Reviewerly/exHarmony huggingface dataset:
Task-Prompt Generalised World Model in Multi-Environment Offline Reinforcement Learning

Offline reinforcement learning (RL) circumvents costly interactions with the environment by utilising historical trajectories. Incorporating a world model into this method could substantially enhance the transfer performance of various tasks without expensive calculations from scratch. However, due to the complexity arising from different types of generalisation, previous works have focused almost exclusively on single-environment tasks. In this study, we introduce a multi-environment offline RL setting to investigate whether a generalised world model can be learned from large, diverse datasets and serve as a good surrogate for policy learning in different tasks. Inspired by the success of multi-task prompt methods, we propose the Task-prompt Generalised World Model (TGW) framework, which demonstrates notable performance in this setting. TGW comprises three modules: a task-state prompter, a generalised dynamics module, and a reward module. We implement the generalised dynamics module as a transformer-based recurrent state-space model and employ prompts to provide task-specific instructions, enabling TGW to address the internal stochasticity of the generalised world model. On the MuJoCo control benchmarks, TGW significantly outperforms previous offline RL algorithms in multi-environment setting.
Instance-Aware Diffusion Implicit Process for Box-Based Instance Segmentation

The diffusion model has demonstrated impressive performance in image generation, but its potential for discriminative tasks such as instance segmentation remains unexplored. In this paper, we propose an Instance-aware Diffusion Implicit Process (IDIP) framework for instance segmentation based on boxes. During training, IDIP diffuses ground-truth boxes across various time steps, extracting corresponding Region of Interest (RoI) features. Dynamic convolution is then used to predict boxes and categories for each RoI, and the mask head generates masks from these predictions. During inference, IDIP iteratively refines randomly generated boxes with the denoising diffusion implicit model, while the mask head derives final masks from RoIs based on the refined boxes. Our method surpasses existing approaches on the COCO benchmark, requiring fewer training steps and less memory resources due to its dynamic design and instance-aware characteristic.
Fingerprint Attack: Client De-Anonymization in Federated Learning

Federated Learning allows collaborative training without data sharing in settings where participants do not trust the central server and one another. Privacy can be further improved by ensuring that communication between the participants and the server is anonymized through a shuffle; decoupling the participant identity from their data. This paper seeks to examine whether such a defense is adequate to guarantee anonymity, by proposing a novel fingerprinting attack over gradients sent by the participants to the server. We show that clustering of gradients can easily break the anonymization in an empirical study of learning federated language models on two language corpora. We then show that training with differential privacy can provide a practical defense against our fingerprint attack.
Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation

Multiple intent detection and slot filling are two fundamental and crucial tasks in spoken language understanding. Motivated by the fact that the two tasks are closely related, joint models that can detect intents and extract slots simultaneously are preferred to individual models that perform each task independently. The accuracy of a joint model depends heavily on the ability of the model to transfer information between the two tasks so that the result of one task can correct the result of the other. In addition, since a joint model has multiple outputs, how to train the model effectively is also challenging. In this paper, we present a method for multiple intent detection and slot filling by addressing these challenges. First, we propose a bidirectional joint model that explicitly employs intent information to recognize slots and slot features to detect intents. Second, we introduce a novel method for training the proposed joint model using supervised contrastive learning and self-distillation. Experimental results on two benchmark datasets MixATIS and MixSNIPS show that our method outperforms state-of-the-art models in both tasks. The results also demonstrate the contributions of both bidirectional design and the training method to the accuracy improvement. Our source code is available at https://github.com/anhtunguyen98/BiSLU.
XFed: Improving Explainability in Federated Learning by Intersection Over Union Ratio Extended Client Selection

