Reviewerly/exHarmony huggingface dataset:
An Efficient Algorithm for Computing the Set of Semi-stable Extensions

Argumentation is one of the most relevant fields in the sphere of Artificial Intelligence. In particular, Dung’s abstract argumentation framework (AF) has received much attention in the last twenty years, and many computational issues have been investigated for different argumentation semantics. Specifically, enumerating the sets of arguments prescribed by an argumentation semantics (i.e., extensions) is arguably one of the most challenging problems for AFs, and this is the case also for the well-known semi-stable semantics. In this paper, we propose an algorithm for efficiently computing the set of semi-stable extensions of a given AF. Our technique relies on exploiting the computation of grounded extension to snip some arguments in order to obtain a smaller framework (called cut-AF) over which state-of-the-art solvers for enumerating the semi-stable extensions are called, as needed to return the extensions of the input AF. We experimentally evaluated our technique and found that our approach is orders of magnitude faster than the computation over the whole AF.
Hypothetical Analytic Filter for the tracking with bearing and range mixture measurements

This paper introduces a hypothetical analytic filter, which is used for the nonlinear tracking problems with noisy bearing and range mixture measurements. The main idea of this filter is to divide the tracking process into two parts, track the target with the bearing information first, and then use the range information to make the tracking process more accurate. This separation is complimentary for that the bearing-correction could overcome the bimodal problem of range-only estimation and the range-only tracking can make the performance more accurate. Compared with other nonlinear filters, it converges very quickly and works well in some scenarios. The lower computation demand and its robust convergence are two main advantages of this filter.
Design and Implementation of Keyword Extraction Framework in Military Domain

In order to improve the performance of keywords extraction in the military domain, this paper analyzes the charateristics of military texts, and gives five principles of military keyword extraction. By imitating "six-step process" that human follow when extracting keywords of military documents, a new technical framework is put forward. Combined multiple features of term frequency, topic and position, a new military keyword extraction algorithm (TTL) is designed and implemented. By making full use of the role of "human in the loop", this framework can meet the demands of multi-user online and distributed application in the Military Knowledge Engineering System. The experimental results show the self-iterative upgradability, self-adaptability and intelligence of the proposed framework. In addtion, the TTL algorithm demonstrates its better performance and effectiveness than TextRank, TF-IDF, and LDA algorithm.
Interdependence Model for Multi-label Classification

The multi-label classification problem is a supervised learning problem that aims to predict multiple labels for each data instance. One of the key issues in designing multi-label learning approaches is how to incorporate dependencies among different labels. In this study, we propose a new approach called the interdependence model, which consists of a set of single-label predictors each of which predicts a particular label using the other labels. The proposed model can directly consider label interdependencies by reusing arbitrary conventional probabilistic models for single-label classification. We consider three prediction methods and one accelerated method for making predictions with the interdependence model. Experiments show the superior prediction performance of the proposed methods in several evaluation metrics, especially when there is a large number of candidate labels or when labels are partially given in the test phase.
Research of Group Recommendation Based on Matrix Factorization

In current big data era, it is more and more important to extract valuable data from big data, i.e. information filtering. Information filtering is categorized into personalized recommendation for the individual users and group recommendation; the later is the extension of the former and is more complex. The current group recommendation has quite a lot of shortages, such as ignoring the items' natural property, the users' interest and their behavior's temporal dynamics. Based on the advantages of matrix factorization on personalized recommendation, the paper adopts ridge regression analysis to propose a matrix factorization model (WMF model) weighted by the items; and combine the WMF model with time effect function to get further improvement. Experiments show that the proposed group recommendation has better precision and recall rate.
Towards Model-Driven Role Engineering in BPM Software Systems

This work introduces a new concept of role engineering inside a Model-Driven Engineering modern trend. In case of developing a complex solution comprehensive definition of all needed business roles and their access rights becomes a difficult task. So, the present contribution analyzes different approaches towards the role engineering realized by DEMO and BPMN methodologies based on the Comindware (CBAP) support.
Deep Deterministic Policy Gradients with Transfer Learning Framework in StarCraft Micromanagement

