Reviewerly/exHarmony huggingface dataset:
The Application of the MEE Model with Mobile Educational Game in Pharmacology Teaching

As an information-based teaching tool, educational games that run on mobile phones can effectively improve students' interest in pharmacology learning and solve the problems in pharmacology teaching under the background of vocational education. In this research, the instructional design is based on the MEE model and the teaching implementation using mobile games, online teaching platform app and social software is supported by Internet and smartphone. Before class, learning motivation of students is stimulated after experiencing the work of pharmacists by playing the mobile game and shooting videos. In class, the level of learning motivation is maintained during the process of exploring, practicing and consolidating knowledge through micro-course, video error correction and the mobile game. After class, activities including recording electronic portfolio, publishing tips on WeChat and updating the game help realize the interest extension. This teaching practice is creative and reformative. The application of the model in pharmacology teaching of pharmacy specialty can significantly improve the level of students' learning motivation, which is worthy of promotion.
The Influence of Respect on the Commitment Between Buyer-Seller Relationships

There is an evidence that commitment plays a crucial role in buyer-seller relationship. Extant of literature has discussed various factors affecting commitment, but none has tackled the dimensions and effects of respect towards commitment. In this sense, and from an analysis of existing literature, we will try to determine the impact of respect on commitment between buyer and seller.The findings of this study show that respect has a positive effect on the commitment between buyer and seller through its two dimensions namely: Attention to the particularity and Responsibility. Our goal is to establish and to test a conceptual model representing this positive effect.
Application of an improved oriented object detection algorithm in remote sensing images

In recent years, in the field of remote sensing image target detection, oriented target detection has received extensive attention. Traditional detectors use horizontal bounding box (HBB) to locate targets, which may cause inaccurate detection when detecting targets with arbitrary orientation angles, dense distribution and large aspect ratios. The Oriented Bounding Box (OBB) adds different rotation angles to the horizontal bounding box, which can better deal with the above problems. However, with the introduction of oriented bounding boxes in the detector, new problems have emerged, such as the increase in the number of anchor points with different scales, angles, and aspect ratios, and the imbalance of IoU matching of positive and negative anchor boxes, which leads to classification confidence and Inconsistency between positioning accuracy. In order to overcome these shortcomings while using the oriented bounding box, this paper proposes a novel oriented target detector and redesigns a matching strategy for evaluating the position of the anchor frame. By selecting a higher quality anchor frame to achieve more accurate target detection, thereby reducing the instability of the angle in the matching process. Experiments conducted on public remote sensing data sets such as HRSC2016 and UCAS-AOD show that the proposed method achieves advanced detection accuracy.
Spatial and Temporal Monocular Visual Odometry

We present an unsupervised learning framework for the monocular depth and camera motion pose estimation from monocular stereo image which combines deep learning. Unsupervised learning does not need to consider separate supervisory information and is based to poxel-wise and gradient-based losses in local region. At the same time we demand to recovery absolute scale. The two tasks in Monocular Visual Odometry system are unsupervised learnig and scale recovery called UMVO. We train neural networks which is single-view depth and multiview pose network from the spatial and temporal information effectively and respectively. we apply UMVO to the monocular stereo images sequences to improve the accuracy. We train the neural network according to decreas spatial and temporal loss synthetically to improve the accuary and robustness of UMVO.
Civic education of students in the conditions of digitalization and informatization of the education system

Nowadays the preconditions for the upbringing of the younger generation in the context of the digitalization of society have emerged. This means that changes are taking place in the social, economic and political spheres of life, which entails changes in the methods of teaching and upbringing of the younger generation. The active use of digital technologies in civic education of students creates a high-tech living environment, which leads to the education of competent and competitive digital citizens. In these conditions, the possibilities of education and upbringing of students are expanding, taking into account individual characteristics. The aforementioned emphasizes the relevance of the problem of informatization of society and the introduction of information technologies in the process of civic education of students. The research problem lies in the informatization of society and the widespread use of information systems in the process of civic education of the younger generation. The purpose of the research is to identify topical issues of civic education in the educational process of an educational institution in the context of digitalization and informatization of society. The research methods used are analysis, synthesis, induction, deduction, generalization. The research results: a generalized definition of the concept of civic education in the context of digitalization has been presented; the ways of using multimedia technologies in the educational process have been shown; telecommunication projects for solving problems of civic education and social and psychological attitudes of the younger generation have been described. Key conclusions: information systems have a high educational potential, as they ensure the availability and quality of the formation of civic values among students at a distance; Internet resources can be the means of civic education; educational work, in the context of the digitalization of society, is a technological process; education of digital citizens requires appropriate pedagogical tools; multimedia technologies allow to instill in students the civic values of education.
Intelligent Security Control Based on the New Criterion of Edwards and Montgomery Curves, Isogenous of These Curves Supersingularity

