Reviewerly/exHarmony huggingface dataset:
SSOV: A Single Sign-on Protocol for Accessing Vehicular Application Services with the Support of Secret Credential Management System

With the rapid development of 5G networks, vehicle-to-everything (V2X) applications will become an essential part of vehicular functions. However, existing login methods for mobile applications such as typing passwords or scanning QR codes are unsafe and inconvenient for moving vehicles. The identity privacy from being tracked is also desired for vehicular applications. In this work, we propose a single sign-on (SSO) protocol, SSOV, for accessing vehicular application services. This protocol is built upon the security credential management system (SCMS) defined in the IEEE 1609.2 standard, and requires minor changes to the original system. This protocol allows the vehicles to access the application services via the pseudonym certificates in the SCMS, and the vehicles can remain anonymous during the login process. This design shares some similarities with OAuth or OpenID Connect, but also bears two unique features. First, SSOV is specifically designed for vehicles and does not require any password for the login process. Second, unlike the other two SSO standards, SSOV does not require multiple identity providers, which will introduce extra difficulties in verification and also increase security risks. We analyze the security of SSOV in terms of the confidentiality of the key parameters, the impact of a specific compromised role on the system, and how we will prevent it in the threat model.
Conflict Consulting Clinic as a Method of Practical Training of Humanities Students

Conflictology as an educational program aimed at training specialists in the field of conflict resolution includes not only theoretical aspects, it is focused on modern practice. This stems both from the interdisciplinary subject field of conflict management, which combines the fundamental and applied components, as well as the needs of the labor market and the students themselves. At the same time, being in the circle of social sciences and humanities, conflict management faces the traditional challenges for this industry, associated with the most relevant forms of organizing students’ practical work. The article analyzes the unique experience of the clinical form of organizing the practice of students majoring in conflictology, which has been implemented at St. Petersburg State University for 10 years. The practice of students, organized in the form of a conflict consulting clinic, first of all solves the problem of adapting students to the conditions and requirements of the practical activity of a conflictologist. The authors focus on the issues of correlating the clinical form to the requirements of the newest educational standard. The main forms and stages of students’ work in the clinic are presented. The article describes the opportunities of students in acquiring the skills and abilities in conflict resolution, organizing and conducting the negotiation process, mastering conflict management strategies. The authors conclude that the conflict consulting clinic is a complex pedagogical methodology that includes both in-class and out-of-class forms of student practical activity in working with real cases based on client requests and promoting mediation, as well as teacher supervision with rhythmically organized discussion and correction of students’ actions, which ensures due professionalism and quality services provided by students to external consumers.
Resolving ownership rights of video data using visible and invisible watermarking in DWT-SVD domain

Abstract This paper addresses the concern of video piracy and also resolves ownership rights. Piracy entails utilizing, modifying and sharing the data without owner concern. Identifying the owner of the piracy and also the legal owner is difficult. For resolving these problems, this paper employs the prominent technique like watermarking with legal data base creation and SVD in wavelet domain. In addition to that, master key frame selection concept is used for identifying the key frames. Here the problem is resolved by two approaches. That is visible and invisible watermark to the authorized data. The watermark may be legal information about the owner like photo, thumb impression, signature of the owner and legal id. The legal id is the visible watermark and photo, thumb impression or signature of the owner is inserted as an invisible watermark. This methodology resists several video and image processing attacks. The experimental results show that the algorithm produces good perceptual quality and robustness to the watermarked video as well as watermark.
Score-based Aggregation for Attention Modules in Image Classification Tasks

Deep Convolutional Neural Networks (CNNs) have been widely used for various computer vision tasks because they hierarchically extract bountiful features from a highdimensional image. Also, some CNNs incorporate channel attention mechanisms that re-scale each channel of intermediate feature maps based on their importance. The channel attention modules squeeze the spatial information of a feature into a representative value to transform it as a re-scaling value. In order to reduce the amount of information, attention modules have utilized hand-designed pooling functions such as max pooling or average pooling which have been widely adopted in CNNs, because they add negligible computational complexity. However, a significant amount of spatial information is lost due to these pooling functions. In this paper, we propose a generalized pooling function that scales down spatial information with respect to the importance of each pixel. Unlike max pooling or average pooling, our score-based aggregation is capable of flexibly adjusting to input. Also, the score-based aggregation function learns how to squeeze the spatial information into the must appropriate representative value, which will convert the pooling into a spatial attention mechanism. Finally, we propose a novel method called Score-based Aggregated Attention Module (SAAM) that utilizes the proposed score-based aggregation. Our experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that SAAM achieves the highest classification accuracy improvement among existing channel attention modules since the score-based aggregation in SAAM is a more dynamic and effective method than the hand-designed aggregations.
LA RESPONSABILITE SOCIETALE EST-ELLE FEMININE ? L’EXPERIENCE DES BANQUES TRANSFRONTALIERES EN AFRIQUE

