Reviewerly/exHarmony huggingface dataset:
A Smart Room to Promote Autonomy of Disabled People due to Stroke

A cerebral vascular accident, known as common language stroke, is one of the main causes of mortality and remains the primary cause of acquired disabilities in adults.Those disabled people spend most of their time at home in their living rooms.In most cases, appliances of a living room (TV, light, cooler/heater, window blinds, etc.) are generally controlled by direct manipulation of a set of remote controls.Handling many remote controls can be disturbing and inappropriate for these people.In addition, in many cases these people could be alone at home and must open the door for visitors after their identification by either moving to the door or using an intercom system which requires in both cases a physical activity.Furthermore, these people need a continuous health monitoring especially blood pressure to avoid a recurrent stroke.Smart spaces and assisted technologies would be beneficial to assist person with disabilities to live independently, enhance their quality of life and empower their autonomy.A complete system which improves and facilitates the daily life and covers all aspects such as appliances automation, appropriate interaction mode and health monitoring of these people is still lacking.The aim of this work is to create a safe and high-quality living environment for persons with disabilities to enable them to live more independently by automating the operation of a living room appliances according to the current context without the need to use remote control devices, the use of a suitable interaction modality with appliances that require direct interaction and a remote health monitoring system which can alert relatives and caregivers in case of an emergency.
Development and Supervision of Financial Technology Based on Blockchain

Decentralization, stability, security, and immutability are all features of blockchain technology. Blockchain, as the underlying technology of Bitcoin's digital monetary system, is currently sweeping the globe. Blockchain is a revolutionary decentralized database technology that employs encryption, a timestamp chain data structure, a distributed consensus mechanism, and other technologies to achieve decentralization, tamper resistance, easy tracking, and programmable smart contracts. In the face of rising financial technology, we must maintain inclusive, technological, and invasive regulatory principles that not only foster financial innovation, but also conduct dynamic supervision to avoid systemic financial hazards. The consensus algorithm is one of the main blockchain technologies that has a direct impact on the system's functioning. As a result, in this paper, we propose a blockchain-based development and supervision method for financial technology, as well as an application of this technology to commercial settlement, which can significantly reduce data complexity, time consumption, and the structural chain phenomenon in existing transaction settlement. We bring the idea of pow competition into DPoS, construct a consensus algorithm with an upgrade mechanism, and call it delegated proof of work, based on an in-depth investigation of the working principle of pow (proof of work) (dDPoS). The blocking efficiency of the dDPoS consensus method is around one block every 10 seconds, which is significantly higher than the blocking efficiency of the POW and POS consensus algorithms. As a result, it offers a potential answer to traditional centralized institutions' concerns of high brokerage costs and insecure central storage, as well as a wide range of application possibilities.
Automatic Stance Detection for Twitter Data

Stance detection is a measure to understand the user view over the content posted on social media. Research associated with stance detection also includes modeling text to identify trends, opinions, feedback, reviews, and political alignment of users on social media. This paper focuses on identifying user stance for the 2019 Indian General election. We collected the data with a hashtag search. We have applied machine learning classifier and neural network approaches to detect user stance.
Technology of Designing an Effective Educational Program when Implementing an Engineering Education in the Digital Economy

The article discusses modern educational technologies of higher education used to train engineering personnel in demand by the economy at this stage of the digital state development. Modern state educational standards are based on the methods of the competent approach when designing educational results, focused on correlation with professional standards. However, the labor functions specified in these standards require additions in accordance with the situation of the labor market, the needs of a particular employer (production). It is also necessary to take into account the key competencies of the digital economy, which should be presented in the form of digital competencies - sets of skills and knowledge necessary to perform labor actions using digital technologies. The article discusses modern methods of designing a competence-based educational model and proposes a technology for the formation of educational results taking into account the requirements of the employer, which is the basis of an intelligent system for designing an optimal competence-based model of a higher educational institution graduate–one of the tools of informatization of the educational process of the higher educational institution.
Solid Oxide Fuel Cell Parameter Extraction via Chaos Game Optimization

