Reviewerly/exHarmony huggingface dataset:
Route Optimization of Robot Groups in Community Environment

The paper studies the path planning and task assignment of robots in the low-efficiency distribution of express delivery in the community. The grid method is used to model the environment and a community is analyzed as an example. In the ant colony optimization (ACO), the heuristic function is reconstructed by the valuation function of the A* algorithm to improve the convergence speed of the ACO. The algorithm has enhanced global search ability in the early stage, and the convergence speed is fast in the later stage with the improvement of pheromone volatilization coefficient, and the experimental parameters simulation analysis is done in MATLAB software. The experimental results show that the improved ACO has faster convergence and higher efficiency than the basic ACO. The rationality of the path planning model and the effectiveness of the optimized ACO are verified.
RTEF-PP: A Robust Trust Evaluation Framework with Privacy Protection for Cloud Services Providers

The trust problem of Cloud Services Providers (CSPs) has become one of the most challenging issues for cloud computing. To build trust between Cloud Clients (CCs) and CSPs, a large number of trust evaluation frameworks have been proposed. Most of these trust evaluation frameworks collect and process evidence data such as the feedback and the preferences from CCs. However, evidence data may reveal the CCs’ privacy. So far there are very few trust frameworks study on the privacy protection of CCs. In addition, when the number of malicious CCs’ feedback increases, the accuracy of existing frameworks is greatly reduced. This paper proposes a robust trust evaluation framework RTEF-PP, which uses differential privacy to protect CCs’ privacy. Furthermore, RTEF-PP uses the Euclidean distances between the monitored QoS values and CCs’ feedback to detect malicious CCs’ feedback ratings, and is not affected by the number of malicious CCs’ feedback rating. Experimental results show that RTEF-PP is reliable and will not be affected by the number of malicious CCs’ feedback rating.
Online Budgeted Stochastic Coordinate Ascent for Large-Scale Kernelized Dual Support Vector Machine Training

Non-linear Support Vector Machines yield highly accurate predictions for many types of data. Their main drawback is expensive training. For large training sets, approximate solvers are a must. An a-priori limit on the number of support vectors (the model’s budget), which is maintained by successive merging of support vectors, has proven to be a well suited approach. In this paper we revisit recant advances on budgeted training and enrich them with several novel components to construct an extremely efficient training algorithm for large-scale data. Starting from a recently introduced dual algorithm, we improve the search for merge partners, incorporate a fast merging scheme, adaptive coordinate frequency selection, and an adaptive stopping criterion. We provide an efficient implementation and demonstrate its superiority over several existing solvers.
Change Management and Digital Age Training

Change management is a scientific area that treats all aspects of changes in the global society and contemporary economy. Nowadays, changes have become more universal and essential social and organizational phenomenon. The modern society has brought the imperative of constant change. In order to maintain efficiency, profit and sustainability, all organizations must be proactive, instead of reactive in regards of implementation of changes. It has been discussed many times that today’s changes have become more broad, rapid, unpredictable and severe – however, changes are inevitable part of the life cycles of all organizations. Most institutions have no awareness of or strategic approach to change management at all; they either talk about a change too much (or not at all), make the change, and hope for the best — treating a change as an event instead of an ongoing, everlasting process. The main problem is that changes are not thought about systemically or holistically, as they should be treated. Technology and changes have a huge impact on every aspect of life, with no exception to education. Educational institutions undoubtedly represent significant part of the whole process. Educational institutions are facing big cultural, demographic and especially technological shifts. Today, teachers are challenged with new ways of teaching in the digital age, incorporating technology effectively in the educational settings. In this paper the authors will discuss the matter of change management, digital age teaching and its impact of the educational practices and experiences. In this context, the method used is content analysis of relevant documents, as well as results of previous theoretical and empirical studies of many scientists and researchers in this particular field. The conclusions which have been reached represent a relevant starting point for future action in the framework of educational activities, policies and prospects.
Stability of Kronecker coefficients via discrete tomography

For partitions λ, μ, ν of size n, the Kronecker coefficient g(λ,μ,ν) is the multiplicity of the irreducible complex character χν of the symmetric group Sn in the Kronecker product χλ⊗χμ. About eighty years ago Murnaghan found the first stability property of Kronecker coefficients. Recently Stembridge introduced the notion of stable triple in order to study different instances of stability of Kronecker coefficients and stated two conjectures. In this paper we use the notion of additivity, that first appeared in discrete tomography, to disprove one of them. We also show that additivity implies Stembridge’s condition for a triple of partitions to be stable. In this way we produce several new examples of stable triples. As a byproduct of the interplay of ideas between representation theory and discrete tomography, we obtain a new characterization of additivity.
Simulation of Adaptive Control for Cloud Computing as Queueing System