This paper proposes an intelligent multi-agent approach in a real-time strategy game, StarCraft, based on the deep deterministic policy gradients (DDPG) techniques. An actor and a critic network are established to estimate the optimal control actions and corresponding value functions, respectively. A special reward function is designed based on the agents’ own condition and enemies’ information to help agents make intelligent control in the game. Furthermore, in order to accelerate the learning process, the transfer learning techniques are integrated into the training process. Specifically, the agents are trained initially in a simple task to learn the basic concept for the combat, such as detouring moving, avoiding and joining attacking. Then, we transfer this experience to the target task with a complex and difficult scenario. From the experiment, it is shown that our proposed algorithm with transfer learning can achieve better performance.
Research on Self-Gaming Training Method of Wargame Based on Deep Reinforcement Learning

The paper uses deep reinforcement learning to study the self-training method of wargame, and puts forward the command of using machine instead of human to conduct battlefield game training. The wargame training solves the training problem through analytical methods. According to the type of the problem, such as the power of red and blue, the corresponding mathematical model is established. Generally, the income function is given by the two-valued matrix. The simulation proves that the self-gaming training method of the chess is effective.
A Study of the Region Covariance Descriptor: Impact of Feature Selection and Precise Localization of Target

In visual tracking, selecting the right image descriptors is critical. The popular version of descriptor is known as the covariance descriptor; however, no further studies is yet developed regarding the different methodologies for its construction. This study analyzes the contribution of diverse features of an image to the descriptor and their contribution to the detection of arbitrary targets in sequences of images, in our case: Boy, David3, Bolt and Walking2 in an image sequence. The methodology to determine the performance of the covariance matrix is defined from different sets of characteristics, and a specific combination of features is needed to develop a correlation between them. Finally, when an analysis is performed with the best set of features, F4 the target detection problem reached a performance of average, 0.77, From this experiment, it is believed that we have constructed a greater solution in choosing best features for this descriptor, allowing to move forward to the next issues such as using it on others datasets.
Historical Significance and Suggestions on Future Works of Software Process Improvement in Japan

Various types of software process improvement activities have been carried out in each company to achieve good results in Japan. The process improvement activities and methods have to be organized from comprehensive perspective for more progress in the future [26]. In this paper, we draw up a chronology of important events related to software development to look back on a history of software process improvement in Japan. Furthermore, the chronology is divided into three periods and we try to find historical significance in each period. According to the investigation, we suggest future works in software process improvement in Japan.
Boundary Management Between Factual Constraints and Emotional Ties

Modern mobile information and communication technology (ICT) allows connectivity anyplace and anytime. This entails flexibility and new opportunities for organising life domains (e.g., working from home). At the same time unwanted overlaps between life domains can lead to stress. In this paper we present a qualitative explorative interview study on life domains, which provides insight into their types and boundaries. Furthermore, we report on ecologies of devices and applications, factual constraints and emotional ties. We draw conclusions with implications for the design of mobile ICT.
An approach to detect false design patterns

A false design pattern occurs when software vocabularies indicate the existence of a design pattern, but it is not implemented. Occurrences of false design patterns may make it difficult to understand, maintain, and evolve software. In this context, we present an approach to detect false patterns of projects implemented in the Java language. We evaluated this solution in eighteen open source projects and through three research questions, we identified 435 indications of false design patterns. Of these, we detected a set of 202 real occurrences of false design patterns. In one of the evaluated projects we achieved an accuracy of approximately 81%. In addition, it was possible to relate the false standards to some software metrics. Identifying these occurrences enables better communication and understanding of the code.
Large-scale Certificate Management on Multi-tenant Web Servers

For large-scale certificate management of multi-tenant web servers, preloading numerous certificates for managing numerous hosts under the single server process results in increasing the required memory usage because of the respective page table entry manipulation, which might be a poor resource efficiency and a reduced capacity. To resolve this issue, we propose a method for dynamic loading of certificates bound to the hostnames found during the SSL/TLS handshake sequences without preloading, provided that the Server Name Indication (SNI) extension is available. We implemented the function of choosing the respective certificates with the ngx_mruby module, which extends web server functions using mruby with a small memory footprint while maintaining the execution speed. The proposed method was evaluated by a web hosting service employing the authors.
Russian labor market in education

