Reviewerly/exHarmony huggingface dataset:
Global Knowledge-Aware and Local Attention-Aware Framework for News Recommendation

Nowadays, news recommendations become the most popular approach to finding useful news information when the number of users dramatically increases after the COVID-19 pandemic. Therefore, the recommendation system must extract personalized features to simplify things for users to get the content they want and to make reading more enjoyable. In this paper, we propose the global knowledge-aware and local attention-aware recommendation framework which is improved from a knowledge-aware CNN. The global knowledge-aware module extracts the knowledge-graph via every single word in news titles, corresponding entities of its word, and associated contexts. The local attention-aware module analyzes the relationship between the candidate news and various historical news. Subsequently, from the two above modules, our framework can effectively predict whether the candidate news is clicked by the user or not. The training data is the MIND dataset. The global knowledge-aware and local attention-aware framework has shown remarkable results which outperform the existing methods in news recommendation in AUC, MRR, nDCG@5, and nDCG@10 evaluation metrics.
Research and Development of a Smart Solution for Runtime Web Application Self-Protection

In contemporary times, ensuring web application security is a critical concern for organizations due to the prevalence of numerous types of attacks that serve diverse purposes. While traditional security measures such as web application firewalls (WAF) and intrusion detection systems (IDS) can help mitigate attacks, there is still a possibility of them being circumvented or compromised. A more efficacious approach is to adopt runtime application self-protection (RASP) solutions integrated within the web application. This solution has demonstrated its effectiveness by aiding in early attack detection and rapid attack mitigation. In this research, we propose a smart solution for runtime web application self-protection (RASP) to protect against vulnerabilities, attacks, and common weaknesses that have been rated among the top ten web security risks in 2021 by the Open Web Application Security Project (OWASP). The proposed solution leverages convolutional neural network (CNN) and a family of recurrent neural network (RNN) techniques. It builds a deep learning model with deep neural network architectures that scrutinizes user requests, thereby detecting potential SQL injection (SQLi), Cross-Site scripting (XSS), command injection (CMDi), and other types of attacks. The solution is designed to dynamically adapt to the application's behavior and traffic, with the goal of minimizing false positives and preventing the blocking of legitimate traffic. Furthermore, the proposed solution, based on a microservices architecture, enhances the flexibility of the prediction module during upgrades and automated deployment. It is integrated with MLOps and DevSecOps and is also designed to be compatible with RESTful API servers. Our results have validated the efficacy of this solution in providing real-time application protection.
MCLDA: Multi-level Contrastive Learning for Domain Adaptive Semantic Segmentation

In this paper, we present a domain adaptation method for semantic segmentation in the context of autonomous vehicles. Our method builds upon a self-training approach with teacher-student architecture and integrates multi-level contrastive learning mechanisms. More specifically, we apply the contrastive learning at two levels: feature level and output level, which encourages pixel representations of the same category to cluster together while promoting dispersion between different categories. Additionally, we adapt a mixing technique to address the imbalance problem of dataset. The central concept involves the iterative copying of pixels from common classes in source images and pasting them into target images based on their occurrence frequency. The experiments on the widely used GTA5 → Cityscapes and SYNTHIA → Cityscapes benchmarks yield mIoU scores of 56.76% and 50.0%, respectively. These scores are comparable with the best methods using the same segmentation architecture Deeplabv2 (ResNet101). It is worth noting that our approach performs notably well in difficult classes such as "truck" and "bus" with respective scores of 56.7% and 57.0%. This further underscores the effectiveness of our proposal.
A Novel Approach to Streaming QoE Score Calculation by Integrating Error Impacts

Video streaming services have become prominent in the last decade. As any other cloud service, these services are error-prone, and the errors during startup and/or playback affect the viewing experience of end-users. Hence, the calculation of Quality-of-Experience (QoE) scores should also account for error impacts. In this paper, we introduce a player-based error classification scheme, which classifies errors based on origin and severity. We use this scheme to quantify the quality degradation due to errors, and propose to improve the QoE score by integrating these quality factors. We instrument the open-source media players dash.js and Exoplayer in our proposed system which follows the guidelines of various multimedia streaming standards. We define several scenarios focusing on different QoE influencing factors, and assess our proposed model's performance. Comparisons with various state-of-the-art QoE models show that our model captures the effect on user experience better in scenarios induced with player-related errors.
A Helpful Reviews Composition Assistance System for Online Shopping Users

