Reviewerly/exHarmony huggingface dataset:
Cooperative Game Model of Delegation Computing: Verifier Separated from Calculators

In cloud computing, the delegation computation service is required because end users usually are resource-constrained, and at the same time the correctness of computing results needs to be verified. However, all the existing and available technologies have a high cost to verify which is the main problem faced by the delegation computing under the cloud platform. In order to address this issue, based on the game theory and a smart contract, we construct three protocols. The client and the calculator sign the Prisoner's Protocol to incentivize correct computation by asking the calculator to pay a deposit upfront; The client and the verifier sign the Long-acting Mechanism Protocol to ensure the validity of the verification results; The calculator and the verifier sign the Collusion Protocol to make collusion the most profitable strategy for all colluding parties. Through the combination of these protocols, the work is realized by separation the computing task and the verification task, and the heavy verification task is avoided. Finally, the performance analysis of the results shows that the combination of all the protocols not only solves the problem of verification complexity in the traditional delegation computing, but also guarantees the benefit of the honest player.
Application Development for Hand Gestures Recognition with Using a Depth Camera

The aim of the work is to develop an application for hand gestures identification based on a convolutional neural network using the TensorFlow & Keras deep learning frameworks. The gesture recognition system consists of a gesture presentation, a gesture capture device (sensor), the preprocessing and image segmentation algorithms, the features extraction algorithm, and gestures classification. As a sensor, Intel® Real Sense™ depth camera D435 with USB 3.0 support for connecting to a computer was used. For video processing and extraction both RGB images and depth information from the input data, functions from the Intel Real Sense library are applied. For pre-processing and image segmentation algorithms computer vision methods from the OpenCV library are implemented. The subsystem for the features extracting and gestures classification is based on the modified VGG-16, with weights previously trained on the ImageNet database. Performance of the gesture recognition system is evaluated using a custom dataset. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy (99.4%).
A Scale Mixture Approach to t-Distributed Mixture Regression

Mixture of linear regressions frequently assume Gaussian distributions for the mixture components. Data from many applications follows leptokurtic and platykurtic shapes. The use of scale mixtures of distributions from the exponential family (SME) of distributions is considered due to its ability to provide more flexibility in the tail behaviour of a distribution. The t-distribution is identified as a member of the SME. This chapter considers robust mixture of regressions with errors from SME distributions, specifically the t-distribution. The technique is illustrated using generated data and an application on managed health care data is given. In the application, different behavioural claim segments are identified using the estimated mixture regression model facilitating better claims differentiation strategies.
Experience of Mixed Learning Strategies in Teaching Lean Software Development to Third Year Undergraduate Students

Teaching is always a challenging task, especially in the current fast-paced and changing world. Universities curricula and instructional practices should take into account growing and changing demands of both industry and students themselves. Given all these factors, a Lean Software Development course for third year BS has been developed and continues to evolve at Innopolis University, Russia. In the course, lean methods are used both for teaching lean software development skill, and for teaching Lean via other, not directly related to programming, collaborative tasks, i.e. writing a research paper. Besides, lean methodology is used by the course development team for course design. As a result, this approach helps not only to develop the theoretical and practical skills that students can apply in various spheres of life but also to engage the students and to maintain their attention throughout the course without any overload.
The Security of Website Based on ASP Technology

At present, most of the resources of the website are open to the visitors, but some information is especially for some users. Only users with specific permissions can access it. Therefore, web application security is a hot research issue. This paper introduces the implementation technology and method of the verification, authorization and Simulation of web application security based on asp.net.
Performance prediction for distributed graph computing

Summary Performance prediction for executing graph applications on distributed systems is a prerequisite to improve system performance. Especially for distributed systems optimized by sacrificing the accuracy of results to improve runtime performance, performance prediction can be used to determine accuracy‐related system parameters to achieve a tradeoff between the runtime and inaccuracy of results. This article presents an approach that predicts the runtime and inaccuracy of executing graph algorithms on bulk synchronous parallel (BSP)‐based systems to optimize system parameters by using an artificial neural network (ANN). The proposed approach samples different scales of subgraphs from the input graph by maintaining that the features of subgraphs are similar to the features of the input graph. Then it executes graph algorithm on each subgraph and extracts their runtime features. An ANN‐based performance prediction model is trained off‐line based on the extracted features and is used to predict the performance of executing graph algorithm on the complete input graph. We have validated the proposed approach by conducting single‐source shortest path, connected component, and PageRank on the BSP‐based distributed systems. The experimental results demonstrate that the prediction method can effectively predict the runtime and the inaccuracy with a relative error rate under 14% and under 25%, respectively, compared with the actual performance results.
Development of a Moving Person Tracking System-Obstacle Detection by Distance Sensor

