Reviewerly/exHarmony huggingface dataset:
Learning and Testing Resilience in Cooperative Multi-Agent Systems

State-of-the-art multi-agent reinforcement learning has achieved remarkable success in recent years. The success has been mainly based on the assumption that all teammates perfectly cooperate to optimize a global objective in order to achieve a common goal. While this may be true in the ideal case, these approaches could fail in practice, since in multi-agent systems (MAS), all agents may be a potential source of failure. In this paper, we focus on resilience in cooperative MAS and propose an Antagonist-Ratio Training Scheme (ARTS) by reformulating the original target MAS as a mixed cooperative-competitive game between a group of protagonists which represent agents of the target MAS and a group of antagonists which represent failures in the MAS. While the protagonists can learn robust policies to ensure resilience against failures, the antagonists can learn malicious behavior to provide an adequate test suite for other MAS. We empirically evaluate ARTS in a cyber physical production domain and show the effectiveness of ARTS w.r.t. resilience and testing capabilities.
Motion Deblurring in Image Color Enhancement by WGAN

Motion deblurring and image enhancement are active research areas over the years. Although the CNN-based model has an advanced state of the art in motion deblurring and image enhancement, it fails to produce multitask results when challenged with the images of challenging illumination conditions. The key idea of this paper is to introduce a novel multitask learning algorithm for image motion deblurring and color enhancement, which enables us to enhance the color effect of an image while eliminating motion blur. To achieve this, we explore the synchronization of processing two tasks for the first time by using the framework of generative adversarial networks (GANs). We add L 1 loss to the generator loss to simulate the model to match the target image at the pixel level. To make the generated image closer to the target image at the visual level, we also integrate perceptual style loss into generator loss. After a lot of experiments, we get an effective configuration scheme. The best model trained for about one week has achieved state-of-the-art performance in both deblurring and enhancement. Also, its image processing speed is approximately 1.75 times faster than the best competitor.
Variational Predictive Information Bottleneck

In classic papers, Zellner demonstrated that Bayesian inference could be derived as the solution to an information theoretic functional. Below we derive a generalized form of this functional as a variational lower bound of a predictive information bottleneck objective. This generalized functional encompasses most modern inference procedures and suggests novel ones.
The Cuckoo Search Algorithm Applied to Fuzzy Logic Control Parameter Optimization

In the design of control systems, the tuning of controller parameters has a fundamental role in the performance of both transient and steady-state regimes. From this perspective, the tuning of controller parameters has been carried out using perturbation and observation methods, computational tools based on optimization algorithms for low-complexity systems, and more recently, using metaheuristic algorithms for highly complex systems with improved tuning procedures that guarantee the operation and stability of the systems. Thus, avant-garde optimization algorithms that mimic the evolution of self-organizing biological systems, also called metaheuristic nature-inspired algorithms, have gained high relevance due to their great potential for solving optimization problems. Hence, the Cuckoo Search (CS) algorithm, a very promising and nearly recent developed nature-inspired algorithm, has been used in the design and optimization of Fuzzy Logic Control (FLC) systems due to its great potentiality. In particular, this chapter studies the application of the CS algorithm for tuning controller parameters in two different case studies. The first one is associated with the FLC parameter tuning of a nonlinear magnetic levitation system, and the second case study is related to the FLC optimization of the energy management system of a residential microgrid. Simulation results are provided to emphasize and analyze the features of the optimized controllers for the two cases and compared against other more conventional techniques. Obtained outcomes show that the adjustment of FLC parameters, performed through the CS algorithm, is efficient and improves the performance of the two FLC, which makes the CS algorithm becomes a powerful alternative for performing the controller parameter tuning in modern control systems.
Social Play in Non-Player Character Dialog

Non-player characters in games generally lack believability and deep interactivity. The AI system Comme il Faut begins to tackle this by modeling social state and behaviors for game characters. The player initiates social exchanges and the dialog and outcome are generated and displayed in their entirety. In this paper we present a model called social prac-tices to extend Comme il Faut. Social practices increase the playability of social play by modeling social interactions at a more granular level and adding interactivity at each stage. This model also moves away from dialog trees to a more modular form of authoring to support the additional com-plexity.
Statistical Deep Parsing for Spanish Using Neural Networks