For the purposes of reliably evaluating the actual performance and optimal controlling of solid oxide fuel cell (SOFC) systems, SOFC parameter extraction derived from accurate modeling is indispensable. However, the strong coupling and inherent nonlinearity of SOFC model severely hinder traditional methods to obtain various gratified parameters. An innovative meta-heuristic algorithm based on chaos game optimization is proposed to overpower these thorny obstacles and accurately extract the parameters in this paper. Besides, on the basis of test data provided by the Energy Research Centre of the Netherlands (ECN), the optimization performance of chaos game optimization (CGO) is comprehensively and thoroughly compared with that of grey wolf optimization (GWO). According to the simulation results, the optimum root mean square error (RMSE) achieved by CGO is 29.19% less than that achieved by GWO in parameter extraction, which fully shows that CGO as a complete method can provide better and competitive results in considered design examples. It is proved that compared with that of GWO, the precision, accuracy, and stability of the optimization results can be remarkably improved via the application of CGO.
Attention-based Persian Language Modeling

Attention-based models have proved their superiority on many NLP tasks, especially for English. Despite their great potential and importance of language models, little attention has been paid to attention-based language modeling for Persian. In this paper, we fine-tuned two language models, namely BERT and Persian GPT-2 on Persica corpus. We then evaluated these models by computing their perplexity on a 5-million-word dataset. Both models outperform previous SOTA results on the measure of perplexity. Our results indicate that GPT-2 performs slightly better by approximately 10 percent improvement of perplexity and seems to be a better fit for language modeling. We have proposed a modified version of perplexity, bi-perplexity, which can be a measure for comparison of language models trained with Masked Language Modeling objective. We have also introduced an innovative way of using BERT as a language model by devising a new strategy for sampling.
Research on the Path of Identifying the Online Live-Streaming Marketing Behaviors within the Framework of the Anti-Monopoly Law of the People's Republic of China

Although online live-streaming marketing shows nowadays play an important role in stimulating consumption and activating economic circulation, problems also arise and become more prominent, especially reflected from the marketing participants who are suspected of monopoly damage to the order of market competition. However, due to the absence of applicable antitrust norms for online live-streaming marketing, the legislation by far is insufficient for the determination of the subject in the process of the Anti-Monopoly Law. Worse still, there are legislative defects in constituting monopoly agreements and complex factors affecting the dominant market position. In recent years, foreign countries have attached great importance to the Internet antitrust issue and taken a number of powerful measures, such as the implementation of the hub-and-spoke conspiracy for identifying Internet platform and the clarification of the platform monopoly "synergistic behavior" judgment standards, etc., which is worthy of reference. China, in this sense, should adopt the antitrust law rules for the determination of the unincorporated form of the subjects in online live-streaming marketing, improve the identification path of online live-streaming marketing constituting the hub-and-spoke Conspiracy and collaborative behavior, update the determination criteria of the market dominance in the online live-streaming marketing and enrich the antitrust law determination procedure. All these methods will help to optimize the antitrust law identification path of the online live-streaming marketing.
Neuroeconomics of the Second Track: Processes, outcomes and impact

Recent research in the fields of neuroscience and behavioural economics offer clues to the success of GAP's Second Track process of productive group collaboration. Entrepreneur and philanthropist Peter Fritz AO explores the ways in which individual and group decision making can be optimised through this approach.
Introduction to<scp>S</scp>ten<scp>L</scp>indström's “A semantic approach to nonmonotonic reasoning: Inference operations and choice”

TheoriaEarly View INTRODUCTION Introduction to Sten Lindström's “A semantic approach to nonmonotonic reasoning: Inference operations and choice” Hans Rott, Corresponding Author Hans Rott hans.rott@ur.de Department of Philosophy, University of Regensburg, Regensburg, Germany Correspondence Hans Rott, Department of Philosophy, University of Regensburg, Regensburg, Germany. Email: hans.rott@ur.deSearch for more papers by this author Hans Rott, Corresponding Author Hans Rott hans.rott@ur.de Department of Philosophy, University of Regensburg, Regensburg, Germany Correspondence Hans Rott, Department of Philosophy, University of Regensburg, Regensburg, Germany. Email: hans.rott@ur.deSearch for more papers by this author First published: 02 June 2022 https://doi.org/10.1111/theo.12406Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Handling Temporal Data Imperfections in OWL 2 - Application to Collective Memory Data Entries

Dealing with imperfect temporal data entries in the context of Collective and Personal Memory applications is an imperative matter. Data are structured semantically using an ontology called "Collective Memo Onto". In this paper, we propose an approach that handles temporal data imperfections in OWL 2. We reduce to four types of imperfection defined in our typology of temporal data imperfections which are imprecision, uncertainty, simultaneously uncertainty and imprecision and conflict. The approach consists of representing imperfect quantitative and qualitative time intervals and time points by extending the 4D-fluents approach and defining new components, as well as reasoning about the handled data by extending the Allen's Interval algebra. Based on both extensions, we propose an OWL 2 ontology named "TimeOntoImperfection". The proposed qualitative temporal relations are inferred via a set of 924 SWRL rules. We validate our work by implementing a prototype based on the proposed ontology and we apply it in the context of the Collective Memory Temporal Data.
Improving Privacy in Blockchain by Combining Group Signature and Groth-Sahai Certification System

