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arXiv 2022年

作者： Muralikrishnan, Madhuvanti Hilal, Amr Miller, Chreston Smith-Glaviana, Dina Computer Science Dept Virginia Tech BlacksburgVA United States Informatics Lab Virginia Tech BlacksburgVA United States Apparel Housing and Resource Management Dept Virginia Tech BlacksburgVA United States The Department of Computer and Systems Engineering Alexandria University Egypt

Historic dress artifacts are a valuable source for human studies. In particular, they can provide important insights into the social aspects of their corresponding era. These insights are commonly drawn from garment pictures as well as the accompanying descriptions and are usually stored in a standardized and controlled vocabulary that accurately describes garments and costume items, called the Costume Core Vocabulary. Building an accurate Costume Core from garment descriptions can be challenging because the historic garment items are often donated, and the accompanying descriptions can be based on untrained individuals and use a language common to the period of the items. In this paper, we present an approach to use Natural Language Processing (NLP) to map the free-form text descriptions of the historic items to that of the controlled vocabulary provided by the Costume Core. Despite the limited dataset, we were able to train an NLP model based on the Universal Sentence Encoder to perform this mapping with more than 90% test accuracy for a subset of the Costume Core vocabulary. We describe our methodology, design choices, and development of our approach, and show the feasibility of predicting the Costume Core for unseen descriptions. With more garment descriptions still being curated to be used for training, we expect to have higher accuracy for better generalizability. Copyright © 2022, The Authors. All rights reserved.

关键词： Social aspects

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Clustering Based User Allocation in 5G Networks*

Clustering Based User Allocation in 5G Networks*

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Christos Bouras Rafail Kalogeropoulos Ioannis Sina Computer Technology Institute & Press “Diophantus201D Univ. of Patras Greece Patras Greece Computer Engineering and Informatics Dept.

Machine learning is an extremely efficient technique for solving complex problems without the use of traditional programming but rather enabling machines to learn from an input of data and train them to cope with various problems. The rapid growth in the number of active mobile devices, mobile applications and services dictates an efficient utilization of mobile and wireless networking infrastructure. Communication networks need to evolve and valorize machine learning methods in order to process large volumes of data without introducing excessive time delay in these computations. Upcoming 5G systems are expected to be the first network infrastructure to support exploding mobile traffic volumes and machine learning techniques can be used in order to help manage the rise in data volumes. We present a mechanism for resource allocation in mobile and wireless networks, that effectively utilizes machine learning techniques.

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Mutation types and pathogenicity classification using multi-label multi-class deep networks

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AIP Conference Proceedings 2024年第1期3097卷

作者： Rana H. Saloom Hussein K. Khafaji Informatics Institute Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Computer Communications Engineering Dept. Al-Rafidain University College Baghdad Iraq

In recent years, considerable progress has been made in genomics and proteomics, resulting in much biological data. To draw inferences from this data, advanced computer analysis techniques are required. Bioinformatics is crucial to interpreting and applying this data in any endeavor. As huge amounts of genomic, proteomic, and other data begin to be collected and integrated, the relevance of this emerging area of study will increase. The detection of variation in Next Generation Sequencing is now a standard and indispensable technique in all areas of the biological sciences. Life-threatening conditions like cancer and other diseases may be caused by DNA mutation sequences (genetic mutations). Thus, it’s important to discover these mutations early, classify them, and understand their effects on the DNA sequence. Bioinformatics is mostly concerned with the changes to DNA that will occur in the cells of the next generation. The primary goal of this research is to develop a sophisticated and comprehensive approach for classifying the mutation type and categorizing the influence of known disease-causing genetic variations on an individual’s risk of disease using a multi-label multi-class deep learning classification technique. A deep neural network was trained using the TP53 dataset (The TP53 gene is a tumor suppressor gene on chromosome 17) to discover the mutation type and its impact in a unified model, as described. To evaluate the performance of the suggested method, the accuracy, recall, precision, and F1 score evaluation scales were utilized. and the results demonstrate that it can accurately determine the mutation type and its effects with an accuracy of 97.58%, Precision 98%, Recall 95%, and F1 score 96%.

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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IEEE Symposium on Network Operations and Management

作者： Árpád Somogyi Tamás Tettamanti Pál Varga Zsolt Szalay Dániel Baranyai Tamás Lovas Dept. of Photogrammetry and Geoinformatics Fac. of Civil Engineering Budapest University of Technology and Economics Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Loránd Research Network Budapest Hungary Dept. of Telecommunications and Media Informatics Fac. of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Dept. of Automotive Technologies Fac. of Transportation Eng. and Vehicle Eng. Budapest University of Technology and Economics Budapest Hungary

One major goal of digital twin technology applied in the Architecture, engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated.

