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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Saputra, Carsten Sebastian Wijaya, Norbert Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

For both print and digital media, the accuracy of the information has long been a problem that has impacted society and the business world. Information travels so quickly on social networks and is amplified that it can be skewed, false, or misleading. Inaccurate or incorrect information has great potential to affect millions of real-world users directly. Therefore, finding or getting accurate, trusted, and precise information becomes a problem. People start to spread fake news, which can cause certain events, such as mental disturbance, riots, and unnecessary anxiety. To prevent these problems, we will solve/research them with CountVectorizer and TF-IDF, which is what this study is all about (Discusses the detection of hoax news using CountVectorizer and TF-IDF). It was found that using these methods makes it possible to detect which words are most common in fake news and real news. © 2023 IEEE.

关键词： Digital storage

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Analysis of Performance Expressions Using Sequential Tension Values 23

Analysis of Performance Expressions Using Sequential Tension...

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7th International Conference on computer science and Application Engineering, CSAE 2023

作者： Miura, Shingo Mizutani, Tetsuya Master's Program in Computer Science University of Tsukuba Tsukuba Japan Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798400700590

Human performers interpret and perform a piece of music based on their own musical sensibilities and the musical characteristics they read from the information in the score. In doing so, the changes in speed and volume that appear in the performance are called performance expressions. This performance expression changes each time the music piece or performer changes, and we believe that formalizing this expression will make it possible to perform more human-like performances on a computer. In this study, we focused on the fact that Generative Theory of Tonal Music (GTTM) [1], one of the cognitive music theories, uses a parameter called tension value to analyze a piece of music. However, one method of calculating tension values, hierarchical tension values, is not suitable for automatic analysis because it requires consideration based on advanced musical knowledge when calculating them. Therefore, this study uses a parameter called sequential tension value, which is obtained by a different calculation method. Although sequential tension values are less accurate than hierarchical tension values, they have the advantage of being easy to calculate and suitable for automatic analysis. This paper discusses how to improve the accuracy of analysis of musical pieces using sequential tension values and how each component of the musical structure that makes up the sequential tension value affects the analysis results. Multiple regression analysis is used for this analysis, with the musical expression obtained from the local tempo and volume changes for each event calculated from the score data as the objective variable and the musical symbols in each event as the explanatory variables. The accuracy of the obtained regression model is compared with the methods used in previous studies, indicating that the analysis using sequential tension values is useful for music analysis. © 2023 ACM.

关键词： Music

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Performance Analysis of Distributed Database System in Cloud Computing Environment 9

Performance Analysis of Distributed Database System in Cloud...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Prasetio, Nicholas Ferdinand, Daniel Wijaya, Brandon Christopher Anggreainy, Maria Susan Kumiawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program School of Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350370126

This research discusses the performance evaluation of distributed database systems in a cloud computing environment Cloud computing environments allow data and applications to be stored and deployed on infrastructure located in different parts of the world. However, the use of distributed database systems in cloud computing environments can cause performance issues, such as complex data access and factors such as network latency, security, and scalability that affect system performance. Therefore, performance evaluation of distributed database systems is necessary to ensure effective data management across the infrastructure. The purpose of this research is to measure and understand the performance of distributed database systems in a cloud computing environment. This is important because proper performance evaluation is needed to ensure distributed database systems can operate effectively and efficiently in such environments. This research will analyze the features of distributed database systems, factors that affect performance, how to measure and compare system performance, and how to improve system performance. Analyzing the performance of distributed database systems in a cloud computing environment can help users choose the most appropriate and efficient cloud computing platform for their business needs and improve operational efficiency. © 2023 IEEE.

关键词： Distributed database systems

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Computational Analysis of Jazz Music: Estimating Tonality through Chord Progression Distances 23

Computational Analysis of Jazz Music: Estimating Tonality th...

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7th International Conference on computer science and Application Engineering, CSAE 2023

作者： Yamamoto, Yuta Mizutani, Tetsuya Master's Program in Computer Science University of Tsukuba Tsukuba Japan Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798400700590

Currently, research in music informatics focuses extensively on music theory, particularly on the theoretical systems of Western classical music dating back to the 19th century. However, contemporary popular music genres such as pop, rock, and jazz often rely on jazz music theory, which is an evolution from classical music theory. To make music informatics research more practical and relevant to modern music composition, it is essential to explore music analysis based on jazz music theory using computational methods. In this study, we attempt to perform jazz music analysis computationally. Jazz music theory primarily centers around chord progressions. Therefore, Tonal Pitch Space (TPS) and its extension to jazz music theory, TPS-ExJ, are valuable tools for our investigation. We implement and modify these computational music theories to calculate the "chord distance,"a metric indicating the naturalness of chord progressions. This allows us to estimate the "key"of a piece of music, a concept determined by human sensibility, using computational techniques. Although the experimental results for key estimation are not particularly high, we believe that the theoretical framework proposed in this study forms a solid foundation for complex chord progressions' key estimation in jazz music. © 2023 ACM.

关键词： Music

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs 1

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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1st IEEE International Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Abdullah, Roni Reza Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia National Institute of Science and Technology Faculty of Informatics Engineering Jakarta12630 Indonesia Bina Nusantara University Binus Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience. © 2023 IEEE.