Background: Blockchain as a decentralized technology distributed on a peer-to-peer network has received a lot of attention recently. It implements a link block structure for data validation and storage, and implements a reliable consensus mechanism for synchronizing data changes, making it possible to create a tamper-proof digital platform for data storage and sharing. Questions/Purposes: There are some privacy challenges that may prevent the widespread use of Blockchain. The privacy plan is a double-edged sword. It must also fully protect the privacy of legal users. But if it is not fully controlled, illegal and criminal acts such as money laundering and extortion will be ignored. The aim of this study is to strike a balance between privacy protection and monitoring. Methods: This paper presents a design, based on the group signature and the Groth-Sahai certification system. Results: Reconfigure user anonymous and traceable technology in Blockchain by Groth-Sahai method, including initial stage; user registration; message sending; message verification; user tracking. Conclusions: This article proposes a scheme that can ensure the anonymity and traceability of the sender and receiver at the same time.
Regressive Based Classifier Analytics for the Mechanism of CryptoSystems Security Using EHE Scheme

Due to the improvement in digital technologies, the security of the data gets into drastic trouble. Several type of encryption-based algorithm has been initiated which tended to be insecure and procure threats to the digital world. To avoid the flaws in the digital era, the researchers brought up new encryption algorithm in existing framework. For a better accuracy purpose, support vector machine algorithm has been developed to provide image-oriented encryption. Some of the parameters are been detected to categorize the image dataset. Machine learning can be used to process large amount of vast data. SVM is used to classify each dataset in different aspects. Here in this paper, the enhanced homo morphic encryption (EHE) is used. Logistic regression provides less precise loss by classifying it. Data gets supervised since logistic regression does not have closed solution. Based on classification, it determines whether the data is been secured or violated. EHE is used for computation on the already encrypted data without assuming the value. For the efficient way of securing message transmission in mobile ad hoc network EHE is used.
A Modified Naïve Bayes Classifier for Detecting Spam E-mails based on Feature Selection

E-mail is the fastest and most formal communicating medium worldwide. However, everyday lots of junk e-mails or spam e-mails get piled up in the mailbox. This research aims to classify spam emails using conventional Naïve Bayes classifier and modified Naïve Bayes classifier, and performs the comparative analysis based on evaluating parameters. This research work proposes the framework for detecting spam emails using a modified NB classifier with Feature Selection. In the name of feature selection, wrapper-based feature subset selection is used as an evaluator and Naïve Bayes-based embedded incremental wrapper subset selection is used as a search method. The conventional Naïve Bayes classifier achieves the accuracy of 87.63% and 79.57% for spam corpus and spambase dataset respectively. Whereas the modified Naïve Bayes classifier attains the accuracy of 93.51% and 89.12% for spam corpus and spambase dataset respectively.
Multi-labeled Dataset of Arabic COVID-19 Tweets for Topic-Based Sentiment Classifications

Natural Language Processing (NLP) can analyze and classify the growing number of expressed opinions and feelings of online texts and quickly get the required feedback. The technique of automatically labeling a textual document with the most appropriate collection of labels is known as text classification, whereas supervised text classifiers require extensive human expertise and labeling efforts. This paper seeks to build a multi-labeled Arabic dataset by labeling an Arabic Covid-19 Tweet to two groups based on their lexical features: related topic and associated sentiment. An extensive dataset was created from Twitter posts to achieve this purpose. There are over 32k multi-labeled tweets in the dataset. The dataset will be made freely available to the Arabic computational linguistics research community. This work used both traditional machine learning approaches and a deep-learning approach to investigate this dataset’s performance. This paper demonstrates that traditional ML approaches provide higher accuracy with almost stable performance when experienced on the Twitter dataset for sentiment analysis and topic classification.
Research on Robust Detection System of Vehicle Attitude Fusion Features Based on Multi-Sensor Fusion

