Reviewerly/exHarmony huggingface dataset:
Advanced Machine Learning Techniques in Gomoku: Strategy,Implementation, and Analysis

The strategic board game Gomoku has become a compelling domain for artificial intelligence (AI) research, particularly in developing and applying machine learning techniques. This paper comprehensively analyzes advanced machine learning strategies in Gomoku, focusing on logistic regression for board evaluation, neural networks for pattern recognition, and reinforcement learning for strategic gameplay. We discuss integrating these techniques in creating a sophisticated AI capable of high-level play and adaptability. Through this exploration, we highlight the potential of AI in strategic decision-making and its broader applications beyond board games.
A Comprehensive Review of Text Sentiment Analysis: A Survey of Traditional Methods and Deep Learning Approaches

Text sentiment analysis is a crucial aspect within the realm of natural language processing. This paper, incorporating the latest research advancements, systematically reviews and summarizes mainstream methods in text sentiment analysis. It covers a spectrum of traditional text analysis techniques, including rule-based and dictionary-based methods, as well as machine learning approaches. The paper further delves into an in-depth discussion of text sentiment analysis methods based on deep learning. This encompasses Recurrent Neural Networks and their enhanced structures, Convolutional Neural Networks and their extended techniques, along with applications involving pre-trained models and transfer learning.
Briefly Explore the Combinatorial Game

The paper talks about a series of games called combinatorial games. Combinatorial games are two-person games with perfect information, no chance moves, and a win-or-lose outcome. To be precise, a combinatorial game is a game that satisfies the following conditions. The paper also talks about the rules of the combinatorial game, such as how many players the game has and the rules of the game specify for both players and each position that moves to other positions a legal move. Then, the meaning of the P-positions and N-positions were introduced. After that, the paper talks about the typical combinatorial game- the game of Nim, another example of the combinatorial game, to illustrate the paper’s point of view. The paper talks about the whole rules of this example game, such as the number of players and the initial configuration of the piles/heaps. The author gives several strategies and talks about why these strategies can help the players win combinatorial games. The paper gives two cases to illustrate the game rules and give a better understanding after all. In the end, the paper gives a Python3 program for combinatorial games, making the ideas of combinatorial games more comprehensive.
Tools to Alleviate Teenager’s Anxiety in Learning ESL

This paper explores a variety of different approaches teachers can take with technology assistance to alleviate the negative effects of learning anxiety, specifically in teenage students learning ESL. This paper focuses on online tools that teachers have used in studies before. Still, it includes offline tools teachers can use in the hopes that whatever the issue of learning anxiety may be, the educator and teacher understand the multitude of solutions available.
A Machine Learning Classification of Individuals with Mild Cognitive Impairment into Variants from Writing

Introduction: Individuals with Mild Cognitive Impairment (MCI), a transitional stage between cognitively healthy aging and dementia, are characterized by subtle neurocognitive changes. Clinically, they can be grouped into two main variants, namely into patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). The distinction of the two variants is known to be clinically significant as they exhibit different progression rates to dementia. However, it has been particularly challenging to classify the two variants robustly. Recent research indicates that linguistic changes may manifest as one of the early indicators of pathology. Therefore, we focused on MCI’s discourse-level writing samples in this study. We hypothesized that a written picture description task can provide information that can be used as an ecological, cost-effective classification system between the two variants. Methods: We included one hundred sixty-nine individuals diagnosed with either aMCI or naMCI who received neurophysiological evaluations in addition to a short-written picture description task. Natural Language Processing (NLP) and BERT pre-trained Language Models were utilized to analyze the writing samples. Results: We showed that the written picture description task provided 90% overall classification accuracy for the best classification models, which performs better than cognitive measures. Discussion: Written discourses analyzed the AI models can automatically assess individuals with aMCI and naMCI and facilitate diagnosis, prognosis, therapy planning, and evaluation.
Using collaborative leadership at all levels to build people-oriented organizations

Based on their nationwide work with the US Cooperative Extension Service, the authors have developed four concepts that leaders at all levels can use in creating people-oriented and effective organizations, groups, change teams, and a sense of community: (1) building collaborative leadership throughout the organization; (2) utilizing eight essential questions leaders need to answer; (3) developing a competency framework for leading people-oriented change; and (4) leveraging facilitation skills throughout an organization.
Research on extractive text abstract generation method for graph model based on TextRank

The traditional TextRank algorithm is based on a graph model, and the extracted abstract generated by it does not deviate from the source text, but there may be issues such as polysemy. The BERT model can capture contextual semantics well. It can directly convert sentences in text into vector form, which can better solve the problem of polysemy. BiLSTM can obtain further information, IDCNN can better handle overfitting problems compared to traditional convolutional neural networks, and CRF can better solve the problem of sentence coherence. The model proposed in this article has been compared with other extraction based text summarization methods on the TTNews dataset, and experiments have shown that the method proposed in this article can obtain meaningful text summaries with significant optimization.
Predictive analytics and the collective dimensions of data protection