关键词： Sensitive data

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Towards a Generalized Bayesian Model of Reconstructive Memory: A Generalized Model of Reconstructive Memory

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Computational Brain and Behavior 2024年第1期8卷 134-146页

作者： Xu, Zihao Hemmer, Pernille Zhang, Qiong Computer Science Department Rutgers University–New Brunswick Piscataway United States Psychology Department Rutgers University–New Brunswick Piscataway United States Center for Cognitive Science Rutgers University–New Brunswick Piscataway United States

Prior knowledge has long been known to shape reconstruction from memory. An individual stimulus from a category is often remembered to be closer to the center of that category than its true location. This effect, together with more complex memory effects that involve prior knowledge at multiple levels of abstraction, has been successfully explained by the Category Adjustment Model (CAM;Huttenlocher et al. Journal of Experimental Psychology: General, 129(2), 220, 2000) and its extensions. However, recent experimental results diverge from CAM’s predictions showing that reconstructive memory for atypical category examples is influenced by the category center less than that of typical category examples. To unify these findings, we propose a generalized Bayesian model of reconstructive memory, called the generalized CAM model (g-CAM). We demonstrate through simulations that g-CAM can account for previously known effects of reconstructive memory, while additionally capturing recent empirical findings involving atypical category examples. © The Author(s) 2024.

关键词： Bayesian modeling Category Adjustment Model Category effect Memory distortion Reconstructive memory

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Evolution of moral semantics through metaphorization 44

Evolution of moral semantics through metaphorization

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Ramezani, Aida Stellar, Jennifer E. Feinberg, Matthew Xu, Yang Department of Computer Science University of Toronto Canada Department of Psychology University of Toronto Canada Rotman School of Management University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

Although language is critical to supporting morality within society, it is not clear how moral language itself evolved. We investigate the evolution of moral semantics, hypothesizing that words evolved to take on moral meanings from concrete experiences through metaphorization. We test this hypothesis by analyzing moral semantic change in words from the Moral Foundations Dictionary and the Historical Thesaurus of English over the past hundreds of years. In contrast with the observation that words become concrete over time, we demonstrate that moral words in the English lexicon undergo concrete-to-abstract shifts, reflecting systematic metaphorical mappings to the moral domain. Our results provide large-scale evidence for the role of metaphor in the historical development of the English moral lexicon. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Semantics

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Improving the Learning Efficiency of Hierarchical Reinforcement Learning SUHRL Using Transition Information Between Subgoals 13

Improving the Learning Efficiency of Hierarchical Reinforcem...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kurodo, Yuya Manabe, Yusuke Graduate School of Information and Computer Science Chiba Institute of Technology Japan Chiba Institute of Technology Department of Cognitive and Information Sciences Japan

ISBN: (纸本)9798350373332

The hierarchical reinforcement learning approach incorporates intrinsic motivation into reinforcement learning. In this approach, the agents are hierarchically organized into a higher-level strategy that learns the application order (plan) of sub goals and a lower-level strategy that learns the sequence of actions up to the subgoals. This incorporation has been shown to facilitate problem-solving even in environments with sparse rewards. Stepwise Unified Hierarchical Reinforcement Learning (SUHRL) has been proposed as a hierarchical reinforcement learning algorithm. SUHRL enables autonomous learning without the need for prior knowledge or predefined subgoals. However, learning efficiency is low, and, in some cases, problem solving is not possible. Therefore, we propose the enhancement of SUHRL by constraining the exploration of sub goals based on the number of steps required to transition between them. This improvement is based on Hierarchical Reinforcement Learning with a k-step Adjacency Constraint, a method that can achieve efficient learning when sub goals are predefined. Experimental results using the Frozen Lake environment show that the proposed method exhibits a better learning efficiency than SUHRL. © 2024 IEEE.

关键词： Adversarial machine learning

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BERT and RoBERTa Models for Enhanced Detection of Depression in Social Media Text 9th

BERT and RoBERTa Models for Enhanced Detection of Depression...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kurniadi, Felix Indra Paramita, Ni Luh Putu Satyaning Pradnya Sihotang, Erna Fransisca Angela Anggreainy, Maria Susan Zhang, Rongfang Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Statistic Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11530 Indonesia Department of Rehabilitation and Health Service University of North Texas United States

Depression is a disease that affects everyone, both young and old. This mental illness not only affects the surrounding environment but everyone. Depression is characterized by deep sadness, behavioral changes and many other actions that are risky for people. In this research we try to solve the problem of detecting depression using Natural Language Processing (NLP) approaches, these two methods are Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa), where these two methods are used to detect posts made in reddit. The dataset is taken from Kaggle. The results obtained found that the average use of BERT and RoBERTa resulted in a high accuracy value of around 98% and with a well balanced precision, recall and F1-Score ratio. This research shows that there is a possibility of using BERT and RoBERTa in depression detection. © 2024 The Authors.

关键词： BERT depression natural language processing RoBERTa

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Knowledge Graph-Enhanced Semantic Cache for Low-Latency and Cost-Effective Inference in Large Language Models 9

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and ...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Dominic, Nicholas Pardamean, Bens Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs. © 2024 IEEE.

关键词： Knowledge graph

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