This paper proposes an assistance system for composing helpful reviews for online shopping users. In online shopping, reviews are essential for users to make informed purchase decisions. However, the quality of reviews varies greatly, and it can be difficult for users to write helpful reviews. To address this issue, we propose an assistance system for writing helpful reviews. The proposed system has two modules: a helpfulness evaluation module and a random example providing module. The helpfulness evaluation module is used to assess the helpfulness of a review. It is implemented by learning labeled review texts with fastText1. The random example providing module is used to provide users with examples of helpful reviews. It randomly shows a helpful review from the dataset. We conducted an experiment with 15 participants to evaluate the effectiveness of the proposed system. The results showed that both the helpfulness evaluation module and the random example providing module were effective in supporting writing helpful reviews. Based on the results of our experiment, we conclude that the proposed system is a promising approach for improving the quality of online shopping reviews.
Few-shot Object Counting with Low-cost Counting Phase

Object counting, a fundamental task in computer vision, holds a pivotal role in a wide range of applications, including surveillance systems and environmental monitoring. However, conventional models that rely on the early-fusion mechanism often face difficulties when seamlessly integrating with retrieval systems. While this mechanism is effective, it demands significant computational resources, thereby impeding the scalability of counting processes and limiting their practical applicability. In response to these challenges, our study introduces an innovative two-phase approach to object counting. By breaking down the counting task into distinct phases, we substantially reduce computational overhead while maintaining performance levels comparable to those of previous methods relying on resource-intensive counting decoders. This approach is opening up exciting new possibilities for real-world applications.
Sharpness and Gradient Aware Minimization for Memory-based Continual Learning

Memory-based Continual Learning methods (CL) preserve performance on old data by storing a small buffer of seen samples to re-learn with current data. Despite their impressive results, these methods may still obtain sub-optimal solutions as a result of overfitting training data, especially on the limited buffer. This can be attributed to their employment of empirical risk minimization over training data. To overcome this problem, we leverage Sharpness Aware Minimization (SAM), a recently proposed training technique, to improve models' generalization, and thus CL performance. In particular, SAM seeks for flat minima whose neighbors' loss values are also low by simultaneously guiding a model towards SAM gradient direction corresponding to low-loss regions and flat regions. However, we conjecture that directly applying SAM to replay-based CL methods whose loss function contains multiple objectives may cause gradient conflict among them. We then propose to manipulate each objective's SAM gradient such that their potential conflict is minimized by adopting one gradient aggregation strategy from Multi-task Learning. Finally, through extensive experiments, we empirically verify our hypothesis and show consistent improvements in our method over strong memory-replay baselines.
Enhancing Intrusion Detection and Explanations for Imbalanced Vehicle CAN Network Data

In modern automobiles, the reliance on vehicle Controller Area Network (CAN) networks has surged, underlining the paramount significance of safeguarding these networks against intrusions. In this work, we unveil an innovative approach for intrusion detection and explanation within in-vehicle CAN networks, employing the formidable synergy of Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Our method is tailored to address network imbalance challenges, offering prowess in binary and multiclass classification tasks. Integral to our approach is the seamless integration of SHAP values, serving as illuminating guides that unravel the intricacies of detected intrusions. This fusion elevates the system's interpretability, equipping stakeholders with deeper insights. Our contribution is underpinned by a rigorous evaluation of our approach, featuring a comprehensive analysis of a published dataset alongside comparisons with established literature. The results underscore the exceptional efficacy of our method, showcasing its remarkable accuracy in detecting intrusions. However, the essence of our methodology transcends mere detection precision. The explanatory capabilities of SHAP values come to the forefront. This augments both understanding and decision-making into the contributing factors behind the detected intrusion classification model.
BlazeSearch: A multimomal semantic search engine for retrieving in-video information for AI Challenge HCMC 2023

In the world today, exploring information has become a critical part of modern life. As a result, search engines have shown their ability to enhance the knowledge-seeking process. However, these search engines still focus on searching for websites or images. The capacity to find information in videos is extremely needed to experiment and study more in order to improve the power of search engines. In this study, we investigate the potentiality of in-video information search engines by introducing BlazeSearch, a multimodal search engine designed to search frames of video with simple input text. By leveraging the OpenCLIP model, which is superior for the image-text retrieval task, our search engine can be guaranteed reliability and accuracy. Furthermore, we optimize the searching speed and provide an easy-to-use, fully functional user interface for BlazeSearch, which can help users have a pleasant experience.
DoppelSearch: A Novel Approach to Content-Based Video Retrieval for AI Challenge HCMC 2023