Federated Learning (FL) allows massive clients to collaboratively train a global model without revealing their private data. Because of the participants’ not independently and identically distributed (non-IID) statistical characteristics, it will cause divergence among the client’s Deep Neural Network model weights and require more communication rounds before training can be converged. Moreover, models trained from non-IID data may also extract biased features and the rationale behind the model is still not fully analyzed and exploited. In this paper, we propose eXplainable-Fed (XFed) which is a novel client selection mechanism that takes both accuracy and explainability into account. Specifically, XFed selects participants in each round based on a small test set’s accuracy via cross-entropy loss and interpretability via XAI-accuracy. XAI-accuracy is calculated by Intersection over Union Ratio between the heat map and the truth mask to evaluate the overall rationale of accuracy. The results of our experiments show that our method has comparable accuracy to state-of-the-art methods specially designed for accuracy while increasing explainability by 14%-35% in terms of rationality.
Adam Accumulation to Reduce Memory Footprints of Both Activations and Gradients for Large-Scale DNN Training

Running out of GPU memory has become a main bottleneck for large-scale DNN training. How to reduce the memory footprint during training has received intensive research attention. We find that previous gradient accumulation reduces activation memory but fails to be compatible with gradient memory reduction due to a contradiction between preserving gradients and releasing gradients. To address this issue, we propose a novel optimizer accumulation method for Adam, named Adam Accumulation (AdamA), which enables reducing both activation and gradient memory. Specifically, AdamA directly integrates gradients into optimizer states and accumulates optimizer states over micro-batches, so that gradients can be released immediately after use. We mathematically and experimentally demonstrate AdamA yields the same convergence properties as Adam. Evaluated on transformer-based models, AdamA achieves up to 23% memory reduction compared to gradient accumulation with less than 2% degradation in training throughput. Notably, AdamA can work together with memory reduction methods for optimizer states to fit 1.26×~3.14× larger models over PyTorch and DeepSpeed baseline on GPUs with different memory capacities.
From Decision Trees to Explained Decision Sets

Recent work demonstrated that path explanation redundancy is ubiquitous in decision trees, i.e. most often paths in decision trees include literals that are redundant for explaining a prediction. The implication of this result is that decision trees must be explained. Nevertheless, there are applications of DTs where running an explanation algorithm is impractical. For example, in settings that are time or power constrained, running software algorithms for explaining predictions would be undesirable. Although the explanations for paths in DTs do not generally represent themselves a decision tree, this paper shows that one can construct a decision set from some of the decision tree explanations, such that the decision set is not only explained, but it also exhibits a number of properties that are critical for replacing the original decision tree.
STRUCTURAL MODELS OF ORGANIZING COGNITIVE LEARNING SKILLS

The paper presents the results of a study of the relationship between metacognitive involvement in activities, the severity of intelligence and 4K competencies. The methodology for studying is based on modeling by structural equations. The authors have developed original structural models of cognitive skills that determine the ability to learn. The structural organization of learning success skills was revealed: metacognitive involvement, intelligence characteristics and universal competencies (creativity, critical thinking, communication, cooperation).
Emprical Study for Algorithms Comparison of Classification and Regression Tree and Logistic Regression Using Combined 5×2cv F Test

Classification is a method to estimate the class of an object based on its characteristics. Several learning algorithms can be applied in classification, such as Classification and Regression Tree (CART) and logistic regression. The main goal of classification is to find the best learning algorithm that can be applied to get the best classifier. In comparing two learning algorithms, a direct comparison by seeing the smaller prediction error rate may be possible when the difference is very clear. In this case, direct comparison is misleading and resulting inadequate conclusions. Therefore, a statistical test is needed to determine whether the difference is real or random. The results of the 5×2cv paired t-test sometimes reject and sometimes fail to reject the hypothesis. It is distracting because the changing of the error rate difference should not affect the test result. Meanwhile, the overall results of the combined 5×2cv F test show that the tests fail to reject the hypothesis. This indicates that CART and logistic regression perform identically in this case.
Comparing Classification and Regression Tree and Logistic Regression Algorithms Using 5×2cv Combined F-Test on Diabetes Mellitus Dataset