In recent years, autonomous robot development has progressed rapidly. However, robots that track and support specific people under all circumstances have not yet been developed. This paper discusses part of the tracking system that could help create a welfare robot that tracks a specific person and provides him/her with personal care. In this study, a tracker that identifies the intended object by shape is used. A Kernelized Correlation Filter (KCF) tracker is used to detect and track a specific person, but it has a weakness in the presence of obstacles. If something passes in front of the target person, the obstacle may be detected as the correct target. This study proposes a process that recognizes a potential misrecognition using a distance sensor called Lidar. If both the tracker and Lidar do not take a true value, they will stop tracking immediately.
Implementation Of Goal-Directed Design Method Multimedia English Learning For Early Childhood

Learning media more interesting in the delivery of learning materials to children, should the learning given in the form of multimedia that entertain and educate. The objective of the research is to design and make multimedia-based application of English learning as an alternative, which is easy to learn and understand by early child. Using the Goal Directed Design method developed by Alan Cooper, this concept emphasizes the user's purpose in designing an interface, so the system is expected to be easy to use. At the research stage, the interview was conducted on English teachers in TK Islam Al-Hikmah Tangkilan and psychologist. In product testing, participants were 30 students of Kindergarten B which were divided into 2 test groups. From the results of paired pairs the first group obtained the results of statistical tests before using multimedia average value of 56,00 with standard deviation of 14,541 and correlation pairs of test data of 0.655. In the second group test the average score of 60.67 with the standard deviation of 7.988 before using multimedia and the average value 89.33 with the standard deviation of 8.837 after using the English language multimedia. From the results of paired correlation test of 0.715. Shows a very strong relationship in improving understanding of vocabulary in early childhood.
Towards affine-invariant graph matching: A directed graph based method

Graph matching is an important problem in computer vision and pattern recognition. This paper proposes a graph matching algorithm based on a directed graph structure model. Firstly, the point set is divided into two parts by using the line between two points, and the direction information between the two points is specified according to the number of points on both sides of the line. Secondly, the ratio between the number of points on both sides of the line and the total number of points is used to define the adjacency matrix. Finally, the matching matrix is solved by the convex-concave relaxation procedure method to complete the matching. The invariance of the directed graph model under affine transformation is proved. The experimental results show that the directed graph model constructed can effectively and accurately describe the structure information between feature points and the matching method as well as has a high performance.
Application of Network Intelligent Experiment Management System in Colleges and Universities——Taking Sac Network Intelligent Experiment System as an Example

We have designed and developed the central network management architecture system of the network intelligent experimental system, the network intelligent experimental equipment and system of various courses, which integrates modern information technology, database management technology, network technology and experimental teaching, truly embodies the network and intelligent management function of the experimental teaching point, and makes the management level and teaching of the experimental teaching conditions, teaching quality and openness will be greatly improved. At the same time, the system supports secondary development, allows teachers to add new experimental projects according to their own needs, and realizes the intelligent management of the network. This study can provide insights for the students and teachers on how to use Network Intelligent Experiment System in daily work.
Three-Layer Security for Password Protection Using RDH, AES and ECC

In this work a three-layer password protection approach is proposed by utilizing Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC) and Reversible Data Hiding (RDH). RDH is a sort of data concealing approach whereby the host image can be recuperated precisely. The proposed approach was implemented and evaluated by considering various images. Results have been presented by calculating Peak Signal to Noise Ratio (PSNR) and Rate. Obtained experimental results demonstrate the effectiveness of the proposed approach.
Approaching a pedagogical model of prison education

