Reviewerly/exHarmony huggingface dataset:
Special Issue “Multi-Agent Systems”: Editorial

Multi-agent systems (MAS) are built around the central notions of agents, interaction, and environment. Agents are autonomous computational entities able to pro-actively pursue goals, and re-actively adapt to environment change. In doing so, they leverage on their social and situated capabilities: interacting with peers, and perceiving/acting on the environment. The relevance of MAS is steadily growing as they are extensively and increasingly used to model, simulate, and build heterogeneous systems across many different application scenarios and business domains, ranging from logistics to social sciences, from robotics to supply chain, and more. The reason behind such a widespread and diverse adoption lies in MAS great expressive power in modeling and actually supporting operational execution of a variety of systems demanding decentralized computations, reasoning skills, and adaptiveness to change, which are a perfect fit for MAS central notions introduced above. This special issue gathers 11 contributions sampling the many diverse advancements that are currently ongoing in the MAS field.
Attack and Defence Modelling for Attacks via the Speech Interface

This paper presents a high-level model of attacks via a speech interface, and of defences against such attacks. Specifically, the paper provides a summary of different types of attacks, and of the defences available to counter them, within the framework of the OODA loop model. The model facilitates an inclusive conceptualisation of attacks via the speech interface, and serves as a basis for critical analysis of the currently available defence measures.
Measuring the Quality of Multivariate Statistical Models

Assessing the quality of a statistical model is very important, since it is crucial for the utility of the modelling process’ outcome. There are many different ways of measuring statistical models’ quality. Some of the measures represent a “goodness of fit” approach, some are “prediction ability” orientated. Among them there are absolute and relative measures. It is a researcher’s decision, which model quality measure is the most adequate for the given task. In the paper we present an overview of statistical models’ quality measures and a suggestion of using different ones during the model type selection stage and the stage of assessing the quality of the final model.
Intelligent decision support system for CV evaluation based on natural language processing

A curriculum vitae (CV) has become one of the most important documents for applying or hiring any job positions.The CV document is normally assessed by a hiring committee based on some predefined criteria.However, the assessment process is lengthy and fraught with human-engendered bias.To the best of our knowledge, none tool that is able to read and filter CV documents which are presented in texts form had been introduced.The purpose of this research is to create a tool that is able to screen and filter hundreds of CVs automatically.This paper proposed an approach based on natural language techniques to develop the tool.The tool can be considered as a decision support system (DSS) in recruiting new employees.One hundred seventy-eight CV documents were used to test the proposed approach.Obtained results suggest that the proposed approach is successful.
Study on the Network-Based English Phonetics Teaching Mode for English-Major Students

With the development of Information Technology, internet is accessible to all, especially to the students. Based on the network, English teaching is changing greatly, which is not simply with textbooks, chalk and blackboard, but with multimedia, various apps and other tools. As to study the network-based English phonetics teaching mode for English-major students, the background of the application, the steps of the network-based teaching mode and the theoretical basis are introduced firstly. By selecting college English-major students of 2017 in Anhui Institute of Information Technology as the object of the teaching experiment, the paper describes the investigation and analyzes the data, which shows that the students learning based on the network have better pronunciation in English than those learning in the traditional mode. Thus, the author suggests that teachers use the network-based teaching mode to improve the effectiveness of teaching and learning.
Cascade Attention Machine for Occluded Landmark Detection in 2D X-Ray Angiography

In cardiac interventions, localization of guiding catheter tip in 2D fluoroscopic images is important to specify vessel branches and calibrate vessels with stenosis. While detection of guiding catheter tip is not trivial in contrast-free images due to low dose radiation as well as occlusion by other devices, it is even more challenging in contrast-filled images. As contrast-filled vessels become visible in X-ray imaging, the landmark of guiding catheter tip can often be completely occluded by the contrast medium. It is difficult even for human eyes to precisely localize the catheter tip from a single angiography image. Physicians have to rely on information before the inject of contrast medium to localize the guiding catheter tip occluded by contrast medium. Automatic landmark detection when occlusion happens is important and can significantly simplify the intervention workflow. To address this problem, we propose a novel Cascade Attention Machine (CAM) model. It borrows the idea of how human experts localize the catheter tip by first per-forming landmark detection when occlusion does not happen, then leveraging this information as prior knowledge to assist the occluded detection. Attention maps are computed from non-occluded detection to further refine the heatmaps for occluded detection to guide the inference focusing on related regions. Experiments on X-ray angiography demonstrate the promising performance compared with the state-of-the-art baselines. It shows that the CAM can capture the relation between situations with and without occlusion to achieve precise detection of occluded landmark.
Target Recognition and Behavior Prediction based on Bayesian Network