This paper presents the development of a deep parser for Spanish that uses a HPSG grammar and returns trees that contain both syntactic and semantic information. The parsing process uses a top-down approach implemented using LSTM neural networks, and achieves good performance results in terms of syntactic constituency and dependency metrics, and also SRL. We describe the grammar, corpus and implementation of the parser. Our process outperforms a CKY baseline and other Spanish parsers in terms of global metrics and also for some specific Spanish phenomena, such as clitics reduplication and relative referents.
Option predictive clustering trees for multi-target regression

Decision trees are one of the most widely used predictive modelling methods primarily because they are readily interpretable and fast to learn. These nice properties come at the price of predictive performance. Moreover, the standard induction of decision trees suffers from myopia: a single split is chosen in each internal node which is selected in a greedy manner; hence, the resulting tree may be sub-optimal. To address these issues, option trees have been proposed which can include several alternative splits in a new type of internal nodes called option nodes. Considering all of this, an option tree can be also regarded as a condensed representation of an ensemble. In this work, we propose to learn option trees for multi-target regression (MTR) based on the predictive clustering framework. The resulting models are thus called option predictive clustering trees (OPCTs). Multi-target regression is concerned with learning predictive models for tasks with multiple numeric target variables.We evaluate the proposed OPCTs on 11 benchmark MTR data sets. The results reveal that OPCTs achieve statistically significantly better predictive performance than a single predictive clustering tree (PCT) and are competitive with bagging and random forests of PCTs. By limiting the number of option nodes, we can achieve a good trade-off between predictive power and efficiency (model size and learning time).We also perform parameter sensitivity analysis and bias-variance decomposition of the mean squared error. Our analysis shows that OPCTs can reduce the variance of PCTs nearly as much as ensemble methods do. In terms of bias, OPCTs occasionally outperform other methods. Finally, we demonstrate the potential of OPCTs for multifaceted interpretability and illustrate the potential for inclusion of domain knowledge in the tree learning process.
Multi-Agent Pattern Formation with Deep Reinforcement Learning (Student Abstract)

We propose a decentralized multi-agent deep reinforcement learning architecture to investigate pattern formation under the local information provided by the agents' sensors. It consists of tasking a large number of homogeneous agents to move to a set of specified goal locations, addressing both the assignment and trajectory planning sub-problems concurrently. We then show that agents trained on random patterns can organize themselves into very complex shapes.
Bitcoin Price Prediction Based on Sentiment of News Article and Market Data with LSTM Model

Bitcoin is a digital currency and investment tool that has received worldwide attention recently. However, the fluctuation of Bitcoin price has been a concern to the users and investors. Forecasting the Bitcoin price can serve as a guideline for investor and user to make effective strategy in their investment or usage. With the rapid development of the Internet, online data including news article can facilitate forecasting Bitcoin price. This research aims to study the effect of news article sentiment towards Bitcoin price with study period from September 2017 to August 2019. Accordingly, this study introduces sentiment analysis to understand the relevant information of online news articles and use it as an input feature for Bitcoin price prediction. Two main phases are included in the study, which is sentiment analysis and price prediction. In sentiment analysis, the sentiment is extracted based on a lexicon-based approach to capture the relevant news articles information regarding cryptocurrency markets. In price prediction, the sentiment is used as an input feature and Long Short-Term Memory (LSTM) model is used in price prediction phase. With Bitcoin market data and news articles as samples, the empirical results show that news articles sentiment reduced the overall error in Bitcoin price predictions.
Türkiye’de Bilişim Suçlarının Kriminolojik Açıdan Değerlendirilmesi: Bilişim Suçlarının Hukuksal ve Sosyolojik Boyutlarının Analizi

