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arXiv 2022年

作者： Ziv, Ran Gronau, Ilan Fire, Michael Efi Arazi School of Computer Science Reichman University Department of Software and Information Systems Engineering Ben-Gurion University

Entity Matching is an essential part of all real-world systems that take in structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and integration processes require completion before any data analytics, or further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights, supports data visualization, and guides further analytic approaches to the data. Here, we focus on organization entity matching. We introduce CompanyName2Vec, a novel algorithm to solve company entity matching (CEM) using a neural network model to learn company name semantics from a job ad corpus, without relying on any information on the matched company besides its name. Based on a real-world data, we show that CompanyName2Vec outperforms other evaluated methods and solves the CEM challenge with an average success rate of 89.3%. Copyright © 2022, The Authors. All rights reserved.

关键词： Data Analytics

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Research on Labour Market Efficiency Evaluation Under Impact of Media News Based on Machine Learning and DMP Model 4

Research on Labour Market Efficiency Evaluation Under Impact...

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4th International Conference on Digital Society and Intelligent systems, DSInS 2024

作者： Zhao, Shenghan Lu, Youyuan Gong, Chenwei Xu, Qiming Cornell University Department of Economics Ithaca14850 United States Software Systems and Simulation Engineering Intel Corporation Beijing100013 China University of California Henry Samueli School of Engineering Department of Computer Los Angeles90024 United States Khoury College of Computer Sciences Northeastern University Boston02115 United States

ISBN: (纸本)9798331528829

The labour market constitutes a pivotal element of the national economy, exerting a profound influence on the health and stability of the broader economic system. In recent years, the role of media journalism in the dissemination of information has increased significantly, with a notable impact on supply-demand and price fluctuations in the labour market. By analysing the impact of media news, it is possible to gain a deeper understanding of, and insight into, the dynamics of the labour market. This study employs a combination of machine learning techniques and a dynamic stochastic general equilibrium model (DMP model) to assess the influence of media news on labour market efficiency. The use of machine learning algorithms enables the processing and analysis of substantial quantities of media news data, facilitating the extraction of sentiment and topic changes. The DMP model is employed to simulate the matching process in the labour market and evaluate the impact of news on the efficiency of matching job seekers and job openings. The study demonstrates, through experimental analysis, the disparate impacts of media news on the labour market across different economic cycles, and validates the efficacy and practicality of the model. © 2024 IEEE.

关键词： Employment

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M-CNN-RF: A hybrid deep learning model for accurate pediatric skeletal age estimation using hand bone radiographs

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Alexandria engineering Journal 2025年 129卷 289-301页

作者： Hazrat Bilal Yibin Tian Inam Ullah Sahil Garg Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea École de Technologie Supérieure Montreal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Precise and accurate skeletal age estimation using medical imaging is a pivotal and challenging task in the healthcare sector, particularly for identifying potential bone growth issues in infants and newborns. Therefore, this study addresses the pervasive challenges associated with assessing bone abnormalities in pediatric patients, including injuries and infections. Given the importance of early and precise detection of skeletal development, a novel hybrid model is proposed that integrates a modified convolutional neural network (M-CNN) with a robust machine learning (ML) model, specifically random forest (RF), resulting in the M-CNN-RF framework. This model is designed to enhance pediatric bone health assessment by providing an effective method for skeletal age estimation. The M-CNN-RF model is tailored to accurately evaluate hand bone maturation, overcoming the inherent difficulties in skeletal age assessment. The model utilizes the bone age dataset from the Radiological Society of North America that includes 14,236 left-hand radiological images, focusing on the development of a robust model for a precise evaluation based on hand skeleton guidelines. In addition, to enhance the prediction and generalization of the model, data augmentation techniques were employed to increase the size of the dataset. The M-CNN-RF exhibits exceptional performance using numerous performance measures, achieving an accuracy of 97% and precision and recall exceeding 94%. In addition, the model reaches an F1 score of 97%, highlighting the ability of the model to ensure a balance between precision and recall. Furthermore, low mean absolute error (MAE) and mean square error (MSE) values of 0.0141 and 0.0327, respectively, were computed for the proposed model, which demonstrates its notable efficacy in predicting skeletal age. The findings of this study not only contribute valuable information for clinical applications but also underscore the potential of the adopted approach to address th