This paper takes an interdisciplinary approach, combining legal studies, ethics and technical insights to shed light on the complex issues surrounding the regulation of predictive analytics. We argue that the individualised concept of regulation, shaped by the dogma of fundamental rights, is unable to adequately capture the implications of predictive analytics. We show that predictive analytics is a problem of collective privacy and informational power asymmetries, and conceptualise the form of data power at work in predictive analytics as ‘prediction power’. The unregulated prediction power of certain actors poses societal risks, especially if this form of informational power asymmetry is not normatively represented. The article analyses this legal lacuna in the light of recent case law of the European Court of Justice and new legislation at the EU level. To address these challenges, we develop the concept of ‘predictive privacy’ as a protected good based on collective interests.
Video anomaly detection via improved future frame prediction

Video anomaly detection is extensively utilized across a variety of domains including public transportation, industrial production, city management, and military fields to mitigate risks and bolster enhance safety. To tackle the challenges associated with video anomaly detection in intricate environments, we propose a light but efficient framework that builds upon future frame prediction techniques. Our framework incorporates Convolutional Long Short-Term Memory (ConvLSTM), masked convolution, and attention mechanisms to enhance the detection accuracy. Furthermore, to simplify the model's complexity, we replace the convolutional layers in the network with depthwise separable convolutions (DSC). Through evaluation on public datasets such as CUHK Avenue, UCSD Peds1, and UCSD Peds2, our proposed network model exhibits both high accuracy and real-time performance.
Identification of Factors Influencing Educators Perception of Blended Learning and Triggering Adoption Decisions

This study employs theoretical frameworks from the past to determine the factors that drive instructors to adopt blended learning (BL). Using a quantitative approach, the extended Technology Acceptance Model (TAM) was utilized to examine a sample size of 302 instructors from four multi-campus Lebanese universities that offer a business degree. The research findings indicate that ease of use has a positive impact on both attitude and usefulness towards BL. Furthermore, the perceived usefulness of BL positively influences the attitude and intention to use the system. To achieve greater adoption of BL, technical support should be a central component of educators' provision since it has a positive effect on the perceived ease of use. Additionally, subjective norms, such as positive image and usefulness, have a positive influence on educators, as they are influenced by important people in their lives, including their supervisors, as well as by the group's positive image.
Ultra-short-term forecast of distributed photovoltaic power based on satellite cloud image and LSTM model

Abstract Photovoltaic (PV) power generation, with its volatile, intermittent, and random characteristics, and large-scale PV access pose a threat to grid stability. For this reason, predicting the photovoltaic output will help keep the grid safe and stable. On the basis of the influence of cloud groups on solar radiation, a very short-term forecast of distributed PV energy will be made using satellite cloud picture information to improve the forecast accuracy of PV energy production. The paper presents a method to predict distributed PV power at very short notice based on satellite clouds and a network model with Long Short-Term Memory (LSTM). First, extract a subset of meteorological and PV power data from the forecast area as training samples., and the abnormal part of the samples is cleaned by an isolated forest algorithm. Secondly, the occlusion feature is extracted from the satellite cloud image in the same period. Finally, the measured solar irradiance, meteorological information, and obscuration features are input into the LSTM network for prediction, and the photovoltaic power prediction results in the next 4 hours are obtained. The measured PV power of Jinghai Guangfu Power Station in Hefei, Anhui province on the 5th day was the training sample for the prediction of PV power on the 6th day. The prediction results show that the prediction error is 2.73% when a satellite cloud image is added, and 16.15% when a satellite cloud image is not added, and the prediction error is reduced by 13.42%.
The effect of PIQMAS application on the engagement processes of upper basic stage mathematics and science students

New strategies, methods and projects for the learning of the mathematics and science disciplines are needed. One such project is the project for improving quality of mathematics and science (PIQMAS) education. In the present research, we consider the project implementation in Palestine. Specifically, we examine students’ engagement when implementing the project. To do that, we consider three components of engagement: the cognitive, the behavioral and the emotional. To collect the data, we used interviews and observations, which ensured the triangulation of data collection. We interviewed 13 science and mathematics teachers who carried out the project. We used deductive and inductive content analysis to analyze the interviews and observations transcripts. The research results indicated that the project impacted positively the three components of students’ engagement, which indicates the importance of incorporating new teaching methods, especially PIQMAS, in the teaching of science and mathematics. It is recommended to study different aspects of students’ learning in PIQMAS environment.
Modelling crypto markets by multi-agent reinforcement learning

Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike previous agent-based models (ABM) or multi-agent systems (MAS) which relied on zero-intelligence agents or single autonomous agent methodologies, our approach relies on endowing agents with reinforcement learning (RL) techniques in order to model crypto markets. This integration is designed to emulate, with a bottom-up approach to complexity inference, both individual and collective agents, ensuring robustness in the recent volatile conditions of such markets and during the COVID-19 era. A key feature of our model also lies in the fact that its autonomous agents perform asset price valuation based on two sources of information: the market prices themselves, and the approximation of the crypto assets fundamental values beyond what those market prices are. Our MAS calibration against real market data allows for an accurate emulation of crypto markets microstructure and probing key market behaviors, in both the bearish and bullish regimes of that particular time period.
Digital transformation in higher education

