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45th Annual international IEEE-computer-Society computers, Software, and Applications conference (COMPSAC)

作者： Bhardwaj, Dave Ormandjieva, Olga Concordia Univ Comp Sci & Software Engn Dept Monteal PQ Canada

ISBN: (纸本)9781665424639

Big Data is quickly becoming a chief part of the decision-making process in both industry and academia. As more and more institutions begin relying on Big Data to make strategic decisions, the quality of the underlying data comes into question. The quality of Big Data isn't always transparent and large-scale systems may even lack its visibility, which adversely affects the credibility of the Big Data systems. Continuous monitoring and measurement of data quality is therefore paramount in assessing whether the information can serve its purpose in a particular context (such as Big Data analytics, for example). This research addresses the need for Big Data quality measurement modeling and automation by proposing a novel conceptual quality measurement framework for Big Data (MEGA) with the purpose of assessing the underlying quality characteristics of Big Data (also known as the V's of Big Data) at each step of the Big Data Pipelines. The theoretical quality measurement models for four of the Big Data V's (Volume, Variety, Velocity, Veracity) are currently automated;the remaining 6 V's (Vincularity, Validity, Value, Volatility, Valence and Vitality) will be tackled in our future work. The approach is illustrated on a case study.

关键词： Big Data quality characteristics The V's Big Data Quality measurement Framework Big Data Pipelines

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Bad Seeds: Evaluating Lexical Methods for Bias measurement 59

Bad Seeds: Evaluating Lexical Methods for Bias Measurement

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joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international joint conference on Natural Language Processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Antoniak, Maria Mimno, David Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781954085527

A common factor in bias measurement methods is the use of hand-curated seed lexicons, but there remains little guidance for their selection. We gather seeds used in prior work, documenting their common sources and rationales, and in case studies of three English-language corpora, we enumerate the different types of social biases and linguistic features that, once encoded in the seeds, can affect subsequent bias measurements. Seeds developed in one context are often re-used in other contexts, but documentation and evaluation remain necessary precursors to relying on seeds for sensitive measurements.

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Optimizing E-Commerce with Multi-Objective Recommendations Using Ensemble Learning

Optimizing E-Commerce with Multi-Objective Recommendations U...

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computer systems (ICCS), IEEE international conference on

作者： Jiaxin Lu Trine University Phoenix USA

ISBN: (数字)9798350366341

ISBN: (纸本)9798350366358

In the dynamic landscape of online shopping, pre-dicting user behaviors such as clicks, cart additions, and orders is crucial for optimizing sales strategies. Traditional recommendation systems often focus on a single objective, limiting their effectiveness in a multifaceted e-commerce environment. This article proposes a multi-objective recommendation system that leverages previous events in user sessions to forecast these key metrics, addressing challenges such as imbalanced positive and negative samples, varying session lengths, and the need for effective sampling techniques. Our approach integrates LightGBM, XgBoost, and Recbole GRU4Rec through ensemble learning, combining the strengths of these models to enhance prediction performance. Extensive evaluations demonstrate that our model outperforms existing methods, offering significant improvements in accuracy and robustness. This work provides a comprehensive solution for online retailers to better predict user behaviors and optimize their sales strategies, ultimately enhancing customer satisfaction and business outcomes.

关键词： measurement Accuracy Limiting Computational modeling Predictive models Feature extraction Robustness Electronic commerce Ensemble learning Recommender systems

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Flexibility Dimensions in Cyber-Physical Production systems

Flexibility Dimensions in Cyber-Physical Production Systems

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Robin Huwa Marcus Albrecht Andreas Leinenbach Johannes Schilp Chair of Digital Manufacturing University of Augsburg Augsburg Germany

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

Abstract A key objective in modern and future manufacturing is increasing efficiency and eliminating downtime, even in the face of rapidly changing markets. To achieve this, various concepts and technologies are currently being researched in the context of manufacturing. One of the main topics of research is expanding the flexibility of resources, processes, products and their combination. This would enable and optimize concepts such as cloud manufacturing or equipment-as-a-service. However, in the literature, many authors focus on researching and experimenting with concepts or algorithms without defining flexibility. While the benefits of flexibility are intuitively understandable, often no quantifiable metric or qualitative analysis is proposed. This leads to ambiguities and a lack of comparability between approaches. In this paper, relevant literature and their implicitly covered flexibility dimensions are categorized explicitly along the Product-Process-Resource model. Based on this, an exemplary temporal resource flexibility model and its benefits are discussed. This numerical modeling method can be applied to further define flexibility for simulations or optimization algorithms.