Based on the feminist postulate which stipulates a female advantage in the fulfilment of societal missions by companies, this research analyses the influence of gender diversity on the societal responsibility of 42 banking groups established in Africa between 2005 and 2014. Based on data from annual reports and a generalized least squared regression, we conclude that parity of board of directors has a positive and significant impact on the protection of nature, philanthropy and financial transparency of banking groups. Moreover, the proportion of women in the general management affects positively and strongly the level of altruism. Conversely, the proportion of women leaders influences negatively and significantly the environmental and economic responsibility of banking groups. This research recognizes feminist virtues on the one hand and denounces discriminates against women on the other.
A Novel Clustering Algorithm based on Cooperative and Iterative Evaluation Exchange for University Classification

Clustering algorithms have an important role in exploring data structure via grouping similar data points or objects into a subset called cluster. Numerous clustering algorithms have been proposed, but almost of them need a good initialization, data structural dependent, or require huge computational time. In this paper, we propose a novel clustering algorithm, called Cooperative and Iterative Evaluation Exchange (Co-IEE). The proposed algorithm considers all data points as potential cluster centers, and calculates, then exchanges real-valued evaluations to find the optimal clustering result of data set. The proposed Co-IEE has been applied into problem of Vietnamese university classification, achieving state-of-the-art results from both clustering evaluation, as well as human observation.
Library science knowledge: continuity and dynamics

Continuity of knowledge is the objective regularity of library science development. Library science is characterized by non- cumulated model of cognitive potential growth. The processes of new knowledge generation, actualization and complementation go on continuously, although unevenly, with varying degrees of intensity at different historical stages. The goal of the article is rationale for continuity and dynamics as the main instruments of library science knowledge development. The article is based on data analyses (including content-analyses) in the framework of methodology of inductive approach. Traditions and innovations are the main categories that provide continuality and dynamics in library knowledge growth and its further enhancement. The author considers the ideological impact on library science development, shows the influence of political changes on library science and the professional community. The article gives examples of Marxist ideology blocking the creation of new scientific ideas, determining the nature and content of research programs and the methodological tools chosen by scientists. Continuity in science can be strengthen by organization and fulfilling microhistorical researches. Portraits can creates a good basis for biographical and prosopographic research. Portraits of researchers presented in scientific, popular science, memoirs and other texts give an idea not only about their lives, but also about the features of the historical era. The role of creating portraits, as well as biographical and prosopographical research, is very important for understanding the processes of dynamics and continuity in the development of library science knowledge. Studying the professional career of practitioners and researchers allows getting answers to questions related to the origins of ideas, concepts, and theories, and helps tracing the complex path of transition from practical activities to library science. These types of research help to identify the social background of researchers, understand their beliefs and motivation to improve practice based on the use of scientific knowledge. Biographical and prosopographical research contributes to the removal of many misconceptions on the development of library science at different historical stages. Local history research is also important for studying and understanding the development of library science and the features of professional and personal life of researchers who have contributed to its cognitive enrichment. The integration of the results of local history, biographical and prosopographical research allows the creation a more complete picture of the development of library science in the historical perspective.
Quantization of synthetic aperture microwave radar

This chapter explores quantization theory of the microwave radar known as synthetic aperture radar (SAR). The quantum concept articulates that both electromagnetic microwaves and matter consist of tiny particles, with which wavelike properties are associated. Microwaves are composed of particles called photons, and matter is composed of particles called electrons, protons, and neutrons. It is only when the mass of a particle becomes slight sufficient to vibrate on its orbit that its wavelike properties show up. Therefore, from a quantum perspective, the term “aperture” indicates a device that determines the area in which microwave photons or particles with microwave-like properties are collected. Each photon has a wave function, which describes its path from the emitter through the aperture to the screen. This also provides a novel definition for SAR and quantum SAR illumination based on quantum entanglement.
Implementation of Back Propagation Artificial Neural Network for Heart Disease Abnormality Diagnosis

