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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Sathwik, Abbaraju Sai Rao, A. Koteswara Saiteja, Bheri Madhavi, K. Reddy Doppalapudi, Asutosh Amudha, P. VIT-AP University Amaravati India Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology Vijayawada India AI&ML School of Computing Mohan Babu University Tirupati India Coimbatore Ayya Avinashilingam Nagar641108 India

ISBN: (纸本)9783031889912

A conversational agent, also referred to as a chatbot, is computer software that can communicate with users in natural language. Because of a number of issues, developing a successful chatbot model is still difficult. First, understanding the nuances and context of human language remains a complex task, especially when dealing with slang, dialects, or ambiguous expressions. Second, maintaining engaging and coherent conversations over extended periods can be a hurdle, as chatbots need to adapt to changing topics and user moods. Third, ensuring ethical and responsible behavior in chatbots, such as avoiding biased or harmful responses, requires continuous monitoring and refinement. Lastly, staying up-to-date with the evolving field of NLP and AI is essential to incorporate the latest advancements and provide users with increasingly sophisticated conversational experiences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

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Prompting Continual Person Search 24

Prompting Continual Person Search

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Pengcheng Yu, Xiaohan Bai, Xiao Zheng, Jin Ning, Xin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University Beijing China School of Computing Macquarie University Sydney Australia Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

The development of person search techniques has been greatly promoted in recent years for its superior practicality and challenging goals. Despite their significant progress, existing person search models still lack the ability to continually learn from increasing real-world data and adaptively process input from different domains. To this end, this work introduces the continual person search task that sequentially learns on multiple domains and then performs person search on all seen domains. This requires balancing the stability and plasticity of the model to continually learn new knowledge without catastrophic forgetting. For this, we propose a Prompt-based Continual Person Search (PoPS) model in this paper. First, we design a compositional person search transformer to construct an effective pre-trained transformer without exhaustive pre-training from scratch on large-scale person search data. This serves as the fundamental for prompt-based continual learning. On top of that, we design a domain incremental prompt pool with a diverse attribute matching module. For each domain, we independently learn a set of prompts to encode the domain-oriented knowledge. Meanwhile, we jointly learn a group of diverse attribute projections and prototype embeddings to capture discriminative domain attributes. By matching an input image with the learned attributes across domains, the learned prompts can be properly selected for model inference. Extensive experiments are conducted to validate the proposed method for continual person search. The source code is available at https://***/PatrickZad/PoPS. © 2024 ACM.

关键词： Contrastive Learning

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Rule-oriented Approach for Knowledge acquisition from Database 15

Rule-oriented Approach for Knowledge acquisition from Databa...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Aakash, B. Uma Sankari, S.S. Thangavel, Senthil Kumar Palanisamy, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Vikram Sarabhai Space Centre ISRO Scientist Thiruvananthapuram India Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India

ISBN: (纸本)9798350370249

In today's world, where importance is provided more towards knowledge rather than just information, the need for transformation of databases to knowledgebase in an efficient way such that data is not lost during the process of transformation. This paper explores the scalability of the rule-based approach, demonstrating its effectiveness across various domains and dataset sizes. Through illustrative examples and case studies, it elucidates how this methodology can adapt to evolving data structures and accommodate complex relationships within the information ecosystem. Moreover, the research investigates the interpretability and transparency of the transformed knowledge base. By harnessing the explicit logic encoded in PROLOG rules, users gain insights into the underlying reasoning process, fostering trust and facilitating collaboration between domain experts and data practitioners. The research highlights the practical implications of deploying a rule-oriented transformation strategy in real-world scenarios. From facilitating decision support systems to enabling semantic search functionalities, the transformed knowledge base serves as a valuable asset for organizations seeking to unlock the latent insights buried within their data repositories. This paper discusses one such possible way which is Rule-oriented Approach where the Database transform to Knowledgebase through PROLOG script which has more advantages like Improved Data Understanding, Enhanced Querying and Retrieval and steps of the transformation are well defined in this paper. © 2024 IEEE.

