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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Iure Rosa L. De Oliveira Kevin B. De Carvalho Alexandre S. Brandão Graduate Program in Computer Science Universidade Federal de Viçosa Viçosa Brazil Electrical Engineering Department Universidade Federal de Vicosa Viçosa Brazil

Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training.

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Towards a Reliable Hierarchical Android Malware Detection Through Image-based CNN

Towards a Reliable Hierarchical Android Malware Detection Th...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Jhonatan Geremias Eduardo K. Viegas Altair O. Santin Alceu Britto Pedro Horchulhack Pontificia Universidade Catolica do Parana (PUCPR) - Graduate Program in Computer Science (PPGIa) Brazil Secure Systems Research Center - Technology Innovation Institute (TII) Abu Dhabi

The number of Android malicious applications keeps growing as time passes, even paving their way to official app markets. In recent years, a promising malware detection approach makes use of the compiled app source codes (dex), through convolutional neural networks (CNN) as an image classification task. Unfortunately, current proposals often rely on unrealistic datasets, focusing their detection on the mal-ware families, while neglecting the detection of malware apps in the first place. In this paper, we propose a reliable and hierarchical Android malware detection through an image-based CNN scheme, implemented twofold. First, Android malware classification is performed in a hierarchically-structured local manner, initially identifying malware apps, then, their related family. Second, to ensure reliability and improve classification accuracy, only highly confident classified apps are reported, in a classification with reject option rationale. Experiments performed in a new dataset with over 26 thousand Android apps, divided into 29 malware families, compounding over 13 GB of app dex images, have shown that current image-based CNN for malware detection is unable to provide high detection accuracies. In contrast, our proposed model is able to reliably detect malware apps, improving the true-negative rates by up to 5.5%, and the average true-positive rate of the malware families of accepted apps by up to 12.7%, while rejecting only 10% of Android apps.

关键词： Source coding Focusing Malware Convolutional neural networks Reliability Proposals Task analysis

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Social Network Analysis Algorithms, Techniques and Methods

Social Network Analysis Algorithms, Techniques and Methods

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Sandra, Lidia Trisetyarso, Agung Ramadhan, Arief Abdurachnan, Edi Lumbangaol, Ford Isa, Sani Muhamad Krida Wacana Christian University Psychology Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Trisakti Institute of Trasportation and Logistic Trisakti University Jakarta Indonesia

ISBN: (纸本)9781665499699

Understanding how social structures with the use of a network have been an active field of study for academics in the past five years alone. The need to properly comprehends how Social Network Analysis (SNA) is being studied grows more and more in recent years. In this article, we propose a Systematic Literature Review (SLR) to the SNA to see how the algorithms, techniques, and methods are used also discuss their findings. We select thirty-one research studies on SNA. We found different algorithms and techniques that are being used. It is found that the selected research could be categorized into five different main topics which is academic, health, social media, communication, and technology. From all of the research paper discussed, it is also found that many algorithms and techniques are being used to enhanced the SNA, most of them are being machine learning algorithm such as Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression, K Nearest Neighbor, and Whale Optimization Algorithm. While the common features of the datasets used in the research comes as different arrays of user information from social media platforms, Tweets and posts from multiple platforms, also a photographic input such as self-images, portraits, and context related pictures. This article will serve as a single reference for future researchers to the discovery of the latest SNA findings. © 2021 IEEE.

关键词： Decision trees

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Convergence of Bi-Virus Epidemic Models with Non-Linear Rates on Networks - A Monotone Dynamical Systems Approach

arXiv

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arXiv 2022年

作者： Doshi, Vishwaraj Mallick, Shailaja Eun, Do Young The Operations Research Graduate Program The Department of Computer Science The Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States

We study convergence properties of competing epidemic models of the Susceptible-Infected-Susceptible (SIS) type. The SIS epidemic model has seen widespread popularity in modelling the spreading dynamics of contagions such as viruses, infectious diseases, or even rumors/opinions over contact networks (graphs). We analyze the case of two such viruses spreading on overlaid graphs, with non-linear rates of infection spread and recovery. We call this the non-linear bi-virus model and, building upon recent results, obtain precise conditions for global convergence of the solutions to a trichotomy of possible outcomes: a virus-free state, a single-virus state, and to a coexistence state. Our techniques are based on the theory of monotone dynamical systems (MDS), in contrast to Lyapunov based techniques that have only seen partial success in determining convergence properties in the setting of competing epidemics. We demonstrate how the existing works have been unsuccessful in characterizing a large subset of the model parameter space for bi-virus epidemics, including all scenarios leading to coexistence of the epidemics. To the best of our knowledge, our results are the first in providing complete convergence analysis for the bi-virus system with nonlinear infection and recovery rates on general graphs. © 2022, CC BY.