Abstract Heart is a very important human organ, where in normal people heart beats at 60-100 beats per second, but there are abnormalities in the heart rate that can occur due to certain causes so that it becomes slower (bradycardia) or faster (tachycardia) . Electrical activity of the heart can be detected by an electrocardiogram (ECG) , where the output of this device is a signal that describes the condition of a person’s heart. Artificial neural network (ANN) is one of the learning methods of artificial intelligence that can be used for pattern recognition or the other. One of the ANN learning paradigms is backpropagation where the computation goes through 2 stages, namely advanced calculation and backward calculation. This study aims to simulate backpropagation neural networks to recognize patterns from the output of the electrocardiogram using the MATLAB program. The input is form of printed electrocardiogram recording, and then it is normalized, next the data is processed by backpropagation computing with two phases (training phase and testing phase). The output of this ANN is a description of a patient’s condition whether normal, bradycardia or tachycardia .
Human-Centered Design of GRE Preparation Applications for Non-Native English Speakers

The Graduate Record Examination (GRE) is a primary barrier to entry into US graduate programs for non-native English speakers. The goal of this study is to develop a GRE preparation mobile phone application for non-native English speakers through the human-centered design cycle. We conducted a Cognitive Task Analysis and Knowledge Audit for the Magoosh GRE App to identify the most difficult tasks while using the app. We proposed features to help with the most difficult tasks and implemented the most promising feature through iterative prototyping taking into account user feedback. Usability testing for the most recent prototype demonstrates the need for a GRE preparation app targeted at non-native English speakers. Results show that the additional functionality added in the prototype shows promise for assisting non-native English speakers in studying for the GRE.
Process Prediction Method of Networked Ammunition Cooperative Operation based on Effectiveness Evaluation

Networked munitions coordinated operations is an effective modern warfare method. In order to better apply the networked munitions coordinated operations effectiveness evaluation to the integrated analysis of actual operations, a networked munitions coordinated operations process based on effectiveness evaluation is proposed method of prediction. Analyze the characteristics of the combat plan reflecting the battlefield situation during actual combat or simulation deduction, and construct corresponding effectiveness evaluation indicators for networked ammunition operations. By collecting the target recognition rate in the process of networked ammunition coordinated operations, the target kill rate and false target attack rate are analyzed to obtain the cost-effectiveness ratio. The effectiveness evaluation index is used to accurately predict the cooperative operation process of networked ammunition to achieve the prediction requirements. The simulation results show that the prediction accuracy of the network ammunition cooperative operation process prediction method based on effectiveness evaluation is higher, and it can better realize the efficient and reliable network ammunition cooperative operation effectiveness.
Smart Security System for Theft Protection Using Face Recognition

The number of car robbery attempts at the local and international scale is rising rapidly in this modern era. By inventing robbery techniques, the owners are afraid that their cars will be robbed from their ordinary parking lot or from outside. This makes vehicle protection against robbery important as a result of insecurity. The computer vision based real-time vehicle safety system solves this problem. The proposed car safety system carries through real time user authentication based on image processing using face detection and recognition techniques and a microprocessor-based control system attached to the car. The infrarot sensor attached to the driver's vehicles seat activates the hidden camera, which is fixed inside the vehicle, as the person enters the parked vehicle overcoming the existing security features. The person's face is detected using Viola Jones algorithm once the image is obtained from the activated camera. The extracted face is recognized using the improved Linéar Discriminant Analysis (LDA) algorithm that distinguishes many features rather than looking for an exact pattern based on the Euclidean distance. Authorization requires that the threshold value is established and compared to the Euclidean distance over which the person is not authenticated. The face is sent to the mobile of the owner as an MMS via the operating GSM modem, which is classified as unknown. The owner shall be controlled with the relay in accordance with the owner’s command when the information is received. The way to authenticate the person would be efficient and efficient in terms of vehicle safety.
ARIBC: Online Reporting Based on Identity-Based Cryptography

The reporting of incidents of misconduct, violence, sexual assault, harassment, and other types of crime that constitute a major concern in modern society is of significant value when investigating such incidents. Unfortunately, people involved in such incidents, either as witnesses or victims, are often reluctant to report them when such reporting demands revealing the reporter’s true identity. In this paper, we propose an online reporting system that leverages Identity-Based Cryptography (IBC) and offers data authentication, data integrity, and data confidentiality services to both eponymous and anonymous users. The system, called ARIBC, is founded on a certificate-less, public-key, IBC infrastructure, implemented by employing the Sakai–Kasahara approach and by following the IEEE 1363.3-2013 standard. We develop a proof-of-concept implementation of the proposed scheme, and demonstrate its applicability in environments with constrained human, organizational and/or computational resources. The computational overheads imposed by the scheme are found to be well within the capabilities of modern fixed or mobile devices.
Cloud Storage Forensics: BaiduNetDisk, WeiYun, and 115yun on a Wireless Network

