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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yuan, Xiaosong Shen, Chen Yan, Shaotian Zhang, Xiaofeng Xie, Liang Wang, Wenxiao Guan, Renchu Wang, Ying Ye, Jieping College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering MoE Jilin University China Alibaba Cloud Computing China Shanghai Jiao Tong University China College of Computer Science and Technology Zhejiang University of Technology China College of Software Zhejiang University China

Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of large language models (LLMs) in real-world reasoning tasks. Nonetheless, the efficacy of a singular, task-level prompt uniformly applied across the whole of instances is inherently limited since one prompt cannot be a good partner for all, a more appropriate approach should consider the interaction between the prompt and each instance meticulously. This work introduces an instance-adaptive prompting algorithm as an alternative zero-shot CoT reasoning scheme by adaptively differentiating good and bad prompts. Concretely, we first employ analysis on LLMs through the lens of information flow to detect the mechanism under zero-shot CoT reasoning, in which we discover that information flows from question to prompt and question to rationale jointly influence the reasoning results most. We notice that a better zero-shot CoT reasoning needs the prompt to obtain semantic information from the question, and then the rationale aggregates sufficient information from the question directly and via the prompt indirectly. On the contrary, lacking any of those would probably lead to a bad one. Stem from that, we further propose an instance-adaptive prompting strategy (IAP) for zero-shot CoT reasoning. Experiments conducted with LLaMA-2, LLaMA-3, and Qwen on math, logic, and commonsense reasoning tasks (e.g., GSM8k, MMLU, Causal Judgement) obtain consistent improvement, demonstrating that the instance-adaptive zero-shot CoT prompting performs better than other task-level methods with some curated prompts or sophisticated procedures, showing the significance of our findings in the zero-shot CoT reasoning mechanism. © 2024 Neural information processing systems foundation. All rights reserved.

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Performance Analysis of Lightweight Ciphers for securing images

Performance Analysis of Lightweight Ciphers for securing ima...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Nekkanti, Keerthan Sumanth, Lade Sai Renuka, A. Supriya, Musica Kumar, Arun Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal India YooBee College of Creative Innovation Department of Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350393415

In the digital age, the security of sensitive information and data has become a paramount concern. The modern conventional encryption algorithms are very efficient, but they are very complex and need a lot of resources. This has led to the development and implementation of lightweight ciphers designed specifically for image encryption. Four algorithms namely Present, Lightweight encryption Algorithm (LEA), Twine and Secure IoT(SIT) are implemented to encrypt and decrypt images and their performance is analyzed. The correlation values of the images before encryption is found to be around 0.9 showing strong correlation whereas after encryption it is found to be very less in the range oof 0.0005 which clearly shows the randomization of the pixels and hence good security. The execution times was less than one second for LEA algorithm and hence efficient for multimedia applications. © 2023 IEEE.

关键词： Cryptography

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Improving students' cognitive abilities in online environment based on neurofeedback 14

Improving students' cognitive abilities in online environmen...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Jamil, Nuraini Lakas, Abderrahmane Belkacem, Abdelkader Nasreddine College of Information Technology United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates College of Information Technology United Arab Emirates University Department of Computer and Network Engineering United Arab Emirates

ISBN: (纸本)9798350399431

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style. © 2023 IEEE.

关键词： Brain

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Autonomous Emergency Landing on 3D Terrains: Approaches for Monocular Vision-based UAVs 8

Autonomous Emergency Landing on 3D Terrains: Approaches for ...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Du, Weiming Lin, Junmou Du, Binqing Borhan, Uddin Md. Li, Jianqiang Chen, Jie College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate. © 2023 IEEE.

关键词： Landing

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Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning 8

Defect Screening on Nuclear Power Plant Concrete Structures:...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Huang, Wenlian Zhu, Guanming Huang, Qiang Chen, Zhuangzhuang Chen, Jie Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results. © 2023 IEEE.

关键词： Defects

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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques 3

Enhancing Medical Image-Based Diagnostics Through the Applic...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Byeon, Haewon Nair, Rajit Mahalakshmi, V. Khalaf, Mohammed I. Kaushik, Bharat Shabaz, Mohammad Inje University Department Of Ai And Software Gimhae50834 Korea Republic of School Of Computing Science & Engineering Vit Bhopal University Bhopal India College Of Engineering And Computer Science Jazan University Department Of Computer Science Jazan45142 Saudi Arabia Al Maarif University College Department Of Computer Science Al Anbar 31001 Iraq Masters University Of Northern Iowa IA United States Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques. © 2024 IEEE.

