Reviewerly/exHarmony huggingface dataset:
Scalable Video Block Streaming in UDNs Using a Multi-Agent Multi-Armed Bandit Approach

At present, the densification of base stations (BSs) enables a user to connect to multiple BSs in ultra-dense networks (UDNs). In this paper, we consider the problem of multi-user scalable video streaming in a UDN, which allows different layers of a video block to be downloaded from different BSs. Specifically, we consider that each user simultaneously selects a BS connection combination to download an individual video block. To this end, BS connection combination selection is formulated as a multi-agent multi-armed bandit (MA-MAB) problem with little information exchange with other users. And each user can adapt the BS connection combination in a distributed self-learning system. To obtain the optimal arm for the MA-MAB problem, we propose a multi-user arm decision algorithm based on upper confidence bound (UCB). Finally, we conduct simulations which show the feasibility and superiority of our proposed algorithms.
Implementation of Genetic Pseudo Rehearsal

Deep neural networks suffer from catastrophic forgetting problem when they are deployed in a continual learning scenario. In our main work, we proposed Genetic Pseudo rehearsal, where we generated synthetic data of the previous task using Genetic Algorithms and pseudo rehearsed the neural network on it. We demonstrated the computational and memory efficiency offered by our proposed method. In this work, we discuss the implementation details of our proposed algorithm and the experimental setup in detail.
Research on Large-Scale Knowledge Base Management Frameworks for Open-Domain Question Answering Systems

In recent years, there are increasingly question answering systems based on large-scale knowledge bases that can answer natural questions. In this paper, we analyze the performance and efficiency of different knowledge base management frameworks when retrieving information from large-scale knowledge bases. The data model is built in the structure of a directed graph with vertices denoted the entities and edges denoted their relationships. With RDF (Resource Description Framework) model, Neo4j and Apache Jena built Graph Database Platform and Triple Store respectively to present the meaning networks. We analyzed, measured, and discussed how they store data from a knowledge base in the industry. We briefly showed the strengths and limits of each tool via experiments. Based on particular aims, researchers can choose the appropriate database management framework for their applications in large-scale open domain question answering systems.
Low-Cost and Reduced-Size 3D-Cameras Metrological Evaluation Applied to Industrial Robotic Welding Operations

Nowadays, the robotic welding joint estimation, or weld seam tracking, has improved according to the new developments on computer vision technologies. Typically, the advances are focused on solving inaccurate procedures that advent from the manual positioning of the metal parts in welding workstations, especially in SMEs. Robotic arms, endowed with the appropriate perception capabilities, are a viable solution in this context, aiming for enhancing the production system agility whilst not increasing the production set-up time and costs. In this regard, this paper proposes a local perception pipeline to estimate joint welding points using small-sized/low-cost 3D cameras, following an eyes-on-hand approach. A metrological 3D camera comparison between Intel Realsene D435, D415, and ZED Mini is also discussed, proving that the proposed pipeline associated with standard commercial 3D cameras is viable for welding operations in an industrial environment.
Activity Concentration of Natural Radionuclides and Total Heavy Metals Content in Soils of Urban Agglomeration

The total heavy metal content and activity concentration of natural radionuclides were obtained in native and anthropogenically transformed soil of the Rostov agglomeration (south of the Russia). The specific activity of natural radionuclides in soils of the has been comparable with indicators typical for chernozems of the Rostov region. The increase of radionuclides activity is observed in the lower horizons, which is due to the nature of the radionuclides themselves and to the fact that the main source of their inflow into the soil cover and biosphere is the parent rock. The correlation between activity concentration of radionuclide and total content of heavy metals were derived by Spearman’s Rank-Order Correlation. The study was revealed that the activity concentration of thorium and radium doesn’t provide significant correlation with the total content of heavy metals. The group of buried horizons have shown a positive correlation with naturally occurring 40K in contrast with humus horizon (A). The carbonates-accumulating group of horizons (B, B b) have random correlation sign by each of metals. The most significant correlation was observed for the B horizons group.
Optimal travel strategy model study

Abstract In this paper, we propose an appropriate algorithm to determine the best strategy of an individual in the Through the Desert game, and give the generally applicable models for single-player and multi-player games under known and unknown weather conditions respectively. We integrate ant colony algorithm, branch-and-bound method and game theory to build and solve the optimization model.
Fundamentals of Choosing an LMS Platform for Distance Learning