Video retrieval, which has been considered as a critical task in the field of computer vision and pattern recognition recently, finds extensive applications in several aspects such as education, entertainment, security, and healthcare. However, it faces challenges due to the complexity of video data, the instability in feature extraction methods, or the semantic disparities between videos and text. In this paper, we present a novel approach for content-based video retrieval, named DoppelSearch, leveraging the CLIP (Contrastive Language-Image Pre-training) model architecture to classify and label video segments, offering users the ability to search for videos based on specific content. Our method capitalizes on the ViT-b/32 model for feature extraction and employs feature embedding, in conjunction with the Faiss library, to enhance search efficiency. Experimental results demonstrate our model's high accuracy and swift retrieval times, promising new opportunities in content-based video retrieval for researchers, developers, and end-users. This paper not only introduces the application of the CLIP and ViT-b/32 models, but also elaborates on the specific feature extraction process and the utilization of Faiss for optimizing video retrieval. The DoppelSearch method represents a significant stride in the field of video retrieval and holds promise for diverse applications across various industries.
Vi-ATISO: An Effective Video Search Engine at AI Challenge HCMC 2023

In this paper, we present the first version of Vi-ATISO, a fast and efficient video search engine on medium-scale datasets. The tool provides several search functions based on text-to-image retrieval, text-to-video retrieval, optical character recognition, and object detection algorithms. With diverse algorithms provided, our system can handle a larger amount of data from the AI Challenge HCMC 2023 and achieve good results. In addition, we feel confident that this search engine can be applied in practice because we also consider user experience during the development process.
Shark-Eyes: A multimodal fusion framework for multi-view-based phishing website detection

In the era of escalating cyber threats, phishing attacks continue to exploit vulnerabilities in online security. This paper presents Shark-Eyes, a novel multimodal fusion framework designed for the detection of phishing websites using a multi-view approach. The proposed approach leverages a combination of two distinct attributes, namely domain features and HTML tag features, extracted from the target websites. The framework's effectiveness is evaluated through comprehensive experiments on a dataset sourced from Phishtank, OpenPhish, and Alexa, encompassing real-world phishing instances. Our results demonstrate the robustness and efficiency of the Shark-Eyes framework in accurately identifying phishing websites, showcasing its potential as a powerful tool for enhancing online security and thwarting malicious activities.
Optimizing GANs using Relativistic Discriminator with Margin Losses for Semi-supervised Learning

We introduce a novel framework, termed RMGANs, that merges Relativistic Generative Adversarial Networks (RGANs) with Margin Losses. This framework capitalizes on the strengths of both RGANs' discriminators, which assess the realism of real and fake data, and the advantages of Margin Losses, well-known for their ability to create distinct class separation. We apply this combination to the problem of semi-supervised learning (SSL). Our study delves into the architecture of RMGANs, provides a mathematical analysis to assess the impact of margin utilization on RMGAN losses, and offers guidance on choosing hyper-parameters. We also conduct experiments across the MNIST and CIFAR-10 datasets. The empirical results clearly demonstrate RMGANs' effectiveness in achieving higher accuracy compared to the state-of-the-art work (MarginGAN) in the SSL fashion.
Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education

In this paper, we evaluate the ability of large language models (LLMs) to perform multiple choice symbol binding (MCSB) for multiple choice question answering (MCQA) tasks in zero-shot, one-shot, and few-shot settings. We focus on Vietnamese, with fewer challenging MCQA datasets than in English. The two existing datasets, ViMMRC 1.0 and ViMMRC 2.0, focus on literature. Recent research in Vietnamese natural language processing (NLP) has focused on the Vietnamese National High School Graduation Examination (VNHSGE) from 2019 to 2023 to evaluate ChatGPT. However, these studies have mainly focused on how ChatGPT solves the VNHSGE step by step. We aim to create a novel and high-quality dataset by providing structured guidelines for typing LaTeX formulas for mathematics, physics, chemistry, and biology. This dataset can be used to evaluate the MCSB ability of LLMs and smaller language models (LMs) because it is typed in a strict LaTeX style. We determine the most probable character answer (A, B, C, or D) based on context, instead of finding the answer step by step as in previous Vietnamese works. This reduces computational costs and accelerates the evaluation of LLMs. Our evaluation of six well-known LLMs, namely BLOOMZ-7.1B-MT, LLaMA-2-7B, LLaMA-2-70B, GPT-3, GPT-3.5, and GPT-4.0, on the ViMMRC 1.0 and ViMMRC 2.0 benchmarks and our proposed dataset shows promising results on the MCSB ability of LLMs for Vietnamese. The dataset is available1 for research purposes only.
Have You Ever Been <i>in</i> A Volcano?

