Reviewerly/exHarmony huggingface dataset:
Tutorial on dynamical modeling of eye movements in reading

Dynamical models are crucial for developing process-oriented, quantitative theories in cognition and behavior. Due to the impressive progress in cognitive theory, domain-specific dynamical models are complex, which typically creates challenges in statistical inference. Mathematical models of eye-movement control might be looked upon as a representative case study. In this tutorial, we introduce and analyze the SWIFT model (Engbert et al., 2002, 2005), a dynamical modeling framework for eye-movement control in reading that was developed to explain all types a saccades observed in experiments from an activation- based approach. We provide an introduction to dynamical modeling, which explains the basic concepts of SWIFT and its statistical inference. We discuss the likelihood function of a simplified version of the SWIFT model as a key foundation for Bayesian parameter estimation (Rabe et al., 2021; Seelig et al., 2020). In posterior predictive checks, we demonstrate that the simplified model can reproduce interindividual differences via parameter variation. All computations in this tutorial are implemented in the R-Language for Statistical Computing and are made publicly available. We expect that the tutorial might be helpful for advancing dynamical models in other areas of cognitive science.
THE ANALYSIS OF AFFECTIVE FACTORS ENHANCEMENT ON EFL TEACHING ACTIVITY

Learning English has its own challenge for Junior High school students in Indonesia. One of the ways to cope with the challenges is by enhancing students’ affective factors such as self-confidence, language ego, risk-taking willingness, and connecting with first language culture. However, researches on these types of affective factors are insufficient thus the researchers were interested to conduct research on the topic. The current study was implementing the narrative qualitative method, where EFL teaching activity was observed. It intended to analyze the teachers’ effort to develop EFL students’ affective variables. The EFL class observed the 9th grade of a private junior high school in West Java which was involving one EFL teacher and 29 students. The research shows that teachers did give favor the development of students’ affective variables as many as 32% from the lists observed. However, it is not sufficient, not programmed, and not systematical. The researchers suggest the educators systematically enhance students’ affective factors in the classroom setting. For further research, it is recommended to conduct research related to the effective factors with broader scopes and samples.&#x0D; Keywords: Affective Factors, Affective enhancement, EFL Students, Teacher Effort&#x0D;
Toward Next Generation Mixed Reality Games: AResearch Through Design Approach

Abstract Mixed reality (MR) games refer to games that integrate physical entities with digitally mediated contents. Currently, it entails relatively high development efforts and technical threshold to incorporate multiple virtual and physical building blocks. Moreover, the absence of a coherent technology stack renders the design and development process even more challenging than conventional digital games. This research proposed an MR game technology stack that affords a wide spectrum of virtual and physical modules with flexibility to adapt to specific application domains and on-cite needs. We adopted a Research-through-Design (RtD) approach. We first surveyed 24 state-of-the-art studies, and results were synergized into three different spectra of technological affordances, respectively activity range, user interface and feedback control. Based on the initial insights, we then went through two iterations and resulted in an MR game technology stack. A user co-design workshop was further conducted, where 15 participants with different game making experiences were invited to try the proposed technology stack, and 4 conceptual game designs were generated after hands-on tutorial and group co-ideation sessions. We also collected participant feedback via user survey and semi-structured interview. The proposed technology stack received an overall positive feedback, particularly the innovative in-game interactions and the motivation for game creation it brought to the users.
PENGARUH PENGGUNAAN MULTIMEDIA CD INTERAKTIF TERHADAP HASIL BELAJAR SISWA KELAS IV SD NEGERI 09 TANJUNG SAKTI PUMU

Background of this research that learning activities are less effective because in the teaching and learning process teachers tend to use tools in the form of simple media, so it is less interesting to be noticed by students. This study aims to determine the effect of using Interactive Multimedia CD on the learning outcomes of grade IV students of SD Negeri 09 Tanjung Sakti Pumu. This research was conducted at SD Negeri 09 Tanjung Sakti Pumu located in Gunung Raya Village, Tanjung Sakti Pumu District, Lahat District, South Sumatra, with zip code 31581. This research is a type of experimental research, namely the type of pre-Experimental Design. The population and sample in this study were grade IV students of SD Negeri 09 Tanjung Sakti Pumu, totaling 20 people. Data collection techniques in this study are Test, Observation and Documentation. Data analysis techniques in this study include Normality Test, Homogeneity Test, Hypothesis Test. The result of this study is a hypothesis test showing that t count ≥ t table or 11.251 ≥ 2.110, then it can be concluded that H0 is rejected and H1 is accepted. This means that there is an influence of interactive multimedia CDs on the learning outcomes of grade IV students of SD Negeri 09 Tanjung Sakti Pumu.
PENERAPAN METODE DISCOVERY LEARNING TERHADAP KEMAMPUAN SOSIAL EMOSIONAL MINAT BELAJAR PADA PEMBELAJARAN IPA DI KELAS V