Abstract With the rapid development of artificial intelligence technology, the field technology of intelligent vehicles is becoming more and more mature. In order to solve the problem of difficult to extract the positional information due to the vestigial point cloud of moving vehicles acquired by LiDAR in complex traffic scenes, a real-time positional estimation method for dynamic targets based on point normal vector features of multi-sensing fusion is proposed. The least-squares fitting algorithm is used to solve the point normal vectors on the target surface, the principal component analysis is used to solve the number of cluster centers of the point normal vectors by adaptive clustering algorithm on the high-dimensional data, the clustering point fitting plane is obtained, and the mutually perpendicular planes representing the point normal vectors are selected as the X and Y axes of the coordinate system to which the target to be measured belongs, and the multi-target sensing fusion feature technology is combined with the sensing features of radar laser and vision, and the point cloud position calculation is completed. The experimental results show that the method can estimate the posture parameters of the moving vehicle in real time and exhibit good robustness.
Machine Learning Approach for Obstacle Classification in Automotive Radar

Machine Learning and Deep Learning have made a significant contribution in camera sensor perception, but not much to the Radar sensor, because of its time-series nature of the data and system complexity. Radar is mostly used for object detection and tracking, perception using Radar is not explored as camera sensor. Our previous work on the moving object classification [6] is another attempt for introducing machine learning in Radar systems. In this paper, we discuss the different machine learning/deep learning architecture-based obstacle classification in Radar data for automotive application. We propose novel architectures that can handle time series and instantaneous data simultaneously for classification. Neural Network, LSTM, 1D-CNN, and hybrid architecture are explored in this paper.
Teacher Allocation and Evaluation Based on Fuzzy C-Means Clustering

As an important resource in the education industry, teachers play a key role in promoting the high quality of education. In order to explore the overall level of the teaching staff in various regions of China and the allocation level of teachers’ educational resources, this paper selects the data or proportion of the student-teacher ratio, teachers’ educational background, and teachers’ professional titles as indicators. The clustering algorithm based on Fuzzy C-means conducts a cluster analysis on the allocation of compulsory education teachers in 31 provinces in China. The allocation of teachers in 31 provinces is divided into four categories, and the analysis and evaluation are carried out. At the same time, according to the current situation of teacher allocation in compulsory education, relevant countermeasures and suggestions are put forward to improve the overall balanced development of teacher allocation in China and narrow the gap of teacher allocation in China, in order to promote the balanced development of teacher allocation in compulsory education.
Computationally augmenting traditional embroidery practices: an autobiographical design process with first-person patient experience for amblyopia follow up treatment activity

Amblyopia is a common neurodevelopmental condition affecting people's vision and quality of life. Follow up treatment plays an essential role in improving amblyopia, and experts have proposed embroidery as a potential activity many times. The low compliance of amblyopia patients is one of the impediments. However, there are currently no targeted embroidery activities designed for patients. Designing embroidery activities to meet the needs of amblyopia patients in human-computer interaction and increasing patient compliance has become a design challenge in the current research field. In this research, we present an autobiographical design process to explore the augmentation of traditional embroidery activities with computationally generated patterns based on the stitching preferences of the user. We propose two design considerations for future research: Design with technology to assist traditional handcrafting and personalized design for long-term follow-up treatment through lived experience.
Developing insights from the collective voice of target users in Twitter

This study develops a pragmatic scheme that facilitates insight development from the collective voice of target users in Twitter, which has not been considered in the existing literature. While relying on a wide range of existing approaches to Twitter user profiling, this study provides a novel and generic procedure that enables researchers to identify the right users in Twitter and discover topical and social insights from their tweets. To identify a target audience of Twitter users that meets certain criteria, we first explore user profiling, potentially followed by text-based, customized user profiling leveraging hashtags as features for machine learning. We then present how to mine popular topics and influential actors from Twitter data. Two case studies on 16 thousand young women interested in fashion and 68 thousand people sharing the same interest in the Me Too movement indicate that our approach facilitates discovery of social trends among people in a particular domain.
Improved Public Auditing System of Cloud Storage Based on BLS Signature

Cloud storage and cloud computing technologies have developed rapidly for a long time, and many users outsource the storage burden of their data to the cloud to obtain more convenient cloud storage services. Allowing users to audit the private data’s integrity has become an additional basic function of the cloud server when providing services. In 2021, based on the BLS signature and automatic blocker protocol, Jalil et al. proposed a secure and efficient cloud data auditing protocol. The protocol can realize public audit, batch audit, data update, and protect data privacy. Moreover, the automatic blocker protocol is used to realize the identity authentication of the auditor. The protocol is relatively novel, innovative, and has a larger use space. However, we found that their scheme had security problems. If the cloud server has thoughts of malicious attack, he can forge the proof that he holds users’ data with stored labels and pass the audit. Referring to the original protocol and being inspired by them, we propose an improved audit protocol. The improved protocol solves the security problem and is more effective.