Reviewerly/exHarmony huggingface dataset:
Blockchains and Triple-Entry Accounting for B2B Business Models

A blockchain is a distributed ledger (DL) that records and tracks of transactions on a P2P network. It was originally designed for cryptocurrencies, but it is now used in healthcare, supply chain management, finance, and many more fields due to its security and trustworthiness. Trust and security are critical factors in any business, and the B2B model is no exception. In businesses, trust becomes more critical when the stakes are higher and the relationships are more complex. Centuries-old Double-Entry Accounting (DEA) is still used as an underlying accounting practice, and its reliability and efficiency are beyond question. But a critical review of DEA reveals that it lacks support for B2B transactions, as the two parties maintain their accounting books without cross-checks and verifications that may lead to an implausible situation. Triple-Entry Accounting (TEA) is an emerging accounting practice introduced in the recent past to overcome the limitations of DEA. It only applies if an outside person or business is involved in a transaction and is not meant to record any internal business transactions. Recording transactions on a blockchain and entering the third transaction via TEA are conceptually the same. The potential of blockchain-based TEA can address the challenges of the B2B business model and overcome some specific limitations of DEA. This study aims to survey the current state of the adaptation of blockchains and TEA in B2B transactions. The methodology used in this study can be classified as exploratory qualitative research and is based on the latest literature on the topics. The findings of this study would deepen our understanding of blockchains and TEA for B2B transactions as they highlight new opportunities and challenges.
An assistant algorithm model for a mobile robot to pass through a concave obstacle area

Abstract When a mobile robot passes through a concave obstacle area, the limitations of the traditional control algorithm add a significant challenge to the mobile robot to move through the concave obstacle area efficiently. Improving the conventional path planning algorithm is a common choice to solve this problem. However, because various path planning algorithms adapt to the fixed scene and the algorithm steps are complex, it is difficult to popularize these algorithms. In addition, conventional path planning algorithms usually encounter difficulties when passing through recessed obstacle areas or cause robot deadlock. An auxiliary algorithm strategy is proposed to help mobile robots avoid concave obstacles quickly. The algorithm strategy plays an auxiliary role by incorporating the judgment of the starting position of the mobile robot, the building of a virtual wall to shield the concave obstacle area, and the judgment of the moving direction of the mobile robot. The advantage of this algorithm strategy is that it does not change the operation mechanism of traditional path-planning algorithms and is highly adaptable. The results of the comparison of simulation experiments with the APF (Artificial Potential Field), the ACO (Ant Colony Optimization), the RRT (Rapidly-Exploring Random Trees), the A-star algorithm, and the Dijkstra algorithm show the inclusion of the auxiliary algorithm strategy resulted in a significant increase in the efficiency of the mobile robot through the depressed obstacle zone. Article Highlights ●This auxiliary algorithm strategy can avoid the self-locking caused by the mobile robot falling into the concave obstacle area. ●This auxiliary algorithm strategy does not change the mechanism of the traditional path planning algorithm, and it is simple and easy to be used. ●This auxiliary algorithm has wide adaptability (it can assist the mainstream path planning algorithm and adapt to various scenarios).
FedAcc and FedAccSize: Aggregation Methods for Federated Learning Applications

This paper presents the ability of the federated learning concept to create a collaboration between multiple devices using a shared global model, while still keeping data privacy to meet the General Data Protection Regulation (GDPR). In real-world application scenarios, this concept faces problems related to the defense of the global model from possible attacks and the compatibility with non-independent and identically distributed data (non-IID). This paper presents two aggregation algorithms compatible with non-IID data, which use a refined aggregation of the local model, based on their accuracy. Thus, the proposed algorithms can refine the confidence in each client, eliminate intruders and allow a safe aggregation of the global model. Testing scenarios performed for IID and non-IID data illustrate that the proposed algorithms are able to provide faster training and improved robustness against intruders, w.r.t. the well-known federated average algorithm.
Deep Learning Approaches for Vehicle and Pedestrian Detection in Adverse Weather