Information technologies encompass our lives in every aspect and our lives are becoming digitalized.However, these gigantic developments in the field of informatics make our lives easier and at the same time bring some important risks.In this respect, while information technologies provide a comfortable life in a way that makes our lives quite easy, on the other hand, information technologies have become a potentially strong medium and area for the opportunities where all kinds of crimes can be committed.In this study, the articles of the Turkish Criminal Code and other internal legislation on informatics were examined.The effects of the Law on the Regulation of Publications on the Internet and Combating Crimes Committed by Means of Such Publication, which are known as "The Internet Law", were analyzed from a sociological perspective.Criminal laws related to cybercrimes are examined in terms of their criminological and sociological consequences and effects, and some of the shortcomings that are noteworthy are discussed in terms of their competencies and inadequacies in deterring the committing of cybercrimes.In this study, what kind of the sociological needs can be met by the laws and weaknesses of the laws against the facts will be discussed in detail.Finally, some recommendations on how to prevent cybercrimes from a legal and sociological point of view are also included.
Confidence Calibration for Incremental Learning

Class incremental learning is an online learning paradigm wherein the classes to be recognized are gradually increased with limited memory, storing only a partial set of examples of past tasks. At a task transition, we observe an unintentional imbalance of confidence or likelihood between the classes of the past and the new task. We argue that the imbalance aggravates a catastrophic forgetting for class incremental learning. We propose a simple yet effective learning objective to balance the confidence of classes of old tasks and new task in the class incremental learning setup. In addition, we compare various sample memory configuring strategies and propose a novel sample memory management policy to alleviate the forgetting further. The proposed method outperforms the state of the arts in many evaluation metrics including accuracy and forgetting F by a large margin (up to 5.71% in A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> and 17.1% in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> ) in extensive empirical validations on multiple visual recognition datasets such as CIFAR100, TinyImageNet and a subset of the ImageNet.
Theoretical and methodological bases of organization of cooperative education in higher educational institutions

The article proves the expediency of extensive introduction of co-operative learning technology in the educational process in Ukrainian higher educational institutions.The purpose of the research is to reveal theoretical foundations of the co-operative learning technology and to describe the methodical tools of its implementation in the practice of higher educational institutions in Ukraine.In order to achieve the goal a set of research methods is defined: general scientific (source study analysis, descriptive analytical method, comparison, opposition, classification, systematization) and special (historical genesis, comparison) techniques.The use of given methods has permitted to determine the essence of the technology of cooperative learning (structured learning activitiy of small groups where members are working together (co-operating) to achieve the maximum effectiveness of the learning process); its (the essence's) features (positive interaction, direct support, social competence, own assessment, personal responsibility), teacher functions in terms of the co-operative learning organization (facilitator, assistant, equal member of the group), to uncover the theoretical foundations of the technology of cooperative learning (the theory of social interdependence, the development of cognition (intelligence), the ideas of behaviorism), to describe the basic methods and techniques of organization of cooperative learning (S.Kagan), to give examples and to formulate methodological recommendations for the implementation of the technology of cooperative teaching in the practice of higher education institutions of Ukraine (STAD methods, "Jigso", training in achievement teams, training tournament, team support of individual training, cooperative mutual learning, group research, "Ko-op Ko-op ", searching method, "Studying together", three-tiered interviewing, numbering of students, method of discussion by L. Mitin, method of problematic seminar lesson).The emphasis is placed on the fact that the use of these methods has powerful levers for the development of student's motivation for learning, critical thinking skills, cooperation, cocreation, collective action, ability to solve complex problems basing on the analysis of circumstances and relevant information, to approach in a balanced manner to evaluation of alternative ideas and to discuss.
V-Combiner

An iterative graph algorithm applies a vertex update operation to all vertices in a graph in every iteration. For large graphs, this computation is costly. However, in practice, not all the updates contribute equally to the end result and, in fact, an exact result may not be needed. In this work, we leverage these insights to speed-up iterative graph algorithms. We propose a mechanism to identify the less important vertices and omit computations for them.
Adaptive Feature Generation for Speech Emotion Recognition

