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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Oscar Adam Darmawan Ford Lumban Gaol Haryono Soeparno Yulyani Arifin Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

The technique of calculating the work required for software development is known as software development estimation. The objective cost estimating technique for organizing and carrying out Multi Account Partner (MAP) software projects, COCOMO II, was employed in this study. The MAP software is a software architecture designed to support brokerage cryptocurrency exchanges using the order book and liquidity of established crypto exchanges. This research uses data sets from MAP project development at Indonesia Crypto Exchange Platform. It aims to create a software cost estimation model for MAP software using COCOMO II so that the resulting estimation model can be used as input or reference for estimates of subsequent MAP software development. The result estimated that MAP software finished in about four to five months, with a price range for software development of $7,441 to $8,780. Further research is needed with datasets from other crypto exchanges tested to increase cost estimation accuracy using COCOMO II.

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Attention is Everything You Need: Case on Face Mask Classification 8

Attention is Everything You Need: Case on Face Mask Classifi...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Pratama, Nanda Harianto, Dody Filbert, Stefan Warnars, Harco Leslie Hendric Spits Muyeba, Maybin K. School of Computer Science Bina Nusantara University West Jakarta Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University West Jakarta Jakarta11480 Indonesia MIST Consulting ManchesterM410SA United Kingdom

Automated face mask classification has surfaced recently following the COVID-19 mask wearing regulations. The current State-of-The-Art of this problem uses CNN-based methods such as ResNet. However, attention-based models such as Transformers emerged as one of the alternatives to the status quo. We explored the Transformer-based model on the face mask classification task using three models: Vision Transformer (ViT), Swin Transformer, and MobileViT. Each model is evaluated with a top-1 accuracy score of 0.9996, 0.9983, and 0.9969, respectively. We concluded that the Transformer-based model has the potential to be explored further. We recommended that the research community and industry explore its integration implementation with CCTV. © 2023 The Authors. Published by Elsevier B.V.

关键词： Attention Convolutional Neural Network Deep Learning Face Mask Classification Transformer

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Towards a Healthy AI Tradition: Lessons from Biology and Biomedical science

arXiv

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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Bioinformatics Graduate Program Department of Computer Science Boston University United States Department of Computer Science Tel Aviv University

AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science, neuroscience and more. The number of applications and impact of AI is second to none and the potential of AI to broadly impact future science developments is particularly thrilling. While attempts to understand knowledge, reasoning, cognition and learning go back centuries, AI remains a relatively new field. In part due to the fact it has so many wide-ranging overlaps with other disparate fields it appears to have trouble developing a robust identity and culture. Here we suggest that contrasting the fast-moving AI culture to biological and biomedical sciences is both insightful and useful way to inaugurate a healthy tradition needed to envision and manage our ascent to AGI and beyond (independent of the AI Platforms used). After all, the human brain is a biological organ produced by evolution and human intelligence is a remarkable bi-product of nature and nurture and their complex interaction. In this perspective, we focus on traditions and culture, namely the commonly observed practices of evaluating, recognizing applauding, critiquing, debating and managing all progress including useful advances and discovery of challenging limitations. We are not discussing specific scientific exchanges between AI and Biology that include interdisciplinary cross fertilization of scientific methods, technology, ideas and applications that have been amply demonstrated and will continue to be transformative in the future. In a previous perspective, we suggested that biomedical laboratories or centers can usefully embrace logistic traditions in AI labs that will allow them to be highly collaborative, improve the reproducibility of research, reduce risk aversion and produce faster mentorship pathways for PhDs and fellows. This perspective focuses on the benefits of AI a

关键词： Biological organs

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model

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Procedia computer science 2023年 227卷 168-175页

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app; there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80.

关键词： Sentiment Analysis TikTok SVM VADER Sentiment Google Play Store

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Machine Learning Approaches in Detecting Autism Spectrum Disorder

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Procedia computer science 2023年 227卷 1070-1076页

作者： Daniel Nicholas Dominic Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD.