Vision-based vehicle and pedestrian identification methods to enhance road safety have become more critical during the last decade. Unfortunately, these identification approaches suffer from robustness because of enormous changes in vehi-cle form, crowded surroundings, different light circumstances, weather, and driving behavior. Therefore, developing automated vehicle and pedestrian detection and tracking systems is a demanding research field in Intelligent Transport Systems (ITS). In recent years, image-based, deep-learning object identification algorithms have become effective autonomous road item recog-nition agents. The profound learning processes for identifying road vehicles have produced remarkable achievements. However, while numerous types of research have extensively examined using different kinds of deep learning techniques, further studies still need to integrate poor weather conditions with the standard vehicle detection algorithms for profound learning objects. This paper investigates the qualitative and quantitative analyses of four recent deep-learning object-detection algorithms for vehicle and pedestrian identification, i.e., the faster F-RCN, SSD, HoG, and YOLOv7 and classifying weather conditions utilizing a real-time dataset (DAWN). The efficacy of the suggested technique, which superposes the state-of-the-art vehicle identification and tracking methodology under unfavorable and adverse circum-stances, is verified by experimental findings.
The Influence of Interpersonal Factors on Telecommuting Employees’ Cybercrime Preventative Behaviours During the Pandemic

The pandemic forced a major shift in the way employees were completing their work duties. This meant that there was an increased dependence on cyberspace to perform work duties. Although this had its benefits, it also came with its challenges. Cybercrimes, in specific, increased dramatically as more and more people were using cyberspace. Cybercriminals evolved their attacks, and many more people began to fall victim to these cybercriminals. It was the sudden shift to telecommuting which meant that people did not have the correct cybercrime preventative behaviours which could help them to fight these criminals. Telecommuting also meant that employees now had a different work environment, which meant that the interpersonal factors that employees had were different from traditional workspaces. There are limited studies addressing this problem and certainly very few in the South African context. Therefore, this empirical report presents the influence of interpersonal factors on telecommuting employees' cybercrime preventative behaviours. An adapted framework is proposed and evaluated using data collected from 209 South African employees. Descriptive statistics and data analysis were conducted through IBM SPSS and PLS-SEM, respectively. The results uphold the suitability of the adapted Theory of Interpersonal Behaviour model. The results show that the intention to perform cybercrime preventative behaviours has a strong impact on an employee's performing those cybercrime preventative behaviours. It also shows how the habit of an employee with regard to cybercrime preventative behaviours also has a strong impact on the employees performing these preventative behaviours.
Attribute disclosure risk for k-anonymity: the case of numerical data

Abstract k -Anonymity is one of the most well-known privacy models. Internal and external attacks were discussed for this privacy model, both focusing on categorical data. These attacks can be seen as attribute disclosure for a particular attribute. Then, p -sensitivity and p -diversity were proposed as solutions for these privacy models. That is, as a way to avoid attribute disclosure for this very attribute. In this paper we discuss the case of numerical data, and we show that attribute disclosure can also take place. For this, we use well-known rules to detect sensitive cells in tabular data protection. Our experiments show that k -anonymity is not immune to attribute disclosure in this sense. We have analyzed the results of two different algorithms for achieving k -anonymity. First, MDAV as a way to provide microaggregation and k -anonymity. Second, Mondrian. In fact, to our surprise, the number of cells detected as sensitive is quite significant, and there are no fundamental differences between Mondrian and MDAV. We describe the experiments considered, and the results obtained. We define dominance rule compliant and p%-rule compliant k -anonymity for k -anonymity taking into account attribute disclosure. We conclude with an analysis and directions for future research.
A Multi-Layer Feature Fusion Model Based on Convolution and Attention Mechanisms for Text Classification

Text classification is one of the fundamental tasks in natural language processing and is widely applied in various domains. CNN effectively utilizes local features, while the Attention mechanism performs well in capturing content-based global interactions. In this paper, we propose a multi-layer feature fusion text classification model called CAC, based on the Combination of CNN and Attention. The model adopts the idea of first extracting local features and then calculating global attention, while drawing inspiration from the interaction process between membranes in membrane computing to improve the performance of text classification. Specifically, the CAC model utilizes the local feature extraction capability of CNN to transform the original semantics into a multi-dimensional feature space. Then, global attention is computed in each respective feature space to capture global contextual information within the text. Finally, the locally extracted features and globally extracted features are fused for classification. Experimental results on various public datasets demonstrate that the CAC model, which combines CNN and Attention, outperforms models that solely rely on the Attention mechanism. In terms of accuracy and performance, the CAC model also exhibits significant improvements over other models based on CNN, RNN, and Attention.
AI Literacy for an Ageing Workforce: Leveraging the Experience of Older Workers

