Reviewerly/exHarmony huggingface dataset:
Web application database protection from SQLIA using permutation encoding

Web application is the base of online businesses through the Internet. The emergence of COVID 19 forced almost every job to operate online so as to bridge the distance amongst individuals. The rapid increment in the needs of web application increases security threats on information and data. According to the Open Web Application Security Project, Structured Query Language Injection Attack (SQLIA) is a top security threat for web application. SQLIA inserts malicious code to gain access or to manipulate database information by cheating the server to bypass the code to the database, thereby causing a severe impact on web application. In this paper, permutation encoding method has been proposed to prevent SQLIA, which is based on encoding all database information using the proposed method. Initially, a special character is inserted to restrict the method from reversing. Subsequently, permutation encoding method is applied. Permutation refers to the method wherein the bit location is changed within three characters and then radix encoding is applied. Permutation is based on the primitive root value. Encoding has been used to hide permutation. The proposed method is implemented and tested using PHP and MySQL databases, where the proposal result has been compared with those of other proposal methods. The results with security analysis prove that the proposal method prevents SQLIA and protects database information.
Non-Invasive Air-Writing Using Deep Neural Network

This paper compares the inference performance of different deep neural networks executed on hardware with limited memory and computational resources. Performance comparison is done between densely connected networks (DNN), convolutional neural networks (CNN), and a long-short term memory network (LSTM) trained to classify hand-written characters on the air. Signals from an accelerometer and a gyroscope are sampled from a MEMS sensor when drawing the symbols. The inference is executed directly on the device equipped with an STMF401 microcontroller. The figures of merit used for the comparison are memory occupation, inference time, energy consumption, and classification accuracy.
Developing Software of Global Optimization Method Based on Grey Wolf Optimizer

This article discusses the development of software that allows to simulate the algorithm of the “Grey Wolf Optimizer” method. This algorithm belongs to the class of metaheuristic algorithms that allow finding a global extremum on a set of admissible solutions. This algorithm is being the most efficiently used in a situation where the cost function is specified in the form of a black box. The algorithm belongs to both bioinspired algorithms and to the class of algorithms of Particle Swarm Optimization. To analyze the efficiency of the algorithm, software was created that allows to vary the parameters of the method. The article contains examples of the program’s work on various test functions. The purpose of the program is to collect and analyze statistical results, making possible to evaluate the final result. The program provides to build graphs that make it possible to make a more thorough assessment of the results obtained. The program has a step-by-step function that allows one to analyze the specifics and features of the algorithm. Analysis of statistical data provides more detailed selection of the parameters of the algorithm.
The Role of Embodiment and Simulation in Evaluating HCI: Experiments and Evaluation

In this paper series, we argue for the role embodiment plays in the evaluation of systems developed for Human Computer Interaction. We use a simulation platform, VoxWorld, for building Embodied Human Computer Interactions (EHCI). VoxWorld enables multimodal dialogue systems that communicate through language, gesture, action, facial expressions, and gaze tracking, in the context of task-oriented interactions. A multimodal simulation is an embodied 3D virtual realization of both the situational environment and the co-situated agents, as well as the most salient content denoted by communicative acts in a discourse. It is built on the modeling language VoxML, which encodes objects with rich semantic typing and action affordances, and actions themselves as multimodal programs, enabling contextually salient inferences and decisions in the environment. Through simulation experiments in VoxWorld, we can begin to identify and then evaluate the diverse parameters involved in multimodal communication between agents. In this second part of this paper series, we discuss the consequences of embodiment and common ground, and how they help evaluate parameters of the interaction between humans and agents, and compare and contrast evaluation schemes enabled by different levels of embodied interaction.
Research on the Application of PAD Class Mode in Vocational English Teaching Based on Duifene Platform

Responding to the call of the times for education and teaching reform, the PAD Class fully exerts the advantages of "Internet + Education" and exemplifies the "student-centered" education idea.The PAD Class consists of three explicit learning links: presentation, assimilation, and discussion, which gives a full play to the strengths of teachers, and follows students' learning rules to help students learn and master knowledge efficiently.Duifene Platform, the official teaching platform of PAD Class, enables teachers to deal with meticulous and tedious teaching tasks in an easy, happy and efficient way.It is a new and universal teaching platform.PAD Class and Duifene Platform complement and reinforce each other.
Communication Robot as a Weekly Online Quiz Presenter

