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Electrical Power, Electronics, Communications, Controls and informatics Seminar (EECCIS)

作者： Alifi Lazuardi Gunawan Fitra Abdurrachman Bachtiar Budi Darma Setiawan Informatics Engineering Department Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (数字)9798350379914

ISBN: (纸本)9798350379921

The Faculty of computer Science, Universitas Brawijaya (Filkom UB) is committed to providing quality services for the users especially internal and external stakeholders, one of which is through the HaloFilkom service. HaloFilkom services have limitations in terms of time. HaloFilkom services are not available 24 hours due to limited working hours. Questions asked by users are not answered directly. This weakness in the HaloFilkom system can be overcome by using a chatbot system. Chatbot is an interactive system that works with natural human language and can work 24 hours. Thus, the current study explores the basic chatbot model by classifying the Q&A in the closed domain knowledge. The dataset in this research is in the form of pairs of questions and answers regarding various topics at the Filkom UB. The knowledge is preprocessed using text preprocessing which includes case folding, tokenization, padding, and tensorization. One of the chatbot models is a generative model. Creating a generative chatbot model can be done using the Seq2Seq model mechanism which consists of an encoder and decoder. The model created consists of four different architectures, namely a model with an LSTM encoder without attention and with attention and a BiLSTM model encoder without attention and with attention. Hyperparameter tuning was conducted to obtain the best hyperparameter combination. The experiment results show the best hyperparameter combination obtained is hidden size 448, drop out rate 0.5, learning rate 0.001, batch size 64, and teacher force 0. The model with the best loss is obtained with a BiLSTM encoder architecture without an attention mechanism with a train loss of 0.120. The model with the highest BLEU Score was obtained by a model with a BiLSTM encoder architecture without an attention mechanism with a BLEU Score of 0.8587 on the training data. Testing using prompt testing obtained an average BLEU Score of 0.3745 on the BiLSTM encoder without an attention mechanism mo

关键词： Attention mechanisms Force Bidirectional long short term memory Training data computer architecture Chatbots Data models Space exploration Tuning Testing

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Anomaly Detection Employing a 5G Core Data Analytics Framework 13

Anomaly Detection Employing a 5G Core Data Analytics Framewo...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Oliveira, Junia Maisa Almeida, Jonatan De Britto E Silva, Erik Rodrigues Moreira, Larissa Ferreira Moreira, Rodrigo Silva, Flavio O. MacEdo, Daniel F. Nogueira, Jose Marcos Universidade Federal de Minas Computer Science Department Gerais Brazil University of Bologna Department of Electrical Electronic and Information Engineering Italy Universidade Federal de Minas Electrical Engineering Department Gerais Brazil Brazil Department of Informatics Portugal

ISBN: (纸本)9798350376562

The Network Data Analytics Function (NWDAF) within the 5G core is not an inherent feature of open-source 5G cores, making its implementation necessary based on provider demands. However, for those seeking to integrate NWDAF into the core but lacking expertise in core architecture or application programming interface (API) integration, this task can be daunting, potentially resulting in the abandonment of data analytics implementation efforts. While the current version of NWDAF specifies certain use cases, it does not provide alternatives for developing new analytics scenarios. To fill this gap, this paper proposes a framework for 5G network data analytics designed to facilitate algorithmic modifications and the addition of new analytical contexts. The framework was developed considering the 3GPP technical specifications related to data analytics. Its evaluation was performed through a use case focused on cybersecurity anomaly detection applied to a 5G network core. The results demonstrated that the framework simplifies installation, increases flexibility for new algorithms, and integrates seamlessly into the 5G network core, effectively addressing previously identified shortcomings. © 2024 IEEE.

关键词： 3GPP 5G core 5G network anomaly detection data analytics network data analytics NWDAF

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Lightweight YOLOv5-Based Algorithm to Detect Room Nameplates for Autonomous Smart Wheelchair

Lightweight YOLOv5-Based Algorithm to Detect Room Nameplates...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Ainandafiq Muhammad Alqadri Fitri Utaminingrum Departement of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

About 10% individuals with visual impairment also use wheelchair, which makes them difficult to ambulate into a room that can only be distinguished by text independently. One thing that could be a solution is to implement a room nameplate recognition system on autonomous smart wheelchairs so that it can ambulate the user to the dedicated room autonomously. We proposed a lightweight YOLOv5-based method to detect room nameplates that is more suitable for embedded devices such as smart wheelchair. We improved YOLOv5 with ghost module and coordinate attention to reduce model complexity while still maintain the detection accuracy. Using room nameplate images data that has been collected by our self, the proposed method has 29 % less parameter with about the same accuracy as the original YOLOv5. With a low complexity of object detection model, our study may be utilized for room nameplate recognition system on smart wheelchair.

