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Creating, Using and Assessing a Generative-AI-Based Human-Chatbot-Dialogue Dataset with User-Interaction Learning Capabilities

Creating, Using and Assessing a Generative-AI-Based Human-Ch...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Cuzzocrea, Alfredo Pilato, Giovanni Bringas, Pablo G. University of Calabria IDEA Lab Rende Italy University of Paris City Department of Computer Science Paris France National Research Council of Italy ICAR-CNR Palermo Italy University of Deusto Faculty of Engineering Bilbao Spain

ISBN: (纸本)9798350362480

The study illustrates a first step towards an ongoing work aimed at developing a dataset of dialogues potentially useful for customer service conversation management between humans and AI chatbots. The approach exploits ChatGPT 3.5 to generate dialogues. One of the requirements is that the dialogue is characterized by a specific language proficiency level of the user;the other one is that the user expresses a specific emotion during the interaction. The generated dialogues were then evaluated for overall quality. The complexity of the language used by both humans and AI agents, has been evaluated by using standard complexity measurements. Furthermore, the attitudes and interaction patterns exhibited by the chatbot at each turn have been stored for further detection of common conversation patterns in specific emotional contexts. The methodology could improve human-AI dialogue effectiveness and serve as a basis for systems that can learn from user interactions. © 2024 IEEE.

关键词： Chatbots

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SSDC: A Scalable Sparse Differential Checkpoint for Large-scale Deep Recommendation Models

SSDC: A Scalable Sparse Differential Checkpoint for Large-sc...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Lingrui Xiang Xiaofen Lu Rui Zhang Zheng Hu Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China RAMS Lab Huawei Shenzhen China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Today deep recommendation models have become increasingly large, with parameter sizes reaching hundreds of GB or even TB scale. As a result, it requires large-scale cluster computing resources to train such models. However, large-scale computing clusters tend to experience frequent failures during runtime, so fault-tolerance mechanisms such as checkpointing and restart are widely used in model training. Traditional checkpointing techniques periodically save all parameters of model, resulting in significant overhead. To address this issue, we propose an improved partial checkpointing mechanism for recommendation models named SSDC. SSDC uses an adaptive threshold strategy to reduce expensive operations when saving checkpoints, thereby having good scalability. Furthermore, SSDC saves the differential value of the model parameters, making it feasible to sparsify the otherwise dense embedding tables, thus reducing the bandwidth and time overhead to reconstruct checkpoints. Our evaluations show that compared to state-of-the-art methods, SSDC greatly reduces the time overhead of saving and reconstructing checkpoints, while achieving comparable training accuracy.

关键词： Checkpointing Training Adaptation models Fault tolerance Accuracy Computational modeling Scalability

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Backtracking Restarts for Deep Reinforcement Learning 34th

Backtracking Restarts for Deep Reinforcement Learning

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34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021

作者： Marji, Zaid Licato, John Lab Department of Computer Science and Engineering University of South Florida

Manipulating the starting states of a Markov Decision Process to accelerate the learning of a deep reinforcement learning agent is an idea that has been proposed in several ways in the literature. Examples include starting from random states to improve exploration, taking random walks from desired goal states, and using performance-based metrics for starting states selection policy. In this paper, we explore the idea of exploiting the RL agent’s trajectories generated during training for use as starting states. The main intuition behind this proposal is to focus the training of the RL agent to overcome its current weaknesses by practicing overcoming failure states by resetting the environment to a state in its recent past. We shall call the idea of starting from a fixed (or variable) number of steps back from recent terminal or failure states ‘backtracking restarts’. Our empirical findings show that this modification yields tangible speedups in the learning process. © 2021by the authors. All rights reserved.

关键词： Deep learning

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UP2ME: univariate pre-training to multivariate fine-tuning as a general-purpose framework for multivariate time series analysis 24

UP2ME: univariate pre-training to multivariate fine-tuning a...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yunhao Zhang Minghao Liu Shengyang Zhou Junchi Yan School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong UniversitySchool of Artificial Intelligence & Department of Computer Science and Engineering & MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University