The purpose of this study was to determine the application of the discovery learning method to the social emotional abilities of students’ learning interests at SD Negeri 08 Gelumbang. The subjects in this study were students of SD Negeri 08 Gelumbang, totaling 31 students. The method used is descriptive qualitative which, annalyzes data using observation, questionnaires and documentation. Based on the results of the study that a student’s social emotional abilities are good so they have an interest in learning, where from the distribution of the questionnaire students get good student responses with the final result of an average questionnaire score of 80, with good criteria. From the results of the questionnaire distribution, the percentage of students was obtained, where 2 students had fairly good criteria with a percentage of 60%, and 6 students had very good criteria with a percentage of 30%.
Efficient Video Object Segmentation via Motion-Driven Labeling and Transflow Transformers

Efficient Video Object Segmentation via Motion-Driven Labelingand Transflow Transformers
Exploring Application of LLMs and AI-based Models in Addressing EDI Concerns

Exploring Application of LLMs and AI-basedModels in Addressing EDI Concerns
ANALISIS SENTIMEN PRODUCT TOOLS &amp; HOME MENGGUNAKAN METODE CNN DAN LSTM

Sentiment analysis is gaining popularity as the number of internet users increases. Internet users often express their opinions through reviews on websites. The customer opinions expressed have a huge impact on sellers and customer numbers, as many consumers rely on online reviews as a reference when purchasing products. In order to quickly understand sentimental views and tendencies towards a product or event, a text sentiment analysis is performed on the opinions expressed by users. Sentiment analysis focuses on understanding the sentiments contained in the text. One common approach in sentiment analysis is to use Deep Learning (DL) models. This study aims to analyze product sentiment in the Tools &amp; Home category from Amazon using models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The CNN model is used to extract features from words that reflect short-term sentiment dependencies, while LSTM is used to establish long-term sentiment relationships between words. CNN and LSTM are sophisticated DL models, capable of efficiently processing text data and recognizing relationships and patterns that exist at various levels of abstraction. The purpose of this study is to understand the differences in the performance of the DL model in conducting sentiment analysis, it is hoped that it can also be a reference for those who plan to apply other DL models.
PENGEMBANGAN MEDIA BIG BOOK DUA DIMENSI MATERI PERPINDAHAN KALOR/PANAS PADA MATA PELAJARAN IPA KELAS V SEKOLAH DASAR

This research was motivated by the emergence of problems at SD Negeri 57 Palembang, namely the absence of interesting and varied learning media in the use of learning media in the learning process, so that students were less interested in participating in learning which caused students to find it difficult to understand the material presented by educators. The purpose of the research is to develop two-dimensional digital big book learning media in order to determine the validity and practicality of the developed big book media. The type of research used is Research and Development (R&amp;D) with the ADDIE model which consists of 5 stages, namely the Analysis stage, the Design Stage, the Development Stage, the Implementation Stage and the Evaluation Stage. The results of the big book learning media research were declared valid, shown in the average percentage results with validation results by material expert validation, namely 87.14% in the "Very Valid" category. The average percentage obtained by media expert validation is 71% with the "Valid" category. As for the average percentage obtained by linguist validation, which is equal to 80% with the "Valid" category from the results of this percentage, the big book media has quality, appearance and content design that is in accordance with the development of students. The results obtained in validation one to one obtained a percentage of 90.4% with the criteria "Very Practical", while in the small group trial it was 82.1% with the criteria "Very Practical". The media developed is in accordance with the level of development of students, communicative. It is concluded that the big book learning media on heat transfer material at SD Negeri 57 Palembang which was developed is very feasible, valid and very practical to use as a learning medium.
PRIORITIZING TASKS IN INFORMATION SYSTEM PROJECTS: A NOVEL APPROACH USING VIKOR METHOD

Information Systems (IS) projects often face significant challenges, with many failing to meet their objectives. One of the key factors contributing to these failures is the improper prioritization of tasks. This article addresses this issue by proposing a comprehensive approach to task prioritization in IS projects using the VIKOR method. The approach takes into account four key criteria - cost, time, risk, and complexity - and uses the VIKOR method to rank tasks based on these criteria. The VIKOR method provides a systematic and objective way of prioritizing tasks, ensuring effective resource allocation, appropriate risk management, and the achievement of strategic objectives. The application of the VIKOR method in the context of IS projects is a novel contribution of this article. However, it is also recognized that the proposed approach has its limitations and that further research is needed to consider additional criteria, explore other decision-making methods, and examine the dynamic nature of IS projects. Overall, this article contributes to the growing body of knowledge on project management in IS and provides a practical tool for project managers to improve their decision-making processes.
Deep Hierarchical Communication Graph in Multi-Agent Reinforcement Learning

