Reviewerly/exHarmony huggingface dataset:
Lessons Learned from the COVID-19 Pandemic

Abstract The COVID-19 pandemic is having a tragic and profound impact on our planet. Thousands of lives have been lost, millions of jobs have been destroyed, and the life of billions of people has been changed and disrupted. In this dramatic turmoil, digital technologies have been playing an essential role. The Internet and all its services have enabled our societies to keep working and operating; social networks have provided valuable channels to disseminate information and kept people connected despite lockdowns and the block of most travels; most importantly, digital technologies are key to support researchers, epidemiologists, and public officers in studying, monitoring, controlling, and managing this unprecedented emergency. After more than a year, it is possible and worthwhile to propose some reflections on the strengths and weaknesses we have experienced and, most importantly, on the lessons learned that must drive our future policies and roadmaps. This is unavoidable not just to improve our ability to react to these dramatic situation, but, most importantly, to proactively design and develop a better future for our society.
An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a mainstream method estimating the most defect-prone components of software with limited historical data. Several studies investigate how software metrics are used and how modeling techniques influence prediction performance. However, the software’s metrics diversity impact on the predictor remains unclear. Thus, this paper aims to assess the impact of various metric sets on CPDP and investigate the feasibility of CPDP with hybrid metrics. Based on four software metrics types, we investigate the impact of various metric sets on CPDP in terms of F-measure and statistical methods. Then, we validate the dominant performance of CPDP with hybrid metrics. Finally, we further verify the CPDP-OSS feasibility built with three types of metrics (orient-object, semantic, and structural metrics) and challenge them against two current models. The experimental results suggest that the impact of different metric sets on the performance of CPDP is significantly distinct, with semantic and structural metrics performing better. Additionally, trials indicate that it is helpful for CPDP to increase the software’s metrics diversity appropriately, as the CPDP-OSS improvement is up to 53.8%. Finally, compared with two baseline methods, TCA+ and TDSelector, the optimized CPDP model is viable in practice, and the improvement rate is up to 50.6% and 25.7%, respectively.
Objects Detection Using Sensors Data Fusion in Autonomous Driving Scenarios

The relatively complex task of detecting 3D objects is essential in the realm of autonomous driving. The related algorithmic processes generally produce an output that consists of a series of 3D bounding boxes that are placed around specific objects of interest. The related scientific literature usually suggests that the data that are generated by different sensors or data acquisition devices are combined in order to work around inherent limitations that are determined by the consideration of singular devices. Nevertheless, there are practical issues that cannot be addressed reliably and efficiently through this strategy, such as the limited field-of-view, and the low-point density of acquired data. This paper reports a contribution that analyzes the possibility of efficiently and effectively using 3D object detection in a cooperative fashion. The evaluation of the described approach is performed through the consideration of driving data that is collected through a partnership with several car manufacturers. Considering their real-world relevance, two driving contexts are analyzed: a roundabout, and a T-junction. The evaluation shows that cooperative perception is able to isolate more than 90% of the 3D entities, as compared to approximately 25% in the case when singular sensing devices are used. The experimental setup that generated the data that this paper describes, and the related 3D object detection system, are currently actively used by the respective car manufacturers’ research groups in order to fine tune and improve their autonomous cars’ driving modules.
Computational tools for genomic data de-identification: facilitating data protection law compliance

In this opinion piece, we discuss why computational tools to limit the identifiability of genomic data are a promising avenue for privacy-preservation and legal compliance. Even where these technologies do not eliminate all residual risk of individual identification, the law may still consider such data anonymised.
Statechart-Based AI in Practice

Layered Statechart-based AI shows considerable promise by being a highly modular, reusable, and designer friendly approach to game AI. Here we demonstrate the viability of this approach by replicating the functionality of a full-featured and commercial-scale behaviour tree AI within a non-commercial game framework. As well as demonstrating that layered Statecharts are both usable and amply expressive, our experience highlights the value of several, previously unidentified design considerations, such as sensor patterns, the necessity of subsumption, and the utility of orthogonal regions. These observations point towards simplified, higher-level AI construction techniques that can reduce the complexity of AI design and further enhance reuse.
Noise reduction in speckle interferometry fringe pattern using adaptive Kalman filter