To add a humorous interface to online quizzes for elementary school students, we demonstrate a video of a humanoid robot as a quiz presenter. The quiz was a fun way to learn various related knowledge starting with eating habits. The purpose of using a robot was to make the quiz interesting for students of all grades in elementary school and for them to enjoy the quiz with their parents. The video was a short quiz that lasted a few minutes wherein the robot interacts with a human voice, presenting the viewer with a multiple-choice quiz. We distributed one robot-video quiz every week for six months, obtaining the following results. The number of students who responded to the quiz decreased sharply in the first month and stabilized at ~10% of the total number of students in the school. At first, most of the comments were about the quiz’s difficulty, but gradually, most of the comments became about quiz’s contents, suggesting the viewers established interest in the quiz topic. Many comments about the robot as a unique entity with its personality suggest that the viewers empathetically accepted the robot’s existence.
Evaluation of a method for measuring speech quality based on an authentication approach using a correlation criterion

When interacting with the Smart environment using speech interfaces, one of the important aspects is to assess the quality of the pronunciation of phrases. Therefore, obtaining objective quantitative estimates of the proximity to the initial standard as one of the quality measures is relevant. The paper proposes a new method for assessing the quality of phrases pronunciation based on a user authentication approach using deep learning of neural networks. This approach can be used to assess the proximity of the presented sample within the Smart-environment in relation to the initial standard. Such an assessment can be useful both in determining the quality of pronunciation of repeated phrases in relation to the reference one (for example, for assessing the channel used when interacting with the Smart environment or for assessing the quality of the speaker's speech to identify qualitative changes related to his condition or health), so and directly during the procedure for confirming the identity of the speaker. A significant correlation of a new approach for these tasks in comparison with the existing ones based on speech recognition (for quality assessment tasks) is shown.
Bonded design in the virtual environment: the transition of a participatory design methodology

Purpose This paper examines how bonded design (BD), a participatory design methodology, was influenced by the transition to working in a virtual environment necessitated by the coronavirus disease 2019 (COVID-19) pandemic. Design/methodology/approach Abiding by the participatory design tenets of creativity, learning-by-doing and mutual learning, the BD methodology was created for the specific purpose of fostering meaningful communication and interaction between two disparate groups. Previous iterations of BD are discussed, including its naissance with intergenerational teams, its adaptation to provide a framework for a university-wide initiative, the Faculty Information Technology (IT) Liaison Program that brought together faculty members and IT professional staff, and its current use in helping public librarians to develop with older adults, targeted library programming and services. Findings Analysis of the findings from the assessment of the BD methodology in different physical contexts demonstrates that the flexibility in the makeup and order of design techniques (discussion, evaluation, brainstorming, prototyping, consensus-building) makes BD potentially adaptable to online spaces. Recommendations for implementing the BD methodology online are outlined. It is argued that BD’s adaptability makes it an ideal method for creating meaningful and productive collaborations within both physical and virtual environments. Originality/value The proposed iteration of the BD methodology responds to a need for innovative practices to foster collaborative work in a virtual environment. BD is a unique, inclusive and cost-effective methodology to encourage meaningful interaction and communication between disparate groups in physical or online contexts.
Private Routing in the Internet

Despite the breakthroughs in end-to-end encryption that keeps the content of Internet data confidential, the fact that packet headers contain source and IP addresses remains a strong violation of users' privacy. This paper describes a routing mechanism that allows for connections to be established where no provider, including the final destination, knows who is connecting to whom. The system makes use of inter-domain source routing with public key cryptography to establish connections and simple private symmetric encryption in the data path that allows for fully stateless packet transmission. We discuss the potential implications of real deployment of our routing mechanism in the Internet.
Super-Resolution Appearance Transfer for 4D Human Performances

A common problem in the 4D reconstruction of people from multi-view video is the quality of the captured dynamic texture appearance which depends on both the camera resolution and capture volume. Typically the requirement to frame cameras to capture the volume of a dynamic performance (> 50m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) results in the person occupying only a small proportion < 10% of the field of view. Even with ultra high-definition 4k video acquisition this results in sampling the person at less-than standard definition 0.5k video resolution resulting in low-quality rendering. In this paper we propose a solution to this problem through super-resolution appearance transfer from a static high-resolution appearance capture rig using digital stills cameras (> 8k) to capture the person in a small volume (< 8m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). A pipeline is proposed for super-resolution appearance transfer from high-resolution static capture to dynamic video performance capture to produce super-resolution dynamic textures. This addresses two key problems: colour mapping between different camera systems; and dynamic texture map super-resolution using a learnt model. Comparative evaluation demonstrates a significant qualitative and quantitative improvement in rendering the 4D performance capture with super-resolution dynamic texture appearance. The proposed approach reproduces the high-resolution detail of the static capture whilst maintaining the appearance dynamics of the captured video.
Using Graph Algorithms for Skills Gap Analysis