关键词： Latent semantic analysis

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QoS Improvement using Federated Learning-based Position Prediction for mobile IoT 10

QoS Improvement using Federated Learning-based Position Pred...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Zafar, Saniya Alshehri, Mohammed S. Shah, Syed Aziz Ahmad, Jawad Zafar, Adnan Khattak, Aizaz Ahmad Institute of Space Technology Department of Electrical Engineering Islamabad Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Saudi Arabia Coventry University Research Centre for Intelligent Healthcare United Kingdom Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Edinburgh Napier University School of Computing Engineering and the Built Environment United Kingdom

ISBN: (纸本)9798350373011

The rapid advancement in mobile broadband technology has evolved wireless infrastructure influencing internet-of-things (IoTs). This evolution has made mobile IoTs (mIoTs) as centre of attention due to its potential of providing seamless connectivity to enable intelligent transportation systems with huge data demands of vehicular users. To address the ever-escalating data demands of vehicular mobile users, vehicular edge computing (VEC) and moving small cells (mSCs) play a pivotal role. mSCs together with VEC are capable to provide improved service quality for mIoTs. However, incorporating the mSCs in vehicles for mIoTs presents challenges while assigning the resources in mIoT-mSC network. This is due to the existence of time varying interference among mSCs. This work explores the distributed resource assignment mechanism for RSU assisted mIoT-mSC network formed in vehicular environment. In the proposed work, location prediction of city buses deployed with mSCs is done using federated learning (FL) which is subsequently utilized for efficient assignment of resources in mIoT-mSC network. Numerical results are provided to validate the proposed resource assignment mechanism. © 2024 IEEE.

关键词： Intelligent systems

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Interpretable Drug Response Prediction through Molecule Structure-aware and Knowledge-Guided Visible Neural Network

Interpretable Drug Response Prediction through Molecule Stru...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xie, Jiancong Zhang, Zhe Li, Youyou Rao, Jiahua Yang, Yuedong Sun Yat-sen University School of Computer Science and Engineering China University of Chicago Department of Chemistry IL United States Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing of Moe China

ISBN: (纸本)9798350386226

Precise prediction of anti-cancer drug responses has become a crucial obstruction in anti-cancer drug design and clinical applications. In recent years, various deep learning methods have been applied to drug response prediction and become more accurate. However, they are still criticized as being non-transparent. To offer reliable drug response prediction in real-world applications, there is still a pressing demand to develop a model with high predictive performance as well as interpretability. In this study, we propose DrugVNN, an end-to-end interpretable drug response prediction framework, which extracts gene features of cell lines through a knowledge-guided visible neural network (VNN) and learns drug representation through a node-edge communicative message passing network (CMPNN). Additionally, between these two networks, a novel drug-aware gene attention gate is designed to direct the drug representation to VNN to simulate the effects of drugs. By evaluating on the GDSC dataset, DrugVNN achieved state-of-the-art performance. Moreover, DrugVNN can identify active genes and relevant signaling pathways for specific drug-cell line pairs with supporting evidence in the literature, implying the interpretability of our model. © 2024 IEEE.

关键词： Prediction models

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Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches

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Machine Intelligence Research 2022年第5期19卷 350-365页

作者： Yi-Jun Zhang Zhao-Fei Yu Jian.K.Liu Tie-Jun Huang Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science Peking UniversityBeijing 100190China Institute for Artificial Intelligence Peking UniversityBeijing 100190China School of Computing University of LeedsLeeds LS29JTUK Beijing Academy of Artificial Intelligence Beijing 100190Chin

Vision plays a peculiar role in *** information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent *** advances have led to the development of brain-inspired algorithms and models for machine *** of the key components of these methods is the utilization of the computational principles underlying biological ***,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual ***,there is a high demand for mapping out functional models for reading out visual information from neural ***,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.