关键词： measurement Production systems Mechatronics Heuristic algorithms Computational modeling Production control Numerical models Manufacturing Optimization Faces

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Domain-Specific Energy modeling for Drug Discovery and Magnetohydrodynamics Applications

Domain-Specific Energy Modeling for Drug Discovery and Magne...

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2023 international conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Carpentieri, Lorenzo D'Antonio, Marco Fan, Kaijie Crisci, Luigi Cosenza, Biagio Ficarelli, Federico Cesarini, Daniele Accordi, Gianmarco Gadioli, Davide Palermo, Gianluca Thoman, Peter Salzmann, Philip Gschwandtner, Philipp Wippler, Markus Marchetti, Filippo Gregori, Daniele Beccari, Andrea Rosario University of Salerno Italy Cineca Italy Politecnico di Milano Italy University of Innsbruck Austria PH3 GmbH Austria E4 Computer Engineering Italy Dompé Farmaceutici Spa Italy

ISBN: (纸本)9798400707858

Over the past few years, the adoption of energy efficiency techniques in modern computer systems is becoming increasingly relevant for sustainable computing. A well-known power management software technique for energy-efficient computing is frequency scaling which modulates the device frequency to explore the energy-performance trade-off. To achieve energy savings, a frequency tuning phase is required because different applications can have different energy and runtime behaviors depending on the frequency setting. Machine learning models can be used to predict energy and runtime, and therefore optimal frequency configurations, based on static or dynamic features extracted from the target application. While general-purpose energy models can be very accurate for a wide range of applications, their accuracy can be limited by the specific input of the target application. We present an energy characterization that spans the fields of drug discovery and magnetohydrodynamics by using two real-world applications as case studies: LiGen and Cronos. Additionally, to overcome the limitations of general-purpose approaches, we define two domain-specific energy models, which enhance the general-purpose energy models by leveraging the target application's input parameter to increase the final accuracy. Experimental results show that for both applications, domain-specific models achieve a ten times lower error compared to the general-purpose energy models. © 2023 Owner/Author.

关键词： Energy efficiency

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modeling simulation and inverter control strategy research of microgrid in grid-connected and island mode

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ENERGY REPORTS 2022年 8卷 206-218页

作者： Wang, Hui Wang, Mingyue Cheng, Qi Lv, Shuaishuai Ji, Xiu Changchun Inst Technol Changchun 130012 Peoples R China Natl & Local Joint Engn Res Ctr Measurement Contr Changchun 130012 Peoples R China

Under the "double carbon" goal, distributed generation (DG) with inverters will show an explosive growth trend. The microgrid can operate in different modes as a channel for DG to connect to the main grid. In the microgrid, the fast response characteristics of power electronics exacerbate the instability of the microgrid when switching between grid-connected and islanded modes. On the PSCAD/EMTDC simulation platform, a refined power generation model with wind-solar-load-storage microgrid is built to capture the behavior of the system, rather than using a highly simplified model. At the same time, a reasonable control strategy is necessary, which is the key to maintaining the stability of the system. Existing control strategies can only operate in a single mode or the control effect is not ideal. Based on this, on the basis of droop control, the virtual adaptive control loop is added in this study, and the virtual impedance value is adaptively changed according to different output conditions to achieve the purpose of precise control;In the active power control loop, the frequency signal is drawn from the phase-locked loop, and becomes a power feedback variable through the signal transformation process, thereby participating in the power control process and improving the accuracy of frequency control. A standard microgrid power generation model and an inverter control model suitable for grid-connected and off-grid microgrids are built, and the voltage and frequency fluctuations in the two modes are analyzed to verify the effectiveness of the strategy.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Microgrid Droop control Adaptive virtual resistance Stability

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Image Captioning using CNN and LSTMs

Image Captioning using CNN and LSTMs

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Rajeel Ahmad Ansari Divyapratap Singh Chauhan B. Jothi Krishnaveni S Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Image captioning is a pivotal field in AI, enabling machines to generate descriptive text for images, with applications in accessibility, content creation, and human-computer interaction. This study identifies the shortcomings of the conventional CNN-RNN model by proposing an advanced architecture that incorporates InceptionV3, LSTM, attention mechanism, and beam search. The image features are extracted using Incep-tionV3; captions are generated using LSTMs, where an attention mechanism is used to guide the model to identify important regions of the image for each word. Beam search has been employed to choose the most optimal captions. The suggested model is tested on the Flickr8k data set with the help of the BLEU metric. The outcomes show that the caption relevance, diversity, and interpretability of the model are enhanced, with BLEU-1 and BLEU-2 scores of 0.5365 and 0.3218 respectively, demonstrating its potential in real-world applications such as providing accessibility and machine learning-based content generation.