Despite the success of self-supervised pretraining in texts and images, applying it to multivariate time series (MTS) falls behind tailored methods for tasks like forecasting, imputation and anomaly detection. We propose a general-purpose framework, named UP2ME (Univariate Pretraining to Multivariate Fine-tuning). It conducts task-agnostic pre-training when downstream tasks are unspecified. Once the task and setting (e.g. forecasting length) are determined, it gives sensible solutions with frozen pre-trained parameters, which has not been achieved before. UP2ME is further refined by fine-tuning. A univariate-to-multivariate paradigm is devised to address the heterogeneity of temporal and cross-channel dependencies. In univariate pre-training, univariate instances with diverse lengths are generated for Masked AutoEncoder (MAE) pre-training, discarding cross-channel dependency. The pretrained model handles downstream tasks by formulating them into specific mask-reconstruction problems. In multivariate fine-tuning, it constructs a dependency graph among channels using the pre-trained encoder to enhance cross-channel dependency capture. Experiments on eight real-world datasets show its SOTA performance in forecasting and imputation, approaching task-specific performance in anomaly detection. Our code is available at https://***/Thinklab-SJTU/UP2ME.

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows

FlowEval: A Consensus-Based Dialogue Evaluation Framework Us...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zhao, Jianqiao Li, Yanyang Du, Wanyu Ji, Yangfeng Yu, Dong Lyu, Michael R. Wang, Liwei Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Shanghai AI Laboratory China Department of Computer Science University of Virginia United States Tencent AI Lab Bellevue United States

Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions. Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it. To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval. This framework provides a reference-free approach for dialog evaluation by finding pseudo-references. Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation. © 2022 Association for Computational Linguistics.

关键词： Large dataset

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An Open and Fully Decentralised Platform for Safe Food Traceability

An Open and Fully Decentralised Platform for Safe Food Trace...

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Tchao, Eric Tutu Gyabeng, Elton Modestus Tang, Andrews Benyin, Joseph Barnes Nana Keelson, Eliel Kponyo, John Jerry Kwame Nkrumah University of Science and Technology Department of Computer Engineering Ghana Distributed IoT Platforms Privacy and Edge-Intelligence Research Lab Kwame Nkrumah University of Science and Technology Department of Telecommunications Engineering Ghana Responsible Artificial Intelligence Lab

ISBN: (纸本)9798350320282

Concerns about food safety have grown across society in recent years. Building a trustworthy traceability system is essential for effectively identifying and preventing food safety issues as well as tracing the responsible parties. The entire food supply chain, which includes the stages of production, processing, warehousing, transportation, and sale, must be precisely recorded, shared, and traced. Traditional traceability systems suffer from problems like data invisibility, tampering, and the leakage of sensitive information. This paper proposes an open platform for a food safety traceability system that indefinitely and incessantly stores and records all transactions, events, and activities on the blockchain's immutable ledger linked with IPFS - a peer-to-peer decentralised file system - for storing and providing maximum transparency and traceability. The platform leverages the blockchain's characteristics such as immutability, transparency, smart contracts, and consensus algorithms to make it ideal for food safety traceability systems. But more importantly, it mirrors the food supply chain making it a pluggable toolbox for all stakeholders across the food chain to adapt to their system irrespective of the food products they deal with since it is a multi-asset system as well. It could be even adapted for non-food products that have a supply chain similar to the typical food supply chain. Simulation results show that there is the complete success of all the blockchain transactions on our platform with real-time responsiveness. © 2022 IEEE.

关键词： Smart contract

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The Fair k-Center with Outliers Problem: FPT and Polynomial Approximations 1

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17th International Joint Conference on Theoretical computer science - Frontier of Algorithmic Wisdom, IJTCS-FAW 2023

作者： Wu, Xiaoliang Feng, Qilong Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Engineering State University of New York at Buffalo New York14260-1660 United States Hunan Provincial Key Lab on Bioinformatics Central South University Changsha410083 China

ISBN: (数字)9783031393440

ISBN: (纸本)9783031393433

The fair k-center and k-center with outliers problems are two important variants of the k-center problem in computer science, which have attracted lots of attention. The previous best results for the above two problems are a 3-approximation (ICML 2020) and a 2-approximation (ICALP 2016), respectively. In this paper, we consider a common generalization of the two mentioned variants of the k-center problem, denoted as the fair k-center with outliers (FkCO) problem. For the FkCO problem, we are given a set X of points in a metric space and parameters k and z, where the points in X are divided into several groups, and each point is assigned a color to denote which group it belongs to. The goal is to find a subset C⊆ X of k centers and a set Z of at most z outliers such that C satisfies fairness constraints, and the objective maxx∈X\Zminc∈Cd(x, c) is minimized. In this paper, we study the Fixed-Parameter Tractability (FPT) approximation algorithm and polynomial approximation algorithm for the FkCO problem. Our main contributions are: (1) we propose a (1 + ϵ) -approximation algorithm in FPT time for the FkCO problem in a low-dimensional doubling metric space;(2) we achieve a polynomial 3-approximation algorithm for the FkCO problem with the reasonable assumptions that all optimal clusters are well separated and have size greater than z. © 2023, Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Towards Faster Graph Partitioning via Pre-Training and Inductive Inference