The identification of target identity attributes and its behavioral prediction are important means for providing command and decision support in modern warfare.This paper analyzes the key steps in the process of target recognition and behavior prediction and proposes a target recognition and behavior prediction model based on the Bayesian network.In the simulation example, by integrating the sensor information and combining expert knowledge, the model can effectively and accurately conduct battlefield situational awareness.Combined with the background of big data, this paper introduces the distributed processing system Hadoop, and prospects its application in target recognition and behavior prediction.
Feature Model Configuration Based on Two-Layer Modelling in Software Product Lines

The aim of the Software Product Line (SPL) approach is to improve the software development process by producing software products that match the stakeholders’ requirements. One of the important topics in SPLs is the feature model (FM) configuration process. The purpose of configuration here is to select and remove specific features from the FM in order to produce the required software product. At the same time, detection of differences between application’s requirements and the available capabilities of the implementation platform is a major concern of application requirements engineering. It is possible that the implementation of the selected features of FM needs certain software and hardware infrastructures such as database, operating system and hardware that cannot be made available by stakeholders. We address the FM configuration problem by proposing a method, which employs a two-layer FM comprising the application and infrastructure layers. We also show this method in the context of a case study in the SPL of a sample E-Shop website. The results demonstrate that this method can support both functional and non-functional requirements and can solve the problems arising from lack of attention to implementation requirements in SPL FM selection phase.
Combining Strengths of Optimal Multi-Agent Path Finding Algorithms

The problem of multi-agent path finding (MAPF) is studied in this paper. Solving MAPF optimally is a computationally hard problem and many different optimal algorithms have been designed over the years. These algorithms have good runtimes for some problem instances, while performing badly for other instances. Interestingly, these hard instances are often different across the algorithms. This leads to an idea of combining the strengths of different algorithms in such a way that an input problem instance is split into disjoint subproblems and each subproblem is solved by appropriate algorithm resulting in faster computation than using either of the algorithms for the whole instance. By manual problem decomposition we will empirically show that the above idea is viable. We will also sketch a possible future work on automated problem decomposition.
Authentication issues for cloud applications

The challenges of security in cloud computing can be categorized into network level (network protocols and network security such as distributed nodes, distributed data, Internode communication), user authentication level (encryption/decryption techniques, authentication methods), data level (data integrity and availability such as data protection and distributed data), and generic issues (traditional security tools, and the use of different technologies). Authentication, in the cloud applications, is the process of validating and guaranteeing the identity of cloud service subscribers or users. The reliability and security of the cloud computing environment especially are based on authentication. Therefore, this chapter discusses security issues for cloud applications and then focus on the authentication technologies in the cloud system.
Expert System Identification Of Learning Patterns The VARK Method

Nowadays computers have been widely used by many people, both parents, children, and adolescents. Basically, they only follow technological progress. And it eventually led to progress in the field of science. On the other hand, the background of the need for the seriousness of parents in knowing the pattern of their children's learning style is the amount of potential free time that is not well utilized by children in the learning process. Plus the number of parents who do not support children in developing their own mindset. Or the learning atmosphere that is still not conducive. In line with the advancement of technology and science, parents should be able to easily find out the right pattern of children's learning styles, to make it easier for their children to develop their mindset and imagination in the world of learning. And one of them can be by using an expert system. The expert system for identifying patterns of children's learning styles is an expert system designed as a tool for parents to identify patterns of children's learning styles with a dynamic knowledge base. This knowledge is obtained from various sources, including research carried out by experts in their fields and books related to learning styles. The knowledge base is arranged in such a way into logic with several provisions including the vark method (visual, auditory, read, kinesthetic), in order to facilitate system performance in making conclusions. Drawing conclusions in this expert system use the certainty factor method. This expert system will display several questions as indicators of the characteristics of the child's learning style that are felt, then later arrive at the final question. In the final result, the expert system will display the types of characteristics of the child's learning style.
IO Challenges for Human Brain Atlasing Using Deep Learning Methods - An In-Depth Analysis

