Reviewerly/exHarmony huggingface dataset:
Enterprise Human Resource Allocation under the Background of Wireless Communication and Internet of Things

With the rapid development of computer software and hardware technology, new progress has been made in wireless communication technology, and the Internet of Things technology has gradually penetrated into the daily work of citizens, which has led to a comprehensive upgrade of the work mode. In the context of increasing technological progress, market competition in various industries has gradually intensified, and enterprises also need to make corresponding adjustments to adapt to the development of the times. Human resource management, as the most important management link of contemporary enterprises, has a non-negligible influence on the development of enterprises, especially large and medium-sized enterprises. In order to adapt to the development of the times, most enterprises will choose to make timely adjustments to human resource allocation strategies. If an enterprise wants to develop in the fiercely competitive market, it needs to excavate talents and configure a reasonable human resource system. Starting from the related concepts of wireless communication, Internet of Things technology, and enterprise human resource allocation, this paper adopts the method of questionnaire survey to analyze the current situation and existing problems of human resource allocation in a large banking enterprise and then proposes targeted solutions for the existing problems. The ultimate goal of the plan is to optimize the allocation of human resources in the enterprise. The contribution of this paper is to select the relatively special field of banking for investigation and analysis, which fills the research gap to a large extent.
Service-aware Recommendation and Justification of Results

The opinions of people who previously experienced items are crucial to decision-making. My Ph.D. research project is focused on finding a better way to recommend experience goods and in particular services such as apartments and tourism experiences by exploiting a description of the service underlying item fruition, such as Service Journey Maps or Blueprint. Regarding the recommender system, I propose an extension of a Top-N algorithm that takes into account service-based dimensions. For the presentation of the results, I plan to develop an incremental view that holistically summarizes the items, showing quantitative data in bar graphs and qualitative data extracted from previous consumer feedback. As a testbed for the research, I exploited the home-booking domain, using publicly available data from Airbnb.
Towards a Standard Testing Data Set in Privacy

More and more information is being rendered publicly available through open data.Consequently, the need for private mechanisms is growing.The issue of the privacy-accuracy trade-off is more prominent than ever: keeping the information private and secure can seriously hamper the performance of queries of interest.Having perfectly secure open data that no one can interrogate is a paradox against the principles upon which open data themselves were founded.But how can one test said accuracy and performance?Much like in Machine Learning, data-sets for benchmarking are becoming necessary.The best one can do without them is theoretically compare private mechanisms among themselves, while the implications of these theoretical guarantees in daily practice remain unclear.A preliminary analysis that takes ideas from theory and tries to identify the characteristics of a potential benchmark is presented in this work.
Conservation Laws and Symmetries

This advanced undergraduate physics textbook presents an accessible treatment of classical mechanics using plain language and clear examples. While comprehensive, the book can be tailored to a one-semester course. An early introduction of the Lagrangian and Hamiltonian formalisms gives students an opportunity to utilize these important techniques in the easily visualized context of classical mechanics. The inclusion of 321 simple in-chapter exercises, 82 worked examples, 550 more challenging end-of-chapter problems, and 65 computational projects reinforce students' understanding of key physical concepts and give instructors freedom to choose from a wide variety of assessment and support materials. This new edition has been reorganized. Numerous sections were rewritten. New problems, a chapter on fluid dynamics, and brief optional studies of advanced topics such as general relativity and orbital mechanics have been incorporated. Online resources include a solutions manual for instructors, lecture slides, and a set of student-oriented video lectures.
Cybersecurity from the perspective of a new technology user

New technologies are, in the world of advancing processes of globalization, scientific and technical revolution, one of the most important indicators for designing many areas of social life.The useage of information technology has initiated many social phenomena generating many previously unknown concepts, threats, and in response, new areas of expert and research interest.One of them is cyber security, which focuses on building national, European or international policies to secure the functioning of information systems.The aim of this paper is to analyze and evaluate cyber security from the perspective of a user of new technologies.The applied research method will be a synthetic and analytical analysis of domestic and foreign literature in the field of new technologies and cyber security.
Real-Time Control-Flow Integrity for Multicore Mixed-Criticality IoT Systems

