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IEEE International Conference on Communications (ICC)

作者： Chao Fang Xiangheng Meng Zhaoming Hu Xiaoping Yang Fangmin Xu Peng Li Mianxiong Dong Faculty of Information Technology Beijing University of Technology Beijing China Purple Mountain Laboratories Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering University of Aizu Japan Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

With the widespread utilization of intelligent devices, massive mobile users' needs for rich multimedia services bring serious challenges in the aspects of network traffic, energy consumption and carbon emission. How to realize green content distribution by optimizing resource allocation is an urgent problem to solve in complex and dynamic networks. In this paper, we design a cross-layer cooperative scheme to promote energy efficiency in non-orthogonal multiple access (NOMA)-assisted cloud-edge-side environments. To be specific, we formulate the joint optimization issue of computation, caching and communication resources as an energy minimization model while considering request aggregation. Next, we propose a new deep reinforcement learning (DRL)-based task offloading strategy to minimize energy consumption by making optimal resource allocation decisions according to content request history and resource availability. Simulation results show that the proposed solution has better performance than current typical strategies in cloud-edge-end collaboration environments.

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Adaptive latent fingerprint image segmentation and matching using Chan-vese technique based on EDTV model 2

Adaptive latent fingerprint image segmentation and matching ...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Altrad, Abdullah Mahmoud Miaikil, Othman A.M. El-Ebiary, Yousef A. Baker Contreras, Jennifer Hilles, Mohanad M Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia FEU Institute of Technology Department of Information Technology Manila Philippines University College of Applied Science Information Technology Department Gaza Palestine

ISBN: (纸本)9781665432221

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National institute of Standard and technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image. © 2021 IEEE.

关键词： Biometrics

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Latent fingerprint enhancement and segmentation technique based on hybrid edge adaptive DTV model 2

Latent fingerprint enhancement and segmentation technique ba...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Miaikil, Othman A.M. Mahmoud Altrad, Abdullah Baker El-Ebiary, Yousef A. Hilles, Mohanad M Contreras, Jennifer Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia University College of Applied Science Information Technology Department Gaza Palestine FEU Institute of Technology Department of Information Technology Manila Philippines

ISBN: (纸本)9781665432221

Image enhancement and segmentation is widely used for fingerprint identification and authorization in biometrics devices, criminal scene is most challenges due to low quality of fingerprint, the most significant efforts is to develop algorithm for latent fingerprint enhancement which become challenging problem due to the complex and existing problem for instance, developing algorithms of latent fingerprint is able to extract features of image blocks and removing overlapping and isolate the poor and noisy background. however, it's still challenging and interested problem specifically latent fingerprint enhancement and segmentation. The aim study of this paper is to propose latent fingerprint enhancement and segmentation based on hybrid model and Chan-Vese method for segmentation, in order to reduce low image quality and increase the accuracy of fingerprint. The desired characteristics of intended technique are adaptive, effective and accurate, hybrid model of edge adaptive direction achieves accurate latent fingerprint enhancement and segmentation, the target needs to improve feature detection and performance, this research has proposed system architecture of research method in fingerprint enhancement and segmentation where is the method content two stages, the first is normalization and second is reconstruction, using EDTV model is required for adaptive noise, in addition Chan-vase technique contributed for identification of fingerprint image features, the result and testing using RMSE with three categories of fingerprint images good, bad and ugly show better performance for all three categories, as well RMSE shows the average of good latent fingerprint before and after enhancement. Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Model Edge Adaptive Directional Total Variation © 2021 IEEE.

关键词： Image enhancement

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RERRFACT: Reduced Evidence Retrieval Representations for Scientific Claim Verification

arXiv

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arXiv 2022年

作者： rana, Ashish Khanna, Deepanshu Ghosal, Tirthankar Singh, Muskaan Singh, Harpreet rana, Prashant Singh School of Business Informatics and Mathematics University of Mannheim Germany Department of Electronics & Communication Engineering Thapar Institute of Engineering & Technology India Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University Czech Republic Department of Computer Science & Engineering Thapar Institute of Engineering & Technology India

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SCIFACT dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SCIFACT leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RERRFACT with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. We make our codebase available at https://***/ashishrana160796/RerrFact. © 2022, CC BY.

