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71-76GHz Traveling Wave Tube Amplifier for High-data-rate Wireless Communication

71-76GHz Traveling Wave Tube Amplifier for High-data-rate Wi...

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international Wireless Communications and Mobile computing conference, IWCMC

作者： Shishuo Liu Qingmei Xie Zhaofei Chen Yujuan Wu Zhangxiong Zi Jianhui Sun Yinxing Chen Jun Cai Jinjun Feng National Key Laboratory of Science and Technology on Vacuum Electronics Beijing Vacuum Electronics Research Institute Beijing China

A 71-76GHz traveling wave tube amplifier is successfully developed to meet the demand of future high-data-rate wireless communication, breaking through a series of key core technologies including beam-wave interaction circuit, two-stage depressed collector, electronic power conditioner and module integration. The test results show that the amplifier is capable of providing more than 63.1W CW output power,37.8dB saturated gain,23.6% total efficiency in 71-76GHz band. The 1dB compression point is over 21W. The third-order intermodulation is <-12.2dBc at 6dB OBO. The dimension and weight of the amplifier are $379\boldsymbol{mm}^{*}200\boldsymbol{mm}^{*}63\boldsymbol{mm}$ and 6.5kg respectively with -48VDC electronic power conditioner embedded.

关键词： Wireless communication Traveling wave tubes Power amplifiers Power generation Mobile computing

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Markerless Augmented Reality Application for Interior Designing

Markerless Augmented Reality Application for Interior Design...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Nagashree R R Rakshitha M Ruchira Bharadwaj S Shabana Sultana Narasimha Mahesh Nadig Department of Computer Science The National Institute of Engineering Mysuru India The National Institute of Engineering Mysuru India

ISBN: (纸本)9781665493970

Markerless Augmented Reality - by using the screen of your smartphone, tablet, or AR glasses, you may place and examine digital things in the real world without the use of a marker like a QR Code, location, image, etc. Pokémon GO was one of the games that made AR a popular topic. Since then, several AR-enabled goods have been created, including social networking app filters, virtual footwear, sunglasses, and more. Often, we need to download applications such as Instagram or Facebook to view content. The introduction of web-based augmented reality implies that downloading a data-intensive and compute-intensive app is longer needed. Today, augmented reality material may be viewed through a browser. This web-based AR methodology is a new potential, due to its low bandwidth, accessibility, and speed. The advantages of WebAR are double. First, the restriction is too lax for viewers to access and share your content. People are more likely to browse and share content when an app is absent. This guarantees greater accessibility, increased connectedness, and a greater sharing feature. Because there is no application, there is minimal expense and flexibility, which leads to the second benefit. You can link to ongoing initiatives, for instance, by pushing fresh material quicker and more affordably. With our software, users may examine a specified 3D model, which is a representation of visual furniture without markers and can be viewed and altered in real-time. This is a step toward Web-based AR. This study suggests a novel method for applying augmented reality technology to furniture. Using a versatile online interface on a variety of devices, the user can connect 3D virtual furniture data and observe visual furniture.

关键词： Visualization Solid modeling Three-dimensional displays Social networking (online) Multimedia Web sites QR codes Information filters

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Multimodal Hate Speech Detection from Bengali Memes and Texts 1st

Multimodal Hate Speech Detection from Bengali Memes and Te...

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1st international conference on Speech and Language technologies for Low-resource Languages, SPELLL 2022

作者： Karim, Md. Rezaul Dey, Sumon Kanti Islam, Tanhim Shajalal, Md Chakravarthi, Bharathi Raja Fraunhofer Institute for Applied Information Technology FIT Sankt Augustin Germany RWTH Aachen University Aachen Germany Noakhali Science and Technology University Noakhali Bangladesh University of Siegen Siegen Germany University of Galway Galway Ireland

