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IEEE International Conference on e-science and Grid Computing

作者： Sho Nakazono Yutaro Bessho Hideyuki Kawashima Tatsuhiro Nakamori Computer and Data Science Laboratories NTT Tokyo Japan Faculty of Environment and Information Studies Keio University Kanagawa Japan

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Index access is one of the dominant performance factors in transactional database systems. Many systems use a B+tree or one of its variants to handle point and range operations. This access pattern has room for performance improvement. Firstly, point operations can potentially be processed in O(1) with a hash table. Secondly, to ensure serializability of transactions, range operations incur overhead from phantom avoidance techniques that involve additional processing or synchronization, such as an extra traversal of the B+-tree. To address these issues, we propose a hybrid index architecture, Griffin. For point operations, Griffin has a hash table that provides access paths in O(1) time, along with a B+-tree. For phantom avoidance, Griffin employs a precision locking method, which does not involve additional traversal of the B+-tree. Despite its hybrid architecture, Griffin transparently provides linearizable operations and an interface of a single database index. We built a Griffin index combining a hash table and BwTree. Compared to a baseline index that is composed of a BwTree only, it achieves up to 3.1x higher throughput in a point operation dominant workload, and up to 5.4x higher throughput in a range operation dominant workload.

关键词： Phantoms Throughput data structures Indexes Synchronization Periodic structures

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Connecting Cross-Modal Representations for Compact and Robust Multimodal Sentiment Analysis with Sentiment Word Substitution Error

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IEEE Transactions on Affective Computing 2024年

作者： Sun, Qiyuan Zuo, Haolin Liu, Rui Li, Haizhou The Department of Computer Science Inner Mongolia University Hohhot010021 China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong CUHKShenzhen Shenzhen518172 China University of Bremen Faculty3 Computer Science / Mathematics Enrique-Schmidt-Str. 5 Cartesium Bremen28359 Germany

Multimodal Sentiment Analysis (MSA) seeks to fuse textual, acoustic, and visual information to predict a speaker’s sentiment states effectively. However, in real-world scenarios, the text modality received by MSA systems is often obtained through automatic speech recognition (ASR) models. Unfortunately, ASR may erroneously recognize sentiment words as phonetically similar neutral alternatives, leading to sentiment degradation in text and impacting MSA accuracy. Recent attempts aim to first identify the sentiment word substitution (SWS) error in ASR results and then refine the corrupted word embeddings using multimodal information for final multimodal fusion. However, such a method includes a burdensome and ambiguous detection operation and ignores the inherent correlations and heterogeneity among different modalities. To address these issues, we propose a more compact system, termed ARF-MSA consisting of three key components to achieving robust MSA with SWS errors: 1) Alignment: we establish connections between the "text-acoustic’ and "text-visual" representations to effectively map the "text-acoustic-visual" data into a unified sentiment space by leveraging their multimodal correlation knowledge;2) Refinement: we perform fine-grained comparisons between the text modality and the other two modalities in the unified sentiment space, enabling refinement of the sentiment expression within the text modality more concisely;3) Fusion: Finally, we hierarchically fuse the dominant and non-dominant representation from three heterogeneity modalities to obtain the multimodal feature for MSA. We conduct extensive experiments on the real-world datasets and the results demonstrate the effectiveness of our model. © 2010-2012 IEEE.

关键词： Alignment

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A new class of generalized quasi-variational inequalities with applications to Oseen problems under nonsmooth boundary conditions

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science China Mathematics 2024年第2期67卷 315-338页

作者： Shengda Zeng Akhtar A.Khan Stanislaw Migórski Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China Faculty of Mathematics and Computer Science Jagiellonian University in Krak´owKrak´ow 30348Poland Center for Applied and Computational Mathematics School of Mathematical SciencesRochester Institute of TechnologyRochesterNY 14623USA College of Sciences Beibu Gulf UniversityQinzhou 535000China

In this paper, we study a generalized quasi-variational inequality (GQVI for short) with twomultivalued operators and two bifunctions in a Banach space setting. A coupling of the Tychonov fixedpoint principle and the Katutani-Ky Fan theorem for multivalued maps is employed to prove a new existencetheorem for the GQVI. We also study a nonlinear optimal control problem driven by the GQVI and givesufficient conditions ensuring the existence of an optimal control. Finally, we illustrate the applicability of thetheoretical results in the study of a complicated Oseen problem for non-Newtonian fluids with a nonmonotone andmultivalued slip boundary condition (i.e., a generalized friction constitutive law), a generalized leak boundarycondition, a unilateral contact condition of Signorini’s type and an implicit obstacle effect, in which themultivalued slip boundary condition is described by the generalized Clarke subgradient, and the leak boundarycondition is formulated by the convex subdifferential operator for a convex superpotential.