The modern world is constantly changing and developing. Companies have to adapt to the conditions of the market environment, building flexible relationships not only with business partners, but also with their staff. There is a need to develop strategies corresponding to the changes that are taking place, to identify the main trends both in the development of the organization itself and in the selection of training methods for its employees. The article is devoted to the problem of distance education, or rather, software tools that help to put this type of education into practice. The definition is given to the concept of the LMS platform and its capabilities. The classification of existing LMS systems is presented. The criteria for choosing a specific platform for the organization are described in detail, depending on the requirements for educational content and learning outcomes. In conclusion, the advantages and disadvantages of using LMS platforms in training are indicated.
Analysis of Object Detection Algorithms Using Neural Networks

This paper presents our study, analysis and comparison of algorithms that are commonly used for object detection projects, specifically those that use neural networks in their functioning. The idea for this research and paper arose from a simple problem that we programmers face before we start any computer vision project. It is of utmost importance to understand the sort of performance that is expected from an algorithm before using it in an application. Furthermore, we present a blueprint of a new algorithm which is based on the algorithms discussed and aims to slightly improve the performance of an existing method.
Triplet-path Dilated Network for Detection and Segmentation of General Pathological Images

Deep learning has been widely applied in the field of medical image processing. However, compared with flourishing visual tasks in natural images, the progress achieved in pathological images is not remarkable, and detection and segmentation, which are among basic tasks of computer vision, are regarded as two independent tasks. In this paper, we make full use of existing datasets and construct a triplet-path network using dilated convolutions to cooperatively accomplish one-stage object detection and nuclei segmentation for general pathological images. First, in order to meet the requirement of detection and segmentation, a novel structure called triplet feature generation (TFG) is designed to extract high-resolution and multiscale features, where features from different layers can be properly integrated. Second, considering that pathological datasets are usually small, a location-aware and partially truncated loss function is proposed to improve the classification accuracy of datasets with few images and widely varying targets. We compare the performance of both object detection and instance segmentation with state-of-the-art methods. Experimental results demonstrate the effectiveness and efficiency of the proposed network on two datasets collected from multiple organs.
Transcending the rational symbol system

Abstract Symbols support the uniquely human capabilities of language, culture, and thinking. Therefore, cognitive scientists have tried to explain intelligence as founded on Rational Symbol Systems (RSS). RSS use syntactical and logical rules to combine discrete symbols into meaningful expressions and inferences. However, these symbols fail to capture continuous, felt experience. The proposed solution is to ground symbols in situated interactions and subsymbolic networks of associations. Historically, different approaches have attempted to overcome the shortcomings of RSS. These include science, by formalizing and operationalizing symbols; philosophy, by critically analyzing the relation between symbols and reality; art, by evoking subjective experiences; and spirituality, by expanding consciousness. Information technologies, such as artificial intelligence, neural networks, simulations and virtual reality, make it possible to integrate their results. That would allow externalizing and controlling creativity and intuition, thus inaugurating an evolutionary transition to a supra-human level of intelligence, the “Global Brain”.
Performance Evaluation of Meta-Heuristic Algorithms for Task Scheduling in Cloud Environment

Cloud Computing is one of the important fields in the current time of technological era. Here, the resources are available virtually for users according to pay-per-usage. Many industries are providing cloud services nowadays as pay for usage which reduces the computing cost drastically. The updated software services, hardware services can be provided to the user at a minimum cost. The target of the industries and scientists is to reduce the computing cost by various technologies. Resource management or task scheduling may also play a positive role in this regard. There are various virtual machine management algorithms available that can be tested and enhanced for research and benefit of the society. In this paper, three famous Max-Min, Ant Colony Optimization, and Particle Swarm Optimization algorithms have been used for experiments. After simulation results, it is found that the PSO algorithm is performing well for makes pan and cost factors. Further, a new algorithm can be proposed or a meta-heuristic technique can be enhanced or modified for getting better results.
Bird.s Eye: Analysis of Video Surveillance Data using Deep Learning

Video surveillance data is widely used as a measure for security in various places like residential areas, shopping malls and so on. This video data is recorded 24x7 and therefore the task to find a particular object or event becomes tedious. Precious manual hours are wasted by security officials trying to skim through the video data trying to find the object they are searching for. This paper aims to reduce the manual effort required in this process by automating and focusing on the object the user is searching for. We have implemented multi-class object detection using YOLO algorithm and deep neural networks. The application will detect commonplace objects, classify them accordingly and extract the features of these objects. The user will be able to query the video data using the name of the object and the features. The paper is expected to detect objects of interest and extract basic features with reasonable accuracy and processing times through which it can be deployed in real-world surveillance applications.
Spatial-related and Scale-aware Network for Crowd Counting