ISBN: (纸本)9783031332302

Numerous machine learning (ML) and deep learning (DL)-based approaches have been proposed to utilize textual data from social media for anti-social behavior analysis like cyberbullying, fake news detection, and identification of hate speech mainly for highly-resourced languages such as English. However, despite of having a lot of diversity and millions of native speakers, some languages like Bengali are under-resourced, which is due to lack of computational resources for natural language processing (NLP). Similar to other languages, Bengali social media contents also include images along with texts (e.g., multimodal memes are posted by embedding short texts into images on Facebook). Therefore, only the textual data is not enough to judge them since images might give extra context to make a proper judgement. This paper is about hate speech detection from multimodal Bengali memes and texts. We prepared the only multimodal hate speech dataset for-a-kind of problem for Bengali, which we use to train state-of-the-art neural architectures (e.g., Bi-LSTM/Conv-LSTM with word embeddings, ConvNets + pre-trained language models, e.g., monolingual Bangla BERT, multilingual BERT-cased/uncased, and XLM-RoBERTa) to jointly analyze textual and visual information for hate speech detection. Conv-LSTM and XLM-RoBERTa models performed best for texts, yielding F1 scores of 0.78 and 0.82, respectively. As of memes, ResNet-152 and DenseNet-161 models yield F1 scores of 0.78 and 0.79, respectively. As of multimodal fusion, XLM-RoBERTa + DenseNet-161 performed the best, yielding an F1 score of 0.83. Our study suggest that text modality is most useful for hate speech detection, while memes are moderately useful. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Embeddings

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Image Processing and Machine Learning for Tumor Tissue Detection Using MRI Images in Bacteria Based Cancer Therapy

Image Processing and Machine Learning for Tumor Tissue Detec...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Emily Hitchcock Sarah Hodges Kevin Zuang Rosy J. Lu Yuanwei Jin Qiuhong He Enyue Lu Department of Computer Science Salisbury University Salisbury MD School of Engineering Liberty University Lynchburg VA Department of Computer Science Brown University Providence RI Molecular Biophy & Biochem Yale University New Haven CT Engineering & Aviation Sciences University of Maryland Eastern Shore Princess Anne MD School of Health Sciences Purdue University West Lafayette IN

Bacteria Based Cancer Therapy (BBCT) is a unique form of cancer treatment where bacteria is genetically engineered to induce production of the anticancer drug from within the tumor without affecting normal tissues. This project aims to study and develop image processing and machine learning algorithms to segment and track the size of the tumor regions affected by the anti-cancer bacteria based on the diffusion-weighted MRI images (DWI) collected from animal tumor models over the course of treatment, providing methods of evaluating its effectiveness. We employ emerging image processing and machine learning techniques that are implemented in MATLAB and Python to create algorithms consisting of pre-processing, processing, and post-processing steps, with the goal of calculating the destructed tumor tissue volume at each stage of the treatment. To automate the pre-processing and identification steps of the tumor region, a convolutional neural network called a U-Net is utilized to predict a binary mask to identify and crop the raw MRI images around the tumor prior to the processing and the post-processing steps, then the algorithm calculates the total volume of the tumor, dead tissue, and live tissue. The U-Net is trained using binary masks of the tumor regions alongside the original MRI images. The developed algorithms allow researchers to quantitatively determine the effectiveness of the BBCT by tracking the volume of dead tumor tissues. Ultimately, this research could lead to an automated 3D volumetric tumor tissue destruction measured in vivo longitudinally in the DWI experiments.

关键词： Microorganisms Machine learning algorithms Magnetic resonance imaging Volume measurement Machine learning Prediction algorithms Convolutional neural networks

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Video Analysis and Deep Learning for Contact Rate Estimation in Public Places and Mass Gatherings in Saudi Arabia

Video Analysis and Deep Learning for Contact Rate Estimation...

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international conference on Information Technology (INCIT)

作者： Asma Alghamdi Suha Bako Zahrah Al-Safwan Sultanah M. Alshammari Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah Saudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz University Jeddah Saudi Arabia

Contact patterns and social mixing are crucial parameters in studying disease epidemics. Various approaches, such as diaries, surveys, and interviews, were used to collect data about contact rates and interactions between individuals in different settings. advances in technology and artificial intelligence approaches have provided new methods to determine contact rates, including wearable sensors and contact-tracing smartphone apps. This study describes an automated approach to estimating contact rates in public places and mass gatherings in Saudi Arabia. The proposed model integrates computer vision, image processing, and deep learning algorithms to compute the average contact rate from video recordings. Our results showed that the proposed model estimated the number and duration of contacts per individual from video recordings.