关键词： generalized quasi-variational inequality existence theorem optimal control Kakutani-Ky Fan theorem Oseen problem non-Newtonian fluid nonmonotone slip boundary condition

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Complexity and Approximation Schemes for Social Welfare Maximization in the High-Multiplicity Setting 27

Complexity and Approximation Schemes for Social Welfare Maxi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Nguyen, Trung Thanh Elbassioni, Khaled Rothe, Jörg Faculty of Data Science and Artificial Intelligence National Economics University Hanoi11616 Viet Nam Dept.Computer Science Center for Cyber-Physical Systems Khalifa University Abu Dhabi127788 United Arab Emirates Institut für Informatik MNF Heinrich-Heine-Universität Düsseldorf Düsseldorf40225 Germany

ISBN: (纸本)9781643685489

We study the social welfare maximization problem in the high-multiplicity setting where agents and/or items are available in multiple types, provided that the numbers of types are *** focus on the egalitarian and Nash social welfare maximization problems, and show that they are NP-hard even when the number of item types is a ***, we present two polynomial-time approximation schemes (PTAS), one for egalitarian social welfare with two item types, and one for Nash social welfare with any constant number of agent *** first PTAS can be applied to the unrelated machine scheduling problem, thus partially solving an open question raised by Jansen and Maack in *** second PTAS significantly improves upon the existing PTAS for identical agents. © 2024 The Authors.

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Smart Energy Management System Using IoT for Real-Time Monitoring and Theft Detection

Smart Energy Management System Using IoT for Real-Time Monit...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Rajermani Thinakaran Somasekar Jalari Vikram Neerugatti Ravindra Raman Cholla K Nagendra Rao Faculty of Data Science and Information Technology INTI International University Malaysia Department of Computer Science and Engineering Faculty of Engineering and technology Jain (Deemed-to-be University) Bangalore India Department of Computer Science and Engineering S V College of Engineering Tirupati Andhra Pradesh India

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. System can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss.

关键词： Meters GSM Energy consumption Renewable energy sources Costs Electricity Real-time systems Recording Monitoring Wireless fidelity

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Online Affinity of Instructing Methods for Using Personal-Efficacy and Reading Capacious During Covid-19 5th

Online Affinity of Instructing Methods for Using Personal-Ef...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： PremaLatha, V. Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Bhoi, Akash Kumar Ghosh, Uttam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Guntur Vaddeswaram522502 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Data Science Meharry School of Computational Sciences Nashville NashvilleTN United States

ISBN: (纸本)9789811920035

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be "more expensive, difficult, and time-consuming". Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes "comprehension reading work" is treated as fundamental in the proposal of disclosures. In case of any, "the programme appeared to positively affect contributing students". Techniques of classification like "decision tree and Mixed Model database Miner (MMDBM)" are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive lice

关键词： Students

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Multilingual Resources for Offensive Language Detection 6

Multilingual Resources for Offensive Language Detection

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6th Workshop on Online Abuse and Harms, WOAH 2022

作者： Arango, Aymé Pérez, Jorge Poblete, Bárbara Proust, Valentina Saldaña, Magdalena Departament of Computer Science University of Chile Chile Communication Faculty Pontifical Catholic University of Chile Chile Millennium Institute of Data Foundation Santiago Chile

ISBN: (纸本)9781955917841

Most of the published approaches and resources for offensive language and hate speech detection are tailored for the English language. In consequence, cross-lingual and cross-cultural perspectives lack some essential resources. The lack of diversity of the datasets in Spanish is notable. Variations throughout Spanish-speaking countries make existing datasets not enough to encompass the task in the different Spanish variants. We manually annotated 9834 tweets from Chile to enrich the existing Spanish resources with different words and new targets of hate that have not been considered in previous studies. We conducted several cross-dataset evaluation experiments of the models published in the literature using our Chilean dataset and two others in English and Spanish. We propose a comparative framework for quickly conducting comparative experiments using different previously published models. In addition, we set up a Codalab competition for further comparison of new models in a standard scenario, that is, data partitions and evaluation metrics. All resources can be accessed through a centralized repository for researchers to get a complete picture of the progress on the multilingual hate speech and offensive language detection task. © 2022 Association for Computational Linguistics.

