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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Makarova, Maria Trinitatova, Daria Liu, Qian Tsetserukou, Dzmitry Intelligent Space Robotics Laboratory Moscow121205 Russia Dalian University of Technology Department of Computer Science and Technology China

ISBN: (纸本)9781665481090

Many modern robotic systems operate autonomously, however they often lack the ability to accurately analyze the environment and adapt to changing external conditions, while teleoperation systems often require special operator skills. In the field of laboratory automation, the number of automated processes is growing, however such systems are usually developed to perform specific tasks. In addition, many of the objects used in this field are transparent, making it difficult to analyze them using visual channels. The contributions of this work include the development of a robotic framework with autonomous mode for manipulating liquid-filled objects with different degrees of transparency in complex pose combinations. The conducted experiments demonstrated the robustness of the designed visual perception system to accurately estimate object poses for autonomous manipulation, and confirmed the performance of the algorithms in dexterous operations such as liquid dispensing. The proposed robotic framework can be applied for laboratory automation, since it allows solving the problem of performing non-trivial manipulation tasks with the analysis of object poses of varying degrees of transparency and liquid levels, requiring high accuracy and repeatability. © 2024 IEEE.

关键词： Remote control

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Software Vulnerability Detection Using LLM: Does Additional Information Help? 40

Software Vulnerability Detection Using LLM: Does Additional ...

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40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Shimmi, Samiha Saini, Yash Schaefer, Mark Okhravi, Hamed Rahimi, Mona Northern Illinois University Department of Computer Science IL United States MIT Lincoln Laboratory MA United States

ISBN: (纸本)9798331532819

Unlike conventional machine learning (ML) or deep learning (DL) methods, Large Language Models (LLM) possess the ability to tackle complex tasks through intricate chains of reasoning, a facet often overlooked in existing work on vulnerability detection. Nevertheless, these models have demonstrated variable performance when presented with different prompts (inputs), motivating a surge of research into prompt engineering-the process of optimizing prompts to enhance their performance. This paper studies different prompt settings (zero-shot and few-shot) when using LLMs for software vulnerability detection. Our exploration involves harnessing the power of both natural language (NL) unimodal and NL-PL (programming language) bimodal models within the prompt engineering process. Experimental results indicate that LLM, when provided only with source code or zero-shot prompts, tends to classify most code snippets as vulnerable, resulting in unacceptably high recall. These findings suggest that, despite their advanced capabilities, LLMs may not inherently possess the knowledge for vulnerability detection tasks. However, few-shot learning benefits from additional domain-specific knowledge, offering a promising direction for future research in optimizing LLMs for vulnerability detection. © 2024 IEEE.

关键词： Zero-shot learning

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Smart Food Sorting Using Deep Learning 3

Smart Food Sorting Using Deep Learning

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3rd International Conference on Advanced Electrical Engineering, ICAEE 2024

作者： El Amin, Gasbaoui Mohammed Soumia, Benkrama Mostefa, Bendjima Laboratory of Tit Algeria Tahri Mohammed University Faculty of Exact Sciences Mathematics and Computer Science Department Bechar Algeria

ISBN: (纸本)9798350349351

Food quality detection refers to the process of evaluating and assessing the characteristics, properties, and safety of food products to determine their overall quality. It ensures that the food is free from harmful contaminants, such as bacteria and viruses. Traditional methods involve various testing techniques to identify potential issues related to safety and nutritional value. However, these methods often require manual labor, resulting in extended detection times and laborious processes. Food fruit sorting techniques, on the other hand, are methods used to categorize and separate fruits based on attributes such as size, color, shape, ripeness, and quality. In this article, we present an embedded system remotely connected to a server-trained model for fruit image classification using the Convolutional Neural Network (CNN) deep learning algorithm. The accuracy of the model reached 94% in the validation set. The embedded system is based on a Raspberry Pi microcontroller equipped with a camera, enabling real-time sorting of fruits into six categories: fresh orange, fresh banana, fresh apple, rotten banana, rotten apple, and rotten orange. Additionally, it incorporates a weight sensor to determine the total weight of each category. © 2024 IEEE.

关键词： Convolutional neural networks

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QoS-Aware Precoding for Dual-Polarized Downlink Massive MIMO LEO Satellite Communications

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Space(science & Technology) 2024年第1期4卷 69-78页

作者： Yan Huang Li You Kezhi Wang Xiqi Gao National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211100China Department of Computer Science Brunel UniversityLondon UxbridgeUK

Low Earth Orbit (LEO) satellite communications (SATCOM) are important for wireless networks and offer better global wireless access services than higher altitude SATCOM options. SATCOM performance is affected by the number of antennas, which is usually constrained by the satellite space. In this paper, the application of dual-polarized technology is considered to assist LEO SATCOM. Compared with single-polarized antennas, dual-polarized antennas utilize two different polarization orientations to transmit and receive data. This can improve the quality of service (QoS) performance, as it allows for twice the amount of data to be transmitted over the same time–frequency resources. Specifically, we characterize the dual-polarized channel in massive multiple-input multiple-output (MIMO) LEO SATCOM and investigate the precoding design for enhancing the system performance. In particular, a precoding optimization problem is formulated, which aims to jointly optimize the system throughput and the QoS performance. We divide the problem into two stages and develop novel algorithms for each stage to obtain the precoding vectors. The numerical results demonstrate that the dual-polarized technology can improve the system throughput, and the proposed algorithm leads to a higher percentage of users achieving their QoS requirements compared to conventional approaches.

