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Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution

Reducing Redundant Computation in Multi-Agent Coordination t...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Yidong Bai Toshiharu Sugawara Dept. of Computer Science and Communications Eng. Waseda University Tokyo Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

In multi-agent reinforcement learning, decentralized execution is a common approach, yet it suffers from the re-dundant computation problem. This occurs when multiple agents redundantly perform the same or similar computation due to overlapping observations. To address this issue, this study introduces a novel method referred to as locally centralized team transformer (LCTT). LCTT establishes a locally centralized execution framework where selec.ed agents serve as leaders, issuing instructions, while the rest agents, designated as workers, act as these instructions without activating their policy networks. For LCTT, we proposed the team-transformer (T-Trans) archi-tecture that allows leaders to provide specific instructions to each worker, and the leadership shift mechanism that allows agents autonomously decide their roles as leaders or workers. Our experimental results demonstrate that the proposed method effectively reduces redundant computation, does not decrease reward levels, and leads to faster learning convergence.

关键词： Measurement Leadership Redundancy Reinforcement learning Transformers Computational efficiency Informatics Convergence Multi-agent systems

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Improved Multilingual text Identification using Embedding Visualization and Deep Learning techniques

Improved Multilingual text Identification using Embedding Vi...

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2024 International Conference on Recent Advances in elec.rical, elec.ronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Wyawhare, Arinjay Basuli, Aadiptya Das, Saikat Jana, Rounak Jha, Purnendu Dutta Samanta, Pravin Kumar School of Computer Science & Eng. KIIT University BBSR India School of Electronics Eng. KIIT University BBSR India Brillio Technologies Pvt. Ltd. Dept. of Data Sc. & AI India Bosch Global Software Technologies Bangalore India

ISBN: (纸本)9798350354539

Classification of multilingual text is a difficult subject that has attracted a lot of interest lately. This study compares various methods for language recognition and classification that make use of embedding visualization and deep learning techniques. A dataset with text and language columns in 17 distinct languages is used in this work. Sentence Transformer is utilized for embedding, and built-in modules like langdetect, langId, and fasttext are utilized for language identification. The embeddings are visualized after dimensionality reduction with t-SNE, while classification is carried out with CNN, LSTM, and MLP models. Based on the results, the FastText MLP model has the highest accuracy of 0.998, precision of 0.998, recall 0.9980, and F -1 score 0.9985. However, the Sentence Transformer multi-layer perceptron model has an F -1 score of 0.956, recall of 0.957, precision of 0.958, and an accuracy rate of 0.957. Since FastText embeddings were trained on a large multilingual corpus, they are clearly clustered in two dimensions;thus the number of dimensions of the embeddings impacted language settings. It can be argued that for language classification, the FastText model works best with 16-dimensional embeddings. © 2024 IEEE.

关键词： Visualization

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A Space-Saving Algorithm for Enumerating Minimal Generators in Negative Rule Mining

A Space-Saving Algorithm for Enumerating Minimal Generators ...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Shogo Mochizuki Koji Iwanuma Dept. of Computer Science & Eng. University of Yamanashi Kofu-shi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

A positive/negative association rule describes the relationship between co-occurrence and anti-co-occurrence of events (item sets) in the data. The number of association rules extracted from the data, especially negative rules, is extremely huge, and some compression is absolutely necessary. In [3], we proposed a lossless compression method for rule sets, using minimal generators. Thus, the efficient enumeration of minimal generators is definitely important for negative association rule mining. In [6], an algorithm for enumerating minimal generators from closed itemsets was proposed. By recording all itemsets examined once to a hash table, all minimal generators can be enumerated without any support calculations, which can speed up the enumeration computation. The size of the hash table, however, becomes very large. In this study, we propose a method of enumerating minimal generators from a closed item set using a bucket-type membership table in a vertical format, instead of a hash table. The proposed algorithm can decrease the memory size required for computing minimal generators.