关键词： autism spectrum disorder brain atlas brain region machine learning

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Metrics and Red Teaming in Cyber Resilience and Effectiveness: A Systematic Literature Review

Metrics and Red Teaming in Cyber Resilience and Effectivenes...

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International Conference on Telecommunications (ICT)

作者： Semi Yulianto Benfano Soewito Ford Lumban Gaol Aditiya Kurniawan Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program. Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Rising cyber risks have compelled organizations to adopt better cyber-protection measures. This study focused on discovering crucial security metrics and assessing the function of red teaming in enhancing cybersecurity defenses against novel cyber hazards. The PRISMA standard considered nine core research works issued between 2014 and 2023. The inclusion of red teaming best practices can significantly enhance cybersecurity architecture. Accurate simulations of cyber threats during red teaming exercises help identify vulnerabilities, and actively embracing red teaming can amplify an organization's capacity to repel future cyber assaults. Researchers and practitioners can utilize the study's insights to pioneer novel security solutions. Combining red teaming methodologies with relevant metrics is essential for enhancing cybersecurity posture. The study's discoveries grant companies a priceless benefit in navigating the rapidly changing cyber threat environment and reinforcing their cyber protection mechanisms.

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OVERLAPPING ERROR CORRECTION CODES ON TWO-DIMENSIONAL STRUCTURES

arXiv

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arXiv 2025年

作者： Fritsch, Andrew Rafael Marcon, César Augusto Missio Pontifical Catholic University of Rio Grande Do Sul School of Technology Computer Science Graduate Program Brazil

The growing demand for highly reliable communication systems drives research and development of algorithms capable of identifying and correcting errors that occur during data transmission and storage. This need becomes even more critical in hard-to-access or sensitive systems, such as those used in space applications, passenger transportation, and the financial sector. In this context, Error Correction Codes (ECCs) are essential tools for ensuring a certain level of reliability. This work proposes a technique to enhance ECC error correction capability through the overlapping of data regions. The approach consists of protecting the same data area with multiple ECCs organized in a two-dimensional structure, enabling logical inferences that correlate the codes and improve their error detection and correction capabilities. More specifically, the overlapping is characterized by the organization of multiple ECCs, whose intersection exclusively covers the entire data region. To evaluate the proposal, different configurations of overlapping ECCs were analyzed in terms of error detection and correction capability, scalability, and reliability. Experimental results confirm the effectiveness of the technique and demonstrate its high scalability potential, reducing the need for redundancy bits relative to the number of data bits. Furthermore, comparisons with state-of-the-art ECC approaches indicate the applicability of the technique in critical systems that require high reliability. © 2025, CC BY.

关键词： Scalability

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A motion-level-aware denoising framework for x-ray fluoroscopic images

A motion-level-aware denoising framework for x-ray fluorosco...

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Medical Imaging 2023: Physics of Medical Imaging

作者： Jo, Geonhui Jang, Minhee Jeon, Sun-Young Kim, Wonjin Choi, Jang-Hwan Department of Computer Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of

ISBN: (纸本)9781510660311

Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering the radiation dose inevitably increases random noise in x-ray images, resulting in poor diagnostic image quality, which requires noise reduction for accurate diagnosis. Also, in the case of non-static objects, the image is blurred due to motion. The most-used denoiser with a recursive filter (RF) preserves details well when applied to temporal data, but it is vulnerable to motion blur. Existing convolutional neural network (CNN)-based algorithms with single-frame input cannot use the temporary context, and others with multi-frame input are good for motion detection but poor for detail preservation. Therefore, we propose a motion-level-aware denoising framework to combine the results of RF- and CNN-based algorithms depending on the pixel-wise magnitude of motion to complement each other. The data we use are fluoroscopy images taken in continuous time, and we aim at many-to-one so that one frame is denoised by considering sequential frames. Also, since both RF- and CNN-based algorithms used in our architecture are many-to-one methods, they can consider spatiotemporal information. In the multi-frame input, the difference in intensity of each pixel between frames is calculated to obtain a moving map. Depending on the factor value from the moving map, the final image is obtained by reflecting the outputs of the RF- and CNN-based algorithms. If the factor value is high, the pixel intensity of the final image is like the CNN-based output, which is good for motion detection, and vice versa, it more reflects the intensity of RF output, which is excellent in perceptual quality. Therefore, it prevents motion blur and does not over-smooth microdetails, such as bones and muscles. The results show that combining the two outputs together records higher peak signal-to-noise ratio (PSNR) and has better perceptual quality for diagnosis than using only one method. F