The spread of the Internet of Things (IoT) and the use of smart control systems in many mission-critical or safety-critical applications domains, like automotive or aeronautical, make devices attractive targets for attackers. Nowadays, several of these are mixed-criticality systems, i.e., they run both high-criticality tasks (e.g., a car control system) and low-criticality ones (e.g., infotainment). High-criticality routines often employ Real-Time Operating Systems (RTOS) to enforce hard real-time requirements, while the tasks with lower constraints can be delegated to more generic-purpose operating systems (GPOS).Much of the control code for these devices is written in memory-unsafe languages such as C and C++. This makes them susceptible to powerful binary attacks, such as the famous Return-Oriented Programming (ROP). Control-Flow Integrity (CFI) is the most investigated security technique to protect against such threats. At now, CFI solutions for real-time embedded systems are not as mature as the ones for general-purpose systems, and even more, there is a lack of in-depth studies on how different operating systems with different security requirements and timing constraints can coexist on a single multicore platform.This paper aims at drawing attention to the subject, discussing the current scientific proposal, and in turn proposing a solution for an optimized asymmetric verification system for execution integrity. By using an embedded hypervisor, predefined cores could be dedicated to only high or low-criticality tasks, with the high-priority core being monitored by the lower-criticality core, relying on offline binary instrumentation and a light exchange of information and signals at runtime. The work also presents preliminary results about a possible implementation for multicore ARM platforms, running both RTOS and GPOS, both in terms of security and performance penalties.
Cybersecurity and International Criminal Law

In this paper I thoroughly discuss the possibility of committing crimes which would fall under the Rome Statute, with regards to the very notion of cybersecurity.I conclude that we need extensively empirical research and solution to many aspects pointed out in my paper.First, we need to find an acceptable definition of cybersecurity to work with this process, and in order to understand the world and possibilities it creates for us.Furthermore, the 1998 Rome Statute needs an update, because the world of the internet creates opportunities never seen before, and the international case law is unable to cope with such distinct acts.Therefore, I believe that we need to find the next "Nuremberg Trials", the next generational solution to a world and crimes never seen before.We need the theoretical and legal revolution which did come after the World World II, and after the ICTY, ICTR and Sierra Leone ad hoc Courts.I truly hope that this short and hence mashup summarizing paper is just an indicator of papers and conferences to come, with solutions and more and more ideas on how we solve these two phased problems, namely having to find 1.An ultimate definition for cybersecurity 2. Having a solution of how to interpret it to the classical notion of International Criminal Law.if we conclude that we are unable to handle this issue, I suggest to create a panel on international or V4 level on either the reorganization of customary international criminal law under the Rome Statute or on the ever-changing definition of cybersecurity.
Access to Disclosure of Disasters Information for Deaf People through Sign Language Interpreter

There are plenty of information or news regarding natural disasters being inaccessible for Deaf people, and this stems from lack of Sign Language Interpreters. Urgency Sign Language Interpreters have an important role in bridging communication between society and the entire Deaf population, Sign Language Interpreters can also help people with Deafness in accessing information comprehensively on the aforementioned subject (natural disasters). Mostly, Deaf people communicate through sign language in their everyday lives, although Deaf people are still experiencing exclusion in discerning the newest and developing information on the current state of world affairs. This study aimed to elaborate situations faced by Deaf people in accessing information on natural disasters around them, their needs on accessing said information, and also urgency as well as obstacles faced in providing Sign Language Interpreters. This research will use qualitative approach by means of document/literature study to gather data from various international as well as national articles in general.
Design of Text Summarization System Based on Finetuned-BERT and TF-IDF

In today’s world, information is exploding. Internet readers need to judge the readability of the text by reading abstracts when searching for some information, such as blogs and technology papers, but the current text information is generally lack of abstracts. The development of NLP (Natural Language Processing) technology has a great help to fill the blanks. The purpose of this paper is to propose a text summary system, which has been tested in the corpus (including CLTS and some educational papers) and shown great results in the shorter samples. Our text summary system provides two options for the text without abstracts or with poor original abstracts, which are based on finetune-BERT and TF-IDF respectively.These two strategies are different from processing speed and semantic fit. In addition, in order to apply to more diverse scenarios, this article makes it possible to generate a summary of the image text by incorporating the OCR (Optical Character Recognition) technology.
Edge Node Information Model under the Framework of Edge Computing

Aiming at the high latency and high load of cloud computing under the Internet of Everything, an edge computing framework is proposed. Secondly, in view of the high heterogeneity of node data under the edge computing framework, which leads to the difficulty of data analysis and processing, this paper proposes an information model with generalization capability to carry the state data of nodes, which reduces the heterogeneity of node data. Ensure that data can be unified access to the edge computing platform. Finally, the information model forms of edge nodes in a variety of application scenarios are discussed, which is helpful to promote the development of edge computing.
Quantum information science in the optics curriculum

