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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Takuya Suzuki Hayato Yamana Department of Computer Science and Communications Engineering Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

Data providers are concerned about data privacy when a third party handles their data. A typical example is cloud computing. In such cases, homomorphic encryption can be used to ensure data privacy. Although homomorphic encryption allows the execution of operations with encrypted data without decryption, its execution time is long. Previous studies have focused on the field-programmable gate array (FPGA)-based acceleration of homomorphic encryption. However, the overhead of data transfer between a host and an FPGA is a bottleneck owing to the limited bandwidth of a peripheral component interconnect express (PCIe). In-storage computing (ISC), i.e., using a computational storage device (CSD), can reduce the data-transfer overhead. In this study, a computational model coupled with a CSD is proposed to accelerate homomorphic operations. Simulations show that our proposed ISC model reduces latency by 71.2% and achieves 3. 5x throughput of a homomorphic encrypted convolutional neural network compared with 16 FPGA boards with 16 PCIe lanes and sufficient solid-state drives (SSDs), i.e., without CSDs.

关键词： Data privacy Solid state drives Computational modeling Random access memory Bandwidth Throughput Convolutional neural networks

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A Study on Impact-Aware Aerial Robots Colliding with the Environment at Non-Vanishing Speed

A Study on Impact-Aware Aerial Robots Colliding with the Env...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Gayatri Indukumar Alessandro Saccon Antonio Franchi Chiara Gabellieri Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands Department of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Enabling aerial robots to handle dynamic contacts happening at non-vanishing speeds can enlarge the range of their applications. In this work, we propose an impactaware strategy to allow aerial multirotor robots to recover from impacts. The method leverages a reactive strategy not requiring low-level changes to the motion controller commonly implemented onboard quadrotors, which might be not viable or not desirable for most users. Extensive simulation tests show that the proposed strategy considerably increases the tolerated velocity at impact in tasks in which the robot either picks an object up or collides against an object to clear its way. Preliminary experimental results using Crazyflie UAVs are also presented.

关键词： Trajectory tracking Dynamics Lead Autonomous aerial vehicles Stability analysis Robustness computer crashes Trajectory Robots Quadrotors

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Sentiment Analysis of Tweets Using Emojis and Texts 21

Sentiment Analysis of Tweets Using Emojis and Texts

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4th International Conference on Information science and Systems, ICISS 2021

作者： Saleh Alzubaidi, Mohammed Bourennani, Farid Department of Computer Science and Artificial Intelligence Faculty of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Information Systems and Technology Faculty of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9781450389136

Nowadays, social media generate massive volumes of data containing valuable information to several applications such as marketing, business, and politics which are interested to analyze opinions and sentiments of individuals. A lot of efforts have been directed towards sentiment analysis (SA) based on texts;however, limited efforts have been directed towards the study and analysis of other DTs such as emojis and other multimedia. The processing of other data types (DTs) not only can generate complementary information, but we argue that the combined processing of heterogeneous DTs (HDTs), called heterogeneous data mining (HDM), should lead to more accurate results. In this work, we propose the HDM of tweets using both texts and emojis for bi-sense SA;the SA results using HDM were more accurate than the exclusive use of either texts or emojis, and interestingly the use of emojis achieved a higher accuracy than texts. © 2021 ACM.

关键词： Sentiment analysis

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An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

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computers, Materials & Continua 2021年第4期67卷 1191-1206页

作者： Ayesha Jabeen Sitara Afzal Muazzam Maqsood Irfan Mehmood Sadaf Yasmin Muhammad Tabish Niaz Yunyoung Nam Department of Computer Science COMSATS University IslamabadAttock CampusPakistan Department of Media Design and Technology Faculty of Engineering&InformaticsUniversity of BradfordBradfordBD71AZUK Department of Smart Device Engineering School of Intelligent Mechatronics EngineeringSejong UniversitySeoulKorea Department of Computer Science&Engineering Soonchunhyang UniversityAsan36538Korea

Stock market forecasting is an important research area,especially for better business decision *** stock predictions continue to be significant for business *** short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily ***,major events’news also contains significant information regarding market *** effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock *** research proposes an efficient model for stock market *** current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and *** use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock *** LSTM has the advantage of analyzing relationship between time-series data through memory *** performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.