关键词： Training measurement Human computer interaction Technological innovation Attention mechanisms Computational modeling Feature extraction Intelligent systems Long short term memory Tuning

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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management

Waste Sorting System Using AI: Development of An Embedded So...

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international Image Processing, Applications and systems conference (IPAS)

作者： Amal Remili Mouna Zouari Mahdi Jihen Frikha Elleuch Ahmed Fakhfakh Dorra Sellami Electrical Engineering Department National Engineering School of Sfax Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge.

关键词： Waste management measurement computer vision Computational modeling Image processing Real-time systems Plastics Artificial intelligence System validation Sorting

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Accurate 3D Body Shape Regression using Metric and Semantic Attributes

Accurate 3D Body Shape Regression using Metric and Semantic ...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Choutas, Vasileios Mueller, Lea Huang, Chun-Hao P. Tang, Siyu Tzionas, Dimitrios Black, Michael J. Max Planck Inst Intelligent Syst Tubingen Germany Swiss Fed Inst Technol Zurich Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While methods that regress 3D human meshes from images have progressed rapidly, the estimated body shapes often do not capture the true human shape. This is problematic since, for many applications, accurate body shape is as important as pose. The key reason that body shape accuracy lags pose accuracy is the lack of data. While humans can label 2D joints, and these constrain 3D pose, it is not so easy to "label" 3D body shape. Since paired data with images and 3D body shape are rare, we exploit two sources of information: (1) we collect internet images of diverse "fashion" models together with a small set of anthropometric measurements;(2) we collect linguistic shape attributes for a wide range of 3D body meshes and the model images. Taken together, these datasets provide sufficient constraints to infer dense 3D shape. We exploit the anthropometric measurements and linguistic shape attributes in several novel ways to train a neural network, called SHAPY, that regresses 3D human pose and shape from an RGB image. We evaluate SHAPY on public benchmarks, but note that they either lack significant body shape variation, ground-truth shape, or clothing variation. Thus, we collect a new dataset for evaluating 3D human shape estimation, called HBW, containing photos of "Human Bodies in the Wild" for which we have ground-truth 3D body scans. On this new benchmark, SHAPY significantly outperforms state-of-the-art methods on the task of 3D body shape estimation. This is the first demonstration that 3D body shape regression from images can be trained from easy-to-obtain anthropometric measurements and linguistic shape attributes. Our model and data are available at: ***

关键词： Solid modeling Three-dimensional displays Shape Annotations Shape measurement Biological system modeling Clothing

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Boosting Entity Mention Detection for Targetted Twitter Streams with Global Contextual Embeddings 38

Boosting Entity Mention Detection for Targetted Twitter Stre...

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38th IEEE international conference on Data Engineering (ICDE)

作者： Bhowmick, Satadisha Saha Dragut, Eduard C. Meng, Weiyi SUNY Binghamton Dept Comp Sci Binghamton NY 13902 USA Temple Univ Dept Comp Sci Philadelphia PA USA

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Microblogging sites, like Twitter, have emerged as ubiquitous sources of information. Two important tasks related to the automatic extraction and analysis of information in Microblogs are Entity Mention Detection (EMD) and Entity Detection (ED). The state-of-the-art EMD systems aim to model the non-literary nature of microblog text by training upon offline static datasets. They extract a combination of surface-level features - orthographic, lexical, and semantic - from individual messages for noisy text modeling and entity extraction. But given the constantly evolving nature of microblog streams, detecting all entity mentions from such varying yet limited context of short messages remains a difficult problem. To this end, we propose a framework named EMD Globalizer, better suited for the execution of EMD learners on microblog streams. It deviates from the processing of isolated microblog messages by existing EMD systems, where learned knowledge from the immediate context of a message is used to suggest entities. Instead, it recognizes that messages within a microblog stream are topically related and often repeat entity mentions, thereby leaving the scope for EMD systems to go beyond the localized processing of individual messages. After an initial extraction of entity candidates by an EMD system, the proposed framework leverages occurrence mining to find additional candidate mentions that are missed during this first detection. Aggregating the local contextual representations of these mentions, a global embedding is drawn from the collective context of an entity candidate within a stream. The global embeddings are then utilized to separate entities within the candidates from false positives. All mentions of said entities from the stream are produced in the framework's final outputs. Our experiments show that EMD Globalizer can enhance the effectiveness of all existing EMD systems that we tested (on average by 25.61%) with a small additional computational overhead.

关键词： Training Social networking (online) Blogs Semantics Pipelines Feature extraction Noise measurement

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