Reviewerly/exHarmony huggingface dataset:
Measuring Virtual Reality (VR) Technology Application and Adoption in Chinese Construction Risk Management

In recent years, the improvement of risk management and the Occupational Health and Safety (OHS) training process has become a hot topic. Virtual Reality (VR) technology is an efficient method to improve training efficiency and reduce accidents. Previous studies have validated its feasibility and usefulness in the construction industry. However, there is currently a lack of research on the adoption of VR technology in the Chinese construction industry. The present work explores the current applications of VR and the model for possible adoption by the Chinese construction companies using a mixed research methodology. Furthermore, this research has quantified the relationships between each adoption variable in China.
Automatic Speech Recognition of Bengali Using Kaldi

Bengali is a prominent language of the Indian subcontinent. This paper presents a comparison between different Bengali speech recognition models built with the Kaldi and Pytorch toolkits. Deep learning has been employed to improve speech recognition performance, and we explore the performance of the techniques for Bengali datasets. Seven different deep neural techniques have been employed in this paper for comparison. Grapheme to phoneme (G2P) is an important module for Indian languages which helps to decode phones from words in Unicode format. We develop a G2P model for Bangla using RNN, and we have shown that it performs well for the purpose. This research also demonstrated that using Kaldi-based feature extraction with DNN-HMM acoustic models yielded the best WER of 4.16 when combined with the Li-GRU neural network. The aim is to demonstrate the performance of the Bengali language using the current state-of-the-art (Kaldi) method.
A Formal Approach to Coercion Resistance and Its Application to E-Voting

The outbreak of the COVID-19 pandemic brought renewed attention to electronic voting—this time as a potential option to contain the spread during elections. One of the long unresolved topics with remote voting is the risk of voter’s coercion due to the uncontrolled environment in which it takes place, indicating the importance of the coercion resistance property. In the present article, the authors conduct a database analysis of over 350 articles to present different formal definitions of coercion resistance based on three frameworks (game-based definitions, applied pi-calculus, and logic). Finally, the different security properties of each one are studied and compared in order to facilitate the development of electronic voting schemes.
Unsupervised Text Segmentation Predicts Eye Fixations During Reading

Words typically form the basis of psycholinguistic and computational linguistic studies about sentence processing. However, recent evidence shows the basic units during reading, i.e., the items in the mental lexicon, are not always words, but could also be sub-word and supra-word units. To recognize these units, human readers require a cognitive mechanism to learn and detect them. In this paper, we assume eye fixations during reading reveal the locations of the cognitive units, and that the cognitive units are analogous with the text units discovered by unsupervised segmentation models. We predict eye fixations by model-segmented units on both English and Dutch text. The results show the model-segmented units predict eye fixations better than word units. This finding suggests that the predictive performance of model-segmented units indicates their plausibility as cognitive units. The Less-is-Better (LiB) model, which finds the units that minimize both long-term and working memory load, offers advantages both in terms of prediction score and efficiency among alternative models. Our results also suggest that modeling the least-effort principle for the management of long-term and working memory can lead to inferring cognitive units. Overall, the study supports the theory that the mental lexicon stores not only words but also smaller and larger units, suggests that fixation locations during reading depend on these units, and shows that unsupervised segmentation models can discover these units.
Releasing Differentially Private Trajectories with Optimized Data Utility

The ubiquity of GPS-enabled devices has resulted in an abundance of data about individual trajectories. Releasing trajectories enables a range of data analysis tasks, such as urban planning, but it also poses a risk in compromising individual location privacy. To tackle this issue, a number of location privacy protection algorithms are proposed. However, existing works are primarily concerned with maintaining the trajectory data geographic utility and neglect the semantic utility. Thus, many data analysis tasks relying on utility, e.g., semantic annotation, suffer from poor performance. Furthermore, the released trajectories are vulnerable to location inference attacks and de-anonymization attacks due to insufficient privacy guarantee. In this paper, to design a location privacy protection algorithm for releasing an offline trajectory dataset to potentially untrusted analyzers, we propose a utility-optimized and differentially private trajectory synthesizer (UDPT) with two novel features. First, UDPT simultaneously preserves both geographical utility and semantic utility by solving a data utility optimization problem with a genetic algorithm. Second, UDPT provides a formal and provable guarantee against privacy attacks by synthesizing obfuscated trajectories in a differentially private manner. Extensive experimental evaluations on real-world datasets demonstrate UDPT’s outperformance against state-of-the-art works in terms of data utility and privacy.
A Verifiable Credentials System with Privacy-Preserving Based on Blockchain