Cloud computing is a complex system with high uncertainty factors. To guarantee the reliability and performance of the cloud computing despite the presence of uncertainty factors, a control system is required. In this paper simulation of control system in cloud computing is presented. The cloud computing is modeled as a queueing system with one unlimited queue and one server, and a simple adaptive control is employed to keep the achievement of the desired performance. The variation of arrival rate of clients, λ, is represent the uncertainty factor and the service rate, μ, is used as the control signal. Simulation results show that the proposed controller has a promising potential to be used in cloud computing.
Integrating Research into Information Technology Education

Given the centrality of research and innovation to the information technology (IT) industry, teaching IT students about research is important and can potentially provide students with a range of benefits. This study investigated student perceptions of their achievement of the proposed benefits of explicit inclusion of research into undergraduate information technology degrees. Students enrolled in one or both of two dedicated research skills courses that first introduce them to information technology research and then allow them to undertake a complete research project were surveyed about their perceptions of the benefits of undertaking the courses. The results showed that they considered increased learning about information technology to be the major benefit, followed by improved lifelong learning and employability skills. The study also explored the factors proposed to determine whether students gain these benefits, and found motivation positively influenced whether students believed they had achieved these benefits. Beliefs and extent of participation in research also played a role in achievement of perceived benefits but influenced different subsets of them. This improved understanding of integration of research into information technology education should provide guidance to academics as they design courses and degrees that support achievement of these benefits.
Image Reconstruction based on Bayesian total variation and Hidden Markov model

Several methods of inversion in Fourier synthesis are based on interpolation of the data and fast inverse Fourier Transform (FT), but, the results obtained by such methods are not satisfactory when the data do not fill uniformly the Fourier domain which is the case in many applications in tomographic imaging because this inverse problem is known to be nonlinear and ill-posed. In this paper, we propose a regularization method based on the Bayesian estimation by introducing some prior information. In the first step, we assume that knowledge of the noise nature is considered as a prior information, for this and in order to improve the quality of image reconstruction, we use a total variation prior whose model parameter is automatically determined using the Evidence Analysis within the Hierarchical Bayesian Paradigm. Secondly, we assume that the original image is composed of homogeneous regions, in this case we propose the Hidden Markov Modelling for image classification. This approach allows to have an algorithm that unifies restoration and reconstruction via classification in Fourier synthesis. This method is applied on synthetics and real images.
Designing and Validating a Model for Databases in Open and Distance Universities

Background: Humans have always needed to store and retrieve information. In the last decades, one of the most important phenomena in the information industry was the emergence and popularity of machine reader databases, especially online databases. The main purpose of this study was to design and validate a model for databases in open and distance universities. Methods: This research is a descriptive study conducted by survey method. The statistical population of the present study consists of all specialists in the field of studying databases and open and distance educational universities. In a small part, sampling was done by a census method and in a purposeful manner. The responses were analyzed using descriptive and inferential statistics and by SPSS and Lisrel software. For the purpose of examining the parametric deflections, the Kolmogorov–Smirnov test was used to check the normality of the data. The Levene’s test was used to examine the default of variances and obtain the inferential part of factor analysis. The current research was carried out in 2 qualitative and quantitative phases. In the qualitative phase, some of the distance education scholars and database experts were selected as a purposive sample to analyze the basic parameters obtained in the first phase. In the quantitative phase, sampling was done through census and purposive manner from professors’ at the University of Isfahan (N=211). 22 questionnaires were excluded due to reasons such as non-functional responses, and statisctical analysis was carried out for 189 participants. To measure content validity index (CVI), Waltz and Bausell index was utilized and the instrument under investigation was presented to 8 experts who were engaged in the content validity stage. Results: The results of the research showed that databases in open and distance universities in Iran have high quality and quantity regarding the search and search features and the technical aspects. Conclusion: Based on this research a major issue of concern is to find a framework and model which can be used by executives and managers from distant backgrounds to evaluate the quality of their existing databases.
Multi-Attribute Data Visualization Analysis Model for Multimedia