关键词： Viruses

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Drawing Lines for Measurement-Based Probabilistic Timing Analysis

Drawing Lines for Measurement-Based Probabilistic Timing Ana...

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Real-Time Systems Symposium (RTSS)

作者： Tadeu Nogueira C. Andrade George Lima Verônica Maria Cadena Lima Graduate Program in Computer Science Federal University of Bahia Salvador Bahia Brazil Institute of Computing Federal University of Bahia Salvador Bahia Brazil Institute of Mathematics and Statistics Federal University of Bahia Salvador Bahia Brazil

ISBN: (数字)9798331540265

ISBN: (纸本)9798331540272

We describe DBL-MBPTA, a new approach for measurement-based probabilistic timing analysis (MBPTA). Unlike the usual MBPTA, which treats the time a program executes as a random variable, we consider both the number n of instructions executed in each measurement and the time $T(n)$ they took to execute. By taking tuples $(n, T(n))$, for various values of n, DBL-MBPTA allows for multiple execution path analysis. We show that $(n, T(n))$ can be bounded from below and above by two reference lines. The modeled random variable is the relative distance ($n, T(n)$) from these lines, which explains the term distance between lines (DBL) given to the approach. According to DBL-MBPTA, samples can be analyzed and improved, for which we employ deep neural networks. Once sample coverage is deemed representative, probabilistic bounds on execution time are estimated via the modeled relative distance. We evaluate our approach using both synthetic data and data collected through measurements on a multi-core platform. The results obtained demonstrate the effectiveness of DBL-MBPTA.

关键词： Lower bound Codes Processor scheduling Bandwidth Artificial neural networks Probabilistic logic Real-time systems Timing Random variables Synthetic data

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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SHIFTING ATTENTION TO YOU: PERSONALIZED BRAIN-INSPIRED AI MODELS

arXiv

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arXiv 2025年

作者： Zhao, Stephen Chong Hu, Yang Lee, Jason Bender, Andrew Wallace, Mark Tovar, David A. Mazumdar, Trisha Data Science Institute Vanderbilt University Nashville United States Department of Computer Science Vanderbilt University Nashville United States Neurosciences Graduate Program University of California San Diego San Diego United States Department of Psychology Vanderbilt University Nashville United States

The integration of human and artificial intelligence offers a powerful avenue for advancing our understanding of information processing, as each system provides unique computational insights. However, despite the promise of human-AI integration, current AI models are largely trained on massive datasets, optimized for population-level performance, lacking mechanisms to align their computations with individual users’ perceptual semantics and neural dynamics. Here we show that integrating human behavioral insights and millisecond scale neural data within a fine tuned CLIP based model not only captures generalized and individualized aspects of perception but also over doubles behavioral performance compared to the unmodified CLIP baseline. By embedding human inductive biases and mirroring dynamic neural processes during training, personalized neural fine tuning improves predictions of human similarity judgments and tracks the temporal evolution of individual neural responses. Our work establishes a novel, interpretable framework for designing adaptive AI systems, with broad implications for neuroscience, personalized medicine, and human–computer interaction. © 2025, CC BY-NC-ND.