With the development of graph databases, organizations can utilize this technology to enhance human capital allocation by better understanding and connecting employee skillsets with the requirements of positions. Specifically, by storing data in the form of a knowledge graph, organizations are enabled to profile the competencies of their employees and optimize the deployment of human capital to the company’s objectives. This study explores data provided by a large engineering organization which merges employee data, including project assignment and skills, with a public library of competency profiles from O*NET. The objective is to explore employee skills profiling, optimize project staffing, and identify employees best suited for upskilling through the use of graph databases and machine learning algorithms. The findings show that knowledge graphs present an opportunity for organizations to better understand their workforces and more optimally allocate and strengthen their human capital.
Automobile Insurance Claims Auditing: A Comprehensive Survey on Handling Awry Datasets

Soufiane, Ezzaim EL Baghdadi, Salah-Eddine Berrahou, Aissam Mesbah, Abderrahim Berbia, Hassan Fraud is a very costly criminal activity. Insurance companies face the very challenging task of identifying and preventing fraudulent claims. Just like any big problem in recent years, Machine Learning has been heavily applied to fraud detection in both a supervised and non-supervised manner. But, usually supervised models do not perform well in the presence of awry, asymmetrical Datasets. This paper presents a novel approach for auditing claims in automobile insurance. Our data pipeline consists of preprocessing, feature selection, data balancing, and classification. This robust fraud detection model, built upon existing fraud detection research, gives very promising results compared to state of the art in the industry.
Multiunmanned Aerial Vehicle Path Planner on Graphics Processing Unit

Using multiple unmanned aerial vehicles (UAVs) improves the efficiency of reconnaissance, surveillance, and search and rescue missions. This article presents a path-planning software for a team of UAVs utilizing graphics processing units (GPUs). The UAVs are tasked to visit multiple points of interest (POIs) in a 3-D environment, and the software finds an optimized solution that assigns the POIs to the UAVs, selects the order in which the POIs are visited, and calculates the paths between the POIs. The software uses a multistep approach using a single-source-shortest-path algorithm to find the optimal paths between all combinations of POIs followed by a genetic algorithm to solve the multitraveling salesperson problem. The algorithm can minimize distance, time, or energy consumption depending on the setting selected by the user. The proposed GPU implementation is tested on eight different maps from around the world and executes in just 0.6 s, a 48.3× speedup compared to a sequential execution on CPU. This performance improvement is a real asset in a mission-changing environment.
Diaspore: Diagnosing Performance Interference in Apache Spark

Apache Spark is being increasingly used to execute big data applications on cluster computing platforms. To increase system utilization, cluster operators often configure their clusters such that multiple co-located applications can simultaneously share the resources of a cluster node. With resource sharing, applications can compete with each other for shared node resources thereby interfering with each other's performance. Many Spark applications take a long time to execute. Performance interference from other applications can thus cause a Spark application to fail or take even longer time to execute thereby wasting cluster resources and frustrating users. This motivates the need for an automated technique that can detect interference quickly and also diagnose the root cause of the interference to facilitate mitigation of the problem. Most existing approaches are not designed to offer quick interference detection and diagnosis. For example, they typically require extensive training data for every application of interest under various possible input data sizes and resource allocations. In this paper, we systematically investigate the design of a Machine Learning (ML) based technique that addresses this open problem. We implement a tool called Diaspore that integrates our findings. We evaluate the tool with a diverse set of 13 Spark applications executing on a real cluster. Experimental results show that Diaspore requires only small scale training data, i.e., executions under small input sizes and resource allocations. Furthermore, our results show that the tool can offer accurate predictions for applications not present in the training data. Consequently, Diaspore reduces the training time needed to offer predictions. Finally, the feature engineering underlying Diaspore ensures that the tool can detect and diagnose interference quickly in an online manner by sampling only a small fraction of a long running application's execution. This can allow cluster operators to mitigate interference in an agile manner.
The usage of pedagogical principles for the adaptation of foreign experience to the educational process of the educational institutions of the Ministry of Internal Affairs of Russia

