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International Conference on Semantic Computing

作者： Mallika Boyapati Ramazan Aygun School of Data Science and Analytics Kennesaw State University Kennesaw GA USA Department of Computer Science Kennesaw State University Kennesaw GA USA

In recent years, transformer-based models, particularly BERT (Bidirectional encoder Representations from Transformers), have revolutionized natural language processing tasks, achieving state-of-the-art performance in various domains. In the context of natural language processing (NLP) and linguistics, understanding the semantic aspects of text is crucial for tasks like information retrieval, sentiment analysis, machine translation, and many others. However, the high dimensionality of BERT embeddings presents challenges in real-world applications due to increased memory and computational requirements. Reducing the dimensionality of BERT embeddings would benefit many application downstream tasks by reducing the computational requirements. Although there are prevalently used dimensionality reduction methods which focus on feature representation with lower dimensions, their application on NLP tasks may not yield semantically correct results. We propose a novel framework named as semanformer (semantics-aware encoder-decoder dimensionality reduction method) that leverages transformer-based encoder-decoder model architecture to perform dimensionality reduction on BERT embeddings for a corpus while preserving crucial semantic information. To evaluate the effectiveness of our approach, we conduct a comprehensive use case evaluation on diverse text datasets by sentence reconstruction. Our experiments show that our proposed method achieves high sentence reconstruction accuracy (SRA) more than 83% compared to the traditional dimensionality reduction methods such as PCA (SRA < 66%) and t-SNE (SRA < 9%).

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Ethical Considerations in the Development and Deployment of Large Language Models

Ethical Considerations in the Development and Deployment of ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Al Khaldy Youcef Gheraibia Department of Business Intelligence and Data Analytics University of Petra Amman Jordan School of Computer Science and Informatics De Montfort University Leicester UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Large Language Models (LLMs) have emerged as a powerful tool with the potential to revolutionize various industries and aspects of human life. However, their rapid development and deployment raise significant ethical concerns. This paper delves into the key ethical considerations that must be addressed to ensure the responsible and beneficial use of LLMs. We explore issues such as bias and fairness, privacy and security, transparency and accountability, and the potential for misuse. Addressing bias requires careful data curation, algorithmic fairness techniques, and regular audits. Protecting privacy necessitates strong data privacy and security measures, as well as robust security protocols. To enhance transparency, developing techniques to interpret and explain LLM outputs is crucial. Mitigating the potential for misuse involves developing tools to detect and filter harmful content, implementing responsible AI practices, and fostering a proactive approach to addressing societal implications. By examining these challenges, we aim to foster a thoughtful and ethical approach to LLM development and deployment. A collaborative effort between researchers, developers, policymakers, and the public is essential to ensure that LLMs are used responsibly and ethically.

关键词： Hands Ethics Technological innovation Privacy data privacy Reviews Large language models Psychology Security Stakeholders

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ACCELERATING DISTRIBUTED STOCHASTIC OPTIMIZATION VIA SELF-REPELLENT RANDOM WALKS

arXiv

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arXiv 2024年

作者： Hu, Jie Doshi, Vishwaraj Eun, Do Young Department of Electrical and Computer Engineering North Carolina State University United States Data Science & Advanced Analytics IQVIA Inc.

We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion. Typically, these random-walks are chosen to be Markov chains that asymptotically sample from a desired target distribution, and play a critical role in the convergence of the optimization iterates. In this paper, we take a novel approach by replacing the standard linear Markovian token by one which follows a nonlinear Markov chain - namely the Self-Repellent Radom Walk (SRRW). Defined for any given ‘base’ Markov chain, the SRRW, parameterized by a positive scalar α, is less likely to transition to states that were highly visited in the past, thus the name. In the context of MCMC sampling on a graph, a recent breakthrough in Doshi et al. (2023) shows that the SRRW achieves O(1/α) decrease in the asymptotic variance for sampling. We propose the use of a ‘generalized’ version of the SRRW to drive token algorithms for distributed stochastic optimization in the form of stochastic approximation, termed SA-SRRW. We prove that the optimization iterate errors of the resulting SA-SRRW converge to zero almost surely and prove a central limit theorem, deriving the explicit form of the resulting asymptotic covariance matrix corresponding to iterate errors. This asymptotic covariance is always smaller than that of an algorithm driven by the base Markov chain and decreases at rate O(1/α2) - the performance benefit of using SRRW thereby amplified in the stochastic optimization context. Empirical results support our theoretical findings. © 2024, CC0.

关键词： Optimization

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A Topological Perspective of Port Networks from Three Years (2017-2019) of AIS data 30

A Topological Perspective of Port Networks from Three Years ...

