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International Conference on Computing, Networking and Communications (ICNC)

作者： Liudong Zuo Daqing Yun Computer Science Department California State University Dominguez Hills Carson CA USA Computer and Information Sciences Program Harrisburg University of Science and Technology Harrisburg PA USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

The proliferation of large-scale applications has led to the generation of vast datasets across diverse scientific domains. The subsequent need to transfer such expansive data across geographical distances is essential for collaborative data storage and analysis. While reserving bandwidth on dedicated links within high-performance networks (HPNs) has proved as an efficient means for such extensive data transfers, certain crucial challenges remain to be investigated. In this paper, we delve into the intricate tradeoff between cost and completion time of data transfers using bandwidth reservation on fixed paths with fixed bandwidth of the HPNs, the most common type of bandwidth reservation or data transfer paths. Our focus centers on the scheduling of two types of bandwidth reservation requests (BRRs) that encapsulate this tradeoff: (i) minimizing data transfer cost within prescribed deadlines, and (ii) achieving the earliest data transfer completion time while adhering to predefined cost constraints. We propose two algorithms to optimize the scheduling of individual BRRs of these two types. We then compare the proposed algorithms with existing ones from the perspective of different performance metrics, and efficacy of the proposed algorithms is verified through extensive simulations.

关键词： Measurement Costs Processor scheduling Computational modeling Memory Collaboration Bandwidth

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Proposed IT Governance Model for Smart Tourism Destinations based on COBIT 2019 Framework 7

Proposed IT Governance Model for Smart Tourism Destinations ...

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7th International Conference on Information Management and technology, ICIMTech 2022

作者： Hiererra, Siti Elda Gaol, Ford Lumban Ranti, Benny Supangkat, Suhono Harso BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia University of Indonesia Faculty of Computer Science Computer Science Department Depok16424 Indonesia Institute of Technology Bandung Smart Cities and Communities Innovation Centre Faculty of Digital Communication and Hotel & Tourism Bandung40116 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Tourism is one of Indonesia's main economic drivers because it can absorb many workers and bring in foreign exchange through tourism activities. Research related to Smart Tourism Destinations (STDs) and the technology that supports them has gained popularity and has received special attention in the last ten years. STDs show that various technologies are applied to a tourist destination, one of which aims to increase added value for the surrounding community and tourists. This study employs the systematic literature review (SLR) method to investigate smart tourism destinations and governance topics. This research aims to identify and analyze the main components of Smart Tourism Destinations Governance. Two research questions were developed and incorporated into the search term. This research resulted in 24 research papers on smart tourism destinations and governance. The result of this study is a proposed IT Governance Model, which can be a guideline for tourism's DMOs (Destination Management Organizations), tourism enterprises, IT companies that support smart tourism concepts, and policymakers (government) to improve their service to society/community. The model can be used as a guide to governing the IT implementation in smart tourism destinations projects. Limitations of this study regarding the scalability of database and keyword usage and their methodologies. Theoretical implication for scholars in the next research is that research can be conducted on how to implement this model and whether this model can be easily adopted. This future work also regarding the IT Governance Maturity Model or the measurement of IT Gov readiness in smart tourism destinations. © 2022 IEEE.

关键词： Tourism

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A Simple Deep Learning Model for Classifying Oranges in Embedded Manufacturing Settings

SSRN

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SSRN 2023年

作者： Salaiwarakul, Anongporn Mungklachaiya, Sujitranan Program in Computer and Information Technology Faculty of Science and Technology Loei Rajabaht University Loei42000 Thailand Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok65000 Thailand

Recently, deep learning has emerged as a powerful tool for automating various tasks in manufacturing industry. Deep learning has shown promise particularly in classifying food products, e.g., identifying sweet oranges in a factory setting. Traditionally, classification of oranges based on sweetness is a labor-intensive and time-consuming process, requiring manual inspection and tasting by trained personnel. However, by employing deep learning techniques, this process can be automated, giving rise to accurate and consistent results at a much faster rate. In this study, we propose a deep learning model, combined with computer vision, for classifying the sweetness of oranges in a factory setting. By training a deep neural network on a dataset of labeled images, we developed a simple Convolutional Neural Network (sCNN) model with few layers that can accurately predict the sweetness of new oranges based on their images. We evaluated the performance of the model and compare it with four CNN architecture-transfer learning models of orange classification. Upon comparison with four different CNN architecture-transfer learning models, namely, MobileNet-V2, Inception-V3, VGG-16, and ResNet-50, we found that while each of these models yields a satisfactory result, learning rates of 0.001 or 0.0001 yielded superior results in every model. Without the application of dropout and image augmentation, ResNet-50 yielded the best classification accuracy (81.43%) of the five models, and the model performance improved upon employment of these methods. However, the provided images were not suitable for Inception-V3 models. Nevertheless, Simple CNN shows that the augmentation method enables the model to learn, achieving an accuracy of 81.43% with a dropout of 0.3, which is close to the VGG-16's highest accuracy of 83.57% (dropout = 0.0) and MobileNet-V2’s 74.29% (dropout = 0.3). © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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AI Recommendations: Friend or Foe? Unraveling the Impact on Consumer Benefits and Privacy 11

AI Recommendations: Friend or Foe? Unraveling the Impact on ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Dewanthi, Desyra Sukma Kristopo, Hari Qomariyah, Nunung Nurul Axel, Muhammad Business Management & Marketing Program Binus Business School International Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Technology Department Jakarta11480 Indonesia School of Computing and Creative Arts Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389654