The use of Deep Learning methods have been identified as a key opportunity for enabling processing of extreme-scale scientific datasets. Feeding data into compute nodes equipped with several high-end GPUs at sufficiently high rate is a known challenge. Facilitating processing of these datasets thus requires the ability to store petabytes of data as well as to access the data with very high bandwidth. In this work, we look at two Deep Learning use cases for cytoarchitectonic brain mapping. These applications are very challenging for the underlying IO system. We present an in depth analysis of their IO requirements and performance. Both applications are limited by the IO performance, as the training processes often have to wait several seconds for new training data. Both applications read random patches from a collection of large HDF5 datasets or TIFF files, which result in many small non-consecutive accesses to the parallel file systems. By using a chunked data format or storing temporally copies of the required patches, the IO performance can be improved significantly. These leads to a decrease of the total runtime of up to 80%.
DETEKSI ALAT PELINDUNG KEPALA (HELM) MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER

Use of protective gear helmet head is often considered unimportant and trivial by workers. Whereas the use of protective headgear helmet is very important and affect the safety and health of workers. Kedisiplina workers to use protective gear head is still low so that the risk of accidents that could endanger workers large enough. In this research aims to detect protective equipment head helmet on video. In this study, the method used is the Haar Cascade Classifier. The system consists of two main processes, namely the process of training data and the detection process. This method of training process has four main processes, haar-like feature, integral image, no-boost and cascade classifier. Haar-like feature is a collection of special features presented the head, face and helmet. Citra is how to quickly calculate integrals haar feature. While no-boost are statistically weighted feature values are obtained and filtered using a cascade classifier. The detection process in this study there are two processes, the first detection process whether human or not, if the result of human detected will continue the process of detection of whether to use a helmet or not. Detection system testing is done individually using helmet colors red, blue and yellow. It obtained accuracy rate of 92%, while the testing group obtained the degree of accuracy of 71%.
What do we mean when we talk about transparency? Towards meaningful transparency in commercial content moderation

This paper seeks to provide greater specificity to demands for transparency in the commercial content moderation practices of digital platforms. We identify gaps in knowledge through a thematic analysis of 380 survey responses from individuals who have been the subject of content moderation decisions. We argue that meaningful transparency should be understood as a component of a communicative process of accountability (rendering account) to independent stakeholders. We make specific recommendations for platforms to provide people with clear information about decisions that affect them, including what content is moderated, which rule was breached, and a description of the people and automated processes responsible for identifying content and making the decision. Beyond providing more information to individuals about particular decisions, however, we note the major challenge of improving understanding of content moderation at a systems level. General demands for greater transparency should be reframed to focus on enhanced access to large-scale disaggregated data that can enable new methods and collaborations between academia, civil society, and journalists to make these systems more understandable and accountable.
A new efficient way based on special stabilizer multiplier permutations to attack the hardness of the minimum weight search problem for large BCH codes

&lt;span&gt;BCH codes represent an important class of cyclic error-correcting codes; their minimum distances are known only for some cases and remains an open NP-Hard problem in coding theory especially for large lengths. This paper presents an efficient scheme ZSSMP (Zimmermann Special Stabilizer Multiplier Permutation) to find the true value of the minimum distance for many large BCH codes. The proposed method consists in searching a codeword having the minimum weight by Zimmermann algorithm in the sub codes fixed by special stabilizer multiplier permutations. These few sub codes had very small dimensions compared to the dimension of the considered code itself and therefore the search of a codeword of global minimum weight is simplified in terms of run time complexity. ZSSMP is validated on all BCH codes of length 255 for which it gives the exact value of the minimum distance. For BCH codes of length 511, the proposed technique passes considerably the famous known powerful scheme of Canteaut and Chabaud used to attack the public-key cryptosystems based on codes. ZSSMP is very rapid and allows catching the smallest weight codewords in few seconds. By exploiting the efficiency and the quickness of ZSSMP, the true minimum distances and consequently the error correcting capability of all the set of 165 BCH codes of length up to 1023 are determined except the two cases of the BCH(511,148) and BCH(511,259) codes. The comparison of ZSSMP with other powerful methods proves its quality for attacking the hardness of minimum weight search problem at least for the codes studied in this paper.&lt;/span&gt;
Reconstructed Saliency for Infrared Pedestrian Images