Fringe pattern denoising is of prime importance in phase demodulation, especially for a single fringe pattern in speckle interferometry. A fringe speckle noise removal algorithm using the Kalman filter is proposed. The conventional linear Kalman filter is implemented with a fixed value of the process and measurement noise covariances; the adaptive Kalman filter is implemented with the process, and measurement noise covariances are estimated in an adaptive manner. The fringe denoising is performed sequentially in a rowwise and columnwise manner. The simulated and experimental results demonstrate the practical applicability of the proposed method, and its performance is appraised using metrics such as the quality index, peak signal-to-noise-ratio, and speckle index.
A Regulatable Data Privacy Protection Scheme for Energy Transactions Based on Consortium Blockchain

In the blockchain-based energy transaction scenario, the decentralization and transparency of the ledger will cause the users’ transaction details to be disclosed to all participants. Attackers can use data mining algorithms to obtain and analyze users’ private data, which will lead to the disclosure of transaction information. Simultaneously, it is also necessary for regulatory authorities to implement effective supervision of private data. Therefore, we propose a supervisable energy transaction data privacy protection scheme, which aims to trade off the supervision of energy transaction data by the supervisory authority and the privacy protection of transaction data. First, the concealment of the transaction amount is realized by Pedersen commitment and Bulletproof range proof. Next, the combination of ElGamal encryption and zero-knowledge proof technology ensures the authenticity of audit tickets, which allows regulators to achieve reliable supervision of the transaction privacy data without opening the commitment. Finally, the multibase decomposition method is used to improve the decryption efficiency of the supervisor. Experiments and security analysis show that the scheme can well satisfy transaction privacy and auditability.
A Hash-Based Quantum-Resistant Chameleon Signature Scheme

As a standard digital signature may be verified by anybody, it is unsuitable for personal or economically sensitive applications. The chameleon signature system was presented by Krawczyk and Rabin as a solution to this problem. It is based on a hash then sign model. The chameleon hash function enables the trapdoor information holder to compute a message digest collision. The holder of a chameleon signature is the recipient of a chameleon signature. He could compute collision on the hash value using the trapdoor information. This keeps the recipient from disclosing his conviction to a third party and ensures the privacy of the signature. The majority of the extant chameleon signature methods are built on the computationally infeasible number theory problems, like integer factorization and discrete log. Unfortunately, the construction of quantum computers would be rendered insecure to those schemes. This creates a solid requirement for construct chameleon signatures for the quantum world. Hence, this paper proposes a novel quantum secure chameleon signature scheme based on hash functions. As a hash-based cryptosystem is an essential candidate of a post-quantum cryptosystem, the proposed hash-based chameleon signature scheme would be a promising alternative to the number of theoretic-based methods. Furthermore, the proposed method is key exposure-free and satisfies the security requirements such as semantic security, non-transferability, and unforgeability.
Inertial Sensor-based Human Activity Recognition Using Hybrid Deep Neural Networks

Currently human activity recognition (HAR) has increasing applications. In HAR, inertial sensors are widely used due to their advantages of low-cost, small size, and portability. In view of device complexity and power consumption, it is hopeful to use a single inertial sensor to realize the recognition of routine activities, such as walking, walking upstairs, or downstairs. Obviously, this is more difficult than using multiple sensors. In this study, we proposed a method combined convolutional neural network (CNN) with deep feedforward sequential memory networks (DFSMN), which is capable of modeling longterm dependency in temporal series, for HAR based on the dataset collected by a single inertial sensor. CNN was responsible for extracting data features, and then DFSMN performed the classification of activities based on these features. Two inertial sensors were placed on waist and thigh of human body, thus two independent datasets were collected separately. The proposed method was applied to these two datasets, and satisfied results were obtained. The overall recognition accuracy for the dataset of waist and thigh was 98.01% and 98.76%, respectively.
Data Security and Privacy-Preserving Framework Using Machine Learning and Blockchain in Big-Data to Data Middle Platform in the Era of IR 4.0

The modem data is collected by using IoT, stored in distributed cloud storage, and issued for data mining or training artificial intelligence. These new digital technologies integrate into the data middle platform have facilitated the progress of industry, promoted the fourth industrial revolution. And it also has caused challenges in security and privacy-preventing. The privacy data breach can happen in any phase of the Big-Data life cycle, and the Data Middle Platform also faces similar situations. How to make the privacy avoid leakage is exigency. The traditional privacy-preventing model is not enough, we need the help of Machine-Learning and the Blockchain. In this research, the researcher reviews the security and privacy-preventing in Big-Data, Machine Learning, Blockchain, and other related works at first. And then finding some gaps between the theory and the actual work. Based on these gaps, trying to create a suitable framework to guide the industry to protect their privacy when the organization contribute and operate their data middle platform. No only academicians, but also industry practitioners especially SMEs will get the benefit from this research.
The Usability of BenKids Mobile Learning App in Vocabulary Teaching for Preschool