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Refinement of an Epilepsy Dictionary through Human Annotation of Health-related posts on Instagram

arXiv

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arXiv 2024年

作者： Min, Aehong Wang, Xuan Correia, Rion Brattig Rozum, Jordan Miller, Wendy R. Rocha, Luis M. Donald Bren School of Information & Computer Sciences University of California IrvineCA United States Luddy School of Informatics Computing & Engineering Indiana University BloomingtonIN United States Instituto Gulbenkian de Ciência Oeiras Portugal Dept. of Systems Science & Industrial Engineering Binghamton University BinghamtonNY United States School of Nursing Indiana University IndianapolisIN United States

Objective — To (1) identify health-related terms used on social media posts that do not precisely match the health-related meaning of terms in a biomedical dictionary, (2) decide which terms need to be removed in order to improve the quality of the dictionary in the scope of biomedical text mining tasks, (3) evaluate the effect of removing imprecise terms on such tasks, and (4) discuss how human-centered annotation complements automated annotation in social media mining for biomedical purposes. Materials and Methods — We used a dictionary built from biomedical terminology extracted from various sources such as DrugBank, MedDRA, MedlinePlus, TCMGeneDIT, to tag more than 8 million Instagram posts by users who have mentioned an epilepsy-relevant drug at least once, between 2010 and early 2016. A random sample of 1,771 posts with 2,947 term matches was evaluated by human annotators to identify false-positives. Frequent terms with a high false-positive rate were removed from the dictionary. To study the effect of removing those terms, we constructed knowledge networks using the refined and the original dictionaries and performed an eigenvector-centrality analysis on both networks. OpenAI’s GPT series models were compared against human annotation. Results — Analysis of the estimated false-positive rates of the annotated terms revealed 8 ambiguous terms (plus synonyms) used in Instagram posts, which were removed from the original dictionary. We show that the refined dictionary thus produced leads to a significantly different rank of important terms, as measured by their eigenvector-centrality of the knowledge networks. Furthermore, the most important terms obtained after refinement are of greater medical relevance. In addition, we show that OpenAI’s GPT series models fare worse than human annotators in this task. Discussion — Dictionaries built from traditional clinical terminology are not tailored for social media language and can bias results when used in biomedical infe

关键词： Eigenvalues and eigenfunctions

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High Speed Photonic Neuromorphic Computing Using Recurrent Optical Spectrum Slicing Neural Networks

arXiv

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arXiv 2022年

作者： Sozos, K. Bogris, A. Bienstman, P. Sarantoglou, G. Deligiannidis, S. Mesaritakis, Charis Dept. of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo 12243 Greece Dept. of Information Technology Ghent University-Imec Technologiepark Zwijnaarde 126 Gent9052 Belgium Dept. Information and Communication Systems Engineering Engineering School University of the Aegean Palama 2 Samos83200 Greece

Neuromorphic Computing implemented in photonic hardware is one of the most promising routes towards achieving machine learning processing at the picosecond scale, with minimum power consumption. In this work, we present a new concept for realizing photonic recurrent neural networks and reservoir computing architectures with the use of recurrent optical spectrum slicing. This is accomplished through simple optical filters placed in an loop, where each filter processes a specific spectral slice of the incoming optical signal. The synaptic weights in our scheme are equivalent to filters' central frequencies and bandwidths. This new method for implementing recurrent neural processing in the photonic domain, which we call Recurrent Optical Spectrum Slicing Neural Networks (ROSS-NNs), is numerically evaluated on a demanding, industry-relevant task such as high baud rate optical signal equalization (>100 Gbaud), exhibiting ground-breaking performance. The performance enhancement surpasses state-of-the-art digital processing techniques by doubling the reach while minimizing complexity and power consumption by a factor of 10 compared to state-of-the-art solutions. In this respect, ROSS-NNs can pave the way for the implementation of ultra-efficient photonic hardware accelerators tailored for processing high-bandwidth (> 100 GHz) optical signals in optical communication and high-speed imaging applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical communication

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Development of Image-Based Emotion Recognition using Convolutional Neural Networks 7

Development of Image-Based Emotion Recognition using Convolu...