关键词： Itemsets Memory management Generators Association rule learning Recording Informatics

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Portable Acoustic and Bioimpedance Sensing System to Support Vocal Pathology Assessments

Portable Acoustic and Bioimpedance Sensing System to Support...

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IEEE Southeastcon

作者： Joseph Layton Memorie Gosa Todd J. Freeborn Dept. Elec. Computer Eng The University of Alabama Tuscaloosa USA Dept. Communicative Disorders The University of Alabama Tuscaloosa USA Dept. Elec. Comp. Eng The University of Alabama Tuscaloosa USA

This paper presents the design and development of a portable platform (using commercially available hardware) to collect tissue bioimpedance and acoustic data to support applications/research in speech pathology. Hardware (Raspberry Pi, Arduino Due, MAX30001) and software of the prototype system are presented with test data to validate prototype operation/functionality. Compared to reference measurements, bioimpedance data has less than 6% difference from 500 Hz to 128 kHz for resistance and less than 30% for reactance. Audio sound pressure levels have less than 5% difference for measurements captured by the prototype system and a reference desktop.

关键词： Pathology Software architecture Prototypes Bioimpedance Acoustic measurements Acoustics Hardware

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On Unsupervised Reconstruction with Dressed Multilayered Variational Quantum Circuits

On Unsupervised Reconstruction with Dressed Multilayered Var...

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

作者： Rivas, Pablo Zhao, Liang School of Eng. and Computer Science Baylor University Dept. of Computer Science United States St. Ambrose University Marketing Department United States

ISBN: (纸本)9798350320282

The advantages of unsupervised quantum machine learning are still under study and appear to be very promising. Trainable variational quantum circuits are one example of successful approaches to combining classic machine learning and quantum. However, there is no clear path toward a quantum advantage for different types of variational circuits. This paper furthers the research efforts in understanding the potential and applications of hybrid quantum circuits. We study different circuits and see how similar they perform in an unsupervised learning task in an autoencoder configuration over a large multimodal dataset. © 2022 IEEE.

关键词： Machine learning

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On Neurodevelopmental Disorder based on Brain computer Interface for Enhancing the Learning Process 53

On Neurodevelopmental Disorder based on Brain Computer Inter...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Jamil, Nuraini Alawa, Omar Samir Manar, Saad Mohammed Alaidarous, Saeed Alawi Adam, Abdulrahman Saeed Eldemerdash, Shehab Adel Belkacem, Abdelkader Nasreddine College of It Uae University Dept. of Computer Science and Software Eng. Al Ain United Arab Emirates College of Science Uae University Dept. of Biology Al Ain United Arab Emirates College of It Uae University Dept. of Information Technology Al Ain United Arab Emirates College of Science Uae University Dept. of Chemistry Al Ain United Arab Emirates College of It Uae University Dept. of Computer and Network Eng. Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

This paper discusses neurodevelopmental disorders and their effects on the brain and nervous system. Autism, attention deficit hyperactivity disorder (ADHD), dyslexia, and cerebral palsy are just a few examples of the many conditions classified as neurodevelopmental disorders. These conditions can potentially influence wide-ranging facets of a person's life, including communication skills, behavior, ability to learn (and thus overall educational achievement), and motor skills. Hereditary and environmental variables are thought to contribute to the development of various developmental disorders, although the exact causes are unknown. The effects of neurodevelopmental disorders on a student's educational opportunities are highly variable according to the particular condition and specific requirements of the individual. Students with dyslexia, for instance, may have trouble reading and writing, whereas those with ADHD may have difficulty paying attention and controlling their impulses. Autism, meanwhile, is characterized by social interaction and communication difficulties. Students with neurodevelopmental problems need to be identified and treated as soon as possible to obtain the appropriate assistance and accommodations to facilitate success in their educational endeavors. In the classroom, teachers have a significant opportunity to recognize and respond to the needs of particular students on an individual and group level. Brain-computer interfaces (BCIs) are an exciting new field of study with potentially substantial ramifications for how persons with neurodevelopmental problems fare in educational settings. In particular, BCIs could help students with neu-rodevelopmental problems to enhance their cognitive function, ability to communicate, and motor skills. Accordingly, this paper investigates the potential applications of BCIs for people with neurodevelopmental disorders. Specifically, we conducted a systematic review and a meta-analysis of the literature indexed

关键词： Brain computer interface Education Neurodevelopmental disease

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Zero-Load: A Zero Touch Network based router management scheme underlying 6G-IoT ecosystems 15

Zero-Load: A Zero Touch Network based router management sche...