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30th Italian Symposium on Advanced database Systems, SEBD 2022

作者： Carlini, Emanuele De Lira, Vinicius Monteiro Soares, Amilcar Etemad, Mohammad Brandoli, Bruno Matwin, Stan Pisa Italy Department of Computer Science Memorial University of Newfoundland St. John's Canada Institute for Big Data Analytics Dalhousie University Halifax Canada

Complex network analysis is a fundamental tool to understand non-trivial aspects of graphs and networks and is widely used in many fields. In this paper, we apply complex network techniques to study port networks, in which nodes are ports and edges are maritime lines between ports. In particular, we study the temporal evolution of several topological features of a network of ports, including connected components, shortest paths, and clustering coefficients. We built the network with three years of Automatic Identification System data from 2017 to 2019. We highlight several interesting trends and behaviors that differentiate long-range vessels from short-range vessels. © 2022 CEUR-WS. All rights reserved.

关键词： Complex networks

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data Security System in Maintaining the Medical Records Using Wireless Network Cryptography Management Integrated with the Artificial Intelligence 3

Data Security System in Maintaining the Medical Records Usin...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Bhatt, Rahul Salman, Hayder Mahmood Kaduskar, Vikas P. Jakka, Geethamanikanta Aich, Shibani C Verma, Devvret Dev Bhoomi Uttarakhand University Department of Computer Science and Engineering Uttarakhand Dehradun India Al-TurathUniversiy College Computer Science Department Baghdad Iraq College of Engineering Pune India University of the Cumberlands Data Analytics Information Technology United States Amrita Vishwa Vidyapeetham Department of English Amritapuri India Graphic Era Deemed to be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Therefore, we will study here. One of the technological fields in the communication industry that is actively developing and increasing the fastest is wireless technology. Interconnection is the ability to communicate without the usage of cables, wires, or maybe another removable drives. This network architecture, which focused on offering timely action versus numerous problems, is able to evaluate the info and practically through and cellular networking and it will protect the health records from possible threat by keeping it. In order to protect material from possible harm, encrypted methods have been employed extensively throughout the years. Using communication devices may help increase the information is available protection. In this way, only authorized persons may view the information. Ensuring that now the data is secure from threats, and no one else can. A variety of sensors are used in wireless sensor systems to measure environmental factors and transmit information to a central site. Originally designed for defense use, these sensor nodes are now extensively used in a wide range of business and commercial situations in addition to cutting-edge industries like medicine. Wireless sensor networks for remote health monitoring certainly improve the standard of care today. There are two frequent security flaws in healthcare applications: dropping and impersonating. © 2023 IEEE.

关键词： Sensor nodes

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Multi-Agent RAG Chatbot Architecture for Decision Support in Net-Zero Emission Energy Systems

Multi-Agent RAG Chatbot Architecture for Decision Support in...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Gihan Gamage Nishan Mills Daswin De Silva Milos Manic Harsha Moraliyage Andrew Jennings Damminda Alahakoon Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Department of Computer Science Virginia Commonwealth University Richmond USA

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Modern energy platforms are increasingly leveraging Artificial Intelligence (AI) for effective decision-making and efficient operations. This has led to the development of expansive data spaces that comprise both structured and unstructured energy data in various modalities. Conversational agents with the most recent advancements in Large Language Models (LLM) are primed to facilitate the efficient retrieval of this diverse information for decision support. In this paper, we propose a multi-agent chatbot architecture for decision support in net-zero emissions energy systems, leveraging LLMs and Retrieval-Augmented Generation (RAG). This architecture consists of a Chatbot User Interface (UI), an advanced Natural Language Understanding (NLU) module for precise entity and intent recognition, a robust Chatbot Core with four specialized agents: Observer, Knowledge Retriever, Behavior Analyzer, and Visualizer and Response Construction Module. These components work together to address diverse decision support needs in energy environments, specifically for net zero carbon emissions initiatives that need to consider diverse parameters and large volumes of data. We showcase the chatbot's successful integration and evaluation for decision support in the net-zero emissions energy system of a large tertiary education institution.

关键词： Intent recognition Net zero Education Decision making data visualization Collaboration Observers

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DeCrypt: a 3DES inspired optimised cryptographic algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4745-4755页

作者： Chowdhury, Deepraj Dey, Ajoy Garai, Ritam Adhikary, Subhrangshu Dwivedi, Ashutosh Dhar Ghosh, Uttam Alnumay, Waleed S. Department of Electronics and Communication Engineering IIIT Naya Raipur India Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Dr. B.C. Roy Engineering College Durgapur India Centre for Business Data Analytics Department of Digitalization Copenhagen Business School Frederiksberg2000 Denmark Meharry Medical College 1005 Dr DB Todd Jr Blvd NashvilleTN37208 United States Riyadh Community College Computer Science Department King Saud University Riyadh Saudi Arabia