关键词： Diagnosis

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Building the metaverse using digital twins at all scales,states,and relations

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Virtual Reality & Intelligent Hardware 2022年第6期4卷 459-470页

作者： Zhihan LV Shuxuan XIE Yuxi LI M.Shamim HOSSAIN Abdulmotaleb El SADDIK Department of Game Design Faculty of ArtsUppsala UniversitySweden College of Computer Science and Technology Qingdao UniversityQingdaoChina Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Electrical Engineering and Computer Science(EECS) University of OttawaOttawaOntarioCanada

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the *** objects can be at the micro-,meso-,or *** Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain ***,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social ***,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

关键词： Digital twins Metaverse Sensor

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CCMOP: A Runtime Verification Tool for C/C++ Programs 23rd

CCMOP: A Runtime Verification Tool for C/C++ Programs

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23rd International Conference on Runtime Verification, RV 2023

作者： Xing, Yongchao Chen, Zhenbang Xu, Shibo Zhang, Yufeng College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9783031442667

Runtime verification (RV) is an effective lightweight formal method for improving software’s reliability at runtime. There exist no RV tools specially designed for C++ programs. This paper introduces the first one, i.e., CCMOP, which implements an AOP-based RV approach and supports the RV of general properties for C/C++ program. CCMOP provides an AOP language specially designed for C++ program to define the events in RV. The instrumentation of RV monitor is done at AST-level, which improves the efficiency of compilation and the accuracy of RV. CCMOP is implemented based on JavaMOP and an industrial-strength compiler. The results of extensive experiments on 100 real-world C/C++ programs (5584.3K LOCs in total) indicate that CCMOP is robust and supports the RV of real-world C/C++ programs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： C++ (programming language)

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Nawaz, Adil Saeed, Faisal Leung, Victor C.M. Li, Jianqiang Wang, Zhaoxia Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Children's Hospital Department of Gastroenterology China

ISBN: (纸本)9798350386226

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB's input regions with high mutual information with the network's output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets - CNMC, ISBI2016, and BreaKHis - demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains. © 2024 IEEE.

关键词： Diseases

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Malaria Disease Prediction Using Frequency-Based Machine Learning Algorithms and Ensembles Algorithms 2

Malaria Disease Prediction Using Frequency-Based Machine Lea...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Ahmad, Hamisu Ismail Prasad, Rajesh Sharma, Birendra Kumar Madaki, Aisha Yakubu Shuaibu, Aliyu Rabiu Nile University of Nigeria Department of Computer Science Abuja Nigeria Ajay Kumar Garg Engineering College Department of Computer Science and Engineering UP Ghaziabad India Ajay Kumar Garg Engineering College Department of Mca UP Ghaziabad India Nile University of Nigeria Department of Software Engineering Abuja Nigeria

ISBN: (纸本)9798350358834

Malaria is one of the most serious and threatening diseases in Sub-Saharan Africa. Its cases increase drastically during the rainy season. It can spread throughout an infected person's entire body in less than an hour. The diagnosis process is time-consuming, and its accuracy is negatively affected due to the lack of technical tools and infrastructure in many laboratories in malaria-prone countries. Hospitals, however, keep patient data during the diagnosis period. Due to the vast amount of diagnostic data gathered and stored on a daily basis in various mediums such as files, it has become increasingly crucial to develop powerful techniques for analysing and interpreting the data. This analysis aims to extract meaningful knowledge and insights that can significantly contribute to malaria diagnosis and decision-making processes. This paper selected four frequency-based algorithms, namely Naïve Bayes, (J48) Decision Tree, ZeroR, and the OneR algorithm, to develop a hybrid model for frequency-based classification algorithms using the available dataset of malaria diagnoses collected from the Federal Medical Centre in Yola, Adamawa state. The research results indicate that the highest accuracy, 88.4%, was achieved by the (J48) Decision Tree model. Additionally, this research employed ensemble methods to enhance the performance of each classification model. The results demonstrated that the accuracy of the Decision Tree and ZeroR models remained the same at 88.4% and 60.9% before and after boosting, respectively. In contrast, the accuracy of the Naïve Bayes and OneR models increased from 79.9% to 87.0% and from 79.8% to 87.6% before and after boosting, respectively. In conclusion, among the frequency-based classification models, the Decision Tree model consistently exhibited the highest accuracy both before and after boosting. © 2023 IEEE.

关键词： Decision trees

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