At present, learning mobile applications (apps) have become highly popular and are widely used. However, they will only be accepted by users if their usability is of an acceptable level. Usability tests assist to measure the impact of a mobile learning app; researchers focus on the compatibility of the app interface with the cognitive skills of children and easy usage. The “BenKids” app has been designed especially for pre-school children (aged three to five). It was created for android devices to teach vocabulary to pre-school children by providing the user with an easy and simple interface. This study aimed to demonstrate a relationship between learning and the usability of the “BenKids” mobile app and explore its impact on children’s learning. The usability of the “BenKids” app was measured with 10 pre-school children. The results showed the effective usability of the app and strongly impacts learning.
Clustering Method Based on Artificial Algae Algorithm

For decades, the researchers have developed many ways as optimization procedures with the aim of find the best solution in short time for many problems under certain conditions in the field of engineering, medicine and banking.These ways also used for parameter updating of algorithms.The most popular Optimization algorithms methods known are mining classification and clustering.In this article, the clustering used to identify the most important point in the best cluster centers of set data.Artificial Algae Algorithm (AAA) optimization algorithm used in the clustering process and implemented on UCI datasets.Balance, Breast Cancer Wisconsin Diagnostic, Breast Cancer Wisconsin original, Pima Diabetes, Glass, Iris, Wine, Urban Land Cover and Hill Valley UCI datasets used to assess the performing of the Algae Algorithm-based clustering algorithm.Euclides method used to calculate the distance between the data.The performance of the AAA based clustering algorithm, Total square distance values in different iteration numbers calculated for each data set.The total square error rate value calculated for each iteration and as the number of iterations progresses, the total square error rate value decreases smoothly.The obtained results compared with k-means, Differential Evolution (DE), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) clustering algorithms.According to the experimental results in this study, the proposed AAA-based clustering algorithm achieved better results in iris and wine data sets compared to other clustering algorithms, while it obtained close to good results in other data sets.As a result, the Artificial Algae Algorithm-based clustering algorithm showed that the method showed a stable appearance and the performance of the clusters also increased, which shows that this study successfully achieved its purpose.
A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

The primary purpose of trading in stock markets is to profit from buying and selling listed stocks. However, numerous factors can influence the stock prices, such as the company's present financial situation, news, rumor, macroeconomics, psychological, economic, political, and geopolitical factors. Consequently, tremendous challenges already exist in predicting noisy stock prices. This paper proposes a hybrid model integrating the singular spectrum analysis (SSA) and the backpropagation neural network (BPNN) to forecast daily closing prices in stock markets. The model first decomposes the stock prices into several components using the SSA. Then, the extracted components are utilized for training BPNNs to forecast future prices. Compared with the BPNN, the hybrid SSA-BPNN model demonstrates a better predictive performance, indicating the SSA's ability to extract hidden information and reduce the noise effect of the original time series.
Perceptual Image Hashing Based on Multitask Neural Network

With the advent of the era of multimedia and in-depth development, the whole human society has been produced and spread a huge amount of image data, but at the same time, in view of the digital image and tamper with the attack of piracy phenomenon also more and more serious, malicious attacks will produce serious social, military, and political influence, therefore, to protect the authenticity of the original image content, which is also more and more important. In order to further improve the performance of image hashing and enhance the protection of image data, we proposed an end-to-end dual-branch multitask neural network based on VGG-19 to produce a perceptual hash sequence and used prepart of network of pretrained VGG-19 model to extract image features, and then, the image features are transformed into a hash sequence through a convolutional and fully connected network. At the same time, in order to enhance the function of the network and improve the adaptability of the proposed network to using scenarios, the rest part of the network layer of the VGG-19 model was used as another branch for image classification, so as to realize the multitask characteristics of the network. Through the experiment of the testing set, the network can not only resist many kinds of attack operations (content retention operations), but also realize accurate classification about the image, and has a satisfactory tampering detection ability.
The Influence of Fraud Prevention Mechanisms on Fraud Awareness in The Federal Ministries in Malaysia