The article deals with the tasks of increasing the efficiency of interaction between the education sector and the labor market, the characteristics of the traditional education system, and the role of the teacher in the modern Russian education system.
Maximum Information Gain Relief Feature Weighting

Features selection can improve the accuracy of the model by reducing the number of input features and has become the focus of many researches in areas of application [15]. Feature selection mainly includes two classes: Filter and Wrapper [16]. In this paper, an extensively-used filter method, Relief feature weighting algorithm, is analyzed.
Dynamic Analysis of Access Control Based on Partially Observable Markov Decision Process for Media Distribution Network

For the access control problem of media distribution network in software-defined network (SDN) environment, an access control scheme considering the optimization of service node and transmission link service performance is proposed. The scheme utilizes the SDN controller's direct control over the router and the perception of the entire network, and jointly optimizes the service performance of the service node on the application layer and the service performance of the transmission link between the service node and the user on the network layer, reducing the impact of link congestion on data transmission and user service quality. Firstly, a partial observable Markov decision process (POMDP) model is established for the access control process of the SDN service system; Then, the observation-based random strategy is used as the access control strategy of the system; Finally, the access control strategy is optimized by the policy gradient algorithm, solve out the optimal strategy of the model. The simulation results show that compared with the best-effort service strategy, the optimal access control strategy based on POMDP model improves the system performance by 10%, and verifies the effectiveness of the proposed method.
On radicals and coordinate partial orders

Abstract The solution of equations and systems of equations over real, complex, rational and integer numbers is a classic topic of research in various areas of mathematics for several thousand years. In the last 20 years, the so-called universal algebraic geometry has been actively developed, in which systems of equations over arbitrary algebraic systems are researched. Many practically important problems on finite graphs, finite fields, and finite orders can be formulated as problems related to solving systems of equations over these systems, which leads to the need to develop algebraic geometry. Many modern models of informational defence represents by graphs and partial orders (posets). This article presents polynomial algorithms for constructing radical and coordinate partial order of systems of equations over finite partial orders in a language without constants.
A Study on the Game System of Dots and Boxes Based on Reinforcement Learning

In this paper, based on AlphaZero, we propose a model with deep convolutional neural networks using value function and policy function for Dots-and-Boxes game system. In supervised game learning strategies, one challenge is the lack of sample data of high-quality. Our approach for solving this problem is to generate sample data effectively by iteration and self-playing. Experiments demonstrate that our novel method can significantly enhance the gaming skill of Dots-and-Boxes.
Reinforcement Learning by Using Dual Q-table for the Whole Space and a Partial Space

In this paper, we propose a Q-learning method by using dual Q-table. Concretely, the proposed method has two Q-tables: “Whole Q-table'' is larger, based on the whole space (in enough detail for learning optimal actions) of the environment and “Partial Q-table'' is smaller, based on a subspace (rough for learning outline actions) of the whole space. The two Q-tables simultaneously learn the environment based on a selected action. The action is selected by using the more learned Q-table out of the two Q-tables by each step. We simulated the proposed method, comparing with conventional ones, under three conditions of the learning environments, where the partial Q-table can learn optimal actions at the highest rate, middle rate and the lowest rate of the situations in the environment. As a result, we indicated that the proposed method can learn the optimal actions at any rates. As the rate is higher, it converges earlier. Even if at the lowest rate, the proposed method is almost as effective as conventional one. And we indicated the proposed method to be effective by using mathematical analysis. Furthermore, we verified that the proposed method was effective under an actual environment.
On Interaction of School and University in the Framework of Engineering Education

The article discusses the problems that arise when engineering classes are organized in secondary schools with participation of universities, and ways to solve them. To strengthen the connection between the school and the university, it is necessary to use both methodological and technological capabilities. The analysis of topics close to the standard programs of higher engineering education, which have appeared recently in the school curriculum in mathematics, is carried out. Methods have been proposed for wider engagement of intellectual capacity of university staff by involving them in creation of teaching methods in schools and especially in the in-depth study of mathematics in engineering classes. The experience of a special mathematical seminar organized by university departments of mathematics for school teachers of engineering classes is considered.
Blacklisted Password Authentication System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Central-Diffused Instance Generation Method in Class Incremental Learning