关键词： Machine learning Pediatric bone health assessment Bone X-ray images Modified-CNN model Hybrid M-CNN-RF model Skeletal age estimation

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CompanyName2Vec: Company Entity Matching based on Job Ads

CompanyName2Vec: Company Entity Matching based on Job Ads

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Ran Ziv Ilan Gronau Michael Fire Efi Arazi School of Computer Science Reichman University Department of Software and Information Systems Engineering Ben-Gurion University

ISBN: (纸本)9781665473316

关键词： Couplings Semantics Neural networks Data visualization Companies Data science Cleaning

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The New Frontier in Media: AI-Driven Content Creation for Ad-Supported TV using Generative Adversarial Network

The New Frontier in Media: AI-Driven Content Creation for Ad...

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International Conference of computer and Informatics engineering (IC2IE)

作者： Shashishekhar Ramagundam Niharika Karne Software Development & Engineering in Content Discovery & AI Comcast USA AL. Department of Computer Science Masters in Computer Science Troy University Montgomery Campus USA Sr. Principal Systems Engineer in C& RS Platform Services DT Amtrak USA Department of Electrical Engineering Masters in Electrical Engineering University of South Alabama USA

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

The use of Artificial intelligence (AI) technology in the content creation to produce creative aspects like editing, audience analysis, creating ideas, writing copy, etc. The major aim is to streamline and automate the process of content creation and convert it into a more efficient and effective way. There is a lack of transparency in the AI content production process and hence it does not exactly mimic human activities, which includes fantasizing and picking up new abilities. AI content creation requires more creativity while investigating with a specific goal in mind. Although, AI faces many challenges since it constantly expands its base knowledge. One of the biggest ethical problems is the probability that AI content is used to deceive or influence people. Hence, to overcome these difficulties a machine learning-based Artificial Intelligent content creation framework is generated for content creation in Ad-supported TV. Here, an effective model Generative Adversarial Network (GAN) introduced to deliver enterprise data, customize descriptions, and adjust content based on consumer behaviors. This developed model produces material that is closely related to the preferences of the user. AI content creation increases efficiency and productivity by saving time and money. It also leads to the development of better content. Finally, the experimental analysis is performed to find the effectiveness of the developed deep learning-based AI content creation framework via various metrics.

关键词： Productivity Measurement Deep learning Ethics TV Media Generative adversarial networks Artificial intelligence Informatics Faces

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How Do Hackathons Foster Creativity? Towards AI Collaborative Evaluation of Creativity at Scale

arXiv

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arXiv 2025年

作者： Falk, Jeanette Chen, Yiyi Rafner, Janet Zhang, Mike Bjerva, Johannes Nolte, Alexander Aalborg University Copenhagen Department of Computer Science Copenhagen Denmark Center for Hybrid Intelligence Department of Management and Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Aalborg University Copenhagen Department of Computer Science Pioneer Centre for Artificial Intelligence Copenhagen Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands and Software and Societal Systems Carnegie Mellon University PittsburghPA United States

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large. © 2025, CC BY.

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An n-MOSFET Layout with Multi-Finger Z Gate for Radiation Tolerance 5

An n-MOSFET Layout with Multi-Finger Z Gate for Radiation To...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Shan, Chan Liu, Ying Chen, Huan Huo, Dong-Yang Xu, Tian-Rong Jimei University College of Ocean Information Engineering Xiamen China Xiamen Institute of Software Technology Department of Software Technology Xiamen China Minjiang University School of Journalism and Communication Fuzhou China Universiti Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia Universiti Malaya Planning Faculty of Built Environment Department of Urban and Regional Kuala Lumpur Malaysia

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

In this paper, a new n-MOSFET layout with multi-finger Z gate is proposed to reduce the total ionizing dose (TID) effect. In addition to the proposed layout, multi-finger single gate layout is also simulated using Sentaurus TCAD in 3D. Firstly, drain current - gate voltage curves of both layouts are compared in order to verify the radiation hardening ability of the proposed layout. Secondly, we compare TID responses by extracting threshold voltages and leakage currents from both layouts. Thirdly, the finger number is investigated for both layouts because it is an important factor for multi-finger layout. The results have indicated that the finger number significantly affects the multi-finger single gate layout, whereas the proposed multi-finger Z gate layout is relatively little affected by the finger number in radiation environment. © 2022 IEEE.