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MassSpecGym: A benchmark for the discovery and identification of molecules

arXiv

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arXiv 2024年

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of Computing Science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Germany Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag: Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym – the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use

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Health Data Science 2022年第1期2022卷 124-135页

作者： Mohammed Ali Al-Garadi Yuan-Chi Yang Yuting Guo Sangmi Kim Jennifer S.Love Jeanmarie Perrone Abeed Sarker Department of Biomedical Informatics School of MedicineEmory UniversityAtlantaGAUSA Department of Computer Science Emory UniversityAtlantaGAUSA School of Nursing Emory UniversityAtlantaGAUSA Department of Emergency Medicine School of MedicineOregon Health&Science UniversityPortlandORUSA Department of Emergency Medicine Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGAUSA

*** behaviors and emotions associated with and reasons for nonmedical prescription drug use(NMPDU)are notwell-captured through traditional instruments such as surveys and insurance *** available NMPDU-related postson social media can potentially be leveraged to study these aspects unobtrusively and at *** applied a machinelearning classifier to detect self-reports of NMPDU on Twitter and extracted all public posts of the associated *** approximately 137 million posts from 87,718 Twitter users in terms of expressed emotions,sentiments,concerns,andpossible reasons for NMPDU via natural language *** in the NMPDU group express more negativeemotions and less positive emotions,more concerns about family,the past,and body,and less concerns related to work,leisure,home,money,religion,health,and achievement compared to a control group(i.e.,users who never reported NMPDU).NMPDU posts tend to be highly polarized,indicating potential emotional ***-specific analyses show that femaleusers in the NMPDU group express more content related to positive emotions,anticipation,sadness,joy,concerns aboutfamily,friends,home,health,and the past,and less about anger than *** findings are consistent across distinctprescription drug categories(opioids,benzodiazepines,stimulants,and polysubstance).*** analyses of large-scaledata show that substantial differences exist between the texts of the posts from users who self-report NMPDU on Twitter andthose who do not,and between males and females who report *** findings can enrich our understanding ofNMPDU and the population involved.

关键词： money finding users

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Modeling global solar radiation using ambient temperature

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Cleaner Energy Systems 2024年 7卷

作者： Ali, Mohamed A. Elsayed, Ashraf Elkabani, Islam Youssef, M. Elsayed Hassan, Gasser E. Computer Based Engineering Applications Department Informatics Research Institute City of Scientific Research and Technological Applications (SRTA-City) New Borg El-Arab City Alexandria 21934 Egypt Department of Mathematics and Computer Science Faculty of Science Alexandria University Alexandria Egypt Faculty of Industrial and Energy Technology Borg Al Arab Technological University Alexandria Egypt Faculty of Computer Science and Engineering AlAlamein International University El Alamein Egypt

Accurate information about solar radiation is regarded as the most important stage in determining the availability of solar energy. As well, it is considered the principal input for various applications of solar energy. Because solar radiation measurements are unavailable at many locations throughout the world, many solar radiation models have been developed to predict global solar radiation. In this regard, this study objects to develop accurate and quick Global Solar radiation (GSR) models for new locations, which currently lack an accurate model. Additionally, assess the performance of a recently introduced model, one of the best temperature-based models for GSR estimation, in these five new sites. Moreover, the proposed model's generalization capacity is investigated over the whole zone, the Suez Canal Zone, and a comparative analysis of its performance is presented. Models’ estimation is compared to the observed values, and the most common performance indicators are obtained to assess models’ performance. The findings indicate that the developed models in this study can predict global solar radiation accurately. Where the Models’ performance, both the local and general models, are larger than 95 % at all sites except for the local one at Port Said City (coastal site), it is 91 %. Additionally, the developed models have good RMSE and MABE values which range from 0.8 to 1.8 (MJ/m² day⁻¹) and 0.7 to 1.7 (MJ/m² day⁻¹), successively. Besides, they have excellent performance with coefficient of determination (R²) values ranging from 0.95 to 0.98, whereas prior research's ranges are 0.884–0.895 for R² and 2.69–3.367 (MJ/m² day⁻¹) for RMSE. Therefore, the developed models in this study can be utilized for accurate GSR forecast, with their high applicability which may be achieved by combining them with various long- or short-term weather forecasting approaches, that are mostly used t

关键词： Egypt Empirical models Solar energy Solar radiation Suez canal

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Detecting Anomalous Insiders Using Network Analysis 1