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings The process of digital transformation is accelerating among organizations and institutions within different settings. Firms' quest to build digital capability can be aided by following guidance provided by a suitable framework and taking necessary actions with regard to each of its dimensions. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Local unitary equivalence of arbitrary-dimensional multipartite quantum states

Local unitary equivalence is an important ingredient for quantifying and classifying entanglement. Verifying whether or not two quantum states are local unitary equivalent is a crucial problem, where only the case of multipartite pure states is solved. For mixed states, however, the verification of local unitary equivalence is still a challenging problem. In this paper, based on the coefficient matrices of generalized Bloch representations of quantum states, we find a variety of local unitary invariants for arbitrary-dimensional bipartite quantum states. These invariants are operational and can be used as necessary conditions for verifying the local unitary equivalence of two quantum states. Furthermore, we extend the construction to the arbitrary-dimensional multipartite case. We finally apply these invariants to estimate concurrence, a vital entanglement measure, showing the practicability of local unitary invariants in characterizing entanglement.
Detecting DeFi securities violations from token smart contract code

Abstract Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In recent years, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, particularly various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses. This study aims to determine whether this problem is suited to a machine learning approach, namely, whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’ smart contract code. We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’ tokens’ smart contract code (specifically, opcode-based features). Our final model was a random forest model that achieved an 80% F-1 score against a baseline of 50%. Notably, we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’ Solidity code and conducting cosine similarity analyses. We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library, although this does not account for the entirety of our features. Another contribution of our study is a new dataset, comprising (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of legitimate tokens from a reputable DeFi aggregator. This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.
Interval reservoir computing: theory and case studies

The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases.
Joint optimization of operational cost and carbon emission in multiple data center micro-grids

As the internet data centers are mushrooming, the energy consumption and carbon emission of data centers are increasing rapidly. To cut down the electricity cost and carbon emission of the data centers, we proposed an optimization method to reduce the electricity cost and carbon emission in geo-distributed multiple data centers. In the proposed method, the carbon tax is considered in the overall operation cost to optimize the carbon emission. The spatial and temporal flexibility of computational workload is fully utilized by considering the difference in renewable energy power output, local electricity and carbon emission of multiple geo-distributed data centers to achieve a better performance. Furthermore, the nonlinear characteristics of the power loss of uninterruptible power supply (UPS) are considered in the optimization. To verify the proposed optimization method, simulation of six cases is carried out with realistic data, and results have proved the proposed method can reduce the operational costs by 4.93%–12.7% and decrease carbon emissions by up to 10%.
Tutt la tosse in una figura

Cough is the most common cause of unscheduled paediatric visits. The figure in the article shows the main causes of paediatric coughs differentiated by duration, quality (dry or wet) and night-time coughing. With these few simple key anamnestic elements (and espe- cially if confirmed and reinforced by being able to see, even in a simple video, the child coughing) a diagnosis may be made with reasonable certainty, saving specialist visits and diagnostic tests that are unlikely useful and potentially harmful.
The Effect of Role Conflict, Role Ambiguity and Ethical Sensitivity on Auditor Performance with Intellectual Intelligence as a Moderating Variable

This study aims to analyze the effect of role conflict, role ambiguity, and ethical sensitivity on auditor performance and intellectual intelligence as a moderating variable on the relationship between these factors and auditor performance. Data were collected from 200 respondents who were auditors at the Makassar City BPKP Office using a questionnaire. The data analysis technique used is multiple regression with a moderated regression analysis (MRA) approach. The results showed that role conflict and role ambiguity have a significant negative effect on auditor performance, while ethical sensitivity has a significant positive effect on their performance. In addition, intellectual intelligence moderates the relationship between role conflict and auditor performance so that the higher the intellectual intelligence of an auditor, the negative impact of role conflict on his performance will be reduced. However, intellectual intelligence does not moderate the relationship between role ambiguity and performance.
Self-Guided Robust Graph Structure Refinement

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. To defend against such attacks, robust graph structure refinement (GSR) methods aim at minimizing the effect of adversarial edges based on node features, graph structure, or external information. However, we have discovered that existing GSR methods are limited by narrowassumptions, such as assuming clean node features, moderate structural attacks, and the availability of external clean graphs, resulting in the restricted applicability in real-world scenarios. In this paper, we propose a self-guided GSR framework (SG-GSR), which utilizes a clean sub-graph found within the given attacked graph itself. Furthermore, we propose a novel graph augmentation and a group-training strategy to handle the two technical challenges in the clean sub-graph extraction: 1) loss of structural information, and 2) imbalanced node degree distribution. Extensive experiments demonstrate the effectiveness of SG-GSR under various scenarios including non-targeted attacks, targeted attacks, feature attacks, e-commerce fraud, and noisy node labels. Our code is available at https://github.com/yeonjun-in/torch-SG-GSR.