This study examined the extent of fraud prevention mechanisms that influence the awareness of curbing the occurrence of fraudulent activities in public sector organisations, particularly the Federal Ministries in Malaysia. The key factors in this study thereby attributed as the fraud prevention mechanisms that influenced by ethical leadership, internal control system, and fraud prevention programmes. Following this, the Agency Theory was employed to guide the study. A survey was conducted among the officers of various Federal Ministries who were sampled via the purposive sampling method and yielded 210 total responses. The results showed that ethical leadership and the implementation of a virtuous internal control system ranked as the most effective fraud prevention mechanism that influenced their awareness towards curbing the occurrence of fraud. A regression analysis further revealed that the fraud prevention programmes were not statistically significant in influencing awareness of fraud. The main value of this paper is the effect of fraud prevention mechanisms; namely, ethical leadership, internal control systems, and fraud prevention programmes, on awareness of fraud amongst officers in the Federal Ministries in Malaysia. The findings of this study provide useful insights for the Federal government to improve and reconsider current fraud prevention mechanisms, especially fraud prevention programmes to improve awareness of fraud and recover public trust. Keywords: fraud awareness, ethical leadership, internal control system, prevention programme, Malaysian federal ministries
Mobile Teleoperation: Feasibility of Wireless Wearable Sensing of the Operator’s Arm Motion

Teleoperation platforms often require the user to be situated at a fixed location to both visualize and control the movement of the robot and thus do not provide the operator with much mobility. One example is in existing robotic surgery solutions that require the surgeons to be away from the patient, attached to consoles where their heads must be fixed and their arms can only move in a limited space. This creates a barrier between physicians and patients that does not exist in normal surgery. To address this issue, we propose a mobile telesurgery solution where the surgeons are no longer mechanically limited to control consoles and are able to teleoperate the robots from the patient bedside, using their arms equipped with wireless sensors and viewing the endoscope video via optical see-through head-mounted displays (HMDs). We evaluate the feasibility and efficiency of our user interaction method compared to a standard surgical robotic manipulator via two tasks with different levels of required dexterity. The results indicate that with sufficient training our proposed platform can attain similar efficiency while providing added mobility for the operator.
Effects of the social isolation generated by Covid-19 on the quality of life of the population in two Brazilian cities

Introduction: COVID-19 is a highly transmissible virus that is causing very serious problems in the health of the world, generating major political and financial problems. There are numerous risk factors related to this disease, ranging from morbidities to social and family issues. Objective: to evaluate the effects of the social isolation generated by Covid-19 on the quality of life in the population of two Brazilian cities. Methods: cross-sectional study. The research was carried out in two different cities of the Brazilian confederation. One in Santo André, São Paulo and the other in Rio Branco, Acre, both in Brazil. This is a survey with an online platform, using the drive’s Google Forms tool to assess the population’s quality of life through the SF-36 protocol. The selection was carried out randomly in both groups (Acre and São Paulo), having respectively 109 and 62 participants in this first moment. Results: regarding gender, women are in greater numbers participating in the city of Santo André, São Paulo than in Rio Branco, Acre, both in Brazil, with (80.6% and 48.6%), respectively. Likewise, in relation to the age group Santo André has an average age younger than in Rio Branco. When we compared the dimensions between the two states, performing a Mann-Whitney statistical analysis, statistical differences appeared in three dimensions, which were functional capacity, pain, and vitality, with better quality of life in these three domains for the city of Rio Branco. Conclusion: the city of Rio Branco, Acre, Brazil, has a positive score when compared to the city of Santo André, São Paulo, Brazil, in most of the domains analyzed.
Breaking Data Encryption Standard with a Reduced Number of Rounds Using Metaheuristics Differential Cryptanalysis

This article presents the author's own metaheuristic cryptanalytic attack based on the use of differential cryptanalysis (DC) methods and memetic algorithms (MA) that improve the local search process through simulated annealing (SA). The suggested attack will be verified on a set of ciphertexts generated with the well-known DES (data encryption standard) reduced to six rounds. The aim of the attack is to guess the last encryption subkey, for each of the two characteristics Ω. Knowing the last subkey, it is possible to recreate the complete encryption key and thus decrypt the cryptogram. The suggested approach makes it possible to automatically reject solutions (keys) that represent the worst fitness function, owing to which we are able to significantly reduce the attack search space. The memetic algorithm (MASA) created in such a way will be compared with other metaheuristic techniques suggested in literature, in particular, with the genetic algorithm (NGA) and the classical differential cryptanalysis attack, in terms of consumption of memory and time needed to guess the key. The article also investigated the entropy of MASA and NGA attacks.