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15th International Conference on elec.ronics, computers and Artificial Intelligence, ECAI 2023

作者： Bhattacharya, Pronaya Mukherjee, Anwesha Tanwar, Sudeep Pricop, Emil School of Eng. and Tech. Amity University Dept. of Computer Science and Engg. Kolkata India Mahishadal Raj College Dept. of Computer Science West Bengal Mahishadal India Inst. of Tech. Nirma University Dept. of Computer Science and Engg. Ahmedabad India Petroleum-Gas University of Ploiesti Automatic Control Comp. & Elect. Dept. Ploiesti Romania

ISBN: (纸本)9798350321388

The rising data volumes force significant bottlenecks on the 6G for IoT (6G-IoT) network management functions, which limits the control, flexibility, and interoperability among devices, protocols, and end applications. Solutions like software-defined networking (SDN), and network function virtualization (NFV) are proposed with 6G, but the core management operations are still manual. Thus, to automatically upscale these 6G-IoT networks at reduced cost orchestration complexity, zero-touch networks (ZTN) are proposed. ZTN in 6G-IoT allows a high degree of automation and seamless integration of services. The article proposes a scheme, Zero-Load, that integrates ZTN at the core routing functionality of the 6G-IoT applications. We present a load balancing and traffic classification scheme through the ZTN networking stack for core routers. The ZTN router configuration fabric connects applications with the core services. Further, we present a Gaussian kernel-based support vector machine (SVM) classifier at the ZTN automation layer, which classifies the normal traffic and attack traffic. The proposed work is compared for parameters like mean time to response (MTTR), and resolution latency against baseline SDN and NFV schemes. Using ZTN, an average improvement of 32.45% is obtained in MTTR, and 87.89% in resolution latency (against a query). Using the Gaussian RBF kernel, an accuracy of 0.9914 is reported. These results indicate that ZTN-based management paves the way toward a more dense and intelligent 6G-IoT network. © 2023 IEEE.

关键词： Network function virtualization

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Enhance Undergraduate Research by Incorporating an Entrepreneurial Mindset 53

Enhance Undergraduate Research by Incorporating an Entrepren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Nezafati, Maysam Reizman, Irene Benton, Mary Lauren Peponis, John Payne, Michelle Marincel Johnson, Blake E. Van Treuren, Kenneth Liu, Liping Georgia Tech Emory Uni. Dept. of Biomedical Eng. AtlantaGA United States Rose-Hulman Inst. of Technology Dept. of Chemical Eng. Terre HauteIN United States Baylor University Dept. of Computer Science WacoTX United States Lawrence Technological University Dept. of Biomedical Eng. SouthfieldMI United States Rose-Hulman Inst. of Technology Dept. of Civil & Env. Eng. Terre HauteIN United States Uni. of Illinois Urbana-Champaign Dept. of Mechanical Eng. UrbanaIL United States Baylor University Dept. of Mechanical Eng. WacoTX United States