Crowd counting aims to estimate the number of people in images. Although promising progresses have been made with the prevalence of deep Convolutional Neural Networks, there still remains a challenging task due to cluttered backgrounds and varying scales of people within an image. In this paper, we propose a learnable spatial attention module which can get the spatial relations to diminish the negative impact of backgrounds. Besides, a dense hybrid dilated convolution module is also brought up to preserve information derived from varied scales. With these two modules, our network can deal with the problem caused by scale variance and background interference. To demonstrate the effectiveness of our method, we compare it with state-of-the-art algorithms on three representative crowd counting benchmarks (ShanghaiTech [1], UCF-QNRF [2], UCF_CC_50 [3]). Experimental results show that our proposed network can achieve significant improvements on all the three datasets.
FACTORS AFFECTING WOMEN CAREER SUCCESS

The objective of this study was to assess the success of women in leadership and entrepreneurship in developing countries, including the Iraqi Kurdistan region. The study used structured survey questionnaires in order to gain a clearer understanding of the reasons for success and the factors that motivate women to lead / entrepreneurial activity in the area. This survey was a web search and created by Survey Monkey, where respondents allow you to do email research and use social networking links.Are male and female leadership roles the same? Specify the same number of male and female executives in the company? Do you see that men and women have senior management positions?This study used multidisciplinary approach that uses question lists for research and data collection. For this reason, the leadership and entrepreneurship of women in the Iraqi Kurdistan region was studied. The sample size could not be determined for this study because there is no official or published information about women in executive/business positions in the Iraqi Kurdistan region. It is assumed that this study was sampling, i.e. the sample size could not be determined initially, so the sample size would increase during the process until sufficient data was collected.
A New Approach for Mapping of Soccer Robot Agents Position to Real Filed Based on Multi-Core Fuzzy Clustering

Mapping the position of soccer robot agents to a real field, is one of the essential issues in the practical implementation of scientific contributions in this context. The lack of a proper assignment affects the scientific implementation of many subjects, such as routing, obstacle avoidance, and robot guidance. For this reason, the use of a clustering method is proposed in this article. Upon the entrance of a new agent, its position is mapped to the real field based on the clustering algorithm. After this mapping, the system begins to work according to the position of the agents, which is defined as the position of the centers of the clusters, as well as the rules defined in the knowledge-base. Considering the unknown and dynamic environment of the robot, some objects inherit common traits from multiple clusters. One reasonable solution for considering the cluster overlaps is to assign a set of membership degrees to each of them. Multiple membership degree assignments result from the fuzzy nature of the clusters. Due to the reduction of segmentations and the shrinkage of the search space, fuzzy clustering generally faces less computational overhead, while the identification and handling of vague, noisy, and outlier data also become much easier in them. The approach of the proposed method is based on the feasibility ideas and uses multi-core learning to identify clusters with complex data structures. The feasibility score of each data represents the percentages of the properties that data inherits from the clusters. Automatically adjusting the weights of the cores in an optimization framework, the proposed method avoids the damage caused by problems such as adopting inefficient cores, or irrelevant features.
A Tracking Control Method for Autonomous Intelligent Park Vehicle Based on Model Predictive Control

In order to make our autonomous intelligent park vehicles against established track has a good path tracking effect, a path tracking control method based on model prediction control (MPC) is proposed, using particle swarm optimization algorithm control to automatically calculate and adjust the MPC controller parameters under different working conditions. The CarSim/Simulink co-simulation platform is built to complete path following control simulation for dual lane under various speed. The simulation and experiment show that the intelligent vehicle in the parks has a good accuracy in following the target path under the control strategy.
Facilitation in Abstract Argumentation with Abstract Interpretation

Cycles of attacking arguments pose non-trivial issues in abstract argumentation. In particular, when arguments in a cycle cannot be determined either to be accepted or rejected, their acceptance statuses simply become undecided. The undecided statuses can then propagate out of the cycles to other parts of the graph, contaminating even more arguments with the undecided status. This is less than desirable from the perspective of obtaining as much useful information as possible. For a remedy, we draw inspiration from facilitation in real-life argumentation with abstractive summarisation of conflicting arguments, which assists a positive discussion progression. As facilitation for real-life argumentations requires awareness of a certain semantic relation among arguments, so, too, do we need argument graphs together with a lattice-like ontological semantic structure over arguments. With the semantic-argumentgraphic hybrid approach, we show that, even where no arguments in a cycle could be selected sensibly, we could say more about acceptability of arguments in an argumentation graph.
Analysis of the compensation equation for rational aggregation functions