Sharing intentions is crucial for efficient cooperation in communication-enabled multi-agent reinforcement learning. Recent work applies static or undirected graphs to determine the order of interaction. However, the static graph is not general for complex cooperative tasks, and the parallel message-passing update in the undirected graph with cycles cannot guarantee convergence. To solve this problem, we propose Deep Hierarchical Communication Graph (DHCG) to learn the dependency relationships between agents based on their messages. The relationships are formulated as directed acyclic graphs (DAGs), where the selection of the proper topology is viewed as an action and trained in an end-to-end fashion. To eliminate the cycles in the graph, we apply an acyclicity constraint as intrinsic rewards and then project the graph in the admissible solution set of DAGs. As a result, DHCG removes redundant communication edges for cost improvement and guarantees convergence. To show the effectiveness of the learned graphs, we propose policy-based and value-based DHCG. Policy-based DHCG factorizes the joint policy in an auto-regressive manner, and value-based DHCG factorizes the joint value function to individual value functions and pairwise payoff functions. Empirical results show that our method improves performance across various cooperative multi-agent tasks, including Predator-Prey, Multi-Agent Coordination Challenge, and StarCraft Multi-Agent Challenge.
Safe Multi-agent Learning via Trapping Regions

One of the main challenges of multi-agent learning lies in establishing convergence of the algorithms, as, in general, a collection of individual, self-serving agents is not guaranteed to converge with their joint policy, when learning concurrently. This is in stark contrast to most single-agent environments, and sets a prohibitive barrier for deployment in practical applications, as it induces uncertainty in long term behavior of the system. In this work, we apply the concept of trapping regions, known from qualitative theory of dynamical systems, to create safety sets in the joint strategy space for decentralized learning. We propose a binary partitioning algorithm for verification that candidate sets form trapping regions in systems with known learning dynamics, and a heuristic sampling algorithm for scenarios where learning dynamics are not known. We demonstrate the applications to a regularized version of Dirac Generative Adversarial Network, a four-intersection traffic control scenario run in a state of the art open-source microscopic traffic simulator SUMO, and a mathematical model of economic competition.
UPAYA MENINGKATKAN HASIL BELAJAR SISWA TENTANG PERKALIAN DENGAN MENGGUNAKAN MEDIA GAMBAR PADA SISWA KELAS II DI SD N 24 PAKAN SINAYAN

This research is motivated by the low learning outcomes of students caused by the lack of proper learning models used in learning mathematics so that in the learning process the majority of students are still passive and lack interaction. This study aims to determine how much influence the use of learning using media images has on the learning outcomes of students in Class II SD N 24 Pakan Sinayan. This type of research is quantitative research with the subjects in this study being grade II students at SD N 24 Pakan Sinayan with a total of 22 students. This research was conducted in two cycles. Each cycle consists of two meetings. Data collection techniques using observation, tests and non-tests. Research data were analyzed using qualitative descriptive techniques to analyze the results of observations and quantitative descriptive techniques to analyze test results. Based on the results of the 2nd cycle research, very satisfying results were obtained where there was an increase in student learning outcomes from cycle 1 to cycle 2. Thus it can be concluded that the use of Media Pictures has an effect on student learning outcomes in Mathematics learning in Elementary Schools.
The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks

Deep Neural Networks are increasingly adopted in critical tasks that require a high level of safety, e.g., autonomous driving. While state-of-the-art verifiers can be employed to check whether a DNN is unsafe w.r.t. some given property (i.e., whether there is at least one unsafe input configuration), their yes/no output is not informative enough for other purposes, such as shielding, model selection, or training improvements. In this paper, we introduce the #DNN-Verification problem, which involves counting the number of input configurations of a DNN that result in a violation of a particular safety property. We analyze the complexity of this problem and propose a novel approach that returns the exact count of violations. Due to the #P-completeness of the problem, we also propose a randomized, approximate method that provides a provable probabilistic bound of the correct count while significantly reducing computational requirements. We present experimental results on a set of safety-critical benchmarks that demonstrate the effectiveness of our approximate method and evaluate the tightness of the bound.
Evidence Combination for Pattern Classification With Missing Data