关键词： YOLO computer science Accuracy Embedded systems Attention mechanisms Wheelchairs Computational modeling

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IoT Service Composition - An Estimation of Distribution Algorithm with Adaptive Bias

IoT Service Composition - An Estimation of Distribution Algo...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Sun, Fengyang Ma, Hui Chen, Gang Hartmann, Sven School of Engineering and Computer Science Victoria University of Wellington Wellington6012 New Zealand Clausthal University of Technology Department of Informatics Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9798350314588

Service composition in Internet of Things (SCIoT), as an emerging topic in service computing, aims to select optimal services to complete user requests according to various user requirements such as minimizing energy consumption and response time. To solve this NP-hard problem, numerous heuristic methods, e.g., local search and population-based algorithms, have been proposed, wherein Estimation of Distribution Algorithm (EDA) gains increasing attention because of its explicit global probabilistic nature. However, existing EDAs increase solution diversity by using fixed bias, yet interfere the stability of the learned distribution in the later stage of optimization. Therefore, this paper proposes an EDA with an adaptive bias strategy (EDA-AdaBias) to solve the service composition in IoT problem. The decreasing bias value is added onto the probability values for all choices of each solution variable over generations, which improves diversity of sampled solutions and avoids dramatic change of constructed distribution. Experiments indicate that EDA-AdaBias presents promising performance compared to other competitive methods on this problem. © 2023 IEEE.

关键词： Heuristic methods

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Modeling heterogeneous behaviors with different strategies in a terrorist attack

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Virtual Reality & Intelligent Hardware 2023年第4期5卷 351-365页

作者： Le BI Tingting LIU Zhen LIU Jason TEO Yumeng ZHAO Yanjie CHAI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Science&Technology Ningbo UniversityCixi 315300China Faculty of Computing&Informatics Universiti Malaysia SabahKota Kinabalu 88000Malaysia

Existing simulations of terrorist attacks do not consider individual *** overcome this lim-itation,we propose a framework to model heterogeneous behavior of individuals during terrorist *** constructed an emotional model that integrated personality and visual perception for *** emotional model was then integrated with pedestrian relationship networks to establish a decision-making model that sup-ported pedestrians’altruistic behaviors.A mapping model has been developed to correlate antisocial personality traits with attack strategies employed by *** demonstrate that the proposed algorithm can generate practical heterogeneous behaviors that align with existing psychological research findings.

关键词： Terrorist attack simulation computer animation Big Five personality Intelligent decision making Heterogeneous behaviors

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Data Analytics for Medical Record Data of Covid-19 Patient with Descriptive Predictive Mining 23

Data Analytics for Medical Record Data of Covid-19 Patient w...

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23rd International Electronics Symposium, IES 2021

作者： Hanafiah, Alvintha Maharani Barakbah, Ali Ridho Karlita, Tita Muliawati, Tri Hadiah Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9781665443463

Currently, the number of COVID-19 patients in Indonesia has not shown a significant decline. One of the reasons is the difficulty of analyzing medical record data of COVID-19 patients. The analysis becomes difficult because of the large volume of data and the heterogeneous characteristics of the content. This is what makes medical personnel take a long time in the process of analyzing data on COVID-19 patients, and causes the slow handling of cases in Indonesia. Therefore, this study proposes a new approach to analyze and classify medical record data of COVID-19 patients using a data analytics approach with descriptive and predictive mining methods. Where descriptive method is used to grouping patient's disease risk categorization and predictive method is used to classify positive/negative patients. The input data used for the analysis process was sourced from the results of the RT-PCR test of COVID-19 patients. Before being analyzed, the data is normalized first, and the features are extracted. Next, the descriptive mining process was carried out using the Hierarchical Clustering Complete Linkage method and predictive mining using the Decision Tree and K-Nearest Neighbor (KNN) methods. If so, then the results of the process are tested using the confusion matrix and error ratio methods. From the whole process, it can be concluded that the error ratio of this approach is 8% for predictive mining and 21% for descriptive mining. And the features found at the end of the process were 19 features, including Leukocytes, Eosinophils, Red blood cells, Patient age quantile, etc. © 2021 IEEE.

关键词： COVID-19

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Reconstruct and match: out-of-distribution robustness via topological homogeneity 24

Reconstruct and match: out-of-distribution robustness via to...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chaoqi Chen Luyao Tang Hui Huang College of Computer Science and Software Engineering Shenzhen University School of Informatics Xiamen University

ISBN: (纸本)9798331314385

Since deep learning models are usually deployed in non-stationary environments, it is imperative to improve their robustness to out-of-distribution (OOD) data. A common approach to mitigate distribution shift is to regularize internal representations or predictors learned from in-distribution (ID) data to be domain invariant. Past studies have primarily learned pairwise invariances, ignoring the intrinsic structure and high-order dependencies of the data. Unlike machines, humans recognize objects by first dividing them into major components and then identifying the topological relation of these components. Motivated by this, we propose Reconstruct and Match (REMA), a general learning framework for object recognition tasks to endow deep models with the capability of capturing the topological homogeneity of objects without human prior knowledge or fine-grained annotations. To identify major components from objects, REMA introduces a selective slot-based reconstruction module to dynamically map dense pixels into a sparse and discrete set of slot vectors in an unsupervised manner. Then, to model high-order dependencies among these components, we propose a hypergraph-based relational reasoning module that models the intricate relations of nodes (slots) with structural constraints. Experiments on standard benchmarks show that REMA outperforms state-of-the-art methods in OOD generalization and test-time adaptation settings.