Optics is an essential backbone of quantum information science (QIS). Infusing QIS in the optics curriculum is much needed, but challenging. We report an approach transitioning from classical matrix optics to qubits and quantum gates.
Automated Approach to Detect and Monitor the Development of Turner’s Syndrome

Turner Syndrome (TS) is an illness that primarily affects females and is caused by a defective or partly misplaced X chromosome (sex chromosome). In this paper we discussed about monitoring the prognosis of TS in subjects of age 9-14 years to study how Turner’s affect their growth. This research work presents an algorithm to segment the hand digital X-ray images of children with TS. Identification of TS is proven in this study utilizing the 4th Metacarpal bone from left hand X-ray images centered on Anchor Based Link (ABL) segmentation technique. Then various features such as mean, variance, skewness, and kurtosis are extracted from normal and turner subjects of different age groups from 9-14years. This paper analyzed proposed ABL segmentation through ANOVA analysis which proves that as age of the turner subject increases growth occurs but it is lesser than the healthier subject. Based on the F value analysis which is below 0.5 it accurately differentiates normal and turner subject.
Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation

The involvement of geographic information differentiates point-of-interest recommendation from traditional product recommendation. This geographic influence is usually manifested in the effect of users tending toward visiting nearby locations, but further mobility patterns can be used to model different groups of users. In this study, we characterize the check-in behavior of local and traveling users in a global Foursquare check-in data set. Based on the features that capture the mobility and preferences of the users, we obtain representative groups of travelers and locals through an independent cluster analysis. Interestingly, for locals, the mobility features analyzed in this work seem to aggravate the cluster quality, whereas these signals are fundamental in defining the traveler clusters. To measure the effect of such a cluster analysis when categorizing users, we compare the performance of a set of recommendation algorithms, first on all users together, and then on each user group separately in terms of ranking accuracy, novelty, and diversity. Our results on the Foursquare data set of 139,270 users in five cities show that locals, despite being the most numerous groups of users, tend to obtain lower values than the travelers in terms of ranking accuracy while these locals also seem to receive more novel and diverse POI recommendations. For travelers, we observe the advantages of popularity-based recommendation algorithms in terms of ranking accuracy, by recommending venues related to transportation and large commercial establishments. However, there are huge differences in the respective travelers groups, especially between predominantly domestic and international travelers. Due to the large influence of mobility on the recommendations, this article underlines the importance of analyzing user groups differently when making and evaluating personalized point-of-interest recommendations.
Personalized, context-aware communication in multimodal public transport

The way we experience time is based on its psychological value. Time spent efficiently or pleasantly positively influences the time experience. When choosing a transportation mode, people highly value flexibility, reliability, and autonomy. Public transport, although used frequently, does not always fulfill these values. With personalization, the public transport experience could become more efficient and pleasant. This Ph.D. research aims to personalize public transport information systems (PTIS) through context-aware communication. To achieve this goal, the following activities are planned: (1) a systematic literature review of personalization in public transport; (2) creating an elaborate passenger context model based on existing literature, expert reviews, and user studies; (3) applying this model to offer context-aware information, advice, and inspiration to public transport passengers. At the time of writing, an initial version of the context model has been produced. The next step will be validating and improving the model.
Study on Displaying Images to Prevent VR Sickness as Maintaining Rich-Presence

This paper presents a method, by which displays selectively-blurred images to the users wearing a VR Head Mount Display (HMD) in order to prevent VR sickness. Our proposed method blurs displayed images selectively based on length of local motion in a field of view, i.e., length of optical flow. In addition to that, it takes into account the user's gaze point in order to maintain rich presence of the displayed images. In this paper, as focusing on preventing the VR sickness, our proposed method was experimented in a virtual space using participants. An effect of the VR sickness prevention was confirmed from experimental results.
A Comprehensive Study of Pose Estimation in Human Fall Detection

According to a study, unexpected fall is one of the main causes of sudden demise in elder persons. Therefore, it is very important to take immediate safety measures for the people having age 65 or above, or the people who are physically or mentally disabled. A powerful fall detection system to identify and provide immediate assistance to senior citizens or the people who is prone to falls is needed. A medical alert system with fall detection allows the user to summon assistance without pressing the call button. This review paper identifies the comparison in the approaches used for fall detection based on machine learning algorithm. A brief discussion on the methods used in pose estimation like OpenPose and PoseNet, which are majorly used to detect the fall and non-fall of a person is done. Moreover, we have also discussed the privacy concern of a person while using camera-based technique for detecting fall.
Attribute Selection, Sampling, and Classifier Methods to Address Class Imbalance Issues on Data Set Having Ratio Less Than Five