ISBN: (纸本)9798350336429

Undergraduate (UG) Undergraduate (UG) research is a high-impact practice that has been shown to benefit students' development and has the potential to contribute to faculty research output. However, UG research does not attain the impact that it could have on student development or faculty research productivity. Faculty doing UG research face two main challeng.s: continuity and efficiency. Continuity is important for faculty to achieve research goals. However, onboarding, training, and mentoring UG researchers can be an inefficient, time-consuming process due to a lack of formal mentorship processes. From a student perspective, UG research has two main challeng.s: lack of autonomy and understanding of relevance relative to other co-curricular activities. In some research groups, UG students may not feel that they have ownership of their work, which results in the decay of intrinsic motivation. We are developing an Entrepreneurial Mindset (EM)-focused framework to enhance the research experience both for faculty and students. This framework provides faculty with access to resources and collaborators to support their efforts in eng.ging students in UG research and developing continuity in their laboratories. Students, on the other hand, will benefit from training opportunities designed to spark their curiosity, help them establish connections between their learnings, and enhance the value they create through their work. EM principles can transform UG research experiences by improving students' understanding of the relevance of their research and how it creates value for society. Our goal is to infuse EM into students' understanding of the UG research process, with the objective of broadening their participation, addressing institutional challeng.s, and enhancing research productivity to benefit society. We seek to understand what benefits students perceive they will gain through the UG research experience so that we can understand how entrepreneurially-minded learning

关键词： curriculum development entrepreneurial mindset intrinsic motivation undergraduate research

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FORSA: Exploiting Filter Ordering to Reduce Switching Activity for Low Power CNNs 66

FORSA: Exploiting Filter Ordering to Reduce Switching Activi...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Halawani, Yasmin Tesfai, Huruy Mohammad, Baker Saleh, Hani College of Engineering & It University of Dubai Dubai United Arab Emirates Khalifa University Electrical Eng. and Computer Science Dept. Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350302103

Switching activity in digital circuits depends on the temporal distribution of the data participating in the operation which directly influences the interconnect, dynamic power and timing of the system. This paper proposes an efficient method for reducing both power and latency of matrix-multiplication operations found in many applications such as convolution neural networks (CNNs). The approach takes advantage of the unique characteristics of CNN with input stationary for efficient multiply-add operations. Since most applications use reduced accuracy for MAC, the proposed work assumed 8-bit fixed-point representation for different quantization levels. As a demonstrator, CIFAR-10 data set has been used for end-to-end analysis of the filters on a 3-ConV with 2-FC CNN model structure. The filters' were re-ordered to reduce the switching behaviour between successive weight fetching. This directly impacts the dynamic power consumption and provides an estimation of 20.62% less classification power with 5-bits for quantization levels and 8-bit fixed-point representation for the MATLAB CNN pipeline. While with the full digital implementation, 11.43 % reduction is achieved. The difference could be due to the internal power of the cell or the arrival times of the signals. Moreover, reduced switching activity leads to reduced interconnect power which is normally high compared to computing. © 2023 IEEE.

关键词： CNN Dynamic power efficiency filter interconnect sort switching activity

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EvoSL: A Large Open-Source Corpus of Changes in Simulink Models & Projects 26

EvoSL: A Large Open-Source Corpus of Changes in Simulink Mod...

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26th ACM/IEEE International Conference on Model Driven eng.neering Languages and Systems, MODELS 2023

作者： Shrestha, Sohil Lal Boll, Alexander Chowdhury, Shafiul Azam Kehrer, Timo Csallner, Christoph University of Texas at Arlington Computer Science & Eng. Dept. ArlingtonTX76019 United States University of Bern Software Engineering Group Bern3012 Switzerland

ISBN: (纸本)9798350324808

Having readily available corpora is crucial for performing replication, reproduction, extension, and verification studies of existing research tools and techniques. MAT-LAB/Simulink is a de-facto standard tool in several safety-critical industries for system modeling and analysis, compiling models to code, and deploying code to embedded hardware. There is no commonly used corpus for large-scale model change studies because there is no readily available corpus. EvoSL is the first large corpus of Simulink projects that includes model and project changes and allows redistribution. EvoSL is available under a permissive open-source license and contains its collection and analysis tools. Using a subset of EvoSL, we replicated a case study of model changes on a single closed-source industrial project. © 2023 IEEE.

关键词： Simulink

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