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International Conference on Computing, Engineering, and Design (ICCED)

作者： Ikrar Adinata Arin Dina Fitria Murad Harco Leslie Hendric Spits Warnars Meyliana Information System Department School of Information Systems Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Indonesia Jakarta

After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. The pandemic has changed how people work, study, communicate, shop, and carry out many aspects of daily life. Research reported that since 2018, research on broadband internet has continued to increase until now. This quantitative study examines the impact of the four critical variables on the link between attitude, availability, pricing, and quality of service (QoS) on broadband internet customer satisfaction using the Structural Equation Model approach and the Partial Lease Square technique. Three hundred and sixty-one citizen respondents in DKI Jakarta Province were collected using the snowball sampling method facilitated by Google Forms. At the same time, the COVID-19 post-pandemic was still ongoing in Indonesia. Through rigorous statistical analysis, the results show that customer satisfaction with broadband internet services is influenced by all factors, from attitude towards broadband Internet, broadband Internet availability, broadband Internet price, and service quality. The implications of this research are significant for service providers. They provide actionable insights for developing effective strategies to improve customer satisfaction and increase broadband internet subscriptions.

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Generating High-Quality Distributed Representations of Words Using Wiktionary and Sentence-BERT

Generating High-Quality Distributed Representations of Words...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Ryota Nishiura Seiji Tsuchiya Hirokazu Watabe Dept. of Information and Computer Science Graduate School of Science and Engineering Doshisha University Kyotanabe Kyoto Japan Dept. of Intelligent Information Engineering and Science Faculty of Science and Engineering Doshisha University Kyotanabe Kyoto Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

We propose an improved method to generate high-quality distributed representations of words. In our previous paper, we introduced a novel method to obtain distributed representations of words with BERT using Wiktionary as semantic sources of words. The previous method outperformed a Wrd2Vec model in a word similarity task, while there still had to be some room to improve in term of semantic accuracy. In this paper, our new method introduces one of Sentence-BERT models to text-embed the semantic sources instead of the original BERT model. We demonstrate that our proposed method outperforms both our previous method and the Word2Vec model in the tasks.

关键词： Vocabulary Accuracy Semantics Encyclopedias Internet Online services Informatics

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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computer science, information technology and Engineering (ICCoSITE), International Conference on

作者： Muhammad Fikri Hasani Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.

关键词： computer science Art Text categorization Clustering algorithms Oral communication Chatbots Classification algorithms

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Cultivating Archipelago of Forests: Evolving Robust Decision Trees Through Island Coevolution 39

Cultivating Archipelago of Forests: Evolving Robust Decision...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zychowski, Adam Perrault, Andrew Mańdziuk, Jacek Faculty of Mathematics and Information Science Warsaw University of Technology Poland Department of Computer Science and Engineering The Ohio State University United States Faculty of Computer Science AGH University of Krakow Poland Center of Excellence in Artificial Intelligence AGH University of Krakow Poland

ISBN: (纸本)157735897X

Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The paper proposes a novel island-based coevolutionary algorithm (ICoEvoRDF) for constructing robust decision tree ensembles. The algorithm operates on multiple islands, each containing populations of decision trees and adversarial perturbations. The populations on each island evolve independently, with periodic migration of top-performing decision trees between islands. This approach fosters diversity and enhances the exploration of the solution space, leading to more robust and accurate decision tree ensembles. ICoEvoRDF utilizes a popular game theory concept of mixed Nash equilibrium for ensemble weighting, which further leads to improvement in results. ICoEvoRDF is evaluated on 20 benchmark datasets, demonstrating its superior performance compared to state-ofthe-art methods in optimizing both adversarial accuracy and minimax regret. The flexibility of ICoEvoRDF allows for the integration of decision trees from various existing methods, providing a unified framework for combining diverse solutions. Our approach offers a promising direction for developing robust and interpretable machine learning models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Adversarial machine learning

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An Ensemble Framework for Dropout Prediction in Online Learning

An Ensemble Framework for Dropout Prediction in Online Learn...