Class incremental learning is widely applied in the classification scenarios as the number of classes is usually dynamically changing. Meanwhile, class imbalance learning often occurs simultaneously in class incremental learning when the new class emerges. Previous studies mainly proposed different methods to handle this problem. But these methods focus on classification tasks with a fixed class set and cannot adjust the peripheral contour features of the original instance distribution. As a result, the classification performance degrades seriously in an open dynamic environment, and the synthetic instances are always clustered within the original distribution. In order to solve class imbalance learning effectively in class incremental learning, we propose a Central-diffused Instance Generation Method to generate the instances of minority class as the new class emerging, called CdIGM. The key is to randomly shoot direction vectors of fixed length from the center of new class instances to expand the instance distribution space. The vectors diffuse to form a distribution which is optimized to satisfy properties that produce a multi-classification discriminative classifier with good performance. We conduct the experiments on both artificial data streams with different imbalance rates and real-world ones to compare CdIGM with some other proposed methods, e.g. SMOTE, OPCIL, OB and SDCIL. The experiment results show that CdIGM averagely achieves more than 4.01%, 4.49%, 8.81% and 9.76% performance improvement over SMOTE, OPCIL, OB and SDCIL, respectively, and outperforms in terms of overall and real-time accuracy. Our method is proved to possess the strength of class incremental learning and class imbalance learning with good accuracy and robustness.
Gaussian Mixture Descriptors Learner

In recent decades, various machine learning methods have been proposed to address classification problems. However, most of them do not support incremental (or online) learning and therefore are neither scalable nor robust to dynamic problems that change over time. In this study, a classification method was introduced based on the minimum description length principle, which offered a very good trade-off between model complexity and predictive power. The proposed method is lightweight, multiclass, and online. Moreover, despite its probabilistic nature, it can handle continuous features. Experiments conducted on real-world datasets with different characteristics demonstrated that the proposed method outperforms established online classification methods and is robust to overfitting, which is a desired characteristic for large, dynamic, and real-world classification problems.
A Histogram and GLCM-based Approach for Image Copy-Move Forgery Detection

In this paper, a novel copy-move forgery detection (CMFD) method for the digital image using the histogram and GLCM-based rotation-invariant feature descriptor is proposed. In developing an efficient CMFD method, there are two fundamental challenges needed to be addressed: accuracy and processing time. To achieve this goal, a fast and straightforward histogram-based, and GLCM-based local features are combined to increase the uniqueness and accuracy of the detection while also maintaining the computational cost, resulting in relatively fast detection mechanism suitable for practical use. The detection mechanism, firstly, performs keypoint detection using SURF-based keypoint detection method. The local GLCM-based and histogram-based features for each block are then calculated and combined using the convolution method. All generated features are then sorted and compared. Finally, lines between matched features are drawn to express the relationship between the original and copy-move regions. Experimental results show that the proposed method outperforms some traditional methods in term of accuracy while also greatly reduces the computational complexity of the system compared to some existing techniques.
Sensitivity Studies Exploiting Multivariate Techniques

The classification between signal-like and background like-events exploiting discriminating variables is considered as the most intuitive method in searches for new particles or in measurements at the LHC. It allows for a good understanding and interpretation of the underlying physics processes as well as the possibility for theoreticians to exploit the event selection for further studies. However, the performance of event classification based on discriminating variables can suffer from correlations amongst a subset of those variables which complicate the definition of the ideal selection requirements in order to thoroughly separate signal-like from background-like events. The chapter describes the application of machine learning techniques in the event classification.
Descriptive Data Analysis of Real Estate Using Cube Technology

With the progress and application of data analysis and processing expertise, the technologies such as data warehouse and cube technology had become the research spot of each business area. Online analytical processing technology has applications in the areas of merchandising system, college decision support system, enterprise marketing management system, knowledge data warehouse, and to analyze the curriculum chosen by students and many more. If we consider one business domain that is real estate, it is observed that online transactional processing technology has been applied. This paper focuses on application of online analytical processing and specifically descriptive data analytics, to extract more information from the traditional real estate datasets.