Triple data Encryption Standard (also known as 3DES) is a symmetric encryption algorithm. European Traffic Management System popularly uses 3DES for authentication and encryption. However, as per a draft published by NIST in 2018, 3DES is officially being retired and not suggested to use for new applications. Several attacks were imposed on 3DES, and the biggest threat to 3DES is meet in the middle attack. Therefore for long term security, it is essential to enhance the security of such algorithms. This paper proposed a new cipher DeCrypt inspired by 3DES, an improved version of the 3DES algorithm, and secured against meet in the middle attack. As per the experiment performed, DeCrypt cipher is 61 per cent faster than 3DES, providing long-term and better security against sweet-32 attacks than other symmetric algorithms. The proposed algorithm is also faster than 3DES due to reduced encryption and decryption time. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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RainAI - Precipitation Nowcasting from Satellite data

arXiv

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arXiv 2023年

作者： Sarabia, Rafael Pablos Nyborg, Joachim Birk, Morten Assent, Ira Aarhus University Department of Computer Science Cordulus Denmark Cordulus Denmark Aarhus University Department of Computer Science DIGIT Aarhus University Centre for Digitalisation Big Data and Data Analytics Denmark

This paper presents a solution to the Weather4Cast 2023 competition, where the goal is to forecast high-resolution precipitation with an 8-hour lead time using lower-resolution satellite radiance images. We propose a simple, yet effective method for spatiotemporal feature learning using a 2D U-Net model, that outperforms the official 3D U-Net baseline in both performance and efficiency. We place emphasis on refining the dataset, through importance sampling and dataset preparation, and show that such techniques have a significant impact on performance. We further study an alternative cross-entropy loss function that improves performance over the standard mean squared error loss, while also enabling models to produce probabilistic outputs. Additional techniques are explored regarding the generation of predictions at different lead times, specifically through Conditioning Lead Time. Lastly, to generate high-resolution forecasts, we evaluate standard and learned upsampling methods. The code and trained parameters are available at https://***/rafapablos/w4c23-rainai. © 2023, CC BY.

关键词： Importance sampling

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Almost minimax optimal best arm identification in piecewise stationary linear bandits 24

Almost minimax optimal best arm identification in piecewise ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yunlong Hou Vincent Y. F. Tan Zixin Zhong Department of Mathematics National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore Data Science and Analytics Thrust Hong Kong University of Science and Technology (Guangzhou)

ISBN: (纸本)9798331314385

We propose a novel piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepoint, and the quality of an arm is measured by its return averaged over all contexts. The contexts and their distribution, as well as the changepoints are unknown to the agent. We design Piecewise-Stationary ε-Best Arm Identification+ (PSεBAI+), an algorithm that is guaranteed to identify an ε-optimal arm with probability ≥ 1 - δ and with a minimal number of samples. PSεBAI+ consists of two subroutines, PSεBAI and NAÏVE ε-BAI(NεBAI), which are executed in parallel. PSεBAI actively detects changepoints and aligns contexts to facilitate the arm identification process. When PSεBAI and NεBAI are utilized judiciously in parallel, PSεBAI+ is shown to have a finite expected sample complexity. By proving a lower bound, we show the expected sample complexity of PSεBAI+ is optimal up to a logarithmic factor. We compare PSεBAI+ to baseline algorithms using numerical experiments which demonstrate its efficiency. Both our analytical and numerical results corroborate that the efficacy of PSεBAI+ is due to the delicate change detection and context alignment procedures embedded in PSεBAI.

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Hierarchical community structure in networks

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Physical Review E 2023年第5期107卷 054305-054305页

作者： Michael T. Schaub Jiaze Li Leto Peel Department of Computer Science RWTH Aachen University 52074 Aachen Germany Department of Data Analytics and Digitalisation School of Business and Economics Maastricht University 6211 LM Maastricht The Netherlands

Modular and hierarchical community structures are pervasive in real-world complex systems. A great deal of effort has gone into trying to detect and study these structures. Important theoretical advances in the detection of modular have included identifying fundamental limits of detectability by formally defining community structure using probabilistic generative models. Detecting hierarchical community structure introduces additional challenges alongside those inherited from community detection. Here we present a theoretical study on hierarchical community structure in networks, which has thus far not received the same rigorous attention. We address the following questions. (1) How should we define a hierarchy of communities? (2) How do we determine if there is sufficient evidence of a hierarchical structure in a network? (3) How can we detect hierarchical structure efficiently? We approach these questions by introducing a definition of hierarchy based on the concept of stochastic externally equitable partitions and their relation to probabilistic models, such as the popular stochastic block model. We enumerate the challenges involved in detecting hierarchies and, by studying the spectral properties of hierarchical structure, present an efficient and principled method for detecting them.

关键词： Community structure

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