Reviewerly/exHarmony huggingface dataset:
Accelerating and Improving AlphaZero Using Population Based Training

AlphaZero has been very successful in many games. Unfortunately, it still consumes a huge amount of computing resources, the majority of which is spent in self-play. Hyperparameter tuning exacerbates the training cost since each hyperparameter configuration requires its own time to train one run, during which it will generate its own self-play records. As a result, multiple runs are usually needed for different hyperparameter configurations. This paper proposes using population based training (PBT) to help tune hyperparameters dynamically and improve strength during training time. Another significant advantage is that this method requires a single run only, while incurring a small additional time cost, since the time for generating self-play records remains unchanged though the time for optimization is increased following the AlphaZero training algorithm. In our experiments for 9x9 Go, the PBT method is able to achieve a higher win rate for 9x9 Go than the baselines, each with its own hyperparameter configuration and trained individually. For 19x19 Go, with PBT, we are able to obtain improvements in playing strength. Specifically, the PBT agent can obtain up to 74% win rate against ELF OpenGo, an open-source state-of-the-art AlphaZero program using a neural network of a comparable capacity. This is compared to a saturated non-PBT agent, which achieves a win rate of 47% against ELF OpenGo under the same circumstances.
TEACHER LEADERSHIP AT TECHNICAL UNIVERSITY

The purpose of the article is to study the specificities of students’ perception of teacher leadership based on a comparison of the respondents’ assessments of teachers’ and managers’ personal qualities that define them as leaders in their professional activities. Teacher leadership may be associated with innovation, the use of techniques, pedagogical skills, organisational skills, creativity, personal qualities, etc. The following methods have been used in the research: questionnaires and tests based on the method of personal differentials. To determine the peculiarities of teacher leadership, a comparison between respondents’ answers about teacher-leaders and manager-leaders was made. The first group of respondents who evaluated the teacher-leaders included 87 students enrolled in the 6th year (master course) at the National Technical University “Kharkiv Polytechnic Institute”, Ukraine. The personal qualities of the manager-leaders were assessed by the masters of the “Public Management and Administration” at the same university, who study the master course having working experience from 3 to 23 years. The number of respondents in the second group was 81 students. The results of the research made it possible to determine that students associated pedagogical leadership, first of all, with moral and ethical qualities, pedagogical skills and the subject knowledge of their teachers. On the contrary, the qualities of active life most closely were associated with a manager-leader. The obtained results can be used in the development of university teachers’ training programmes.
Explanations for Inconsistency-Tolerant Query Answering under Existential Rules

Querying inconsistent knowledge bases is a problem that has attracted a great deal of interest over the last decades. While several semantics of query answering have been proposed, and their complexity is rather well-understood, little attention has been paid to the problem of explaining query answers. Explainability has recently become a prominent problem in different areas of AI. In particular, explaining query answers allows users to understand not only what is entailed by an inconsistent knowledge base, but also why. In this paper, we address the problem of explaining query answers for existential rules under three popular inconsistency-tolerant semantics, namely, the ABox repair, the intersection of repairs, and the intersection of closed repairs semantics. We provide a thorough complexity analysis for a wide range of existential rule languages and for different complexity measures.
Online Active Learning of Reject Option Classifiers

Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required.
An Algorithmic Model of Decision Making in the Human Brain

One of the interesting topics in neuroscience is problem solving and decision-making. In this area, everything gets more complicated when events occur sequentially. One of the practical methods for handling the complexity of brain function is to create an empirical model. Model Predictive Control (MPC) is known as a powerful mathematical-based tool often used in industrial environments. We proposed an MPC and its algorithm as a part of the functionalities of the brain to improve the performance of the decision-making process.We used a hybrid methodology whereby combining a powerful nonlinear control system tools and a modular fashion approach in computer science. Our hybrid approach employed the MPC and the Object-Oriented Modeling (OOM) respectively. Therefore, we could model the interaction between most important regions within the brain to simulate the decision-making process.The employed methodology provided the capability to design an algorithm based on the cognitive functionalities of the PFC and Hippocampus. The developed algorithm applied for modulation of neural circuits between cortex and sub-cortex during a decision making process.It is well known that the decision-making process results from communication between the prefrontal cortex (working memory) and hippocampus (long-term memory). However, there are other regions of the brain that play essential roles in making decisions, but their exact mechanisms of action still are unknown. In this study, we modeled those mechanisms with MPC. We showed that MPC controls the stream of data between prefrontal cortex and hippocampus in a closed-loop system to correct actions.
The Effects of Semantics in the Language Development of English as a Foreign Language (EFL) Learners