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsJill RaisorJill Raisor (jmraisor@usi.edu) is an associate professor of education, and Tony Maria (ahmaria@usi.edu) is a professor of geology, both at the University of Southern Indiana in Evansville, Indiana.Tony MariaJill Raisor (jmraisor@usi.edu) is an associate professor of education, and Tony Maria (ahmaria@usi.edu) is a professor of geology, both at the University of Southern Indiana in Evansville, Indiana.
Science 101: Do You Have an Activity for Daily and Seasonal Patterns and Motions of the Earth?

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsMatt BobrowskyMatt Bobrowsky is the lead author of the NSTA Press book series, Phenomenon-Based Learning: Using Physical Science Gadgets & Gizmos. You can let him know if there’s a science concept that you would like to hear more about. Contact him at: DrMatt@msb-science.com
Science 101: What Are Some Fun Activities Related to Thunderstorms?

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsMatt BobrowskyMatt Bobrowsky (DrMatt@msb-science.com) is the lead author of the NSTA Press book series, Phenomenon-Based Learning: Using Physical Science Gadgets & Gizmos.
Commentary: Preparing for the Great American Eclipse of 2024

"Commentary: Preparing for the Great American Eclipse of 2024." Science Scope, 46(6), pp. 6–7 Additional informationNotes on contributorsKurtz MillerKurtz Miller (Kurtz.miller@myhhcs.org) is a science teacher at Huber Heights City Schools in Huber Heights, Ohio.
The 2023 and 2024 Solar Eclipse Double-Header: The Perfect Opportunity to Highlight Three-Dimensional Science Learning

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsDennis SchatzDennis Schatz (schatz@pacsci.org) is Senior Fellow at the Institute for Learning Innovation. He lives in Seattle, Washington.Andrew FraknoiAndrew Fraknoi (fraknoi@fhda.edu) is Professor of Astronomy at the Fromm Institute at the University of San Francisco. They are both leaders in the Solar Eclipse Task Force of the American Astronomical Society and in the project to distribute 5 million safe-viewing glasses (and information) for the upcoming eclipses through 10,000 public libraries, funded by the Gordon and Betty Moore Foundation.
Adaptation of internally displaced students to new learning environment through innovative methods of open teacher training in war perspectives

The article investigates the impact of innovative open learning technologies on training future teachers, particularly those internally displaced in Ukraine due to the war with Russia. It explores the current conditions of these students and how to meet their educational needs through online learning. The successful adaptation of displaced students relies on government's social, legal, and academic support, highlighting the importance of online learning in war response. The methodology involved surveying 437 displaced students from Ukrainian higher education institutions, using various questionnaires. Data on inputs, outputs, and outcomes of online learning programs were collected and analyzed. Both benefits (continuous access to learning, flexibility, low economic cost, access to international programs) and risks (lack of socialization, insufficient technical support, resource imbalance) were assessed. The findings indicate that adaptation challenges are mainly linked to poor living conditions, stress, and changes in learning conditions and curriculum. The study concludes that open learning technologies facilitate the adaptation of displaced students, ensuring educational continuity and adherence to international standards.
EVALUATION OF ACCOUNTING INFORMATION SYSTEMS ON BUDGETING, USE AND REPORTING OF FUNDS REGULAR SCHOOL OPERATIONAL AID (BOS). (CASE STUDY IN SMA NEGERI 4 KOTA SERANG BUDGET YEAR 2020)

Purpose - This study aims to analyze and determine the application of Accounting Information Systems in the field of Budgeting, Use and Reporting of Regular School Operational Assistance (BOS) Funds at SMA Negeri 4 Kota Serang. Design/methodology/approach - The research was conducted at SMA Negeri 4 Kota Serang. This study uses a qualitative approach. Qualitative Approach Method is a research method based on the philosophy of post positivism used to examine the condition of natural objects, where the researcher is the key instrument. Finding - The Accounting Information System related to the BOS Fund has actually existed since 2019 and was socialized in early 2020. This application created by the Ministry of Education and Culture has been able to accommodate all financial activities from the Regular School Operational Assistance Fund but in its implementation there are many components of incomplete item names, making it difficult for users to operate. A good system is a system that is made to make it easier for users to operate, understandable and structured. Originality - The budget will be able to run effectively based on the time of planning before budgeting, good and careful planning will ensure that the budget implementation function can run effectively and efficiently as expected