The Colegio de Estudios Científicos y Tecnológicos del Estado de Durango (CECyTED), offers high school studies based on two pedagogic schemes, the first one is a technique formation, by different schools CECyTED; the second one, a Median High Education of Distance, denominated Educacion Media Superior a Distancia (EMSaD) a general high school that capacitates as Educational workers. They oppress in 71 schools that covers the majority part the state of Durango. Since 1996, attends students of prison centers, based on the schemes EMSaD with a schoolar modality, has at this day near of 700 students in the data base, and hundreds of students graduated in all the prison centers in the State of Durango. In a national level, this is the unique subsystem that provides this service and modality in the prison centers in an international level in Latin America, is the unique model with regular courses in the prison centers. By the way, the pedagogical model is not designed for this context, but it works with a model of students of common scholar context, it says outside of the prison. This project is a part of a series of different works and successful experiences shared for some years in different Prison Centers in the State of Durango. This emphasizes in the creation of a Dirección de Educación Carcelaria, this constituted a line that make the opportunity to recognize the pedagogical precepts, administrative and infrastructure needs of the prison. The objetive is to show a padagogic model to educate in confinement contexts. The method used in this work is action-research, and the contribution is the potentiation and the aplication of the model in México´s jails and other countries.
Enforcing Control Flow Confidentiality with SGX

When a program is executed on a untrusted cloud, the confidentiality of the program logic and related control flow variables should be protected. To obtain this goal, control flow obfuscation can be used. However, previous work has not been effective in terms of performance overhead and security. In this paper, we propose E-CFHider, a hardware-based method to protect the confidentiality of logics and variables involved in control flow. By using the Intel SGX technology and program transformation, we store the control flow variables and execute statements related to those variables in the trusted execution environment, i.e., the SGX enclave. We found this method can better protect the confidentiality of control flow and achieve acceptable performance overhead.
An Intelligent Power Grid Oriented Data Mining Engine Construction Approach

With the control operation and business management as the demand orientation, the overall architecture of data mining engine and machine learning component construction is established. Using the analysis mining adaptive modeling method based on the machine learning algorithm library, the application framework based on the data mining engine is constructed to fully mine the value of regulating operation data and provide technical support for the dispatcher to acquire data resources and regulating knowledge quickly and effectively. Through the method of knowledge discovery and knowledge acquisition oriented to regulation operation, the support ability of comprehensive application of regulation information and centralized analysis and decision-making in the whole grid can be improved.
Design and Implementation of an Efficient Database Management Tool

In order to efficiently operate and process big data in MongoDB database, a data management tool is proposed with re-design and encapsulation of low-level drivers using Java. The tool is composed of five functional modules and established through database connection verification, output queries and connection break. The research focuses on parameter configuration, validity verification and the run-time order of result returning, in the case of document query. Experiment shows the convenience and stability of the tool for improving development efficiency.
Transformations for Generating Type Refinements

We present transformations for incrementally defining both inductive sum/variant types and coinductive product/record types in a formal refinement setting. Inductive types are built by incrementally accumulating constructors. Coinductive types are built by incrementally accumulating observers. In each case, when the developer decides that the constructor (resp. observer) set is complete, a transformation is applied that generates a canonical definition for the type. It also generates definitions for functions that have been characterized in terms of patterns over the constructors (resp. copatterns over the observers). Functions that input a possibly-recursive sum/variant type are defined inductively via patterns on the input data. Dually, functions that output a possibly-recursive record type are defined coinductively via copatterns on the function’s output. The transformations have been implemented in the Specware system [4] and have been used extensively in the automated synthesis of concurrent garbage collection algorithms [9, 12] and families of protocol-processing codes for distributed vehicle control [5].
Enhanced Homomorphic Encryption Scheme with Particle Swarm Optimization for Encryption of Cloud Data

Cloud Computing is the decentralized type of architecture which is vulnerable to various type of security attacks. Homomorphic encryption is the encryption scheme to encrypt objects which are used to access data from the cloud server. Homomorphic encryption scheme has major disadvantage of key management and key sharing which reduce its efficiency. Particle swarm optimization algorithms (PSO) are nature-inspired meta-heuristic algorithms that are population dependent known for social behavior of birds and fishes. These concepts are used as an inspiration to build scientific approach for complex problem solving. Depending upon the quality of measure, the solutions are enhanced by the algorithms initiating from a randomly distributed set of particles. By moving the particles around the search space using a set of mathematical expressions, the improvisations are achieved. In this research work, technique of PSO is applied which generate key for the encryption. PSO in is the optimization algorithm used to generate fixed number key for the encryption. PSO based homomorphic algorithm is implemented in MATLAB and simulation results shows that it performs well in terms of execution time, resource utilization. The execution time and resource utilization of PSO based homomorphic algorithm is less as compared to homomorphic algorithm. The result is optimized upto 10% approx in the improved algorithm as compared to existing algorithm.
Successful Practices in ICT Team Building in International Projects

