing environment. GNH provides distributed optimization for workflow placement. GNH utilises resource status to reduce failure risks and costs while still completing tasks on time. Our approach is validated using a simulated rural environment – composed of multiple decentralized controllers sharing the same infrastructure and running a shared FL application. Results show that GNH outperforms three algorithms for deployment of FL tasks: random placement, round-robin load balancer and simple greedy algorithm.
An Integrated Photorefractive Analog Matrix-Vector Multiplier for Machine Learning

AI is fueling explosive growth in compute demand that traditional digital chip architectures cannot keep up with. Analog crossbar arrays enable power efficient synaptic signal processing with linear scaling on neural network size. We present a photonic photorefractive crossbar array for neural network training and inference on local analog memory. We discuss the concept and present results based on the first prototype hardware.