The issue of emotion recognition has received considerable critical attention in artificial intelligence and machine learning. In sentiment analysis fields, researchers recognize emotional states from speech, electroencephalograms, and images, etc. The speech signal is among the most widely used in emotion recognition. There are many speech features, including pitch, energy, linear prediction coefficients, mel-frequency cepstral coefficients, and the Teager energy operator. In this study, we explore the critical speech features for sentiment analysis. We modify our previous feature-generation method, which applies low-variance filtering and principal component analysis (for grouped features) to identify the features. We do not utilize between-class scatter here, but rather, the between class–scatter and within class–scatter ratio. Grouping is achieved according to the number of features—not correlation values. For an objective evaluation, we use the Ryerson Audio-Visual Database of Emotional Speech and Song, with a performance evaluation conducted in terms of classifier accuracy and computational complexity. Finally, we propose an effective feature-generation method to find the critical features for emotion recognition from speech.
Code-switching in teaching English subject for EFL students

This study aims to find the types of code-switching employed by the lecturers in the teaching-learning process of EFL students and also to find the functions of the use of code-switching employed by the lecturers for teaching EFL students in ITB STIKOM Bali. The data source of this study was the interview transcribes of the lecturers who teach English subjects in ITB STIKOM Bali. They were interviewed regarding the use of code-switching in teaching EFL students. The theory of code-switching was used in this study and the results of the previous researches in the same areas were also used as the literature review. The finding shows that the situational and metaphorical types of code-switching were employed by the lecturer in the teaching-learning process. However, the situational type of code-switching was dominantly employed by the lecturers in the teaching-learning process. Another result was regarding the function of the code-switching that employed by the lecturer. It was found that the function of code-switching as a translation frequently employed by the lecturer. It is hoped this study can give information to the lecturer and teacher regarding the topic of code-switching. &nbsp; &nbsp
In situ optical backpropagation training of diffractive optical neural networks: publisher’s note

This publisher’s note corrects the authors’ affiliations in Photon. Res. 8 , 940 ( 2020 ). PRHEIZ 2327-9125 10.1364/PRJ.389553
Pengujian Black Box pada Aplikasi Sistem Informasi Pengelolaan Masjid Menggunakan Teknik Equivalence Partitions

Black-box testing is very important because this technique is able to identify errors in functions, interfaces, data models, and access to external data sources. In implementation problems often arise when testers are never sure whether the software being tested has actually passed the test. This happens because there may be several execution paths that have never been tested. Testers must make every possible input data combination for testing. The selection of input data to find errors is a problem for testers because it has a high probability, so the test case design can automatically become a solution. In this implementation the application to be tested using black-box testing is the Mosque Management Information System. The Mosque Management application will be tested using black box testing where the test is only intended to see if the program matches the function desired by the program without knowing the program code used. To produce test case designs automatically in black box method software testing with equality partition technique. Equivalence Partitions technique is a test based on entering data on each form in the mosque management information system, each menu will be tested and grouped according to function whether it is valid or not.
Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time.
Neural architecture search for sparse DenseNets with dynamic compression

Neural Architecture Search (NAS) algorithms have discovered highly novel state-of-the-art Convolutional Neural Networks (CNNs) for image classification, and are beginning to improve our understanding of CNN architectures. However, within NAS research, there are limited studies focussing on the role of skip-connections, and how the configurations of connections between layers can be optimised to improve CNN performance. Our work focusses on developing a new evolutionary NAS algorithm based on adjacency matrices to optimise skip-connection structures, creating more specialised and powerful skip-connection structures within a DenseNet-BC network than previously seen in the literature. Our work further demonstrates how simple adjacency matrices can be interpreted in a way which allows for a more dynamic variant of DenseNet-BC. The final algorithm, using this novel interpretation of adjacency matrices for architecture design and evolved on the CIFAR100 dataset, finds networks with improved performance relative to a baseline DenseNet-BC network on both the CIFAR10 and CIFAR100 datasets, being the first, to our knowledge, NAS algorithm for skip-connection optimisation to do so. Finally, skip-connection structures discovered by our algorithm are analysed, and some important skip-connection patterns are highlighted.