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An enhanced donkey and smuggler optimization algorithm for choosing the precise job applicant

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Iran Journal of computer Science 2023年第3期6卷 233-243页

作者： Hasan, Nazir M. Rashid, Tarik A. Alsadoon, Abeer Qosaeri, Ahmed S. Abualigah, Laith Yaseen, Zaher Mundher Computer Science and Engineering Department University of Kurdistan Hawler KR Erbil Iraq Charles Sturt University Sydney Australia Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Throughout the last few decades, Nature-Inspired Algorithms (NIA) have become very popular in solving real-world problems by getting inspiration from nature. This work suggests the Modified Donkey and Smuggler Optimization (MDSO) algorithm for solving the selection problem to choose suitable job applicants for a specific position. The original Donkey and Smuggler Optimization algorithm (DSO) has two modes: smuggler and donkey mode (non-adaptive and adaptive). In the smuggler mode, the algorithm tries to find the best solutions, which is the path to send the donkey to the destination;once the best path is found, the smuggler will send the donkey through the selected path. The donkey's actions will start when the smuggler mode's best solution is no longer the best one. Certain modifications have been made in the smuggler mode, including replacing the original fitness with a new fitness equation since that method can work more accurately. The Human Resource (HR) department in Korek-telecom has been used as a resource to achieve real-world data to test the original and Modified DSO. Using MDSO, not only will organizations be able to choose suitable job applicants more accurately but also will manage to do so at a faster pace as well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Donkey and smuggler algorithms Human resource management MDSO Nature-inspired algorithms Optimization algorithm Swarm intelligence

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Judging Phishing under Uncertainty: How Do Users Handle Inaccurate Automated Advice? 25

Judging Phishing under Uncertainty: How Do Users Handle Inac...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Saka, Tarini Vakali, Kalliopi Jenkins, Adam D G Kokciyan, Nadin Vaniea, Kami School of Informatics University of Edinburgh Edinburgh United Kingdom University of Edinburgh Edinburgh United Kingdom School of Informatics King's College London London United Kingdom Electrical and Computer Engineering University of Waterloo WaterlooON Canada

ISBN: (纸本)9798400713941

Providing accurate and actionable advice about phishing emails is challenging. The majority of advice is generic and hard to implement. Phishing emails that pass through filters and land in user inboxes are usually sophisticated and exploit differences between how humans and computers interpret emails. Therefore, users need accurate and relevant guidance to take the right action. This study investigates the effectiveness of guidance based on features extracted from emails, which even in AI-driven systems can sometimes be inaccurate, leading to poor advice. We examined three conditions: control (generic advice), perfect advice, and realistic advice, through an online survey of 489 participants on Prolific, and measured user accuracy and confidence in phishing detection with and without guidance. Our findings indicate that having advice specific to the email is more effective than generic guidance (control). Inaccuracies in the guidance can also impact user decisions and reduce detection accuracy. © 2025 Copyright held by the owner/author(s).

关键词： Feature extraction

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TinyML-based Event Detection: An Edge-Cloud Approach for Smart Agriculture over LoRa WSNs 8

TinyML-based Event Detection: An Edge-Cloud Approach for Sma...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Karras, Aristeidis Karras, Christos Giannaros, Anastasios Giotopoulos, Konstantinos C. Tsolis, Dimitrios Oikonomou, Konstantinos Sioutas, Spyros University of Patras Computer Engineering and Informatics Department Patras Greece University of Patras Department of Management Science and Technology Patras Greece University of Patras Department of History and Archaeology Patras Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350383676

In modern agriculture, the capability to promptly detect and respond to specific events is crucial. This study centres on the transformative potential of TinyML for enhancing event detection in Smart Agriculture, particularly when integrated with LoRa-based Wireless Sensor Networks (WSNs). In this work, we underscore the unique advantages of utilizing TinyML at the edge-bypassing the latency and overhead associated with cloud-centric models and ensuring immediate, on-site analytical insights. Employing LoRa WSNs as the backbone provides a seamless, low-power, and expansive data communication framework. Detailed experiments demonstrate TinyML's efficacy in accurately predicting agricultural events while reducing computational and energy consumption. In conclusion, the synergy between TinyML and LoRa WSNs offers a promising approach for fine-tuned, real-time event detection, aiming for sustainable and high-yield agricultural practices. © 2023 IEEE.

关键词： Wireless sensor networks

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