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7th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2021

作者： Latif, Atiya Gunawan, Teddy Surya Kartiwi, Mira Arifin, Fatchul Mansor, Hasmah International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Universitas Negeri Yogyakarta Electronics and Informatics Eng. Dept. Yogyakarta55281 Indonesia

ISBN: (纸本)9781728175225

In recent years, artificial intelligence has been utilized in many applications. One of the prominent applications is detecting emotion from an image, which can help an intelligent automatic response system respond appropriately based on the user's emotion. This paper presented the development of emotion recognition using Convolutional Neural Networks (CNN) on image input. First, the extended Cohn-Kanade image emotion database was selected with five defined emotions: happy, sad, anger, fear, surprise, and neutral. Second, face detection and facial landmarks extraction was applied to the input image. Then, the AlexNet model is used as the selected deep learning architecture for transfer learning. Results showed that around 98.2% recognition accuracy could be achieved. Furthermore, precision, recall, and F1-score were evaluated, and it showed the effectiveness of our proposed algorithm. © 2021 IEEE.

关键词： Emotion Recognition

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A new clustering-based technique for the acceleration of deep convolutional networks

arXiv

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arXiv 2021年

作者： Pikoulis, Erion Vasilis Mavrokefalidis, Christos Lalos, Aris S. Industrial Systems Institute Patras Science Park Greece Computer Engineering Informatics Dept. University of Patras Greece

Deep learning and especially the use of Deep Neural Networks (DNNs) provides impressive results in various regression and classification tasks. However, to achieve these results, there is a high demand for computing and storing resources. This becomes problematic when, for instance, real-time, mobile applications are considered, in which the involved (embedded) devices have limited resources. A common way of addressing this problem is to transform the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Within the MCA framework, we propose a clustering-based approach that is able to increase the number of employed centroids/representatives, while at the same time, have an acceleration gain compared to conventional, k-means based approaches. This is achieved by imposing a special structure to the employed representatives, which is enabled by the particularities of the problem at hand. Moreover, the theoretical acceleration gains are presented and the key system hyper-parameters that affect that gain, are identified. Extensive evaluation studies carried out using various state-of-the-art DNN models trained in image classification, validate the superiority of the proposed method as compared for its use in MCA tasks. © 2021, CC BY.

关键词： Deep neural networks

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A Location-Independent Flood Prediction Model for Bangladesh's Rivers

A Location-Independent Flood Prediction Model for Bangladesh...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Md Abid Hasan Nirjhor Tahmidur Rouf Md Sajid Hossain Lamia Binta Latif Anika Tasnim Marcin Grzegorzek Institute of Medical Informatics University of Lübeck Lübeck Germany Dept. Of ECE North Carolina State University USA department of EEE American International University Bangladesh Electrical and Electronic Engineering American International University Bangladesh Computer Science and Engineering Jahangirnagar University Bangladesh Silicon Engineering Ulkasemi Private Limited Bangladesh Fraunhofer IMTE Lübeck Germany

The involvement of a multitude of parameters adds to the complexity of modeling a flood. However, floods are among the most destructive of natural disasters and therefore, flood forecasting is one of the key priorities of hydrology. Flood forecasting goes a long way to minimize the loss of lives as well as economic losses. Furthermore, proper modeling of floods can contribute immensely towards future risk reduction and the introduction of necessary policies. At present, the application of machine learning in river and flood analysis has dramatically increased among hydrologists. In this research, we propose a location (District) independent flood prediction model (Random Forest-RF) of commercially significant rivers in Bangladesh. The data Imbalance problem is solved by synthetic minority oversampling of numerical and categorical (SMOTENC) data augmentation techniques. Our results show that the proposed framework outperforms the previously reported results by up to 9%. To the best of our knowledge, our proposed flood prediction framework achieved the best performance in terms of all evaluation matrices on the specific dataset.

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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning 8

Constructing a Cryptocurrency-Price Prediction Model Using D...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Tran, Toai Kim Le, Thanh Thi Tuyet Bui, Thinh Tien Dang, Vang Quang Senkerik, Roman Ho Chi Minh City University of Technology and Education Faculty of Economics No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Viet Nam VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Dept. of Computer Science 17. Listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Tomas Bata University in Zlin Faculty of Applied Informatics T. G. Masaryka 5555 Zlin76001 Czech Republic

ISBN: (纸本)9781665491068

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies. © 2022 IEEE.

关键词： Forecasting

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