This study delves into the potential benefits of Artificial Intelligence (AI) literacy training for late-career-stage workers approaching retirement. These workers often possess extensive experience but may lack the necessary digital and AI skills. AI literacy training can empower them to leverage their experience and become proficient contributors to the digital economy. Furthermore, the article describes how these workers might benefit from AI training, particularly when employing practical applications of AI in their specific field or industry. The article argues that these workers’ knowledge, insights and experience are valuable assets that can be exploited to benefit the business. Their abilities allow them to calibrate the prompts used in Generative-AI systems, maximizing the quality of the AI-generated content. Prompt engineering is a new career path that has followed the rapid emergence of Generative AI content creation. Late-career-stage workers are well suited to act in this role, leveraging their business knowledge to produce quality outputs. The study argues that employers should integrate domain-specific knowledge into the training program's structure, allowing late-career-stage workers to understand the relevance of Generative AI systems and calibrate prompts based on their experience and knowledge. Developing their AI skills can ultimately lead to increased productivity and efficiency. Moreover, this article emphasizes the potential benefits for these workers and businesses in providing AI literacy training. It not only enables late-career-stage workers to enhance their skills, but it can also bring valuable insights and experience to the business, ultimately leading to the growth and success of the business. These opportunities empower such workers to access more strategic positions in their institutions and gain a sense of fulfillment in their roles. The methodology for this study will follow a scoping literature review of academic articles. This approach allows for examining existing research on AI literacy training for late-career-stage workers. Articles were sourced through a comprehensive search of academic databases such as ScienceDirect, EbscoHost and Scopus. Search terms included AI literacy, digital literacy, aging workforce and prompt engineering.
Application of hybrid educational technologies in the context of a personalized approach in adult education

This paper examines the problem of using hybrid educational technologies in the context of the personalized approach in adult education. The innovative use of hybrid technology in pedagogical methods allows faculty and students to communicate on a new technological level. Here it is necessary to consider the motivational component, which is one of the main driving forces in making decisions to continue learning both in the postgraduate period and in professional development. The article contains statistics from international analytical agencies, obtained from studies of hybrid educational methods. Theoretical analysis of the literature has allowed us to find out that the problem is considered widely enough by researchers around the world, which gives us the right to address the problem of hybrid learning as the main point in the personalization of the educational process in the education of the adult population.
Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference

The use of Neural Networks (NNs) for sensitive data processing is becoming increasingly popular, raising concerns about data privacy and security. Homomorphic Encryption (HE) has the potential to be used as a solution to preserve data privacy in NNs. This study provides a comprehensive analysis on the use of HE for NN training and classification, focusing on the techniques and strategies used to enhance data privacy and security. The current state-of-the-art in HE for NNs is analysed, and the challenges and limitations that need to be addressed to make it a reliable and efficient approach for privacy preservation are identified. Also, the different categories of HE schemes and their suitability for NNs are discussed, as well as the techniques used to optimize the accuracy and efficiency of encrypted models. The review reveals that HE has the potential to provide strong data privacy guarantees for NNs, but several challenges need to be addressed, such as limited support for advanced NN operations, scalability issues, and performance trade-offs.
State-of-the-art image and video quality assessment with a metric based on an intrinsically non-linear neural summation model

The development of automatic methods for image and video quality assessment that correlate well with the perception of human observers is a very challenging open problem in vision science, with numerous practical applications in disciplines such as image processing and computer vision, as well as in the media industry. In the past two decades, the goal of image quality research has been to improve upon classical metrics by developing models that emulate some aspects of the visual system, and while the progress has been considerable, state-of-the-art quality assessment methods still share a number of shortcomings, like their performance dropping considerably when they are tested on a database that is quite different from the one used to train them, or their significant limitations in predicting observer scores for high framerate videos. In this work we propose a novel objective method for image and video quality assessment that is based on the recently introduced Intrinsically Non-linear Receptive Field (INRF) formulation, a neural summation model that has been shown to be better at predicting neural activity and visual perception phenomena than the classical linear receptive field. Here we start by optimizing, on a classic image quality database, the four parameters of a very simple INRF-based metric, and proceed to test this metric on three other databases, showing that its performance equals or surpasses that of the state-of-the-art methods, some of them having millions of parameters. Next, we extend to the temporal domain this INRF image quality metric, and test it on several popular video quality datasets; again, the results of our proposed INRF-based video quality metric are shown to be very competitive.
Vertically-Advised Federated Learning for Multi-Strategic Stock Predictions through Stochastic Attention-based LSTM

In recent years, stock price forecasting has become a challenging task commonly used to evaluate the performance of various machine learning solutions. This work explores a Federated Learning (FL) framework within a competitive collaboration scenario with the aim of training a centralised model advised by non-recoverable decentralised strategies so that no exchange of private data is required. The proposed Vertically-Advised Federated Learning (VAFL) framework combines elements from both horizontal and vertical FL, as each client trains two independent models. Furthermore, a novel forecasting architecture, based on a stochastic variant of an Attention-based Long Short Term Memory (LSTM) network, is proposed and validated on a simulated scenario based on real data from the stock market.
A Class of Priors for Color Image Restoration Parameterized by Lie Groups Acting on Pixel Values