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9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020

作者： Dai, Lei Zhang, Liwei Li, Limin Chen, You Beijing Institute of Technology Beijing China China Academy of Electronics and Information Technology Beijing China Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Electrical Engineering & Computer Science School of Engineering Vanderbilt University NashvilleTN United States

ISBN: (数字)9789811584114

ISBN: (纸本)9789811584107

Collaborative information systems (CISs) can improve efficiency and quality of services, because they enable users to coordinate and collaborate on common works with a large scale. In recent decades, increasing number of hospitals start to adopt CIS to improve efficiency and quality of health care treatment, and CIS is becoming an important platform for hospital employees to treat patients. CIS is rich in resources of relations among users, however, people seldom study networks of users existing in the CIS, especially in health information systems. In this paper, we construct collaborative weighted networks of users from two typical CIS: one from health care respect, Vanderbilt University STARPANEL system;and the other from a more general respect, Wikipedia talk system. We learn characteristics of those two networks, and find both networks follow degree-strength relations, which could be used for detecting anomalous behaviors of users in the CIS, for instance, anomalous behaviors of hospital employees and wiki editors. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hospitals

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Various transmission codes for the control of bit error rate in both optical wired and wireless communication channels

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Journal of Optical Communications 2024年第0期45卷 s475-s493页

作者： Zaki rashed, Ahmed Nabih Ahammad, S.K. Hasane Daher, Malek G. Zyoud, Samer H. Sorathiya, Vishal Montalbo, Francis Jesmar P. Asaduzzaman, Sayed Rehana, Hasin Zuhayer, Asif Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Physics Department Islamic University of Gaza P.O. Box 108 Gaza Palestine Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Ajman University Ajman United Arab Emirates School of Physics Universiti Sains Malaysia Penang11800 Malaysia Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Waghodia Road Gujarat Vadodara391760 India College of Informatics and Computing Sciences Batangas State University Batangas4200 Philippines Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Electronics & Telecommunication Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

This study clarifies the data error rates optimization for OFC/OWC channels based on different transmission codes. These codes that are namely multi bits/symbol digital pulse interval modulation (DPIM), multi bits/symbol pulse position modulation (PPM), nonreturn to zero inverted (NRZI), 4 bit data symbol/5 bit code (4B5B), and Manchester for upgrading optical wired/wireless communication systems. The optical power through OFC/OWC channels, S/N ratio, the output power at the receiver side are stimulated with high bit transmission rates. The effects of coding complexity on the Q-factor, BER, optical power, and electrical received power are also stimulated using both DPIM and PPM coding. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Bit error rate

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Multi-behavior Learning for Socially Compatible Autonomous Driving

Multi-behavior Learning for Socially Compatible Autonomous D...

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International Conference on Intelligent Transportation

作者： Sanjula Jayawardana Vindula Jayawardana Kaneeka Vidanage Cathy Wu University of Westminster UK Informatics Institute of Technology Colombo Sri Lanka Department of Electrical Engineering and Computer Science MIT Laboratory for Information & Decision Systems Cambridge MA USA Department of Civil and Environmental Engineering MIT Laboratory for Information Decision Systems Institute of Data Systems and Society Cambridge MA USA

Autonomous vehicles (AVs) have the potential to revolutionize transportation, but their effective integration into the real world requires addressing the challenge of interacting with human drivers. Real-world driving involves negotiating and cooperating with fellow drivers through social cues, necessitating AVs to also demonstrate such social compatibilities. However, despite the popularity, current learning-based control methods for AV policy synthesis often overlook this crucial aspect. In this work, we look at the problem of enabling socially compatible driving when AV control policies are learned. We leverage human driving data to learn a social preference model of human driving and then integrate it with reinforcement learning-based AV policy synthesis using Social Value Orientation theory. In particular, we propose to use multi-task reinforcement learning to learn diverse social compatibility levels in driving (ex: altruistic, prosocial, individualistic, and competitive), focusing on the requirement of having diverse behaviors in real-world driving. Using highway driving scenarios, we demonstrate through experiments that socially compatible AV driving not only enables naturalistic driving behaviors but also reduces collision rates from the baseline. Our findings reveal that without social compatibility, AV policies tend to adopt dangerously competitive driving behaviors, while the incorporation of social compatibility fosters smoother vehicle maneuvers.

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