With the rapid development of Multimedia technology and big data, all areas of application data exploded causing difficult to analyze and mine useful information from big data. Along with the development of Python applications, data analysis with characteristics of multimedia area needs more targeted and efficient new methods. In this paper, aiming at the shortage of existing work, we propose a Multimedia big data analysis platform based on Data crawl and Data visualization by Python.
Scalable Dynamic Analysis of Browsers for Privacy and Performance

Brief Biography: Behnam Pourghassemi is a fth-year Ph.D. student in Computer Engineering at the University of California, Irvine. His research primarily revolves around performance analysis and privacy on the web including work- load characterization of web browsers, online advertising, and tracking ecosystem. He is interested in designing and developing scalable and low-overhead pro ling tools for pri- vacy and performance measurements. His previous research was in HPC and parallel computing with the focus on GPU acceleration and fault-tolerance. He earned his M.Sc. in Computer Engineering from UCI in 2017 and his B.Sc. in Electrical Engineering from Sharif University in 2015 with honor.
Examining the Landscape of Creating World-Class Technology-Oriented Higher Education in China

This chapter focuses on examining the landscape of the technology-oriented innovation education in China. The main way that “knowledge” has a substantial influence on “economy” is technological innovation. In the national innovation system, colleges and universities should not only undertake the task of basic research, but also strengthen the interaction with industry and science and technology and participate in the process of building an innovation-oriented country through science and technology, education, economy, and society. Under the realistic guidance of technological innovation, the three functions of higher education, namely talent cultivation, scientific research, and social service, are comprehensively coordinated and organically reorganized to promote the interaction between tradition and innovation, theory and practice, and school and society in higher education.
Design of Searchable Algorithm for Biological Databased on Homomorphic Encryption

With the rapid development of biotechnology, researchers are able to obtain large number of genome data sets. However, biological data often involves high privacy and data security issues. Thus when storing, transferring or analyzing these data, a safe and effective method is highly needed. This paper aims to propose a practical scheme using searchable homomorphic encryption. We combined the inverted index mechanism with the interactive operation on the homomorphic encrypted ciphertext data files, so as to realize the management and protection of biological data.
MC-Inverse: An Analytical Method for Consistent Motion of Anthropomorphic Dual-arm Robot

In order to keep the consistent of the dual-arm motion, an inverse kinematics method is proposed, which is called MC-inverse (Motion Consistency Inverse). Using the arm angle as an additional parameter to control the dual-arm in joint space, it can generate same arm angles when the anthropomorphic dualarm robot repeats movement from similar initial position to the same target, and not only complete the human-like task, but also the motion is predictable and controllable. In this paper, we designed a dual-arm robot-HR2 as experimental platform to verify the proposed method, select the joint angles and arm angles as the evaluation values, to compare our method with Track-IK and ikfast methods. The comparison results show that our method can keep the consistent of motion better.
Design and Implementation of Smart Mirror for Pilot Assistance

In modern world, high performance is achieved with the advancement of science and technology. In the fast way of life, the developments of automation projects are required. Automation systems are mainly developed by using Internet of Things. Likewise, the project represents the implementation of the Smart Mirror for an individual use instead of a normal mirror. The project is based on the Intel Atom Processor- Z-series which runs with the help of an open source operating system. Using speech processing techniques, the pilot can interact with the Smart Mirror through verbal commands. It actively listens to the pilot’s command and once the subsequent voice command is recognized, it performs the function associated with it. Smart Mirror provides common functionalities such as daily weather, time corresponding to the location, using social applications and more. The Smart Mirror can make the people to use other devices with low contribution and get them the entire world into hands through their voice commands itself.
어린이집 유아반 교사를 위한 교사학습공동체 프로그램 개발 및 적용

Objective: The objective of this study was to develop and apply a TeacherLearning Community Program in order to improve the specialization of infantdaycare center teachers and explore the changes in the learning attitudes ofteachers.Methods: To develop the program, the requirements were analyzed bysurveying 500 teachers of infant classes. The developed program was providedto 25 infant daycare center teachers for a total of 14 sessions. A total of 75sets of collected journal writing materials were analyzed qualitatively.Results: First, the Teacher Learning Community Program for infant daycarecenter teachers was developed. Second, the Teacher Learning CommunityProgram was found to bring a shift in the learning attitudes among the teachersof infant classes towards reflective and communal learning.Conclusion/Implications: The Teacher Learning Community Program brought ashift in the learning attitude towards reflective and communal learning. Thus,the Teacher Learning Community Program can be applied as a teachereducation program for improvement of the specialization of infant daycarecenter teachers.
Kecerdasan Dalam Mencipta Karya Seni Patung Realis Dengan Menggunakan Bahan Baku Akar Kayu Jati (Bonggol Kayu Jati)

