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12th International Conference on Learning Representations, ICLR 2024

作者： Huang, Jen-Tse Wang, Wenxuan Li, Eric John Lam, Man Ho Ren, Shujie Yuan, Youliang Jiao, Wenxiang Tu, Zhaopeng Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Tencent AI Lab China Institute of Psychology Tianjin Medical University China School of Data Science The Chinese University of Hong Kong Shenzhen China

Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical medicine, legal consultation, and education. LLMs become more than mere applications, evolving into assistants capable of addressing diverse user requests. This narrows the distinction between human beings and artificial intelligence agents, raising intriguing questions regarding the potential manifestation of personalities, temperaments, and emotions within LLMs. In this paper, we propose a framework, PsychoBench, for evaluating diverse psychological aspects of LLMs. Comprising thirteen scales commonly used in clinical psychology, PsychoBench further classifies these scales into four distinct categories: personality traits, interpersonal relationships, motivational tests, and emotional abilities. Our study examines five popular models, namely text-davinci-003, ChatGPT, GPT-4, LLaMA-2-7b, and LLaMA-2-13b. Additionally, we employ a jailbreak approach to bypass the safety alignment protocols and test the intrinsic natures of LLMs. We have made PsychoBench openly accessible via https://***/CUHK-ARISE/PsychoBench. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Natural language processing systems

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Multi Label Classification Based on Ensemble Learning: Investigative Study 25

Multi Label Classification Based on Ensemble Learning: Inves...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Hadi, Wael Samara, Ghassan Al Daoud, Essam Owaida, Suhaila Abu Elrashidi, Ali Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan Zarqa University Faculty of Information Technolog Department of Computer Science Zarqa Jordan Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of It Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

In the big data technology nowadays, activities associated with Multi-Label Classification (mlC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing mlC algorithms suffer from low overall performance. This paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. These PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of mlC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. The results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique. © 2024 IEEE.

关键词： Contrastive Learning

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A Predictive Approach For Assessing Medication Responsiveness in Breast Cancer Through Analysis of Gene Data

A Predictive Approach For Assessing Medication Responsivenes...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Kimmi Gupta Jagendra Singh Harender Pratap Singh Manoj Kumar Singh Nitin Gupta Basudeo Singh Roohani School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE (AI & ML) Raj Kumar Goel Institute of Technology Ghaziabad India Computer Science & Engineering IMS Engineering College Ghaziabad India Department of CSE S. D. College of Engineering and Technology Muzaffarnagar India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Breast cancer remains one of the leading causes of cancer-related deaths worldwide, with treatment responses varying wildly among patients. The inability to predict how an individual patient will respond to a given particular medication is an essential area in optimising treatment plans and better outcomes in such deadly diseases. This research aims to address the challenge of predicting medication responsiveness in breast cancer, analysing genomic data by exploiting publicly available datasets from both The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The datasets are comprised of gene expression profiles, somatic mutations, and clinical data such as patient demographics, tumour subtypes, and treatment outcomes. Preprocessing involved cleaning and normalisation of gene expression values, imputation to address missing data, and selection of essential genes that were most relevant for drug response using feature selection techniques. Its dimensionality was reduced using Principal Component Analysis (PCA). Four machine learning models, including artificial neural networks (ANN), Convolutional Neural Networks (CNN), recurrent neural networks with long short-term memory (RNN-LSTM), and autoencoders, are used in this research. All models were trained, validated, and tested against accuracy, precision, recall, F1 score, and AUC-ROC in that the highest performance was achieved at 98.89% accuracy using the CNN model. The findings point towards the fact that deep learning models, especially CNN, open promising avenues for personalised breast cancer treatment. The findings, therefore, hold significant importance for the application of ai in precision medicine, potentially enabling more accurately targeted therapies, minimising side effects, and ultimately improving patient outcomes in oncology.

关键词： Accuracy Recurrent neural networks Precision medicine Genomics Oncology Breast cancer Convolutional neural networks Gene expression Bioinformatics Principal component analysis

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CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions

CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with...