关键词： Neurophysiology

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BERT-based Topic Modeling Approach for Malaria Research Publication

BERT-based Topic Modeling Approach for Malaria Research Publ...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Arif Budiarto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Malaria is a communicable disease with half of the global population at risk due to its high morbidity and mortality rates. A massive number of studies are dedicated to malaria research, so it plays a key role in formulating the proper prevention strategy and effective malaria treatment. With the overwhelming number of updated publications in the field, an unsupervised text mining approach such as topic modeling may provide an alternative method for the malaria researcher to keep pace with new insights. In this work, we collect metadata of malaria publications from the PubMed database to perform BERT-based topic modeling to find well-defined topics regarding malaria research. The method is largely based on the popular BERTopic pipeline. We compare the performance of three different language models to generate document embeddings from the data. The dimension reduction and the density-based clustering algorithm are used to cluster the embeddings. The topic representation is computed based on the semantic similarity of the class TF-IDF representation. The substance of the resulting topics is then manually annotated based on the top words of each topic. We demonstrate that by merging initial topics into larger topics using hierarchical clustering and manual content-based examination, the evaluated coherence measure can be further improved, thus enhancing the topic's interpretability. Our modeling result is able to extract ten major topics recurring in the malaria research publication published from 2017–2022. The result provides preliminary insight to understand the dynamics and patterns of malaria research over the years

关键词： Text mining Heuristic algorithms Multimedia systems Time series analysis Sociology Semantics Pipelines

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Aspect-based Sentiment Analysis in Tourism Industry for Tourism Recommender System

Aspect-based Sentiment Analysis in Tourism Industry for Tour...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Choirul Huda Yaya Heryadi Lukas Widodo Budiharto Computer Science Department Doctor of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia Computer Engineering Research Group (CERG) Atma Jaya University Jakarta Indonesia

The improvement of some aspects in tourism industry needs further study through aspect-based sentiment analysis based on tourist experience. The aim of this study is presenting the empiric results of aspect-based sentiment analysis to extract some useful aspects of services for leveraging tourism industry development based on user's feedback. The study uses customer review data from TripAdvisor website for developing classification model using machine learning algorithms including Decision Tree (DT), Random Forest Classifier (RFC), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) models. Each machine learning model under study is trained using K-Fold Cross Validation. The empiric results found that accuracy of RFC achieved the highest result (99.5%) followed by XGBoost (87.3%), DT (82.9%), and SVM (80.5%). The extracted meaningful aspects from customer feedback give many valuable insights for service quality improvement in tourism industry in the effort to strengthening the rising-up of sustainability economic growth through tourism industry.

关键词： Support vector machines Sentiment analysis Machine learning algorithms Tourism industry Boosting Data models Tokenization

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A Transparent Ultrasound Array for Real-Time Optical,Ultrasound,and Photoacoustic Imaging

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Biomedical Engineering Frontiers 2022年第1期3卷 284-297页

作者： Haoyang Chen Sumit Agrawal Mohamed Osman Josiah Minotto Shubham Mirg Jinyun Liu Ajay Dangi Quyen Tran Thomas Jackson Sri-Rajasekhar Kothapalli Department of Biomedical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA School of Electrical Engineering and Computer Science The Pennsylvania State UniversityUniversity ParkPA 16802USA Penn State Cancer Institute The Pennsylvania State UniversityHersheyPA 17033USA Graduate Program in Acoustics The Pennsylvania State UniversityUniversity ParkPA 16802USA

Objective and Impact *** imaging of ultrasound and optical contrasts can help map structural,functional,and molecular biomarkers inside living subjects with high spatial *** is a need to develop a platform to facilitate this multimodal imaging capability to improve diagnostic sensitivity and ***,combining ultrasound,photoacoustic,and optical imaging modalities is challenging because conventional ultrasound transducer arrays are optically *** a result,complex geometries are used to coalign both optical and ultrasound waves in the same field of *** elegant solution is to make the ultrasound transducer transparent to ***,we demonstrate a novel transparent ultrasound transducer(TUT)linear array fabricated using a transparent lithium niobate piezoelectric material for real-time multimodal *** TUT-array consists of 64 elements and centered at~6 MHz *** demonstrate a quad-mode ultrasound,Doppler ultrasound,photoacoustic,and fluorescence imaging in real-time using the TUT-array directly coupled to the tissue mimicking *** TUT-array successfully showed a multimodal imaging capability and has potential applications in diagnosing cancer,neurological,and vascular diseases,including image-guided endoscopy and wearable imaging.

关键词： Ultrasound specificity transparent

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