Skuratovskii, Ruslan Williams, Aled Osadchyy, YevgenIt is well known supersingular curves due to pairing of Weil and pairing of Tate are used in identity-based cryptosystems so we find criterion of supersingularity of Montgomery and Edwards curves. We consider the algebraic affine and projective curves of Edwards over the finite field $${{\text {F}}_{{{p}^{n}}}}$$ . It is well known that many modern cryptosystems can be naturally transformed into elliptic curves. The criterions of the supersingularity of Montgomery and Edwards curves are found. In this paper, we extend our previous research into those Edwards algebraic curves over a finite field, and we construct birational isomorphism of them with cubic in Weierstrass normal form. One class of twisted Edwards is researched too. We propose a novel effective method of point counting for both Edwards and elliptic curves. In addition to finding a specific set of coefficients with corresponding field characteristics for which these curves are supersingular, we also find a general formula by which one can determine whether or not a curve $${{E}_{d}}[{{\mathbb {F}}_{p}}]$$ is supersingular over this field. The method proposed has complexity $$\mathcal {O}\left(p\log _{2}^{2}p \right) $$ . This is an improvement over both Schoof’s basic algorithm and the variant which makes use of fast arithmetic (suitable for only the Elkis or Atkin primes numbers) with complexities $$\mathcal {O}(\log _{2}^{8}{{p}^{n}})$$ and $$\mathcal {O}(\log _{2}^{4}{{p}^{n}})$$ , respectively. The embedding degree of the supersingular curve of Edwards over $${{\mathbb {F}}_{{{p}^{n}}}}$$ in a finite field is additionally investigated. Singular points of twisted Edwards curve are completely described. Due existing the birational isomorphism between twisted Edwards curve and elliptic curve in Weierstrass normal form the result about order of this curve over finite field is extended on cubic in Weierstrass normal form. Also it is considered minimum degree of an isogeny (distance) between curves of this two classes when such isogeny exists. We extend the existing isogenous of elliptic curves.
The Procedural Framework for the Electronic Blackmail Crime in the Jordanian Criminal Legislation

Investigating the electronic blackmail crime and how to seize and collect digital evidence from emerging topics in Jordan. The nature of digital evidence and how to deal with it by the investigation authorities is one of the issues of legal and practical importance. The Public Prosecution is investigating it, assisted by a judicial officer specialized in cybercrime, which is the Cybercrime Prevention Department in the Criminal Investigation Department, which exercises its jurisdiction according to the traditional rules of verification and evidence. The problem of the study is represented in the difficulties raised by the electronic blackmail crime from a procedural point of view in terms of legislative shortcomings and the inadequacy of the Criminal Procedure Code of 2017 for the investigation and collection of evidence for this type of crime. The aforementioned law does not include texts explaining how to deal with digital electronic evidence, or its value in proof. Therefore, the study dealt with the investigation procedures and the most important methods of proof that pertain to the electronic blackmail crime, which is the digital evidence, by defining it and determining what it is, and the difficulties that the investigation authorities face in considering the evidence Digital and dealing with it as proof.
VRBT: A Non-pharmacological VR approach towards hypertension

Hypertension is a prevalent disease that is known to affect the vascular system especially to the people with poor living habits and lifestyles. Virtual reality (VR) is effective to interact with people to release their pressure and cheer them up, which however is less conducted towards manipulating blood pressure and hypertension. In this paper, we consider how hypertension can be treated with VR devices and design virtual reality river bathing therapy (VRBT) with respect to a combination of traditional methods through sensory stimulation, audio interventions, and motor training.
Hand Detection Based on YOLOv3

Hand detection is paid more attention to in the field of augmented reality, which can enhance user experience of human–computer interaction. It is more and more popular to use deep neural networks to research hand detection. A new method of hand detection based on YOLOv3 Model is proposed in our work. Our target is to contribute to a real-time hand detection system to make it more accuracy and convenient for the experience of the user and can apply in all aspects of our life. 11 K hands dataset is used to perfrom the experiments and the result is sound.
Life Cycle of Student Software Project Development