关键词： Drain current

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On the Convergence of Gossip Learning in the Presence of Node Inaccessibility

On the Convergence of Gossip Learning in the Presence of Nod...

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IEEE International Conference on Communications (ICC)

作者： Tian Liu Yue Cui Xueyang Hu Yecheng Xu Bo Liu Zhejiang Laboratory Hangzhou China Department of Computer Science and Computer Information Systems Auburn University at Montgomery Montgomery USA Department of Computer Science and Software Engineering Auburn University Auburn USA Institute of Rural Development Zhejiang Academy of Agricultural Sciences Hangzhou China DBAPPSecurity Co. Ltd. Hangzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Gossip learning (GL), as a decentralized alternative to federated learning (FL), is more suitable for resourceconstrained wireless networks, such as Flying Ad-Hoc Networks (FANETs) that are formed by unmanned aerial vehicles (UAVs). GL can significantly enhance the efficiency and extend the battery life of UAV networks. Despite the advantages, the performance of GL is strongly affected by data distribution, communication speed, and network connectivity. However, how these factors influence the GL convergence is still unclear. Existing work studied the convergence of GL based on a virtual quantity for the sake of convenience, which failed to reflect the real state of the network when some nodes are inaccessible. In this paper, we formulate and investigate the impact of inaccessible nodes to GL under a dynamic network topology. We first decompose the weight divergence by whether the node is accessible or not. Then, we investigate the GL convergence under the dynamic of node accessibility and theoretically provide how the number of inaccessible nodes, data non-i.i.d.-ness, and duration of inaccessibility affect the convergence. Extensive experiments are carried out in practical settings to comprehensively verify the correctness of our theoretical findings.

关键词： Training Accuracy Network topology Federated learning Wireless networks Autonomous aerial vehicles Ad hoc networks

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MIMO-Terahertz in 6G Nano-Communications:Channel Modeling and Analysis

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computers, Materials & Continua 2021年第1期66卷 263-274页

作者： Shahid Bashir Mohammed H.Alsharif Imran Khan Mahmoud A.Albreem Aduwati Sali Borhanuddin Mohd Ali Wonjong Noh Business Studies Department Namal InstituteMianwali42250Pakistan Department of Electrical Engineering College of Electronics and Information EngineeringSejong UniversitySeoul05006South Korea Department of Electrical Engineering University of Engineering and Technology PeshawarPeshawar814Pakistan Department of Electronics and Communications Engineering College of EngineeringA’Sharqiyah UniversityIbraOman Wireless and Photonics Networks Research Centre of Excellence Department of Computer and Communication Systems EngineeringUnversiti Putra MalaysiaSelangor43400Malaysia School of Software Hallym UniversityHallymdaehak-gilChunchoen-si24252South Korea

With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by *** wireless mobile communication frequency tends to be saturated,which demands for new ***(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information *** communication has great application prospects in military-civilian integration and coordinated *** China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the ***,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond *** paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel ***,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the *** results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.

关键词： Wireless communication 6G mobile communication terahertz communication MIMO channel modeling

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Day-ahead PV Output Power Forecasting Utilizing Boosting Recursive LightGBM-LSTM Framework

Day-ahead PV Output Power Forecasting Utilizing Boosting Rec...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Hossein Nourollahi Hokmabad Oleksandr Husev Dmitri Vinnikov Juri Belikov Eduard Petlenkov Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia

Recently, global photovoltaic (PV) system installations have surged. Precise forecasting is vital for their grid integration and carbon emission cuts. However, due to fluctuating solar radiation, predicting PV output is difficult. Machine learning models, notably Long Short-Term Memory (LSTM) networks, offer a solution. This study presents a novel framework using a boosted recursive Light Gradient Boosting Machine (LightGBM)-LSTM network to forecast daily PV output at hourly intervals. Conducted in Northern Europe—a region with significant solar radiation variability—the models trained on meteorological and historical data showed a 12% improvement in RMSE, a 13% reduction in MAPE, and a 5% increase in the R 2 score compared to standalone LSTM models.

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