Abstract The compensation equation is the law of conservation of a certain quantity with respect to a given class of admissible transformations. This paper discusses approaches to the construction of a compensating function for rational aggregation functions, including those with the associativity property.
Linking Developer Experience to Coding Style in Open-Source Repositories

We, humans, gain experience by doing something for a prolonged period. In this paper, we address whether we can link the use of advanced language features in software to a greater developer experience, indicating higher software quality. The coding style we chose to measure is the usage of lambdas and whether it correlates with programming experience since previous research has shown that less experienced C++ developers face difficulties with lambdas despite them being a language feature for almost ten years. If we established that lambda use and developer experience correlate positively, we could further investigate whether a good understanding of lambdas has a lasting contribution to software quality. Further, we could use it as an indicator within code quality metrics and promote the teaching of functional programming and lambda use in software engineering curricula. To measure experience, we introduce the Mean Repository Experience (MRE) metric, a novel but straightforward way of measuring what we here call repository experience, i.e., the combined assessed experience of contributors in a repository. We use this metric to analyze 500 C++ repositories. The proposed MRE metric shows potential as a proxy for software quality and could further extend to advanced language features other than lambdas. Our results suggest that the developer experience positively correlates with lambda usage. Future research includes understanding how well MRE reflects actual developer experience and further implications.
A Self-paced Tutoring System, ILSA: Which System Utilization aligned with Learners' Goal Orientations?

For several decades, the role of computers has been appreciated in achieving various educational goals. Computer-assisted instructions become very popular in educational settings nowadays. Various intelligent tutoring systems have been developed to support learning in different contexts. This work investigates a tutoring system for learning Finite State Machines (FSM), one of the models of computation in Automata Theory, called <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ILSA.</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ILSA</i> was deliberately developed to appeal to learners' achievement goal orientations. Achievement Goal Orientation theory suggests that learners could be mastery-oriented or performance-oriented. Hence, the specific aim of this investigation is to examine if there is evidence of alignment between system utilization and achievement goal orientations of learners. In other words, did the learners use the features of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ILSA</i> according to their respective orientation? Results showed that the goal orientations in Post-ILSA 1 have high manifestations of alignment between utilization and achievement goal orientations.
Analysis of Search Space in the Domain of Swarm Intelligence

The formulation and analysis of complex problems to get optimum solution is an emerging art which inspired new perspective in optimization world. Derived from biological system swarm intelligence algorithm attract many researcher due to its simplicity and adaptable nature with real-life problem. But swarm algorithms suffer from its search process. Higher rate of exploitation may trap to pre-mature solution and higher rate of exploration may slow down the process or may not true solution. In this paper, we present detailed analysis and sorting of search strategies based on its property and structure to get optimum of exploration and exploitation for swarm algorithms. In our study, we cluster majority of strategies in to information sharing and change in structure. In first part, sharing of information is categorized by population initialization, population update and population control. In second part, change in structure is categorized by modification in governing equation, hybridization and new topology. At last, the analysis attempts to provide understanding for different strategies, current issues and future research for expert and researcher.
Deep-Learning-Based Automatic Modulation Classification Using Imaging Algorithm

본 논문은 convolutional neural network (CNN) 모델에 이미지화 알고리즘을 적용한 자동 변조
A Study on Training Strategies of College Students' Cooperative Learning Ability Based on Online Case Teaching

The fundamental goal of higher vocational education lies in the cultivation of talents, and the key of talent training lies in the classroom. Cooperative learning, as a form of classroom organization that stimulates students' active inquiry and cooperation spirit, has been widely advocated in recent years, but it also has the problems of low implementation, difficult evaluation of learning effect and mere formality. Influenced by the development of computer and information technology, informatization has penetrated into the classroom of major colleges and universities, which has brought innovative ideas for the development of teaching. With the help of scientific and technological means, relying on the information platform and based on the angle of online case teaching, this paper puts forward the improvement ideas of college students' cooperative learning, so as to promote the cultivation of college students' learning initiative, consciousness and profundity.