The study of Semantics is an important area of word meaning, references, senses, logic, and perlocutions and illocutions. That is, the study of Semantics increases students’ understanding and awareness of word meaning, sentence relationships, and discourse and context. It also enables students to create and improve their Semantics maps which are webs of words visually display the meaning-based connections between a word or phrase and a set of related words or concepts. This paper is a product of effort that I make to implement some theoretically-sound strategies in planning and teaching a Semantic course for English Foreign language learners (EFL). The aim of this paper is to show that utilizing the mechanisms of meaning is vital to successful human communication. Alongside with that, lexical development will solidify students’ understanding of language meaning and sense relations. The purpose of the course is to concentrate on teaching key terms in Semantics, Semantics Analysis of Writing Approach (SAW) and ‘agent-action-goal with real-life action’ technique and then employed the knowledge of these terms to improve students’ vocabulary in the short-term, and their language proficiency in the long-term. Scaffolding the Semantics information with L2 vocabulary strategies is pivotal in language development. Implementing semantics strategies in an attempt to expose the relationship between teaching Semantics and improving ELLs’ language skills.
PENGGUNAAN MULTIMEDIA ONLINE DALAM PEMBELAJARAN

The use of technology and information as multimedia learning has a very strategic function. Media is seen as one of the factors that can improve the learning process because the media has strategic roles and functions that can directly or indirectly affect the motivation, interests, and attention of students. The function of multimedia in learning is to overcome the limitations of students’ experiences, be able to transcend classroom boundaries, enable direct interaction between students and their environment, produce uniform observations, instill the basic concepts that are true, concrete, and realistic. Multimedia can arouse new desires and interests and arouse students’ motivation to learn and provide an integral or comprehensive experience from the concrete to the abstract. The advantages of using multimedia in learning include being able to improve students’ ability to understand an abstract concept more easily, in addition to the use of computer media in the form of multimedia can give a positive impression to the teacher because it can help teachers explain the contents of the lesson to students, streamline time, and increase students’ motivation in learning.
Bridging Maximum Likelihood and Adversarial Learning via α-Divergence

Maximum likelihood (ML) and adversarial learning are two popular approaches for training generative models, and from many perspectives these techniques are complementary. ML learning encourages the capture of all data modes, and it is typically characterized by stable training. However, ML learning tends to distribute probability mass diffusely over the data space, e.g., yielding blurry synthetic images. Adversarial learning is well known to synthesize highly realistic natural images, despite practical challenges like mode dropping and delicate training. We propose an α-Bridge to unify the advantages of ML and adversarial learning, enabling the smooth transfer from one to the other via the α-divergence. We reveal that generalizations of the α-Bridge are closely related to approaches developed recently to regularize adversarial learning, providing insights into that prior work, and further understanding of why the α-Bridge performs well in practice.
Multiple Positional Self-Attention Network for Text Classification

Self-attention mechanisms have recently caused many concerns on Natural Language Processing (NLP) tasks. Relative positional information is important to self-attention mechanisms. We propose Faraway Mask focusing on the (2m + 1)-gram words and Scaled-Distance Mask putting the logarithmic distance punishment to avoid and weaken the self-attention of distant words respectively. To exploit different masks, we present Positional Self-Attention Layer for generating different Masked-Self-Attentions and a following Position-Fusion Layer in which fused positional information multiplies the Masked-Self-Attentions for generating sentence embeddings. To evaluate our sentence embeddings approach Multiple Positional Self-Attention Network (MPSAN), we perform the comparison experiments on sentiment analysis, semantic relatedness and sentence classification tasks. The result shows that our MPSAN outperforms state-of-the-art methods on five datasets and the test accuracy is improved by 0.81%, 0.6% on SST, CR datasets, respectively. In addition, we reduce training parameters and improve the time efficiency of MPSAN by lowering the dimension number of self-attention and simplifying fusion mechanism.
The Impact of the Digital Transformation of Business on Corporate Governance. An Overview of Recent Studies