Many modern approaches to classification presume that the underlying training set is uniformly distributed. In a class unbalanced grouping, where the minority class is generally the more fascinating class, the majority class's training set greatly outweighed the minority class's training set. The authors of the current study discuss the difficulties that occur when training the machine as a result of the class imbalance. Research on current techniques checks the performance of the techniques on various parameters. Doing this will help us in getting idea how the standard techniques perform while minority class is suffering from one or more kind of issues. The purpose of this article is to present a comparative analysis of techniques for contemporary imbalance data analysis techniques, with a focus on data pre-processing, attribute selection, and algorithmic analysis, as well as a comparison of these techniques in the context of different data distributions.
Track and Field Image Target Detection Based on Feature Learning

With the advancement of video target positioning technology, the amount of image data captured by people is increasing rapidly, which puts forward more efficient and automated requirements for data processing methods. As the most popular artificial intelligence technology, deep intelligence has become a research hotspot and has shown potential in the field of mobile image target search. This paper focuses on the research of track and field image target detection based on feature learning. Aiming at some of the problems in video target detection, we use feature learning to solve related problems, so as to improve the accuracy of target detection, and carry out experimental verification. As a result, the target detection method based on feature learning proposed in this paper is 5% more accurate than the traditional target detection method.
Simulation Training Auxiliary Model Based on Neural Network and Virtual Reality Technology

Training simulators have been gradually evolving in the direction of software, virtualization, networking, and multiplatform in recent years, with the continuous development of hardware and software technologies, particularly the maturity of VR (Virtual Reality) related technologies. The network intrusion program allows remote hackers to take control of the system, posing a serious threat to the network and computer security. As a result, this paper proposes a VR-based ID (intrusion detection) simulation training system. This paper proposes an ID model based on CNN (Convolutional Neural Networks) and LSTM to address these issues (Long Short-term Memory Networks). This model oversamples data from unbalanced data sets, reducing the difference in category data and thus improving the ID model's performance and existing detection methods to compensate for the flaw. 3DSMAX technology is used to simulate the process visualization scene, as well as some key equipment models and signal transmission simulations, during the system design and implementation process. The experimental results show that CNN LSTM outperforms BP, and the overall evaluation index F1 has significantly improved, particularly the F1 index of D4. CNN LSTM outperforms GA (genetic algorithm) by 12.75 percent and BP by 14.07 percent. The system essentially accomplishes the anticipated simulation training goal, and the simulation training effect is impressive.
Classification of Product Review Sentiment by NLP and Machine Learning

Online marketing and e-commerce firms were already prospering in Bangladesh during this era of internet technology. Because people are under lockdown due to the COVID-19 epidemic, internet shopping has become the major platform for purchasing because it is the safest option. It sped up the time it took for firms to go online. More online product service providers improve people's lives, but it also raises concerns about product quality and service. As a result, it is simple for new clients to dupe while purchasing online. Our objective is to create a system that uses Natural Language Processing to assess client feedback from online purchasing and deliver a ratio of good and bad comments written in Bangla from past customers (NLP). We gathered approximately 6000 comments and views on the product to conduct the study. As classification approaches, we used sentiment analysis, as well as KNN, Decision Tree, Support Vector Machine (SVM), Random Forest, and Logistic Regression. With an accuracy of 94.78 percent, SVM outperformed all other methods.
Triple Extraction Technique for Power Transformer Fault Information Disposal Based on a Residual Dilate Gated Convolution and Self-Attention Mechanism

This article presents a triple extraction technique for a power transformer fault information process based on a residual dilate gated convolution and self-attention mechanism. An optimized word input sequence is designed to improve the effectiveness of triple extraction. A residual dilate gated convolution is used to capture the middle-long distance information in the literature. A self-attention mechanism is applied to learn the internal information and capture the internal structure of input sequences. An improved binary tagging method with position information is presented to mark the start and the end of an entity, which improves the extraction accuracy. An object entity is obtained by a specific relationship r for a given subject. The nearest start-end pair matching the principle and probability estimation is applied to acquire the optimal solution of the set of triples. Testing results showed that the F 1 score of the presented method is 91.98%, and the triple extraction accuracy is much better than the methods of BERT and Bi-LSTM-CRF.