The purpose of the chapter is to address the importance of building the project team in order to contribute to maximize the number of project success histories in an international context. The design science research methodology allowed, based on the literature review on the thematic domain, to elaborate a reference framework in order to obtain the scientific validation of the work. The main conclusions focus on presenting the added value of the team building in the implementation and adaptation of software products. The need to define strategies when setting up the teams is emphasized, so that specialists are incorporated in the various domains, taking into account the specificity of the country in which the project will take place. Thus, the definition of team formation policies is considered a differentiating factor, in order to include the knowledge in the domains of the culture of the country, organizational culture and current legal and fiscal frameworks as well as the interpersonal relations component.
Educating for “Buoyancy”

The future of education matters to all of us. This chapter presents a theoretical-inductive construction of the future of education, inspired by the advancements envisaged in the Fourth Industrial Revolution (also abbreviated to Industry 4.0 or IR4.0). Recent developments in the technological field make it imperative that university syllabi foster and grow technological and non-cognitive soft skills in tandem. The latter—socio-emotional skills—are considered crucial skills that endow “buoyancy” and resilience to the workforce. Empathy, cultural sensitivity, and tolerance are the key professional skills that should be nurtured among the upcoming generation of digital natives. The chapter builds on a previous publication and aims at advancing concrete proposals for the future of university education.
Devising the set of abnormalities for a given defeasible rule

Devising adaptive logics usually starts with a set of abnormalities and a deductive logic. Where the adaptive logic is ampliative, the deductive logic is the lower limit logic, the rules of which are unconditionally valid. Where the adaptive logic is corrective, the deductive logic is the upper limit logic, the rules of which are valid in case the premises do not require any abnormalities to be true. In some cases, the idea for devising an adaptive logic does not relate to a set of abnormalities, but to one or more defeasible rules, and perhaps also to one of the deductive logics. Defeasible rules are not universally valid, but are valid in ‘normal situations’ or for unproblematic parts of premise set. Where the idea is such, the set of abnormalities has to be delineated in view of the rules. The way in which this task may be tackled is by no means obvious and is the main topic studied in the present paper. The outcome is an extremely simple and transparent recipe. It is shown that, except for very special cases, the recipe leads to an adequate result.
Error Types in Natural Language Processing in Inflectional Languages

This article presents the challenges of natural language processing applications when they are used with inflectional languages. Two typical applications are presented: automatic speech recognition and machine translation. An overview of those applications and the properties of inflectional languages is given as well as examples from the highly inflectional Slovene language. Then, an error classification with examples is given, also with an emphasis on inflectional languages, as well as some directions for further research in this area.
Demo abstract: Leveraging AI players for QoE estimation in cloud gaming

Quality of Experience (QoE) assessment in video games is notorious for its burdensomeness. Employing human subjects to understand network impact on the perceived gaming QoE presents major drawbacks in terms of resources requirement, results interpretability and poor transferability across different games. To overcome these shortcomings, we propose to substitute human players with artificial agents trained with state-of-the-art Deep Reinforcement Learning techniques. Equivalently to traditional QoE assessment, we measure the in-game score achieved by an artificial agent for the game of Doom for varying network parameters. Our results show that the proposed methodology can be applied to understand fine-grained impact of network conditions on gaming experience while opening a lot of new opportunities for network operators and game developers.
Recognition of Change in Facial Expressions From Video Sequence Using Incenter-Circumcenter Pair Gradient Signature

Facial expression recognition system provides us enormous scope for studying characteristics of different human facial expression. Basic expressions (anger, disgust, fear, happiness, sadness, and surprise) can be distinguished from one another with minimum overlapping features between them. A human facial emotion can be represented uniquely if we are able to capture such features having high discrimination power. In this paper, we propose a method for recognizing facial expression by using salient landmark-induced gradient signature derived from the line segment joining circumcenter and incenter pair of a triangle obtained by joining relevant landmark points. We use appearance-based models to detect essential landmark points on face. We are considering only those landmark points situated at eyes, lips, eyebrows, and nose to get geometric feature. This landmark-based geometric facial feature has been used to describe changes in emotion from neutral through basic expression. We examine our proposed method on Chon-Kanade (CK+), MMI, and MUG benchmark image sequence databases and compare its performance under different motion image databases. In our experiment, dynamic behavior of every individual expression has been recognized with impressive recognition rate of accuracy.