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

作者： Zhang, Hanchong Cao, Ruisheng Xu, Hongshen Chen, Lu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761148

Recently, Large Language Models (LLMs) have been demonstrated to possess impressive capabilities in a variety of domains and tasks. We investigate the issue of prompt design in the multi-turn text-to-SQL task and attempt to enhance the LLMs’ reasoning capacity when generating SQL queries. In the conversational context, the current SQL query can be modified from the preceding SQL query with only a few operations due to the context dependency. We introduce our method called CoE-SQL1 which can prompt LLMs to generate the SQL query based on the previously generated SQL query with an edition chain. We also conduct extensive ablation studies to determine the optimal configuration of our approach. Our approach outperforms different in-context learning baselines stably and achieves state-of-the-art performances on two benchmarks SParC and CoSQL using LLMs, which is also competitive to the SOTA fine-tuned models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Advanced Persistent Threats (APT): evolution, anatomy, attribution and countermeasures

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9355-9381页

作者： Sharma, Amit Gupta, Brij B. Singh, Awadhesh Kumar Saraswat, V.K. Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra136119 India International Center for AI and Cyber Security Research and Innovations Department of Computer Science and Information Engineering Asia University Taichung Taiwan NITI Aayog New Delhi110001 India Lebanese American University Beirut Lebanon School of Computing Skyline University College Sharjah United Arab Emirates Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India Birkbeck University of London London United Kingdom

In today’s cyber warfare realm, every stakeholder in cyberspace is becoming more potent by developing advanced cyber weapons. They have equipped with the most advanced malware and maintain a hidden attribution. The precocious cyber weapons, targeted and motivated with some specific intention are called as Advanced Persistent Threats (APT). Developing defense mechanisms and performing attribution analysis of such advanced attacks are extremely difficult due to the intricate design of attack vector and sophisticated malware employed with high stealth and evasive techniques. These attacks also include advanced zero-day and negative-day exploits and payloads. This paper provides a comprehensive survey on the evolution of advanced malware design paradigms, APT attack vector and its anatomy, APT attack Tactics, Techniques, and Procedures (TTP) and specific case studies on open-ended APT attacks. The survey covers a detailed discussion on APT attack phases and comparative study on threat life-cycle specification by various organizations. This work also addresses the APT attack attribution and countermeasures against these attacks from classical signature and heuristic based detection to modern machine learning and genetics based detection mechanisms along with sophisticated zero-day and negative day malware countermeasure by various techniques like monitoring of network traffic and DNS logs, moving target based defense, and attack graph based defenses. Furthermore, the survey addresses various research scopes in the domain of APT cyber-attacks. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Life cycle

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Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking 38

Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Fo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xi Qin, Chuan Fang, Chuyu Wang, Chao Zhu, Chen Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Computer Network Information Center Chinese Academy of Sciences China Baidu Inc. China Institute of Artificial Intelligence Beihang University China SKLSDE School of Computer Science Beihang University China AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark. © 2024 Neural information processing systems foundation. All rights reserved.

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Wide-Deep Neural Networks with Randomized Sparse Backpropagation Simplify Demand-Side Load Event Detection

Wide-Deep Neural Networks with Randomized Sparse Backpropaga...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Adusumilli, Manasa Ramesh, G.P. Katkam, Vijayrani Bhargavi Pareek, Piyush Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India St. Peter's Institute for Higher Education and Research Department of Electronics and Communication Engineering Chennai India Ballari Institute of Technology and Management Department of e and Ce Bellary India Hyderabad Institute of Technology and Management Department of Cse Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

Identifying demand-side load events is a critical step in lowering energy use in a number of situations. Using wide-deep neural networks and randomised sparse backpropagation algorithms, this study presents a unique way to identify demand-side load events. To detect subtleties in the input data, we employ randomised sparse backpropagation and both shallow and deep network topologies. We also use the ReLU and sigmoid activation functions to improve the performance of our model. We use the Adam optimizer to train the proposed model, using a learning rate of 0.001 per batch and a batch size of 64. The results were compared to those of other popular technologies, such as support vector machines. According to the findings of our studies, the technique we utilised is more precise and efficient than any other option we investigated. The proposed approach combines wide-deep neural networks with randomised sparse backpropagation to simplify demand-side load event identification. © 2023 IEEE.

关键词： Energy efficiency

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Advancing Deep Learning Models through Robust Feature Extraction and Transfer Learning for Enhanced Image Recognition 2

Advancing Deep Learning Models through Robust Feature Extrac...