Abstract It remains a challenging problem to classify the incomplete patterns with randomly missing values. In some applications, it is difficult for us to collect complete attributes of target due to the complex sensing environment, and the observed patterns are all with more or less missing values. In order to well classify such incomplete patterns, we propose a new classification method based on evidence combination. Because the patterns are all considered with missing attribute values, it is hard to accurately estimate the missing values of object to classify. We propose to classify the object using each available attribute respectively. The K-nearest neighbors (K-NN) of object are found in training data space according to each attribute, and K Basic belief assignments (BBA) are constructed corresponding to the K-NN. The BBA reflects the degree of object belonging to each class. Then a two step evidence combination strategy is developed for combining the K BBA's. The BBA's associated with the same class are combined by classical DS rule at first. When the K-NN belong to different classes, the previous DS combination results associated with different classes may highly conflict, and they are further combined by PCR5 rule, which can well manage the high conflict via proper conflicting masses redistribution. We can classify the object using other attribute one by one in the similar way. When multiple classification results corresponding to different attributes are collected, a weighted evidence combination method is employed to fuse these results. The weighting factors of the results are optimized by minimizing an error criterion. The object is finally classified depending on this combination result. Experimental results with various data sets show the effectiveness of the proposed method comparing with other related methods.
Diagram Visual Grounding: Learning to See with Gestalt-Perceptual Attention

Diagram visual grounding aims to capture the correlation between language expression and local objects in the diagram, and plays an important role in the applications like textbook question answering and cross-modal retrieval. Most diagrams consist of several colors and simple geometries. This results in sparse low-level visual features, which further aggravates the gap between low-level visual and high-level semantic features of diagrams. The phenomenon brings challenges to the diagram visual grounding. To solve the above issues, we propose a gestalt-perceptual attention model to align the diagram objects and language expressions. For low-level visual features, inspired by the gestalt that simulates human visual system, we build a gestalt-perception graph network to make up the features learned by the traditional backbone network. For high-level semantic features, we design a multi-modal context attention mechanism to facilitate the interaction between diagrams and language expressions, so as to enhance the semantics of diagrams. Finally, guided by diagram features and linguistic embedding, the target query is gradually decoded to generate the coordinates of the referred object. By conducting comprehensive experiments on diagrams and natural images, we demonstrate that the proposed model achieves superior performance over the competitors. Our code will be released at https://github.com/AIProCode/GPA.
Decentralized Anomaly Detection in Cooperative Multi-Agent Reinforcement Learning

We consider the problem of detecting adversarial attacks against cooperative multi-agent reinforcement learning. We propose a decentralized scheme that allows agents to detect the abnormal behavior of one compromised agent. Our approach is based on a recurrent neural network (RNN) trained during cooperative learning to predict the action distribution of other agents based on local observations. The predicted distribution is used for computing a normality score for the agents, which allows the detection of the misbehavior of other agents. To explore the robustness of the proposed detection scheme, we formulate the worst-case attack against our scheme as a constrained reinforcement learning problem. We propose to compute an attack policy by optimizing the corresponding dual function using reinforcement learning. Extensive simulations on various multi-agent benchmarks show the effectiveness of the proposed detection scheme in detecting state-of-the-art attacks and in limiting the impact of undetectable attacks.
Exploration via Joint Policy Diversity for Sparse-Reward Multi-Agent Tasks

Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning problems. Previous works argue that complex dynamics between agents and the huge exploration space in MARL scenarios amplify the vulnerability of classical count-based exploration methods when combined with agents parameterized by neural networks, resulting in inefficient exploration. In this paper, we show that introducing constrained joint policy diversity into a classical count-based method can significantly improve exploration when agents are parameterized by neural networks. Specifically, we propose a joint policy diversity to measure the difference between current joint policy and previous joint policies, and then use a filtering-based exploration constraint to further refine the joint policy diversity. Under the sparse-reward setting, we show that the proposed method significantly outperforms the state-of-the-art methods in the multiple-particle environment, the Google Research Football, and StarCraft II micromanagement tasks. To the best of our knowledge, on the hard 3s_vs_5z task which needs non-trivial strategies to defeat enemies, our method is the first to learn winning strategies without domain knowledge under the sparse-reward setting.
On the Fairness Impacts of Private Ensembles Models

The Private Aggregation of Teacher Ensembles (PATE) is a machine learning framework that enables the creation of private models through the combination of multiple "teacher" models and a "student" model. The student model learns to predict an output based on the voting of the teachers, and the resulting model satisfies differential privacy. PATE has been shown to be effective in creating private models in semi-supervised settings or when protecting data labels is a priority. This paper explores whether the use of PATE can result in unfairness, and demonstrates that it can lead to accuracy disparities among groups of individuals. The paper also analyzes the algorithmic and data properties that contribute to these disproportionate impacts, why these aspects are affecting different groups disproportionately, and offers recommendations for mitigating these effects.