Towards Faster Graph Partitioning via Pre-Training and Induc...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Qin, Meng Zhang, Chaorui Gao, Yu Ding, Yibin Jiang, Weipeng Zhang, Weixi Han, Wei Bai, Bo The Hong Kong University of Science & Technology Department of Computer Science & Engineering Hong Kong Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Theory Lab China

ISBN: (纸本)9798350387131

Graph partitioning (GP) is a classic problem that divides the node set of a graph into densely-connected blocks. Following the IEEE HPEC Graph Challenge and recent advances in pre-training techniques (e.g., large-language models), we propose PR-GPT (Pre-trained & Refined Graph ParTitioning) based on a novel pre-training & refinement paradigm. We first conduct the offline pre-training of a deep graph learning (DGL) model on small synthetic graphs with various topology properties. By using the inductive inference of DGL, one can directly generalize the pre-trained model (with frozen model parameters) to large graphs and derive feasible GP results. We also use the derived partition as a good initialization of an efficient GP method (e.g., InfoMap) to further refine the quality of partitioning. In this setting, the online generalization and refinement of PR-GPT can not only benefit from the transfer ability regarding quality but also ensure high inference efficiency without re-training. Based on a mechanism of reducing the scale of a graph to be processed by the refinement method, PR-GPT also has the potential to support streaming GP. Experiments on the Graph Challenge benchmark demonstrate that PR-GPT can ensure faster GP on large-scale graphs without significant quality degradation, compared with running a refinement method from scratch. We will make our code public at https://***/KuroginQin/PRGPT. © 2024 IEEE.

关键词： Community Detection Graph Partitioning Inductive Graph Inference Pre-training & Refinement

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Deep Learning for Automatic Wafer Monitoring System

Deep Learning for Automatic Wafer Monitoring System

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2023 Smart Systems Integration Conference and Exhibition, SSI 2023

作者： Rundo, Francesco Calabretta, Michele Pino, Carmelo Coffa, Salvatore Messina, Angelo Spampinato, Concetto Battiato, Sebastiano Adg Power and Discretes R&d Division STMicroelectronics Catania Italy Ipa Division STMicroelectronics Catania Italy University of Catania PerCeiVe Lab Group Department of Electrical Electronics and Computer Engineering Catania Italy University of Catania IP-LAB Group Department of Mathematics and Computer Science Catania Italy

ISBN: (纸本)9798350325065

Modern semiconductor manufacturing production lines include several technological processes such as diffusion, wafer cleaning and epitaxy and therefore they are subject to degradation phenomena that must be carefully monitored. The processed semiconductor wafer consequently requires a very accurate testing phase which allows to detect the presence of defects to be traced back to the production line and which can have an impact on the downstream yield. The electric wafer testing phase (EWS i.e. Electrical Wafer Sorting) is certainly one of the key stages in semiconductor wafer monitoring in both classical silicon-based technology and modern silicon carbide production lines. The EWS validation phase includes the execution of several electrical tests directly on the integrated circuits (dies) over the wafer. The EWS electrical assessment outcome can be reported in a 2D binary image-map enabling the formation of specific visual patterns in accordance with the defect statistics found in the testing phase. Specifically, from this intelligent EWS analysis it is possible to early-assess defects in the production lines, electrical defects in the devices and finally a proper wafer-yield forecasting estimation. The classification of the EWS image-map visual patterns is a very challenging and time-consuming task. In that context, ad-hoc deep learning solution has been developed in order to perform supervised/unsupervised EWS image-maps patterns recognition and tracking. The related pipeline is composed by ad-hoc convolutional deep architecture with an innovative augmentation system based on spatiotemporal preprocessing of input EWS image-maps. The implemented architecture is able to classify the input EWS binary maps into a well-known pattern-classes whose features at the production line level and the impact on yield are known. The designed deep network is able to identify new EWS patterns not included in the already known classes, so as to address the production-engineer accor

关键词： Defects

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