This paper presents the life cycle of the process of developing software projects by students. It outlines the theoretical basis of the projects as the modern technology. The suggested life cycle, in which students play the role of junior software developers, results in the acquisition of diverse knowledge and practical skills that contribute to their personal growth. The article also describes the process of students forming and developing digital skills and key competences as they develop software projects. The life cycle in question prepares them for success in the everchanging, dynamic life, because it enables them to develop qualities that stimulate multi-level thinking and help them show their full potential.
An Evolutionary-Based Game Model of Honest and Dishonest Sellers

Many sellers are pursuing rapid profit growth, using the information asymmetry and virtuality of e-commerce platforms to destroy the reputation system of e-commerce platforms. Most of the existing defending strategies are implemented from the perspective of process or result governance, but these methods are difficult to fundamentally solve the phenomenon of fraud transactions. In this paper, we propose the seller’s honest asymmetric evolution game model in the monopolistic e-commerce market, based on the relevant knowledge of game theory mechanism design field. First, we theoretically analyze the stability of the model. Then, we verify the influence of the probability of sellers choosing honest strategy on the evolutionary stability of market credit through simulation experiments. Finally, we verified the reward and punishment parameters on the stability of the model. Experiment results show that increasing the probability that the monopolistic sellers choose honest strategies can promote the non-monopolistic of sellers to choose honest strategies, and appropriate reward and punishment measures can promote sellers to choose honest strategies.
A breast cancer diagnose application using deep learning technology

Breast cancer is a cancer which has the highest morbidity in women worldwide. Modern medicine uses imaging methods to analyze pathological images, which may cause the human error, and consume resources and manpower. Deep learning can help people and doctors to detect breast cancer, treat it better. This paper developed a breast cancer diagnosis system based on the Convolutional Neural Networks (CNN). We proposed a convolutional model consists of two convolutional layers and pooling layers. The Breast Cancer Histopathology Image Classification (BreaKHis) dataset was used to contain benign and malignant two classes. BCE loss was used as the loss function during our training process. The learning rate, optimizer, training epochs are 0.001, Adam and 11, respectively. In addition, we also developed a breast cancer diagnosis application using the Flask, which the users can use to detect breast cancer online and know where has cancerous by putting red boxes. Experimental results indicate that our cancer diagnosis system can provide good feedback to breast cancer patients.
The Nature of Intellectual Property Rights for Works Produced by Artificial Intelligence

The sweeping effect of technology cannot be overemphasized in our world today. Before now, computers used to be merely tools to carry out diverse functions, now these computers can now perform these tasks with or without human help. Artificial intelligence is growing in leaps and bounds daily, its impact is huge, and it keeps pervading all sectors. The emergence of artificial intelligence increasingly contends with the hackneyed belief that creativity is the exclusive preserve of human beings. The advent of artificial intelligence, especially considering the fact it is now capable of making creative and inventive works poses a form of legal challenge. Intellectual property laws refer to a body of laws that seek to recognize and protect products of human intellect, and grant creators and inventors certain legal rights to control the commercial exploitation of their work or invention. These forms of protection include but are not limited to copyright and patent. Now that computer algorithms and learning machines have now become a source of creativity, the question is now whether or not these machines can enjoy intellectual property rights on works made by them, even though they are non-human entities. It becomes pertinent to reconsider intellectual property laws generally in the light of this reality. This work seeks to examine the legal questions posed by artificial intelligence and other consequential issues.
Design of parallel Computing based on Web Technology in Soil Moisture and Thermal process Simulation system

Abstract Most of the traditional simulation software of soil water, soil and crops are based on the C-paw S model, and the efficiency of the simulation and calculation is relatively underground, and the organization and management of the data is slightly inferior, most of them are more powerful on the model. From the point of view of the early organization and management of the data, the simulation and optimization of the medium-term data, the visualization of the late platform and the efficiency of calculation, this project completes a new simulation platform with the help of the classical WHCNS model. At the same time, the innovation is to simulate and optimize the data with the help of big data technology, multi-thread technology, Web technology and the classical WHCNS soil-crop management model, so that the system has become an important experimental means of pilot-scale experiment, which can solve the problems of long experimental period, high cost and regional differences in the pilot-scale experiment of soil organic reconstruction. The simulation platform of the pilot test is constructed, and the relevant mechanism models and research models involved in the experimental research are coupled and integrated into the platform system to form a simulation process of data input-model simulation-calculation output and visual display. Used to assist the pilot test.
A CNN-Based Approach Towards Gender Identification from Online Handwritten Bangla Vowel Modifiers