This article presents a survey of recent studies on the impact of digitalisation, and particularly blockchain technology, oncorporate governance and the principal-agent conflict in companies. The principal-agent conflict has been a centerpieceof the corporate governance research for more than 40 years. However, recent technological developments, andblockchain in particular, has created new avenues for exploration.We survey the implications of blockchain for the principal-agent conflict in three parts: 1) the organisationalenvironment, and the creation of the conflict; 2) common observable instances of conflict; 3) actions necessary tomaximise the value of blockchain implementation. We limit the studied conflict to the relationship between shareholdersand management. We also limit the blockchain use cases to those currently in testing. The applications for blockchain insecurities trading and for corporate functions automation via ‘smart’ contracts are both analysed. We also evaluate theimplications for investor activism.Our results indicate that passive investor behaviour is at the core of the environment that creates conflict. One of thekey drivers of low activity is a non-transparent voting process resulting in low participation rates. Studies indicate thatblockchain can solve this issue, thus mitigating the conflict, and is an attractive proposition for board members. Themost frequent instances of conflict are related to the composition of boards of directors and compensation schemesobserved at shareholder voting. Using blockchain for settlement would eliminate ambiguity in shareholder registersand prevent such strategies as “empty voting”. Smart contracts promise automation of governance functions like audit,which also weakens conflict. Even skeptics agree that voting is a promising application for blockchain. However, thereis evidence that blockchain poses its own problems, and that smart contracts are associated with practical risks. Somecritics argue that blockchain is less efficient than conventional corporate procedures.Blockchain is among the top digital technologies that business leaders have to monitor closely. As such, this overview ofthe most up-to-date thinking on the subject is relevant for anyone interested in the future of corporate governance andthe digitalisation of business processes. This evaluation serves to highlight the current status of this innovative resource,outlining for both professionals and newcomers what exactly blockchain’s potential uses and implications are, while alsooutlining where a lack of quantitative research creates opportunities for further contributions to the research field. Thisstudy will also be instructive for those investigating blockchain implementation and the optimal characteristics of thesolution.
Conclusion-Supplement Answer Generation for Non-Factoid Questions

This paper tackles the goal of conclusion-supplement answer generation for non-factoid questions, which is a critical issue in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI), as users often require supplementary information before accepting a conclusion. The current encoder-decoder framework, however, has difficulty generating such answers, since it may become confused when it tries to learn several different long answers to the same non-factoid question. Our solution, called an ensemble network, goes beyond single short sentences and fuses logically connected conclusion statements and supplementary statements. It extracts the context from the conclusion decoder's output sequence and uses it to create supplementary decoder states on the basis of an attention mechanism. It also assesses the closeness of the question encoder's output sequence and the separate outputs of the conclusion and supplement decoders as well as their combination. As a result, it generates answers that match the questions and have natural-sounding supplementary sequences in line with the context expressed by the conclusion sequence. Evaluations conducted on datasets including “Love Advice” and “Arts &amp; Humanities” categories indicate that our model outputs much more accurate results than the tested baseline models do.
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks

Deep generative neural networks (DGNNs) have achieved realistic and high-quality data generation. In particular, the adversarial training scheme has been applied to many DGNNs and has exhibited powerful performance. Despite of recent advances in generative networks, identifying the image generation mechanism still remains challenging. In this paper, we present an explorative sampling algorithm to analyze generation mechanism of DGNNs. Our method efficiently obtains samples with identical attributes from a query image in a perspective of the trained model. We define generative boundaries which determine the activation of nodes in the internal layer and probe inside the model with this information. To handle a large number of boundaries, we obtain the essential set of boundaries using optimization. By gathering samples within the region surrounded by generative boundaries, we can empirically reveal the characteristics of the internal layers of DGNNs. We also demonstrate that our algorithm can find more homogeneous, the model specific samples compared to the variations of ϵ-based sampling method.
Information-Theoretic Understanding of Population Risk Improvement with Model Compression