关键词： Deep learning

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Negation Handling on XLNet Using Dependency Parser for Sentiment Analysis

Negation Handling on XLNet Using Dependency Parser for Senti...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Ferdinand Winaya Abba Suganda Girsang BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Negation handling is often overlooked in Indonesian sentiment analysis, making it difficult to automatically and accurately determine the polarity of sentences containing negation words. Negation is a challenging issue in natural language processing, as it can drastically alter the meaning of a sentence. For example, common Indonesian negation words such as “tidak” (no/not), “belum” (not yet), or “jangan” (do not) can reverse the sentiment polarity in a text. Previous studies have proposed rule-based approaches, relying on linguistic rules or dependency parse trees to handle negation. However, negation is a complex problem that cannot be effectively addressed by simple rules alone, as they often lack flexibility in handling diverse sentence structures and negation complexities. Therefore, a more dynamic approach is needed, such as combining a dependency parser with an embedding layer, which can map syntactic relationships between words and learn vector representations of negation. This allows the model to flexibly determine the scope of negation words, enabling more accurate sentiment analysis even in complex sentences. This study evaluates the impact of negation handling on a transformer-based model, specifically XLNet, in Indonesian sentiment analysis. Using the proposed method, the model's F1 score increased by 2.13%, from 71.42% to 73.55%, compared to the baseline model. This demonstrates that the proposed negation handling strategy enhances sentiment prediction accuracy, making the model more effective at handling texts with negation.

关键词： Sentiment analysis Analytical models Accuracy Syntactics Predictive models Linguistics Transformers Vectors Complexity theory

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Enhancing Efficiency in Yolov8n for Real-Time Detection of Oil Palm Fruits in Plantations Using Mobileone and Cross-Fusion

SSRN

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SSRN 2024年

作者： Hugo, Gregory Mushlik, Muhammad Suharjito Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Accurate detection of fresh fruit bunches (FFB) ripeness in complex plantation environments is challenging due to occlusion, varying lighting, intricate visual features, and limited computational resources of mobile devices. To address this, a resource-efficient, real-time detection system was developed using an optimized YOLOv8n model. The model integrates MobileOne modules and Cross-Fusion blocks to enhance efficiency without compromising accuracy. Specifically, Conv and C2f modules were replaced with MobileOne modules, reducing parameters by 85% and 81%, respectively. Cross-Fusion improved feature fusion, and its combination with MobileOne (CF-MobileOne) achieved a 51% reduction in parameters while maintaining performance. The dataset comprised images from videos captured in plantations, annotated into five maturity stages: flower, unripe, underripe, ripe, and abnormal, and underwent augmentation with class balancing through manual undersampling. The model outperformed baseline YOLOv8 variants and heavier models like YOLOv9c and Faster R-CNN, achieving an mAP50 of 0.795 and an inference speed of 140.57 FPS. Hyperparameter tuning with a batch size of 64, SGD optimizer, and a learning rate of 0.01 yielded optimal results. Despite challenges in detecting certain classes due to visual similarities and environmental variations, the system effectively enhances productivity and efficiency in precision agriculture for the palm oil industry. © 2024, The Authors. All rights reserved.

关键词： Fruits

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