Accurately and completely detecting infrared pedestrian is a challenging problem in an intelligent transportation system due to the low SNR and the inhomogeneous luminance distribution in the infrared images, especially for the complex background environment. In this paper, we introduce a reconstruction optimization-based saliency detection method for infrared pedestrian images to solve the problem. First, appearance-based infrared saliency was introduced to enhance the salient areas of the infrared images from locally and globally contrast features. Then, considering the essential characteristic of the infrared pedestrian images, thermal radiation prior, and pedestrian shape prior was combined to construct infrared object prior information. Finally, the infrared pedestrian saliency map can be calculated through a random walk-based saliency reconstruction optimization method with the appearance saliency and infrared object prior. The extensive experiments on real infrared images captured by intelligent transportation systems demonstrate that our saliency algorithm consistently outperforms the state-of-the-art saliency detection methods, in terms of higher precision, F-measure, and lower mean absolute error.
What Are You Thinking About Where? Syntactic Ambiguity between Abstract Arguments and Concrete Adjuncts in Hungarian, Modulated by Concreteness

Background.The critical importance of lexical concreteness and embodied sensorimotor processes for language comprehension is often assumed to be beyond doubt.Hungarian grammar is unique in that it expresses certain verb arguments using spatial suffixes, which sometimes create ambiguity between literal spatial adjuncts and abstract verb arguments.Objective.In the present study, our goal was to investigate the role and perhaps primacy of concrete spatial meaning when generating the abstract sense of arguments of mental verbs.Design.Towards that end, we embedded ambiguous verb-noun constructs with both a possible locative adjunct reading (i.e.spatial, literal) and a verb argument reading (i.e.abstract, figurative), with a continuously varying preference for one or the other, in disambiguating sentence contexts.Using a selfpaced reading paradigm, we measured reading times of verbs and sentence final nouns of the ambiguous constructs.Results.We found no difference in the reaction times to verbs, which suggests that their argument frames were obligatorily activated regardless of sentential context.Nouns were read more slowly in the argument contexts, yet the slower pace was driven by constructs that had a preferred locative reading.Conclusion.This pattern of results contradicts strong embodiment explanations, and can be better accounted for by dual coding theory.Our findings demonstrate the importance of studying the role of concreteness and metaphoricity in linguistic meaning construction in the context of syntax and sentence processing.
A LEARNING MODEL OF BAHASA INDONESIA AS A FOREIGN LANGUAGE BASED ON LOCAL INTERCULTURAL POLITENESS

The study was aimed for developing a learning model of Bahasa Indonesia based on local intercultural politeness to improve the foreigners’ communicative competencies. This is Research and Development, started with a preliminary study of intercultural learning concepts and Buginese politeness strategies; the distribution of the needs analysis instruments to the 25 foreigners learning Bahasa Indonesia in Makassar, South Sulawesi, Indonesia; model development, focus group discussions; the model finalization and product experiment. The interviews and questionnaires data were analyzed by descriptive quantitative and qualitative. The result, a local politeness intercultural learning model consisting of four phases: noticing, comparing, reflecting, and interacting. 1) The foreign learners noticed the use of local politeness markers in Bahasa Indonesia; 2) They compared the similarities and differences between the use of the markers of local politeness and those they found in their native languages; 3) They reflected themselves; 4) They practiced Bahasa Indonesia through direct interactions. The conclusion showed that the t-score = 3.26 &gt; t-table = 2.45 signifying that the model was effective in overcoming the foreign learners’ problems in oral interactions and improving their Bahasa Indonesia communicative competencies.
ANALISA NASABAH POTENSIAL TABUNGAN DEPOSITO BERJANGKA MENGGUNAKAN TEKNIK KLASIFIKASI DATA MINING

Time deposits are a product of a financial institution, which is currently increasing. The main target of this time deposit marketing is the old customers of the Bank. To increase the effectiveness of marketing customers are grouped into potential and non-potential customers. This means that potential customers have a greater chance to open a time deposit account. Customer data is taken from the UCI repository, originating from Banks in Portugal. Data is processed with rapidminer software using the Decision Tree method with Particle Swarm Optimization, Naïve Bayes with Particle Swarm Optimization and finally processed using Neural Network with Particle Swarm Optimization. Data processing results were compared and showed that the Naïve Bayes Algorithm with Particle Swarm Optimization had the highest accuracy of 97.04%. Therefore an application designed based on Naive Bayes with Particle Swarm Optimization. From the original attribute consisting of 20, only 9 attributes can be used so that the level of accuracy is high. Attributes used have values ​​more than 0.500, while those that have these values ​​are omitted. The design was created using the Unified Modeling Language (UML) and Visual Basic 6.0 to create an User Interface.