关键词： Speech recognition

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Design and Implementation of a Radar-Camera Fusion System for Human Detection and Its Distance Measurement

Design and Implementation of a Radar-Camera Fusion System fo...

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International Electronics Symposium (IES)

作者： Indra Putra Mahayuda Alfan Rizaldy Pratama Bima Sena Bayu Dewantara Dadet Pramadihanto Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Data Science Department Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jawa Timur Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Human distance detection is essential for improving safety and efficiency in applications such as autonomous vehicles, surveillance, and human-robot interaction. Integrating radar and camera data through sensor fusion significantly improves detection accuracy and reliability while reducing the inherent limitations of each sensor type. This research presents the design of a radar-camera fusion system that reads and processes data from both sources. Instance segmentation is used to identify objects for camera data, while radar data is projected to pixel coordinates through coordinate transformation, allowing the two data sets to be fused into the same space. This fusion results in more complete object information. The developed system achieved 100% object detection accuracy in the experimental scenario and demonstrated 93.4% distance measurement accuracy, with average longitudinal and latitudinal errors of 37.1 cm and 17.5 cm, respectively.

关键词： Instance segmentation Accuracy Surveillance Spaceborne radar Radar detection Sensor fusion Cameras

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Multi-Task Learning Frameworks to Classify Food and Estimate Weight From a Single Image

Multi-Task Learning Frameworks to Classify Food and Estimate...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Pakin Siwathammarat Panas Jesadaporn Jakarin Chawachat Data Science Research Center Faculty of Science Chiang Mai University Thailand Department of Internal Medicine Faculty of Medicine Chiang Mai University Thailand Department of Computer Science Faculty of Science Chiang Mai Thailand

Usually, elderly patients in hospitals suffer from malnutrition because they are unable to consume food as prescribed by doctors or nutritionists. Analyzing food intake is labor-intensive and time-consuming. Therefore, machine learning is used to analyze the food intake. Major food analysis tasks include food classification and food weight estimation. The basic machine learning approach to this problem is to combine a food classification model with a food weight estimate model sequentially. When we deployed it, we found that a large amount of memory and models were required. One solution is to use multi-task learning. In this study, we proposed multi-task learning frameworks that could recognize food and predict weight based on a single image. The performance of our frameworks was compared to the baseline models, which only utilized either regression or classification. Although baseline accuracy is higher, our framework has MAPE values that are lower than the baseline. To improve the performance, we explored different approaches for weighting loss, including manual weighting and auto weighting, using uncertainty and auxiliary tasks. From the experiment, our results showed that our multi-task learning framework that adjusted the weight of loss using auxiliary tasks outperformed the baseline models in terms of MAPE and Accuracy. Moreover, we demonstrate our framework when scaling up the backbone from ResNet50 to ResNet101 and ResNet152.

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Optimizing Feature Selection in Machine Learning with E-BPSO: A Dimensionality Reduction Approach 3rd

Optimizing Feature Selection in Machine Learning with E-BP...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM University – Kattankulathur Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819773558

In the era of informatics, the effectiveness of machine learning models is compromised due to the challenge of dimensionality in the data. The presence of redundant and irrelevant features significantly increases computational complexity, posing a central obstacle in the extraction of valuable insights from the extensive dataset. Any machine learning model’s performance suffers because of the issue of the plague of dimensionality. To improve the classifier’s performance, feature selection is applied beforehand on applying the machine learning model. Feature selection is accomplished using Enhanced Binary Particle Swarm Optimization (E-BPSO) with the aid of boosting the performance of the K-Nearest Neighbor (K-NN) classifier and is experimented on benchmarking real-world datasets. The conventional BPSO suffers from the problem of exploration which leads to premature convergence. In order to overcome the drawbacks of conventional BPSO, E-BPSO is proposed. The enhancement is made by integrating the self-adaptive velocity to drive the particle with the aid to balance exploration and exploitation. The performance of the proposed E-BPSO is evaluated against the traditional binary particle swarm optimization algorithm and genetic algorithm, considering metrics like accuracy, fitness, root mean square error, and dimensionality reduction ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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