关键词： two different polarization orientations QoS aware low earth orbit downlink dual polarized precoding wireless networks satellite communications

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Binary Electric Eel Foraging Optimization Algorithm for Solving 0-1 Knapsack Problems 6

Binary Electric Eel Foraging Optimization Algorithm for Solv...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Limane, Abdelhadi Zitouni, Farouq Ferhat, Aridj Lakbichi, Rihab Kasdi Merbah University Laboratory of Artificial Intelligence And Information Technologies Department of Computer Science And Information Technology Ouargla Algeria

ISBN: (纸本)9798350350265

Over the past few decades, the field of metaheuristics optimization has undergone rapid development. More than 650 different metaheuristic algorithms have been created so far. Among these, the Electric Eel Foraging Optimization (EEFO) stands out as a recently developed bio-inspired algorithm. To extend its application to binary optimization, particularly the 0-1 knapsack problem (KP01), we introduce a binary version of EEFO, termed EB-EEFO. The proposed algorithm employs two kinds of transfer functions, S-shaped and V-shaped, to shift from the continuous to binary search space. Additionally, we propose an enhanced repair and improvement (RI) method to address infeasible solutions, aiming to enhance solution diversity by reducing the inherent greedy nature of the traditional RI method. To Study the competitiveness of the new RI method, we develop the binary versions of five different algorithms using the RI method found in the literature, including, EEFO (B-EEFO), B-RIME, One-to-One-Based Optimizer (B-OOBO), Sinh Cosh Optimizer (B-SCHO), and Newton-Raphson-Based Optimizer (B-NRBO). The performance of these algorithms is evaluated using a dataset of 25 instances of KP01. The concluding remarks commend the performance of the EB-EEFO. © 2024 IEEE.

关键词： Transfer functions

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Enhanced E-commerce Recommender System Based on Deep Learning and Ensemble Approaches 24

Enhanced E-commerce Recommender System Based on Deep Learnin...

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7th International Conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Karabila, Ikram Darraz, Nossayba El-Ansari, Anas Alami, Nabil El Mallahi, Mostafa Sidi Mohamed Ben Abdellah University High Normal School Department of Mathematics and Computer Science Ipi Laboratory Fez Morocco Mohammed First University Polydisciplinary Faculty Computer Science Department Masi Laboratory Nador Morocco Mohammed First University Higher School of Technology Masi Laboratory Nador Morocco

ISBN: (纸本)9798400709296

The enhancement of e-commerce conversion rates heavily relies on personalized product recommendations generated by recommendation systems (RS). Despite successful techniques, challenges like sparse data and cold-start issues hinder their effectiveness. To tackle these obstacles, leveraging additional information sources like user and item profiles becomes crucial. This study focuses on a sophisticated RS architecture that combines Neural Collaborative Filtering (NCF) with Global vectors for word representation (GloVe) and Deep Neural Network (DNN), to address sparsity and cold-start problems. By integrating GloVe-DNN, a deeper understanding of item profiles is achieved, enabling a better grasp of user preferences and item traits. This detailed understanding, alongside the Neural Collaborative Filtering model, enables the system to offer highly personalized suggestions. The proposed method involves four main stages: creating the GloVe Embedding model to generate embeddings from item profile text, utilizing these vectors to build a Deep Neural Network, implementing the NCF method, and finally, developing an ensemble learning technique that combines these approaches, followed by a thorough evaluation. Empirical results strongly confirm the effectiveness of our approach, especially the fusion of GloVe-DNN with Neural CF techniques, demonstrating significant improvements across various performance measures. © 2024 ACM.

关键词： Collaborative filtering

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Optimizing Hybrid Recommendations: VADER-Enhanced Sentiment Analysis 24

Optimizing Hybrid Recommendations: VADER-Enhanced Sentiment ...

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7th International Conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Darraz, Nossayba Karabila, Ikram El-Ansari, Anas Alami, Nabil El Mallahi, Mostafa Sidi Mohamed Ben Abdellah University High Normal School Department of Mathematics and Computer Science Ipi Laboratory Fez Morocco Mohammed First University Polydisciplinary Faculty Computer Science Department Masi Laboratory Nador Morocco Mohammed First University Higher School of Technology Masi Laboratory Nador Morocco

ISBN: (纸本)9798400709296

Recommendation systems are essential tools for assisting users in discovering relevant and personalized content. The optimization of hybrid recommendations by incorporating sentiment analysis is explored. By analyzing sentiments expressed in user-generated content, such as reviews and feedback, valuable insights into user preferences are gained. Leveraging sentiment analysis improves the effectiveness of recommendations, leading to enhanced user experiences and increased engagement. Our proposed approach utilizes Singular Value Decomposition (SVD) for collaborative filtering, TF-IDF with Lasso for content-based filtering, and VADER for sentiment analysis. We convert continuous sentiment scores into discrete ratings and replace initial ratings with sentiment-derived ones from user reviews. The resulting hybrid system offers improved RMSE and recommendations tailored to user preferences, showcasing the potential of sentiment-aware approaches in enhancing recommendation systems. Additionally, we propose a hybrid recommendation system with NMF and DecisionTreeRegressor-based models, which outperformed all other models with an impressively low RMSE score of 0.092. © 2024 ACM.