Decentralized identity authentication is generally based on blockchain, with the protection of user privacy as the core appeal.But traditional decentralized credential system requires users to show all the information of the entire credential to the verifier, resulting in unnecessary overexposure of personal information.From the perspective of user privacy, this paper proposed a verifiable credential scheme with selective disclosure based on BLS (Bohen-Lynn-Shacham) aggregate signature.Instead of signing the credentials, we sign the claims in the credentials.When the user needs to present the credential to verifier, the user can select a part of but not all claims to be presented.To reduce the number of signatures of claims after selective disclosure, BLS aggregate signature is achieved to aggregate signatures of claims into one signature.In addition, our scheme also supports the aggregation of credentials from different users.As a result, verifier only needs to verify one signature in the credential to achieve the purpose of batch verification of credentials.We analyze the security of our aggregate signature scheme, which can effectively resist aggregate signature forgery attack and credential theft attack.The simulation results show that our selective disclosure scheme based on BLS aggregate signature is acceptable in terms of verification efficiency, and can reduce the storage cost and communication overhead.As a result, our scheme is suitable for blockchain, which is strict on bandwidth and storage overhead.
Adaptive Hybrid Optimization Based Virtual Machine Placement in Cloud Computing

The rapid evolution of cloud technology has attracted millions and millions of users due to its unlimited services and the cloud service providers have put huge efforts to construct the large scale data centers. The data center is a repository of Physical machines (PMs) and large-scale of Virtual Machines (VMs) are utilized to reduce resource cost of PMs. Typically, cloud computing is accomplished only by means of virtualization technology and the mechanism of allocating VMs to consumers is called VM placement. However, VM placement is performed by considering various parameters, such as allocation time, SLA violation, energy consumption, resource utilization, and so on. The VM placement issue is a major constraint for good VM consolidation and the ultimate intention of this VM placement issue is to reduce quantity of functioning host machines in cloud by alleviating energy consumption and increasing the resource usage. Therefore, an effective mechanism is needed to effectively balance the network by addressing the aforementioned issues. In this research, an effective strategy called Adaptive-Artificial Bee Cat Swarm Optimization (ABCSO) algorithm is developed for VM optimal positioning in cloud environment. The developed Adaptive-ABCSO is derived by the integration of adaptive concept into the classical ABCSO algorithm. The traditional ABCSO algorithm is obtained by combining Artificial Bee Colony (ABC) and Cat Swarm Optimization (CSO). Moreover, the proposed approach has achieved minimum load of 0.165, minimum migration of 0.055 cost and low power consumption of 0.042.
Likelihood of Unintentional Electromagnetic Emanations Compromising IT Equipment Security: Perspectives of Practitioners on Causal Factors

The study explores the influencing factors that Radio Frequency (RF) Engineers take into consideration when assessing the likelihood of unintentional electromagnetic radiation compromising the security of Information Technology Equipment. A focus group research methodology is adopted, and the results analyzed using a cause-and-effect technique. Two focus groups of RF Engineers identified twenty-six influencing factors they considered when assessing likelihood. Through the cause-and-effect technique it was possible to categorize the factors in terms of threat, vulnerability, and impact (the three constituents of a qualitative risk calculation). This information can be used by cyber security practitioners with little or no RF experience to provide a better understanding of the significance RF Engineers place on specific factors particularly when assessing vulnerability likelihood.
Comparison of the Digital Ruble Concept with Foreign Central Bank Digital Currencies

In the context of digitalization and the declining role of cash in the economy, central bank digital currencies (CBDC) are a promising direction for the development of payments. Many countries have started research in this direction. The status of research varies – some countries are at the stage of public discussions; others are preparing the concept of CBDC to test or launch a ready-made solution. This article analyzes the Bank of Russia's program to create a digital ruble and compares the proposed ways to build a CBDC in Russia with the experience of other countries. The focus is on the analysis of the development of digital currencies in Sweden, China, the European Union and the Bahamas. It is established that central banks do not have a clear opinion on the level of authority of the Regulator in ensuring the functioning of the system, centralization and tokenization of CBDC. The article highlights the main trends in the development of digital currencies. It is established that an essential factor for building a CBDC is the central bank's understanding of the purpose of introducing a digital currency in the country. For the digital ruble, recommendations for building relationships with clients and intermediaries based on the experience of other countries are formulated.
Two-point AG codes from the Beelen-Montanucci maximal curve