Mukherjee, Himadri Majumder, Chandrima Biswas, Suparna Saha Dhar, AnkitaGender identification from handwriting has disparate applications from assistance in writer identification to personality analysis. Online handwriting has now become a common affair for the rustics with the advancements in technology. Such systems have developed for Western languages, but it has not been so for Indic languages like Bangla. This is mostly due to the unavailability of data sets coupled with the complex nature of the script itself. Here, a CNN-based system is proposed to distinguish gender from online Bangla handwritten vowel modifiers. At the outset, experiments were performed with 27 writers, and a highest accuracy of 91.15% was obtained.
Protection Method based on Multiple Sub-Detectors against Audio Adversarial Examples

Applications with audio speech recognition usually involve personal and authentication information; therefore, security measurement for audio speech recognition is one of the most important issues. Audio adversarial examples are a safety threat in the real world such that automatic speech recognition systems convert an input voice sound to an incorrect text message. Audio adversarial examples deceive the automatic speech recognition systems and they are created by exploiting the vulnerabilities of the deep-learning-based automatic speech recognition systems with the speech-to-text transcription neural network technique. To protect applications with automatic speech recognition, a new method based on multiple sub-detectors against audio adversarial examples is proposed in this study. The proposed protection method has a detector that is composed of three sub-detectors: dynamic-sampling-based, denoising-based, and temporal-dependency-based sub-detectors. In the experiments, we created 1,000 audio adversarial examples for evaluation. These audio adversarial examples are created on Mozilla-implemented Deep Speech. Deep Speech is a deep-learning-based end-to-end ASR system and it is the main attack target of this study. The experimental results of detecting audio adversarial examples show that the protection performance of the new method is higher than those of conventional methods.
From the Criminal Justice System to Entrepreneurship

An interview with Marcus Bullock, CEO of the Flikshop app, TED speaker, and Techstars alumni on transitioning from the criminal justice system to entrepreneurship.
Safety and maintenance of ATM system using Internet of things

Nowadays, we come across different theft and security problems. The main aim of the proposed system is to provide security and also maintenance-based ATM Operation, Monitoring and Auto Alert System. In this, system uses RFID based Smart ATM card for authentication, if this is ok then it captures immediately person face for bank purpose, whomsoever used the card. If authentication done user can be able to do transaction from the ATM based on user input and get money along with status, then these information updates to the particular bank branch office using IOT Blynk Application. And user can get feedback regarding the balance or transaction alert message to their particular mobile number using GSM. This system also provides ATM anti-theft security using Vibration detection sensor attached to the ATM Machine, if anyone trying to open or break the ATM machine, then this system immediately close the ATM room door along with buzzer alarm and send alert message to the bank Application Here we are using USB camera, vibration sensor, DC motor along with Motor Driver, IR Sensor, LCD and buzzer. All these devices are interfaced to the Raspberry Pi board and these entire systems connect to the bank through Blynk mobile Application over WIFI communication. This system continuously monitors the ATM machine with Multi security and anti-thief detection, and this live information will be updated to the bank office via IOT application. So, this system provides security-based authentication along with user-friendly smart ATM system.
Lymph node sections detection based on deep convolutional neural networks

Due to the severe damage to the health of a human being caused by breast cancer, it is rather crucial to explore novel approaches to detect images of lymph node sections to replace traditional methods, which are both time-consuming and inaccurate. This paper proposed an efficient cancer detection method by applying various kinds of convolutional neural networks (CNN) with a user interface. To achieve the best accuracy, experiments and comparisons are made between VGG16, ResNet-18, and EfficientNet, which are three popular models in the field of image processing. We also made a comparison to emphasize the importance of pre-trained weights of VGG16. Our models achieved high accuracy with pre-trained weights. In particular, VGG16 achieved 0.8340 in accuracy and 0.8326 in recall on the PatchCamelyon dataset and was used in our application. Our source code is available at https://github.com/bqdqj/Cancer-detection-based-on-tensorflow-and-PyQt5.
The Impact of Cloud Computing on the IT Support Function

The development of digital technologies has opened up new opportunities for e-learning in higher education. These technologies include cloud computing, which promises a scalable and reliable computing environment for both staff and students. This has not only changed the teaching, learning, and research environment, but also affected the way IT support services must now operate in the university sector. This chapter investigates the adoption of cloud computing in higher education through a case study of a major UK university and focuses on how this has affected the IT support function. The benefits and challenges of implementing cloud computing are explored, using questionnaires and interviews to generate data and analysis. The chapter concludes that cloud migration is a complex undertaking requiring a robust strategy that pays due attention to a wide range of issues, notably security concerns and the need for reskilling and the development of new support roles.