In a recent paper [T. Batard, G. Haro, and C. Ballester, SIAM J. Imag. Sci., 14 (2021), pp. 1816–1847], a new prior for image restoration was introduced. It relies first on the observation that an image and a degraded version of it can share some visual content and then on the conjecture that an image restoration model can benefit from the use of an image prior encoding this invariance property. This prior considers the restored image as a parallel section of a connection (also called covariant derivative), this latter being a critical point of an energy associated to the Lie group SO(2) acting on image pixel values. In this paper, we propose a twofold generalization of this result. First, we consider other Lie groups acting on image pixels, yielding new optimal connections. Then, we derive a family of -connections from the optimal connections. The corresponding parallel sections describe new invariance properties which we use as priors encoded as penalty terms in variational models for image restoration. Experiments conducted on color image deblurring show that the proposed generalization of the work of Batard, Haro, and Ballester outperforms the original approach.
Deep Learning Application for Detection of Malaria

Malaria continues to be a significant burden on global health with 247 million clinical episodes and 619,000 deaths. Along with biomedical science, technology, and informatics have begun participating in the quest against malaria. Microscopy techniques are frequently used to detect malaria parasites in infected red blood cells. Giemsa stain has been used to stain blood parasites for over a century. The stain is applied after fixing blood smears in methyl alcohol for 25 to 30 minutes [1]. When stained slides are examined under a microscope, the parasites are easily discernible based on morphology and color. We observed that automating the detection of these slides using deep learning is possible with high accuracy. A comparison between deep learning models reveals ResNets provide better performance.
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code

Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the performance of the models. In this paper, we explore the impact of an unsuperivsed feature enrichment approach based on variable roles on the performance of neural models of code. The notion of variable roles (as introduced in the works of Sajaniemi et al. [1], [2]) has been found to help students' abilities in programming. In this paper, we investigate if this notion would improve the performance of neural models of code. To the best of our knowledge, this is the first work to investigate how Sajaniemi et al.'s concept of variable roles can affect neural models of code. In particular, we enrich a source code dataset by adding the role of individual variables in the dataset programs, and thereby conduct a study on the impact of variable role enrichment in training the Code2Seq model. In addition, we shed light on some challenges and opportunities in feature enrichment for neural code intelligence models.
Reinforced Lattice Kalman Filters: A Robust Nonlinear Estimation Strategy

This paper introduces the Sliding Innovation Lattice Filter (SILF), a robust extension of the Lattice Kalman Filter (LKF) that leverages sliding mode theory.SILF incorporates a sliding boundary layer in the measurement update formulation, enabling the filter innovation to slide within predefined upper and lower bounds.This enhances the robustness of SILF, making it resilient to model uncertainties and noise.Additionally, a derivative-free formulation of SILF is developed using statistical linear regression, eliminating the need for Jacobian calculations.To further improve accuracy, robustness, and convergence behavior in the presence of abrupt changes in system model/parameters, SILF is reinforced with the Iterated Sigma Point Filtering and Strong Tracking Filtering strategies, resulting in the Reinforced Lattice Kalman Filter (RLKF).The experimental findings for the estimation of distorted power waveforms illustrate the superior performance of SILF and RLKF over competing methods, especially when operating in scenarios characterized by model uncertainties and noisy environments.
Supervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Vehicles

Unmanned Aerial Networks (UAVs) are prone to several cyber-atttacks, including Global Positioining Spoofing attacks. For this purpose, numerous studies have been conducted to detect, classify, and mitigate these attacks, using Artificial Intelligence technqiues; howver, most of these studies provided techniques with low detection, high misdetection, and high bias rates. To fill this gap, in this paper, we propose three supervised deep learning techniques, namely Deep Neural Network, U Neural Network, and Long Short Term Memory. These models are evaluated in terms of Accuracy, Detection Rate, Misdetection Rate, False Alarm Rate, Training Time per Sample, Prediction Time, and Memory Size. The simulation results indicated that the U Neural Network outperforms other models with accuracy of 98.80%, a probability of detection of 98.85%, a misdetection of 1.15%, a false alarm of 1.8%, a training time per sample of 0.22 seconds, a prediction time of 0.2 seconds, and a memory size of 199.87 MiB. In addition, these results depicted that the Long Short Term Memory model provides the lowest performance among other models for detecting these attacks on UAVs.