关键词： Collaborative filtering

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Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics

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Journal of computer science & Technology 2022年第1期37卷 182-206页

作者： Zi-Jie Huang Zhi-Qing Shao Gui-Sheng Fan Hui-Qun Yu Xing-Guang Yang Kang Yang Department of Computer Science and Engineering East China University of Science and TechnologyShanghai 200237China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai 200237China Shanghai Engineering Research Center of Smart Energy Shanghai 200237China

Community smells are sub-optimal developer community structures that hinder *** studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community ***,refactoring smells also requires bottom-up effort from every ***,supportive measures and guidelines for them are not available at a fine-grained *** recent work revealed developers'personalities and working states could influence community smells'emergence and variation,we build prediction models with experience,sentiment,and development process features of developers considering three smells including Organizational Silo,Lone Wolf,and Bottleneck,as well as two related classes including smelly developer and smelly *** predict the five classes in the individual granularity,and we also generate forecasts for the number of smelly developers in the community *** proposed models achieve F-measures ranging from 0.73 to 0.92 in individual-wide within-project,time-wise,and cross-project prediction,and mean R2 performance of 0.68 in community-wide Smelly Developer *** also exploit SHAP(SHapley Additive exPlanations)to assess feature importance to explain our *** conclusion,we suggest developers with heavy workload should foster more frequent communication in a straightforward and polite way to build healthier communities,and we recommend community shepherds to use the forecasting model for refactoring planning.

关键词： community smell developer sentiment socio-technical analysis empirical software engineering

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Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention

Scalable Efficient Training of Large Language Models with Lo...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lv, Xingtai Ding, Ning Zhang, Kaiyan Hua, Ermo Cui, Ganqu Zhou, Bowen Department of Electronic Engineering Tsinghua University China Shanghai AI Laboratory China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9798891761643

Improving the effectiveness and efficiency of large language models (LLMs) simultaneously is a critical yet challenging research goal. In this paper, we find that low-rank pre-training, normally considered as efficient methods that will compromise performance, can be scalably effective when reduced parameters are precisely targeted. Specifically, applying the low-dimensional module only to the attention layer - resolves this issue and enhances both effectiveness and efficiency. We refer to this structure as Low-dimensional Projected Attention (LPA) and provide an explanatory analysis. Through extensive experimentation at parameter scales of 130M, 370M, and scaling up to 3B, we have validated the effectiveness and scalability of LPA. Our results show that LPA model can save up to 12.4% in time while achieving an approximate 5% improvement in test perplexity (ppl) and on downstream tasks compared with the vanilla Transformer. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Automated Heartbeat Classification for Arrhythmia Patients Using a Deep Convolutional Neural Network 8

Automated Heartbeat Classification for Arrhythmia Patients U...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Kerdoudi, Saoueb Guezouli, Larbi Hattab, Abdessalam Dilekh, Tahar University of Batna 2 LaSTIC Laboratory Computer Science Department Batna Algeria Higher National School of Renewable Energies Environment and Sustainable Development LEREESI Laboratory of Renewable Energy Energy Efficiency and Smart Systems Batna Algeria University of Batna 2 LAMIE Laboratory Computer Science Department Batna Algeria

ISBN: (纸本)9798350309249

The electrocardiogram (ECG) is widely used for diagnosing heart diseases, including arrhythmia, due to its noninvasive nature and simplicity. Accurate detection and classification of arrhythmic types are crucial in preventing heart disease and reducing mortality. To enhance ECG heartbeat classification, we propose an automated system based on a fine-tuned deep convolutional neural network consisting of 13 residual blocks with 28 layers in total. This model was trained using a dataset of 103247 single-lead ECG recordings. Our deep convolutional neural network achieved remarkable results by validating the independent test dataset from the MIT-BIH Arrhythmia Database. It demonstrated an Accuracy of 99%, a Macro avg Precision of 96%, a Macro avg Recall of 93%, a Weighted avg of 99%, and an impressive Macro avg F1-score of 94%. Notably, our approach has demonstrated superior performance to the original architecture, with a 3% increase in F1-score. These findings showcase the potential of our improved deep learning approach to effectively classify a wide range of arrhythmias from single-lead ECGs, yielding high diagnostic performance comparable to that of cardiologists. © 2024 IEEE.

关键词： Electrocardiograms

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