In this paper we investigate two-point algebraic-geometry codes (AG codes) coming from the Beelen-Montanucci (BM) maximal curve. We study properties of certain two-point Weierstrass semigroups of the curve and use them for determining a lower bound on the minimum distance of such codes. AG codes with better parameters with respect to comparable two-point codes from the Garcia-G\"uneri-Stichtenoth (GGS) curve are discovered.
Design and simulation of a Rooftop solar PV system Using PV syst software

One of the most reliable and eco-friendly systems is the grid-connected solar PV system which is in high demand for the last few years. The performance of the photovoltaic system (PV) depends on geographical location, solar irradiance, and type of PV module, and orientation of the module. Therefore, the design of this PV system involves various calculations, depending on the above parameters. This paper includes the theoretical analysis of a particular site (residential building) along with the simulation of the PV system based on load conditions of a building to achieve maximum power, performance ratio, and efficiency using PVsyst software. Here, the focus is on the calculated load conditions and the designing of an efficient solar rooftop PV panel.
Robust Trust Region for Weakly Supervised Segmentation

Acquisition of training data for the standard semantic segmentation is expensive if requiring that each pixel is labeled. Yet, current methods significantly deteriorate in weakly supervised settings, e.g. where a fraction of pixels is labeled or when only image-level tags are available. It has been shown that regularized losses—originally developed for unsupervised low-level segmentation and representing geometric priors on pixel labels—can considerably improve the quality of weakly supervised training. However, many common priors require optimization stronger than gradient descent. Thus, such regularizers have limited applicability in deep learning. We propose a new robust trust region approach <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for regularized losses improving the state-of-the-art results. Our approach can be seen as a higher-order generalization of the classic chain rule. It allows neural network optimization to use strong low-level solvers for the corresponding regularizers, including discrete ones.
A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising Systems

A domain that has gained popularity in the past few years is personalized advertisement. Researchers and developers collect user contextual attributes (e.g., location, time, history, etc.) and apply state-of-the-art algorithms to present relevant ads. A problem occurs when the user has limited or no data available and, therefore, the algorithms cannot work well. This situation is widely referred in the literature as the ‘cold-start’ case. The aim of this manuscript is to explore this problem and present a prediction approach for personalized mobile advertising systems that addresses the cold-start, and especially the frozen user case, when a user has no data at all. The approach consists of three steps: (a) identify existing datasets and use specific attributes that could be gathered from a frozen user, (b) train and test machine learning models in the existing datasets and predict click-through rate, and (c) the development phase and the usage in a system.
A Comprehensive Empirical Study of Query Performance Across GPU DBMSes

In recent years, GPU database management systems (DBMSes) have rapidly become popular largely due to their remarkable acceleration capability obtained through extreme parallelism in query evaluations. However, there has been relatively little study on the characteristics of these GPU DBMSes for a better understanding of their query performance in various contexts. Also, little has been known about what the potential factors could be that affect the query processing jobs within the GPU DBMSes. To fill this gap, we have conducted a study to identify such factors and to propose a structural causal model, including key factors and their relationships, to explicate the variances of the query execution times on the GPU DBMSes. We have also established a set of hypotheses drawn from the model that explained the performance characteristics. To test the model, we have designed and run comprehensive experiments and conducted in-depth statistical analyses on the obtained empirical data. As a result, our model achieves about 77% amount of variance explained on the query time and indicates that reducing kernel time and data transfer time are the key factors to improve the query time. Also, our results show that the studied systems should resolve several concerns such as bounded processing within GPU memory, lack of rich query evaluation operators, limited scalability, and GPU under-utilization.
Sk-Unet Model with Fourier Domain for Mitosis Detection

Mitotic Count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem. Swapping the low-frequency spectrum of source and target images is shown to be effective to alleviate the discrepancy between different scanners. Our Fourier-based segmentation method can achieve F $$_1$$ with 0.7456, recall with 0.8072, and precision with 0.6943 on the preliminary test set. Besides, our method reached 1st place in the MICCAI 2021 MIDOG challenge.
Advanced Model for Improving IoT Security Using Blockchain Technology