With the development of cloud computing and wireless networks, cloud storage services are widely used in daily life. People can get access to cloud storage anytime and anywhere. Cloud storage forensics is a computer forensic scenario that currently appears frequently. In this paper, we first briefly introduce the general methods of traditional cloud storage forensics and then introduce forensic investigations that are conducted on three cloud storage services: BaiduNetDisk, 115yun, and WeiYun. The findings of the forensic analysis are presented in detail to help the development of forensics for cloud storage services.
Tracker Evaluation for Small Object Tracking

The small object problem becomes an increasingly important task because of its wide application. There are three significant challenges for small objects: 1) small objects have extremely vague and variable appearances, 2) due to the low resolution of the input images, their characteristic expression information is inadequate and, therefore, is prone to be absent after downsampling and 3) they draft drastically in the images when lens shake violently. Even though small object detection has been extensively studied, small object tracking is still in its infancy. To further explore small object tracking, we evaluate six latest trackers on OTB100 (normal object dataset) and small90 (small object dataset). According to our observation, we draw three instructive conclusions for the follow-up research of small object tracking. Firstly, due to the weak characteristics of small objects, existing trackers perform worse on small objects than on normal objects. Secondly, based on the results of ATOM, SPSTracker, DIMP, SiamFC and SiamMask, the trackers’ performance on small objects is positively correlated with that on normal objects. Thirdly, trackers tend to perform better on small object datasets when they can handle drift, occlusion and out-of-view.
Adaptive Fuzzy Dynamic Surface Control for IM with Iron Losses

A dynamic surface control (DSC) method combined with adaptive fuzzy backstepping technology [1] is developed for induction motor with iron losses in electric vehicle drive systems in this chapter. The DSC is utilized to overcome the “explosion of complexity” issue of classical backstepping. The fuzzy systems are used to approximate unknown nonlinear functions and the adaptive backstepping is employed to design controllers. The proposed control method can guarantee all the closed-loop signals are bounded. Simulation results illustrate its effectiveness.
Lessons Learned: Medical Library Pilot Testing of a Virtual Reality Anatomy Lab

In recent years many schools have begun to incorporate virtual reality tools and programs into their curriculum. This paper relates the results of a library pilot project on the usefulness of virtual reality headsets and programming for graduate students studying physical therapy and medicine. Authors review the existing literature on virtual reality use in education with a specific focus on libraries. This information is incorporated into lessons learned by researchers regarding hardware and software choice, attracting students, and potential deterrents or safety issues relating to virtual reality use in libraries.
Unsupervised Deep Learning

In this chapter, at the end of Part I, we will explain Boltzmann machines and Generative adversarial networks (GANs). Both models are not the “find an answer” network given in Chap. 3, but rather the network itself giving the probability distribution of the input. Boltzmann machines have historically been the cornerstone of neural networks and are given by the Hamiltonian statistical mechanics of multi-particle spin systems. It is an important bridge between machine learning and physics. Generative adversarial networks are also one of the important topics in deep learning in recent years, and we try to provide an explanation of it from a physical point of view.
Optimization of Inventory Decisions Using Fuzzy Cognitive Mapping in an Automobile Component Manufacturing SME

Inventory optimization is a serious challenge to industries around the globe. It poses a challenge to the decision-makers as poor judgment or decision can be catastrophic to any enterprise’s fortunes regardless of its size and sector. The complexity arises due to the dynamic movement behavior of various inventories in the supply chain. The main aim of the current research work is to identify the most central and important concepts affecting inventory optimization in an automobile component manufacturing SME located in Delhi-NCR. The variables considered for inventory optimization are identified through a thorough literature review. The variables affecting inventory optimization are capital available, usage level, cost of procurement, credit time and material availability. The current research work makes use of Fuzzy cognitive mapping (FCM) for inventory optimization by identifying the interrelations between the various factors and the most central concept. Also, the paper aims at identifying the positive and negative associations among the various variables under study, which affect the inventory optimization decisions. The importance of the various variables is measured by making a note of the incoming and outgoing arcs for each variable in the directed graph. It is found that the most central concept is the cost of procurement.