The research subject is the peculiarities of using foreign experience in the pedagogical process of educational organizations of the Ministry of Internal Affairs of Russia. The research object is the range of conceptual approaches to the application of the general principles of understanding foreign experience and using it for the creation of pedagogical techniques. The authors study such aspects of the topic as the scientific interpretation of pedagogical techniques of embedding foreign experience into the educational process of educational institutions.&amp;nbsp; Special attention is given to the consideration of the common principles of interpretation of foreign experience and their usage for the creation of pedagogical techniques. The authors offer the directions of the development of pedagogical techniques with account for the principles of foreign experience interpretation: the usage of foreign experience as an element of education; the usage of foreign experience for the improvement of teaching methods. The main conclusions of the research are the proposed principles of using international experience as an element of education and for the creation of specific teaching methods. The authors&amp;rsquo; contribution is the proposed principles of embedding foreign experience into the pedagogical process of educational institutions of the Ministry of Internal Affairs of Russia.&amp;nbsp; The scientific novelty of the research consists in the usage of foreign experience based on the following principles: principle of equality of goals; principle of the experience system specification; principle of a real limit of experience; principle of variability; principle of &amp;ldquo;psychological allergy&amp;rdquo; prevention; principle of the formation of an &amp;ldquo;image of experience&amp;rdquo;; principle of information sufficiency; principle of analogies; principle of inevitability of proof; principle of inclusiveness; principle of reasonable non-use of foreign experience.&amp;nbsp; &amp;nbsp;
Analysis of Multi-attribute User Authentication to Against Man-in-the-Room Attack in Virtual Reality

With the popularity of Virtual Reality (VR), most of VR applications focus on content creation and experience design, while the security and privacy of VR applications has been ignored. For example, the Man-in-the-room (MITR) attack is well known for tapping user interactions in VR, including the user’s behaviors, real-time conversations, screenshots and computer audio, etc. Therefore, to provide secure and usable user authentication become one of crucial problems for VR applications. In this work, we propose a multi-attribute user authentication method to against MITR attack in VR. Specifically, any combinations of these selected attributes and values can be password for the certain user. The presented objects that conform to the password from the random object group are provided by our designed principles for identity authentication. The user then can select any presented attributes and values for authentication. Through this process, the attacker could not easy to guess the password even via a MITR attack. We designed a pilot study and evaluated the effectiveness and security of the proposed authentication method. By imitating the behavior of the MITR attacker, the authentication schemes under different settings are used to test the influence of different variables in the proposed security scheme. The experimental result shows that the proposed scheme can effectively resist invisible attack. This work-in-progress can give preliminary suggestions to against MITR for securing VR applications.
Knowledge economy classification in African countries: A model-based clustering approach

Knowledge economy (KE) has been a central issue in the political-economic literature of advanced economies, but little research has focused on the transition towards a KE in Africa. Using a latent profile analysis, six clusters of the KE were found in the region. The clusters range from very prepared with good performance in all KE dimensions (institutional, education, and innovation output) to very unprepared with low performance in each KE dimension. Lastly, we offer policy recommendations that shed some light on the national and international economic policies towards a more knowledge-oriented environment. One such recommendation is that effective policies should consider both the similarities and dissimilarities of African knowledge economies. How precise that can be done is one direction future research can take.
Securing the metrological chain in IoT environments: an architectural framework

The Internet of Things (IoT) paradigm, with its highly distributed and interconnected architecture, is gaining ground in Industry 4.0 and in critical infrastructures like the eHealth sector, the Smart Grid, Intelligent Power Plants and Smart Mobility. In these critical sectors, the preservation of metrological characteristics and their traceability is a strong legal requirement, just like cyber-security, since it offers the ground for liability. Any vulnerability in the system in which the metrological network is embedded can endanger human lives, the environment or entire economies. This paper presents a framework comprised of a methodology and some tools for the governance of the metrological chain. The proposed methodology combines the RAMI 4.0 model, which is a Reference Architecture used in the field of Industrial Internet of Things (IIoT), with the the Reference Model for Information Assurance & Security (RMIAS), a framework employed to guarantee information assurance and security, merging them with the well established paradigms to preserve calibration and referability of metrological instruments. Thus, metrological traceability and cyber-security are taken into account straight from design time, providing a conceptual space to achieve security by design and to support the maintenance of the metrological chain over the entire system lifecycle. The framework lends itself to be completely automatized with Model Checking to support automatic detection of non conformity and anomalies at run time.