关键词： Business intelligence decision making stock prediction long short-term memory COVID-19 event sentiment

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Beyond Euclidean Structures: Collaborative Topological Graph Learning for Multiview Clustering

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10606-10618页

作者： Cheng Liu Rui Li Hangjun Che Man-Fai Leung Si Wu Zhiwen Yu Hau-San Wong Department of Computer Science Shantou University Shantou China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China School of Computing and Information Science Faculty of Science and Engineering Anglia Ruskin University Cambridge U.K. School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Computer Science City University of Hong Kong Hong Kong China

Graph-based multiview clustering (MVC) approaches have demonstrated impressive performance by leveraging the consistency properties of multiview data in an unsupervised manner. However, existing methods for graph learning heavily rely on either Euclidean structures or the manifold topological structures derived from fixed view-specific graphs. Unfortunately, these approaches may not accurately reflect the consensus topological structure in a multiview setting. To address this limitation and enhance the intrinsic graph learning process, an adaptive exploration of a more appropriate consistency topological structure is required. Toward this end, we propose a novel approach called collaborative topological graph learning (CTGL) for MVC. The key idea is to adaptively discover the consistent topological structure to guide intrinsic graph learning. We achieve this by introducing an auxiliary consistency graph that formulates the topological relevance learning function. However, estimating the auxiliary consistency graph is not straightforward, as it is based on the learned view-specific graphs and requires prior availability. To overcome this challenge, we develop a collaborative learning strategy that simultaneously learns both the auxiliary consistency graph and view-specific graphs using tensor learning techniques. This strategy enables the adaptive exploration of the consistency topological structure during graph learning, resulting in more accurate clustering outcomes. Extensive experiments are provided to show the effectiveness of the proposed method. The source code can be found at https://***/CLiu272/CTGL .

关键词： Manifolds Tensors Collaboration Accuracy Learning systems Federated learning Kernel computer science Silicon Adaptive learning

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Ciao AI: the Italian adaptation and validation of the Chatbot Usability Scale

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Personal and Ubiquitous Computing 2023年第6期27卷 2161-2170页

作者： Borsci, Simone Prati, Elisa Malizia, Alessio Schmettow, Martin Chamberlain, Alan Federici, Stefano Faculty of Behavioural Management and Social Sciences Department of Learning Data Analysis and Technology Group of Cognition Data and Education University of Twente Enschede Netherlands Department of Surgery and Cancer Faculty of Medicine NIHR London IVD Imperial College of London London United Kingdom Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia Modena Italy Computer Science Department University of Pisa Pisa Italy Molde University College Molde Norway School of Computer Science University of Nottingham Nottingham United Kingdom Department of Philosophy Social and Human Sciences and Education University of Perugia Perugia Italy

Chatbot-based tools are becoming pervasive in multiple domains from commercial websites to rehabilitation applications. Only recently, an eleven-item satisfaction inventory was developed (the ChatBot Usability Scale, BUS-11) to help designers in the assessment process of their systems. The BUS-11 has been validated in multiple contexts and languages, i.e., English, German, Dutch, and Spanish. This scale forms a solid platform enabling designers to rapidly assess chatbots both during and after the design process. The present work aims to adapt and validate the BUS-11 inventory in Italian. A total of 1360 questionnaires were collected which related to a total of 10 Italian chatbot-based systems using the BUS-11 inventory and also using the lite version of the Usability Metrics for User eXperience for convergent validity purposes. The Italian version of the BUS-11 was adapted in terms of the wording of one item, and a Multi-Group Confirmatory Factorial Analysis was performed to establish the factorial structure of the scale and compare the effects of the wording adaptation. Results indicate that the adapted Italian version of the scale matches the expected factorial structure of the original scale. The Italian BUS-11 is highly reliable (Cronbach alpha: 0.921), and it correlates to other measures of satisfaction (e.g., UMUX-Lite, τb = 0.67;p © 2023, The Author(s).