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2022 IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022

作者： Srinivasan, Sruthi Dewan, M. Ali Akber School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computing and Information Systems Athabasca University Faculty of Sceinece and Technology AB Canada

ISBN: (纸本)9781665456371

Online learning has gained traction over recent years, especially since online education has become more widespread. However, it comes with its own set of challenges of which high dropout is still a major one. Identifying at-risk learners at an early stage is pivotal to offering personalized attention that can potentially prevent them from dropping out from the online courses. This work proposes two methods to analyze students' progress in an online course and subsequently identify dropout prone students. The first method performs fusion of course activity features by concatenating previous weeks' features before training. The second method extracts course activity features from the start date of the courses to a current week instead of concatenating as the first method does. A set of machine learning models and an ensemble framework were trained and tested on these two types of feature sets. On evaluating the models, the benchmark dataset KDDCup15 has been used, where the first method yielded an F1-score 91% while the second method yielded a score 92%. It was observed that both feature fusion methods produce comparable results although we expected that the concatenation of the features over time would produce better results. We also found that using features over a longer duration of time can help in achieving better performance. Further, ensemble model consistently outperformed the base classifiers. © 2022 IEEE.

关键词： Students

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Exploration of IoT Nodes Communication Using LoRaWAN in Forest Environment

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computers, Materials & Continua 2022年第6期71卷 6239-6256页

作者： Anshul Sharma Divneet Singh Kapoor Anand Nayyar Basit Qureshi Kiran Jot Singh Khushal Thakur Embedded Systems&Robotics Research Group Chandigarh UniversityMohali140413PunjabIndia Graduate School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Prince Sultan UniversityRiyadh11586Saudi Arabia

The simultaneous advances in the Internet of Things(IoT),Artificial intelligence(AI)and Robotics is going to revolutionize our world in the near *** recent years,LoRa(Long Range)wireless powered by LoRaWAN(LoRa Wide Area Network)protocol has attracted the attention of researchers for numerous applications in the IoT *** is a low power,unlicensed Industrial,Scientific,and Medical(ISM)bandequipped wireless technology that utilizes a wide area network protocol,i.e.,LoRaWAN,to incorporate itself into the network *** this paper,we have evaluated the LoRaWAN communication protocol for the implementation of the IoT(Internet of Things)nodes’communication in a forest *** outdoor performance of LoRa wireless in LoRaWAN,i.e.,the physical layer,has been evaluated in the forest area of Kashirampur Uttarakhand,***,the present paper aims towards analyzing the performance level of the LoRaWAN technology by observing the changes in Signal to Noise Ratio(SNR),Packet Reception Ratio(PRR)and Received Signal Strength Indicator(RSSI),with respect to the distance between IoT *** article focuses on estimating network lifetime for a specific set of LoRa configuration parameters,hardware selection and power *** the experimental results,it has been observed that transmissions can propagate to a distance of 300 m in the forest environment,while consuming approx.63%less energy for spreading factor 7 at 2 dBm,without incurring significant packet loss with PRR greater than 80%.

关键词： LoRa LoRaWAN IoT communication protocol wireless sensor networks packet reception ratio

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The Evaluation of Trust Towards The Intention to Use of Blockchain-Based Crowdwork Systems (Case Study of Creative Industry) 8

The Evaluation of Trust Towards The Intention to Use of Bloc...

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8th International Conference on information Management and technology, ICIMTech 2023

作者： Hartono, Sugiarto Prabowo, Harjanto Meyliana Hidayanto, Achmad Nizar School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350326093

Current crowdwork research still has issues related to trust. Workers and employers do not know each other, so problems related to trust will arise. This trust issue will influence the desire to use the crowdwork system. This study will evaluate trust in the intention to use blockchain-based crowdwork systems in creative industries. The methodology used is a quantitative approach (questionnaire) which is distributed to 200 respondents consisting of workers and employers. After that, testing the inner and outer models was carried out. The results obtained are technology, risk, and reputation which have the greatest impact on trust. The use of blockchain technology can minimize risks when using a crowdwork system, such as data that cannot be changed, data tracing can be done, and so on. The rating and review features are also important to be able to find out the user's reputation in the system. © 2023 IEEE.

关键词： Blockchain

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An Intelligent Approach for Solving QoS and Multicast Routing Issues 22

An Intelligent Approach for Solving QoS and Multicast Routin...