The purpose of this research is to describe intelligence in creating realist sculpture works using raw materials of teak roots / teak stumps. This is motivated by the large amount of teak root waste that can be used for art work and capable of producing high economic value. But the results of observations made by the author so far, the work of art produced from teak root material is mostly just responded to as is and or made in an unrealistic form. To answer this problem the method used in this research is descriptive qualitative. Data obtained by observation in the field, in-depth observations, through literature and internet sources as well as direct practice writers. The writer as a researcher is an actor in the creation of works of art. The results of this study obtained a description of the smart ways of creating realist sculpture by using teak root / teak wood stumps, namely: smart in choosing raw materials, responding to raw materials, forming globally, forming in detail, the process of patching, drying, and finishing. The expected benefit of the results of this study is that it can help researchers / artists steps in creating realist sculpture works using raw materials of teak roots / teak stumps. Keywords: Intelligent, creative, realist sculpture, teak root (teak hump)
Ludic Applications for Language Teaching Support using Natural Language Processing

This paper describes the development of a tool for English teaching support using a ludic (playful) format, which applies Natural Language Processing (NLP) techniques. The application allows teachers and students to automatically generate three types of games for learning or practicing English: crosswords, word-search and a variant of the battleship game. We describe the main characteristics of the tool, the way the games work and the linguistic resources that were created in order to build it. The use of NLP and Artificial Intelligence techniques for generating this type of exercises shows promising results.
Philosophical and methodological landmarks of value and semantic informatization VS the "dictatorship" of digital information in the modern anthropological situation

The article issue is a reflection of the anthropological situation of the world, which is manifested in the problematic nature of a decent human existence and its responsibility for the course of events of both personal lives and communities. The development of modern strategies for the development of humanity today is associated with the informatization, which "has taken" the knowledge society initiative to be the leader of the progress. The systematization of knowledge and conceptualization of the problem highlighted the contradictory information influence on the existential capabilities of the person and explicated risk factors, ideological and methodological orientations of the anthropologization of the informational-existential situation. Axiological, dialogical, phenomenological approaches and world-view interpretation of familiar concepts of "information society", theoretical reconstruction, content analysis and certain cognitive procedures became the basis for defining certain constructs and receptions: the direction of goals, content, organizational and managerial conditions of informatization to the cultural and spiritual growth, where information becomes "the supreme power that mediates all processes – from economy to spirit"; axiologicalization of the convergent space collision of the information, in particular virtual, world and the real one; taking into account the informatization of significant changes in the world outlook of the person, presenting philosophical reflection (post-metaphysical thinking, linguistic turn, refusal to recognize the superiority of theory over practice, concretization of mind); "humanization" of informatization, as a system-creating process of life, through a discursive, dialogical multicultural virtual world, approaching the cultural and educational space.
Fast clustering methods based on learning spectral embedding

Learning data representation is a fundamental problem in data mining and machine learning. In recent years, semisupervised clustering has aroused considerable interest from the machine-learning and data-mining communities. Spectral embedding is a popular method for learning effective data representations. In this book, we introduce a learning spectral embedding via iterative eigenvalue thresholding, and apply it in semisupervised clustering. A semisupervised clustering method based on enhanced spectral embedding (ESE) is proposed, which not only takes into account the structural information contained in the data set, but also makes use of prior information (such as pairwise constraints).
Heuristic Load Scheduling Algorithm for Stateful Cloud BPM Engine

With the increasing popularity of cloud computing technology, the traditional Business Process Management System (BPMS) begins to transform to the architecture that deployed in the cloud. Since the traditional BPMS is often implemented as a stateful single-instance architecture, the cloud BPMS providers will encounter the stateful service load scheduling problem when they refactor the traditional BPMS to the microservice architecture that deployed on the cloud server. In order to help realize the transformation of traditional BPMS and improve the load capacity of cloud BPMS with limited computing resources, we propose a heuristic load scheduling algorithm for stateful service scheduling. The algorithm makes use of the busyness metrics of the single BPMS engine instance microservice in the cloud BPMS architecture. Because the resource scheduling problem is always defined as online bin packing problem, we improve the Best-Fit algorithm to solve this kind of problem. We come up with the Best-Fit Decreasing algorithm based on cloud BPMS engine busyness measuring and load prediction to schedule the computing resources to business process instances. Compared to some common load scheduling algorithms, our algorithm help cloud BPMS increase load level by at least 15%.