关键词： Buses

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Two-phase nanofluid flow exhibiting Brownian motion and thermophoretic diffusion via a horizontal circular pipe: numerical simulation

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JMST Advances 2024年第1期6卷 37-54页

作者： EL-Dabe, N. T. M. Attia, H. A. Essawy, M. A. I. Ramadan, A. A. Abdel-Hamid, A. H. Department of Mathematics Faculty of Education Ain Shams University Heliopolis Cairo Egypt Department of Engineering Mathematics and Physics Faculty of Engineering Fayoum University Fayoum Egypt Higher Technological Institute (HTI) 6th of October City Giza Egypt Mathematics and Computer Science Department Faculty of Science Beni-Suef University Beni-Suef Egypt

The present study investigates the impact of an unsteady internal flow of a particulate nanofluid within a porous material on the heat and mass transfer along a circular horizontal conduit. It is assumed that both the carrier nanofluid and the dust particles have a high viscosity and are hence incompressible. To kick off this two-phase flow, a constant pressure gradient is applied along the axial direction of the circular pipe. The porous medium’s drag is explained by Darcy’s law and the energy calculations account for the Darcy limit of porous dissipation. A set of nonlinear partial differential equations (PDEs) is used to characterize the nanofluid and dust particle phases, as well as the concentration of suspended nanoparticles. These PDEs were numerically solved using the methodology of finite differences. Coefficients of skin friction and flow rates regarding both phases were also calculated. The novelty lies in the ability of numerical simulation to capture the intricate interplay between Brownian motion, thermophoretic diffusion, and fluid flow within nanofluids. This approach allows for detailed analysis of the complex phenomena involved, which may not be easily achieved through experimental investigations alone. These profiles are formed as a result of the regulating physical factors. Graphs and tabular data are utilized to visually represent the impact of different parameters on solutions. Ultimately, an evaluation of the current solutions for some special cases with previously published findings demonstrates the precision and reliability of the present results.

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Various Modulation Types Classification on the Air by Convolutional Neural Network 7

Various Modulation Types Classification on the Air by Convol...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Hammadi, Yousif I Alsaeedi, Mokhalad Abdulameer Kadhim Mahmood, Omar Abdulkareem Sameen, Aws Zuhair Muthanna, Mohammed Saleh Ali Aziz, Ahmed Bilad Alrafidain University College Diyala Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Medical Instrumentation Engineering Techniques College Of Medical Techniques Al-Farahidi University Baghdad Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan

ISBN: (纸本)9798400709036

Deep learning (DL), a relatively recent AI technique, has been successfully applied to the problem of automated modulation categorization (AMC), with promising results. An essential part of developing the spectrum-sensing capabilities needed by software-defined radio is carrying out radio signal classification jobs, which we did in this research by introducing a better deep neural architecture. Different detection techniques were utilized in the literature;however, DL neural networks are recently utilized in this field. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) which is one variation of the RNN network, and other techniques are some of the employed DL architectures in the field of AMC. In this paper, Convolutional Neural Network (CNN) model has been built to automatically classify ten-modulation formats correctly. The suggested CNN network is constructed mainly of six-Convolutional layers. The overall number of trainable parameters in the proposed network is 1,757,962. Thus, the network accuracy, which was achieved during the training reached more than 90%. Python programming packages have been used to implement the suggested approach using *** platform, which provides wide-range of facilities. © 2023 ACM.

关键词： Network architecture

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Performance Comparison of Homomorphic Encrypted Convolutional Neural Network Inference Among HElib, Microsoft SEAL and OpenFHE

Performance Comparison of Homomorphic Encrypted Convolutiona...

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computer science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Haoyun Zhu Takuya Suzuki Hayato Yamana Department of Computer Science Waseda University Tokyo Japan Department of Computer Science and Communications Engineering Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

Homomorphic encryption (HE) offers a promising solution for maintaining data privacy even during calculation, as it allows for the evaluation of ciphertexts. Several HE libraries have been released, but selecting the best-suited library for HE beginners can be challenging. This paper provides insights into the selection process by comparing three popular HE libraries: HElib, Microsoft SEAL, and OpenFHE. To evaluate the performance of these libraries, we implemented a convolutional neural network inference using each of the three libraries and analyzed its latency. Our experimental results show that Microsoft SEAL achieves the shortest latency, less than 56% of OpenFHE and less than 17% of HElib. In terms of ease of use, OpenFHE surpasses the other libraries because it eliminates the need for programmers to consider rescaling during calculations and has more choices on different key-switching algorithms. In contrast, utilizing automatic rescaling with OpenFHE results in 5x longer latency than manual rescaling.

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