We show that model compression can improve the population risk of a pre-trained model, by studying the tradeoff between the decrease in the generalization error and the increase in the empirical risk with model compression. We first prove that model compression reduces an information-theoretic bound on the generalization error; this allows for an interpretation of model compression as a regularization technique to avoid overfitting. We then characterize the increase in empirical risk with model compression using rate distortion theory. These results imply that the population risk could be improved by model compression if the decrease in generalization error exceeds the increase in empirical risk. We show through a linear regression example that such a decrease in population risk due to model compression is indeed possible. Our theoretical results further suggest that the Hessian-weighted K-means clustering compression approach can be improved by regularizing the distance between the clustering centers. We provide experiments with neural networks to support our theoretical assertions.
Cognitive effect on image quality in full ultra-high definition(8 K)

Resolution for displays are increasing from 4K-UHD to 8K-Full UHD. We conducted a subjective image quality evaluation to compare the image quality between 4 and 8 K by using diverse image stimuli. As a result, we identified perceptual and cognitive characteristics that can be demonstrated in image quality above 8 K. Cognitive and synesthetic factors as well as perceptual factors, and color expression were improved in 8 K image quality. The research findings suggested that both perceptual and cognitive factors are reinforced in 8 K. This tendency was even more reinforced when the main object was placed within the image. Also, the contrast and vividness felt higher at 8 K, of which the vividness influenced the cognitive evaluation of the 8 K image quality. When compared with 4 K, the image quality above 8 K was accompanied by optical illusion, which in turn improved the understanding of the main object. These results suggest that cognitive evaluations for a comprehensive understanding of images may be strengthened at a very high resolution of 8 K or higher beyond the 'presence' of 4 K resolution.
Learning from Interventions Using Hierarchical Policies for Safe Learning

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.
Reinforcing Meltdown Attack by Using a Return Stack Buffer

Meltdown is a microarchitectural side-channel attack that extracts sensitive data in the kernel space of operating systems (OSs).Meltdown deliberately creates transient executions by exploiting an outof-order execution technique and obtains the execution results through a cache covert channel.In a previous attack, an OS signal handler and hardware transactional memory support (i.e., Intel TSX) were used to establish the cache covert channel.However, both methods restricted the effectiveness of the attack owing to the large amount of system noise caused by the context switching of signal handlers and the narrow range of TSX-enabled processors.Hence, we propose a new variant of the Meltdown attack using a return stack buffer (RSB).The RSB enables the establishment of a low-noise cache covert channel without relying on processor-specific hardware features, such as TSX.The wide usage of the RSB in commodity processors further improves the effectiveness of the proposed attack.We present the details of our implementation of the attack and evaluate the performance.Furthermore, we overview several existing countermeasures against the proposed attack. INDEX TERMS Microarchitectural side-
Generative Reconstructive Hashing for Incomplete Video Analysis

In the literature of video analysis, most researches, such as retrieval and recognition, hypothesize that each input video contains at least one complete semantic entity, e.g. an activity, action and event.However, this hypothesis does not hold in many realistic scenarios due to two main reasons. First, complete videos whose qualities are good enough for automatic analysis are not always accessible because of heavy motion blur, occlusions, interruptions, etc. % Second, extracting features from complete videos always fails to meet up with speed and storage requirements in large-scale use cases.To tackle these challenges, incomplete videos are more useful, but researches on them are seldom mentioned. In this paper, we propose a novel and effective hashing framework specialized in large-scale incomplete video analysis called Generative Reconstructive Hashing (GRH). To begin with, an adversarial generative network that is specially designed to map incomplete video features to the feature distributions of complete videos, so that features of incomplete videos become indistinguishable from those of complete videos. Then, the discriminative hashing module further fills the gap between full video features and estimated features from partial videos by projecting both features into a common binary feature space, which allows improvement in efficiency compared with real-value based methods. GRH is the first end-to-end framework for incomplete video analysis. Extensive experiments on various datasets demonstrate GRH's superior effectiveness and efficiency on retrieval and recognition tasks. GRH outperforms the recent state-of-the-art methods by 5.44/3.22/4.82 in terms of MAPs on HMDB51/UCF101/CCV datasets, respectively.