Classification is the process of finding a model that describes and distinguishes data classes that aim to be used to predict the class of objects whose class labels are unknown. There are several algorithms in classification, such as classification trees and regression trees (CART) and logistic regression. The k-fold cross validation method has a weakness for algorithm comparison problems it is possible at different folds to produce different error predictions, so that the results of comparing algorithm performance will also be different. There for in the problem of comparison of algorithms, the researcher will apply the 52cv t test method and the 52cv combined F test. Out of 100 iterations the 10-fold cross validation method was only consistent three times which shows that the k-fold cross validation method has poor consistency in comparing the CART algorithm and logistic regression for diabetes mellitus data. In addition, 52cv combined F test and 52cv t test methods that have been carried out show that 52cv combined F test is better used to get conclusions from the results of a comparison of the two algorithms because it only produces one decision, in contrast to 52cv t test which has the possibility to get different decisions from 10 test statistics which results makes it difficult for researchers to draw conclusions in comparing the cart algorithm and logistic regression
Analysis of Vulnerability Assessment Technique Implementation on Network Using OpenVas

Vulnerability Assessment is an important method for identifying and analyzing security vulnerabilities within a network system. This research aims to identify security vulnerabilities within the PT. Dutakom Wibawa Putra network using OpenVAS as a research tool. In the vulnerability analysis phase, OpenVAS is utilized to scan the PT. Dutakom Wibawa Putra network and identify existing vulnerabilities. Subsequently, an evaluation is conducted on the identified vulnerabilities, including risk assessment and necessary mitigation recommendations. The outcomes of this study provide a clear overview of the security vulnerabilities present in the PT. Dutakom Wibawa Putra network. This research using method analysis of vulnerability assessment technique implementation on network using OpenVas, several significant vulnerabilities that could impact the security of the network and systems have been identified. The findings of this analysis report can serve as a foundation for developing improved security strategies and implementing effective mitigation measures. In conclusion, this study successfully applies Vulnerability Assessment techniques to the PT. Dutakom Wibawa Putra network using OpenVAS. The identified vulnerability analysis results offer valuable insights into security weaknesses that need to be addressed. It is hoped that this research can serve as a reference for PT. Dutakom Wibawa Putra and similar organizations in enhancing their network security through the implementation of effective Vulnerability Assessment techniques.&#x0D;
Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods

Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.
Analisis Aspek Kepribadian Tokoh Utama Dalam Novel Trauma Karya Boy Candra, Implikasinya Terhadap Pembelajaran Sastra Di SMA

The purpose of this study is to describe the 3 aspects of personality proposed by Sigmund Freud, namely the id, ego, and superego contained in the main character of Boy Candra's novel Trauma and their implications for learning literature in high school. The method used in this research is descriptive qualitative research method. The data used in this study are quotes that are included in the 3 aspects of personality in Boy Candra's novel Trauma. The results of this study indicate that (1) Id in the main character, Kimara, originates from the instinct of curiosity about what actually happened. (2) The ego in the kimara character always realizes the demands of the id. The ego in the Kimara character always acts as a conveyer of reality, that is, when the instinct of curiosity exists, the ego will always act. (3) Kimara's superego is based on moral values. (4) This research has implications for learning literature in high school, namely analyzing the content and language of the novel, interpreting the author's view of life in the novels they read.
Upaya Meningkatkan Keterampilan Menulis Deskripsi Dengan Menggunakan Metode Tanya Jawab Berbantuan Media Gambar Berseri Pada Peserta Didik Kelas IV-A Di SDN 8 Menteng Palangka Raya

The research background in learning Indonesia include aspects of listening, speaking, reading, writing. Writing skill as one of the four language skills has a very important role in human life. This study aims to improve the ability to write descriptions in class IV A students at SD Negeri 8 Menteng Palangka Raya and describe the activities of students during learning to write descriptions using the question and answer method assisted by serial picture media to students in class IV A SD Negeri 8 Menteng Palangka Raya .&#x0D; This research uses the Classroom Action Research (CAR) using the question and answer method. The population of this research is SDN 8 Menteng Palangka Raya. The sample of the research is 33 students of Class IV A of SDN 8 Menteng Palangka Raya.&#x0D; The results of this research are improving writing descriptions can be seen from the average score of students during the pretest, namely 65.60. The results of the first cycle of post test writing descriptions get an average of 71.65. while the results of the post test cycle II writing a description get an average of 80.17. increase in the average value of cycles I and II of 8.52. The classical completeness in cycle I was 63.63% and Cycle II was 93.93%.
Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms

Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.
A Longitudinal Case Study on Transformational Videoconferencing-Based EFL Teacher Education for Pre-service Teachers’ Professional Development

New technologies and digital learning have opened up a wide range of learning possibilities for language learners. Even though traditional, face-to-face foreign language teaching remains predominant, there is a growing interest in teaching foreign languages (especially English as a Foreign Language - EFL) on videoconferencing (VC) platforms such as Zoom and MS Teams. Using the
Uncertain Relational Hypergraph Attention Networks for Document-Level Event Factuality Identification

Document-level event factuality identification (DocEFI) is an important task in event knowledge acquisition, which aims to detect whether an event actually occurs or not from the perspective of the document. Unlike the sentence-level task, a document can have multiple sentences with different event factualities, leading to event factuality conflicts in DocEFI. Existing studies attempt to aggregate local event factuality by exploiting document structures, but they mostly consider textual components in the document separately, degrading complicated correlations therein. To address the above issues, this paper proposes a novel approach, namely UR-HAT, to improve DocEFI with uncertain relational hypergraph attention networks. Particularly, we reframe a document graph as a hypergraph, and establish beneficial n-ary correlations among textual nodes with relational hyperedges, which helps to globally consider local factuality features to resolve event factuality conflicts. To better discern the importance of event factuality features, we further represent textual nodes with uncertain Gaussian distributions, and propose novel uncertain relational hypergraph attention networks to refine textual nodes with the document hypergraph. In addition, we select factuality-related keywords as nodes to enrich event factuality features. Experimental results demonstrate the effectiveness of our proposed method, and outperforms previous methods on two widely used benchmark datasets.
MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient

Monocular 3D object detection is an inherently ill-posed problem, as it is challenging to predict accurate 3D localization from a single image. Existing monocular 3D detection knowledge distillation methods usually project the LiDAR onto the image plane and train the teacher network accordingly. Transferring LiDAR-based model knowledge to RGB-based models is more complex, so a general distillation strategy is needed. To alleviate cross-modal problem, we propose MonoSKD, a novel Knowledge Distillation framework for Monocular 3D detection based on Spearman correlation coefficient, to learn the relative correlation between cross-modal features. Considering the large gap between these features, strict alignment of features may mislead the training, so we propose a looser Spearman loss. Furthermore, by selecting appropriate distillation locations and removing redundant modules, our scheme saves more GPU resources and trains faster than existing methods. Extensive experiments are performed to verify the effectiveness of our framework on the challenging KITTI 3D object detection benchmark. Our method achieves state-of-the-art performance until submission with no additional inference computational cost. Our codes are available at https://github.com/Senwang98/MonoSKD.
Reinforcement Learning with Reward Machines in Stochastic Games

We investigate multi-agent reinforcement learning for stochastic games with complex tasks, where the reward functions are non-Markovian. We utilize reward machines to incorporate high-level knowledge of complex tasks. We develop an algorithm called Q-learning with reward machines for stochastic games (QRM-SG), to learn the best-response strategy at Nash equilibrium for each agent. In QRM-SG, we define the Q-function at a Nash equilibrium in augmented state space. The augmented state space integrates the state of the stochastic game and the state of reward machines. Each agent learns the Q-functions of all agents in the system. We prove that Q-functions learned in QRM-SG converge to the Q-functions at a Nash equilibrium if the stage game at each time step during learning has a global optimum point or a saddle point, and the agents update Q-functions based on the best-response strategy at this point. We use the Lemke-Howson method to derive the best-response strategy given current Q-functions. The three case studies show that QRM-SG can learn the best-response strategies effectively. QRM-SG learns the best-response strategies after around 7500 episodes in Case Study I, 1000 episodes in Case Study II, and 1500 episodes in Case Study III, while baseline methods such as Nash Q-learning and MADDPG fail to converge to the Nash equilibrium in all three case studies.