关键词： Neural decoding machine learning deep learning visual decoding brain-inspired vision

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Demand Response in Smart Grids using SDN 14

Demand Response in Smart Grids using SDN

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Gautham Krishna, K. Patnaik, Manav Gunananthaa, K.B. Radhika, N. Paul, Sujni Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Higher Colleges of Technology Dubai Men's Campus Dubai United Arab Emirates

ISBN: (纸本)9798350335095

Digitization of analogue meters has enabled the travel of information into duplex in electric power grids. This has fetched the ability of data collection and analysis from the consumer sinks. Transfer of such data requires extensive digital network transmission infrastructure as a parallel space. Being incorporated with a fresh network environment since proposals, harnessing and applying the modern network technologies(Software Defined Networks, Network Virtualization) in smart grids from elemental level provide opportunities for building applications which are dynamic and generic. This study explores the Demand response event, where the various problems of supply constraints (considerably in peak hours) are dealt with possible load smooth and reducing inefficient power generation. This follows the exploration of Variable Pricing possibilities based on factors like seasons, power generation modes, demand times. The whole network infrastructure is built upon Software Defined Networks' (SDN) Virtual Network Functions (VNF) which gives possibilities for customization, easy adaptation to custom behaviours and much. © 2023 IEEE.

关键词： Smart power grids

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： P. Suthanthiradevi G Revathy K. Rejini Muthu Lakshmi.V Department of Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Department of Computer science and engineering Vels Institute of science Technology and Advanced studies Chennai Department of Computer Science Engineering Amrita college of Engineering and Technology Nagercoil Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Biological system modeling Large language models Text detection Writing Transformers Natural language processing

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Characterization, synthesis, and optimization of quantum circuits over multiple-control Z-rotation gates: A systematic study

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Physical Review A 2023年第2期108卷 022603-022603页

作者： Shihao Zhang Junda Wu Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China

We conduct a systematic study of quantum circuits composed of multiple-control Z-rotation (mczr) gates as primitives, since they are widely used components in quantum algorithms and also have attracted much experimental interest in recent years. We establish a circuit-polynomial correspondence to characterize the functionality of quantum circuits over the mczr gate set with continuous parameters. An analytic method for exactly synthesizing such quantum circuit to implement any given diagonal unitary matrix with an optimal gate count is proposed, which also enables the circuit depth optimal for specific cases with pairs of complementary gates. Furthermore, we present a gate-exchange strategy together with a flexible iterative algorithm for effectively optimizing the depth of any mczr circuit, which can also be applied to quantum circuits over any other commuting gate set. Besides the theoretical investigation, the practical performance of our circuit synthesis and optimization techniques is further evaluated numerically on two typical examples in quantum computing, including diagonal Hermitian operators and quantum approximate optimization algorithm circuits with tens of qubits, which can demonstrate a reduction in circuit depth by 33.40% and 15.55% on average over relevant prior works, respectively. Therefore, our methods and results provide a pathway for implementing quantum circuits and algorithms on recently developed devices.

关键词： Iterative methods

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Infrastructure Development of Non DHT-Based Pyramid Tree Network Architecture 38th

Infrastructure Development of Non DHT-Based Pyramid Tree Net...

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38th International Conference on computers and Their Applications, CATA 2023

作者： Roy, Indranil Rahimi, Nick Gupta, Bidyut Debnath, Narayan Department of Computer Science Southeast Missouri State University Cape GirardeauMO United States School of Computing Sciences & Computer Engineering University of Southern Mississippi HattiesburgMS United States School of Computing Southern Illinois University CarbondaleIL United States School of Computing and Information Technology Eastern International University Viet Nam

In this paper, we have considered a recently reported 2-layer non-DHT-based structured P2P network. It is an interest-based system and consists of different clusters such that peers in a given cluster possess instances of a particular resource type. It offers efficient data look-up protocols with low latency. However, the architecture lacks in one very important aspect: it is assumed that no peer in any cluster can have more than one resource type, and this could be a very hard restriction practically. This is true for all interest-based works existing in the literature. Therefore, in the present work, we have addressed this issue of generalizing the architecture to overcome this restriction and so far, have come up with some significant initial results. Work is being on to complete the generalization process. We have identified some of our previously reported data look-up protocols that will need to be modified in order to accommodate the new findings toward the generalization and while doing so, we aim at keeping the data look-up latencies of these probable modified protocols unchanged. In addition, our objective is to consider security in communication in the generalized architecture as well. To achieve it, we aim at using mainly public key-based approach for the different look-up protocols reported earlier, because results obtained so far in this direction indicate that the required number of public-private key pairs will be much smaller than the number of symmetric keys if symmetric key-based approach is used. © 2023, EasyChair. All rights reserved.

关键词： Network architecture

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