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Proceedings of the 7th International Conference on Sustainable information Engineering and technology

作者： Hazem H. Abdulmajeed Assem Alsawy Hesham Ahmed Hefny Faculty of Graduate Studies for Statistical Reseach Cairo University Egypt) Faculty of Computer Science and Information Technology Al-Ahram Canadian University Egypt)

ISBN: (纸本)9781450397117

A neural network approach is presented to solve the delay constraint and loss data multicast routing problem, an NP-complete problem. The multicast tree is obtained in line with optimal (minimum) routing costs which are subjected to integrated delay and loss data constraints. The proposed routing algorithm is designed to find the optimal (shortest) path taking into account traffic conditions (the incoming traffic flow, routers occupancy, and link capacities), avoiding packet loss data to the input buffer overflow and combine them into the union. The experimental results showed that the proposed method using the discrete-time equation has achieved a significant difference in iterations and execution time compared to other proposals such as Runge–Kutta, Euler equations, ant colony algorithm and Hierarchical Hopfield neural network which contributes to improving the performance of Internet of Things networks in terms of the durability of interconnection between devices. In accordance with the extent of the impact of quality of service restrictions on the network, and the maintenance of the cost-effectiveness of the route, the algorithm achieved a decrease in execution time ranging from 27% to 81%, in addition to a decrease in the number of iterations by 44% to 66%.

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Cognitive Analysis of Medical Decision-Making: An Extended MULTIMOORA-Based Multigranulation Probabilistic Model with Evidential Reasoning

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Cognitive Computation 2024年第6期16卷 3149-3167页

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar Wang, Baoli Li, Wentao School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India School of Mathematics and Information Technology Yuncheng University Shanxi Yuncheng044000 China College of Artificial Intelligence Southwest University Chongqing400715 China

Cognitive computation has leveraged the capabilities of computer algorithms, rendering it an exceptionally efficient approach for addressing multi-attribute group decision-making (MAGDM) problems. Due to the stability of MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) and the capability of evidential reasoning (ER) to combine information from multiple sources, the technique of multigranulation probabilistic rough sets (MG PRSs) holds great promise for solving MAGDM problems. Thus, a new and stable method for MAGDM is proposed. Initially, three forms of multigranulation Pythagorean fuzzy probabilistic rough sets (MG PF PRSs) are constructed using MULTIMOORA approaches. Next, the hierarchical clustering method is employed to cluster similar decision information and consolidate the decision-makers’ preferences. Representatives are chosen from each category to simplify information fusion calculations and reduce complexity by reducing the model’s dimensionality. Following that, the rankings obtained from the three methods are fused using ER. Ultimately, the validity of our method is revealed via a case analysis on chickenpox cases from the UCI data set by employing cognitive analysis. The paper outlines a method for MAGDM that provides significant advantages. Specifically, the use of MULTIMOORA improves the stability of decision results, while the incorporation of ER reduces the overall uncertainty of entire decision processes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Granular computing

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Formal Security Verification for Searchable Symmetric Encryption Using ProVerif

Formal Security Verification for Searchable Symmetric Encryp...

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International Symposium on information Theory and Its Applications, ISITA

作者： Takehiko Mieno Hiroyuki Okazaki Kenichi Arai Yuichi Futa Hiroaki Yamamoto Graduate School of Medicine Science and Technology Shinshu University EPSON AVASYS CORPORATION Graduate School of Science and Technology Shinshu University Graduate School of Engineering Nagasaki University School of Computer Science Tokyo University of Technology Faculty of Engineering Shinshu University

ISBN: (数字)9784885523519

ISBN: (纸本)9798331533335

With the rapid proliferation of various cloud storage services in recent years, the development of technology to efficiently search data while ensuring its confidentiality during cloud usage is an important issue. The technology that enables keyword searches on encrypted files using previously set keywords is called searchable symmetric encryption (SSE). In this paper, we propose a method formally representing encrypted document, and verify the security of SSE using the formal verification tool ProVerif. Our proposed method considers the channel-type terms of ProVerif as a Document that includes different keywords to verify the indistinguishability of encrypted documents.

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