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

作者： Thenmozhi, R. Kavinilavu, A. Malathi, G. Mohanambal, K. Srm Valliammai Engineering College Department of Ai&ds Chennai India Sri Sairam Institute of Technology Computer Science and Engineering Chennai India Velammal Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

Deep learning has changed image recognition forever, which has achieved amazing results in many areas. On the other hand, deep learning models depend a lot on the quality and amount of labeled training data. This study suggests a new way to improve deep learning models for image recognition tasks using transfer learning and strong feature extraction methods. First, we look into how well different feature extraction methods, like autoencoders and convolutional neural networks (CNNs), can distinguish features from pictures. Considering different network structures and pre-training methods, we look at how well they do in terms of accuracy and how quickly they can do their work. Next, we look at transfer learning as using models already trained on big datasets to make deep learning models better at generalization. We look into various transfer learning methods, such as fine-tuning, feature extraction, and domain adaptation, to make the already-trained models work for new picture recognition tasks. We also deal with the domain shift problem by suggesting ways to match the source and target domains' feature distributions. We look into methods like domain adaptation and antagonistic training to reduce the difference in distribution and make the learned features more transferable. We do a lot of tests on standard image recognition datasets like ImageNet and RobustFeatureNet to see how well the proposed method works. Our approach greatly enhances the performance of deep learning models compared to traditional training methods, as shown by the testing results. The suggested framework gets the highest level of accuracy while lowering the need for a large labeled dataset. © 2023 IEEE.

关键词： Convolutional Neural Networks Feature Extraction Image Recognition RobustFeatureNet Transfer Learning

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ARIA: Augmented Reality and Artificial Intelligence enabled mobile application for Yield and grade prediction of tomato crops

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Procedia computer science 2024年 235卷 2693-2702页

作者： Balaji Prabhu B V Shashank R Shreyas B Omkar Subbaram Jois Narsipura Dept. of Computer Science and Engineering (AI&ML) Malnad College of Engineering Hassan Visvesvaraya Technological University Belagavi India 573202 Dept. of Computer Science Viterbi School of Engineering University of Southern California Los Angeles USA 90007 Dept. of Computer Science and Engineering RNS Institute of Technology Bangalore India 560098 Dept. of Aerospace Engineering Indian Institute of Science Bangalore India 560012

Agriculture is the most crucial sector of the Indian economy. Lately, there has been a surge in the usage of technologies like deep learning and computer vision to make the process of agriculture modern and consequently, lessening the mistakes related to conventional processes. This work delivers a user-friendly, accessible, and novel approach for the detection, counting, and grading of tomatoes found on a farm. An Augmented Reality (AR) based mobile application is developed to obtain the images efficiently from a tomato farm in the pre-harvest stage subjected to open situations. The proposed approach uses Faster RCNN, a convolutional neural network model for detection of tomatoes from the input image on a large scale. The proposed model is trained and tested using 2083 images. The results are then analyzed for the overall performance of detection, segmentation, and classification of tomatoes. The results have examined the efficiency of the proposed mobile application and demonstrate the robustness it exhibits for the detection, grading and yield prediction of Tomatoes.

关键词： tomato detection tomato grading tomato yield prediction augmented reality deep learning

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Enhancing Predictive Maintenance in Industrial IoT through Machine Learning Models 4

Enhancing Predictive Maintenance in Industrial IoT through M...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Rani, A. Jaya Mabel Prithivirajan, V. Shiney, S Arumai Babaiah, Ch. Swamy, Hemanth Sivagamidevi, G. Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India S A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Dundigal Hyderabad India Motorola Solutions India Dhanalakshmi College of Engineering Department of Ai & Ds Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

Significant production losses and increased maintenance costs can arise from unplanned downtime in industrial settings caused by machine faults. In order to identify issues early and prevent significant breakdowns, predictive maintenance approaches make use of data collected from Internet of Things (IoT)-enabled devices installed in operating equipment. Here, we present a predictive maintenance system that employs machine learning methods, namely AdaBoost, to categorize various types of machine pauses in real-time, with a focus on knitting machines. Six different types of machine stops were classified using machine learning: idle stop, Lycra stop, full roll stop, gate stop, feeder stop, and needle stop. We did this by preprocessing and inputting machine learning model data, which included machine speeds and steps. After a rigorous training and optimization process that combines cross-validation and hyperparameter tuning, the model achieves an impressive accuracy rate of 92% on the test dataset. These results highlight how our suggested solution might help the textile sector become more productive and efficient overall by precisely detecting machine stoppage and enabling timely maintenance interventions. © 2024 IEEE.

关键词： Predictive maintenance

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