Artificial Intelligence (AI) and marketing have transformed consumer behavior and shopping experiences, especially through Recommender Systems (RSs) in e-commerce. RSs use algorithms to provide personalized recommendations, enhancing shopping convenience. However, this raises data privacy and ethical concerns, as RSs rely on user data. This research explores the quality determinants of RSs and their impact on consumer behavior, focusing on privacy awareness and its effect on repurchase intentions. Using a qualitative approach with semi-structured interviews, the study targets Indonesian respondents aged 18-40 with e-commerce experience. Findings reveal that recommendation quality, especially self-reference and vividness, significantly influences purchasing decisions. Respondents prefer recommendations that match their preferences and are visually appealing. Positive shopping experiences are linked to efficient and accurate RSs, but there's a need for improvements in transparency, novelty, diversity, and advanced technologies like AR. Respondents are aware of digital data storage but have limited understanding of privacy concerns. They trust e-commerce platforms with basic personal details but are hesitant to share sensitive information. Despite privacy issues, e-commerce remains vital, with respondents emphasizing the need for transparency, robust security, and clear explanations for data use to maintain trust. Addressing these issues not only enhances customer experience but also demonstrates that consumer trust directly impacts the sustainability and success of the business. © 2024 IEEE.

关键词： Differential privacy

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IoT Attack Detection and Prevention Through Machine Learning System 2

IoT Attack Detection and Prevention Through Machine Learning...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Agrawal, Kanishk Poli, Pavan Kumar Reddy Gupta, Yash Juneja, Garv Bopaiah, Adhip Kishen Arani, Dinesh Ashok Cse Manglayatan University Department of Iet Uttar Pradesh Aligarh India Managing Information Technology Implementations Program Management for Fortune 6 Healthcare Company United States Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Department of Computer Science and Engineering Delhi India Trinity College Bangalore India Manglayatan University Uttar Pradesh Aligarh India

ISBN: (纸本)9798350369960

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified 'Support Vector Machine (SVM)' as well as 'Random Forest (RF)' ML techniques showed optimal performance in IoT attack detection and prevention. © 2023 IEEE.

关键词： Anomaly detection and Cyber security Internet of Things Machine learning

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Uncovering emotion sequence patterns in different interaction groups using deep learning and sequential pattern mining

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Journal of computer Assisted Learning 2024年第4期40卷 1777-1790页

作者： Huang, Changqin Yu, Jianhui Wu, Fei Wang, Yi Chen, Nian-Shing Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China School of Education Zhejiang International Studies University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Institute for Research Excellence in Learning Sciences Program of Learning Sciences National Taiwan Normal University Taipei Taiwan

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi-directional long short-term memory model for emotion detection yielded the highest predictive performance, with an impressive F-measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low-level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups

关键词： deep learning emotion sequence patterns learning analytics learning behaviour sequential pattern mining

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Automatic Text Summarization of COVID-19 Scientific Research Topics Using Pre-trained Models from Hugging Face

Automatic Text Summarization of COVID-19 Scientific Research...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Ontoum, Sakdipat Chan, Jonathan H. King Mongkut's University of Technology Thonburi Computer Science Program Bangmod Thung-Khru Thailand Research Center School of Information Technology Bangmod Thung-Khru Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Automated text summarizing helps the scientific and medical sectors by identifying and extracting relevant information from articles. Automatic text summarization is a way of compressing text documents so that users may find important and useful information in the original text in reduced time. We will first review some new works in the field of summarization that uses deep learning approaches, and then we will explain the application to COVID-19 related research papers. The ease with which a reader can grasp written text is referred to as the readability test. The substance of text determines its readability in natural language processing. We constructed word clouds using the abstracts' most commonly used text. By looking at those three measurements, we can determine the performance measures of ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-L-SUM. Our findings indicated that Distilbart-mnli-12-6 and GPT2-large outperform than others considered. © 2022 IEEE.

关键词： COVID-19

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Visual Grounding Helps Learn Word Meanings in Low-Data Regimes

Visual Grounding Helps Learn Word Meanings in Low-Data Regim...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Zhuang, Chengxu Fedorenko, Evelina Andreas, Jacob McGovern Institute for Brain Research MIT United States Department of Brain and Cognitive Sciences MIT United States The Program in Speech and Hearing Bioscience and Technology Harvard University United States Computer Science and Artificial Intelligence Laboratory MIT United States

ISBN: (纸本)9798891761148

Modern neural language models (LMs) are powerful tools for modeling human sentence production and comprehension, and their internal representations are remarkably well-aligned with representations of language in the human brain. But to achieve these results, LMs must be trained in distinctly un-human-like ways-requiring orders of magnitude more language data than children receive during development, and without perceptual or social context. Do models trained more naturalistically-with grounded supervision-exhibit more human-like language learning? We investigate this question in the context of word learning, a key sub-task in language acquisition. We train a diverse set of LM architectures, with and without auxiliary visual supervision, on datasets of varying scales. We then evaluate these models’ learning of syntactic categories, lexical relations, semantic features, word similarity, and alignment with human neural representations. We find that visual supervision can indeed improve the efficiency of word learning. However, these improvements are limited: they are present almost exclusively in the low-data regime, and sometimes canceled out by the inclusion of rich distributional signals from text. The information conveyed by text and images is not redundant-models mainly driven by visual information yield qualitatively different from those mainly driven by word co-occurrences. However, our results suggest that current multimodal modeling approaches fail to effectively leverage visual information to build human-like word representations from human-scale data. © 2024 Association for Computational Linguistics.

关键词： Semantics

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