In order to process and transmit large amounts of data generated by a wide range of devices, the advancement of artificial intelligence (AI) is critical. Anonymous users abuse the decentralisation of the blockchain when it is used to manage IoT devices and their associated large data. One of the most common uses of the platform was for illicit transactions and criminal activity. Since it's built into the hash function, no large hard forks are necessary. There are no changes made to its hash code. Intelligent design would allow any errors in the chain to be corrected uniformly and automatically, but this has not been implemented. Big data or any other data-driven business is discouraged from using a new blockchain pattern due to this fact. to propose a advanced blockchain model (SRB) in support of intelligent chain editing in this article as a solution. Hash and an ephemeral trapdoor prevent editorial power from being misused.
A Prototype Web Application to Support Human-Centered Audiovisual Content Authentication and Crowdsourcing

Media authentication relies on the detection of inconsistencies that may indicate malicious editing in audio and video files. Traditionally, authentication processes are performed by forensics professionals using dedicated tools. There is rich research on the automation of this procedure, but the results do not yet guarantee the feasibility of providing automated tools. In the current approach, a computer-supported toolbox is presented, providing online functionality for assisting technically inexperienced users (journalists or the public) to investigate visually the consistency of audio streams. Several algorithms based on previous research have been incorporated on the backend of the proposed system, including a novel CNN model that performs a Signal-to-Reverberation-Ratio (SRR) estimation with a mean square error of 2.9%. The user can access the web application online through a web browser. After providing an audio/video file or a YouTube link, the application returns as output a set of interactive visualizations that can allow the user to investigate the authenticity of the file. The visualizations are generated based on the outcomes of Digital Signal Processing and Machine Learning models. The files are stored in a database, along with their analysis results and annotation. Following a crowdsourcing methodology, users are allowed to contribute by annotating files from the dataset concerning their authenticity. The evaluation version of the web application is publicly available online.
Not perfect but good enough: a primer for creating spherical video-based virtual reality for autistic users

Purpose Previous research provides promising insights to the role of spherical video-based virtual reality (SVVR) applied with and for autistic users. Work already conducted in this area suggests that SVVR delivered via a range of head-mounted displays (HMDs) are useable, acceptable, can enable skill acquisition, can be relevant for delivering training, can help to reduce discomfort and promote skills generalization. However, to date very little research articulates methods or approaches to the design and development of SVVR. Here, the authors share the experiences of working in this space and designing SVVR content with and for autistic groups. Design/methodology/approach The authors draw upon two case studies/projects that were previous worked on with the intention to extrapolate key parts of the production process of SVVR development. The authors also outline key theoretical contexts as related to SVVR development in this field. Findings The goal of this primer on SVVR is to provide researchers and practitioners with an overview of using this technology. The authors provide a set of recommendations that should inform others in creating their own content and developing SVVR for/with/by autistic people. Originality/value This work combines and outlines theoretical, conceptual and practical considerations for practitioners and stakeholders seeking to build and deploy SVVR content; aspects not reported in previous research.
AR in TV: Design and Evaluation of Mid-Air Gestures for Moderators to Control Augmented Reality Applications in TV

Recent developments in augmented reality for TV productions encouraged broadcasters to enhance interaction with virtual content for moderators. However, traditional interaction methods are considered distracting and not intuitive. To overcome these issues, we performed a gesture elicitation study with a follow-up evaluation. For this, we considered TV moderators as primary users of the gestures as well as viewers as recipients. The elicited gesture set consists of five gestures for two types of camera shots (long shot and close shot). Findings of the evaluation study indicate that the derived set of gestures requires low physical and concentration effort from moderators. Also, both moderators and viewers found them appropriate to be used in TV with respect to understandability, distraction, likeability, and appropriateness. Using these gestures would allow moderators to control AR content in TV and tell stories in a modern and more expressive way.
Gender and Age Detection using Deep Convolutional Neural Networks

In social interaction, gender and age play an important role. Natural differences vary in gender, as are the terms used to identify people by their age. Despite the importance of these factors in our daily lives, the machine's capability to measure facial pictures reliably and effectively comes as part of what is required for industrial applications. For many years, automated facial recognition and gender and age estimation using Artificial Intelligence have attracted a lot of attention and have increased popularity. The use of a Deep Convolutional Neural Network(DCNN) to predict gender and age is suggested in this paper. Based on the widespread availability of facial images on the World Wide Web, mainly on social media, automatic facial recognition and prediction of gender and age using machine learning models have attracted a lot of consideration over a decade and have become increasingly popular. In all the existing literatures, gender is predicted as male or female. This paper considers third gender also using DCNN. The proposed approach produces 96.2% classification accuracy.