关键词： Deep learning Computational modeling Urban areas Estimation Detectors data models Planning

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A statistics-based performance testing methodology: a case study for the I/O bound tasks

A statistics-based performance testing methodology: a case s...

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international Computer sciences and Information technologies (CSIT)

作者： Andriy Sultanov Maksym Protsyk Maksym Kuzyshyn Daria Omelkina Vyacheslav Shevchuk Oleg Farenyuk Faculty of Applied Sciences of Ukrainian Catholic University L’viv Ukraine Dialog Semiconductor L’viv Ukraine Institute for Condensed Matter Physics of the National Academy of Sciences of Ukraine L’viv Ukraine

ISBN: (纸本)9798350334326

This research demonstrates the applicability of the established statistical methodology developed for experimental data analysis within physics, biology, and other natural science disciplines to the analysis of computing performance measurements. The methodology is based on the randomization of test samples and test conditions as well as usage of statistical hypothesis testing to determine if the performance differs. The hypothesis testing method is selected based on the statistical distribution of the data-Student’s t-test for the normal distribution and non-parametric tests, such as the Wilcoxon signed-rank test, for other distributions. Multiple comparisons problem is taken into account using the Bonferroni correction. Though other important natural science methods exist-different kinds of blinded experiments and analysis could be beneficial too-they could be too expensive for such contexts without additional justifications. The method will be applied to a model problem: a comparison of the performance of three implementations of web crawlers, compiled with different options and different compilers. Experimental cases were intentionally selected with both large and minor (but definite) performance differences to show how statistical methods can help determine the appearance of the performance difference and confidently discuss its extent to take into account benefits or drawbacks of the various setups.

关键词： data analysis Statistical analysis Programming Time measurement Reliability Noise measurement Task analysis

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A Review on Human-Computer Interaction (HCI)

A Review on Human-Computer Interaction (HCI)

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international conference on Innovative Sustainable Computational technologies (CISCT)

作者： Janhavi Gupta Mohit Ahuja Neha Garg Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9781665474177

Human-Computer Interaction (HCI), has risen to prominence as a cutting–edge research area in recent years. Human-computer interaction has made significant contributions to the development of hazard recognition over the last 20 years, as well as spawned a slew of new research topics, including multimodal data analysis in hazard recognition experiments, the development of efficient devices and sensors, and the human-computer interaction safety management platform based on big data. The basic concepts and terminology, existing technologies, and recent breakthroughs in the field of HCI are all included in this research paper’s overview. The ability to respond and sense efficiently and appropriately in response to user affective feedback, as well as to detect and comprehend the affective states displayed by the user, is the first step toward developing an intelligent HCI. This paper also looks at several methods for HCI design. In addition, this paper includes a thorough list of references for each HCI idea, method, and application. In addition to being user centric, we design with both usability and business goals in mind. Rather than focusing solely on the application’s functional objectives, our design will need to address the entire customer experience. The customer experience will determine how users/customers see the organization as a viable and trustworthy brand both online and offline. To attain these objectives, we must address several fundamental difficulties with the ART (Access, Relationship, and Trust) of interface design.

关键词： Human computer interaction Terminology Subspace constraints World Wide Web Ubiquitous computing Hazards Electronic commerce

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Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging data 2nd

Progression Free Survival Prediction for Head and Neck Cance...

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2021

作者： Naser, Mohamed A. Wahid, Kareem A. Mohamed, Abdallah S. R. Abdelaal, Moamen Abobakr He, Renjie Dede, Cem van Dijk, Lisanne V. Fuller, Clifton D. Department of Radiation Oncology The University of Texas MD Anderson Cancer HoustonTX77030 United States

ISBN: (纸本)9783030982522

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT images and clinical data as separate input channels to predict PFS in days. Internal model validation based on 10-fold cross-validation using the training data (N = 224) yielded C-index values up to 0.622 (without) and 0.842 (with) censoring status considered in C-index computation, respectively. We then implemented model ensembling approaches based on the training data cross-validation folds to predict the PFS of the test set patients (N = 101). External validation on the test set for the best ensembling method yielded a C-index value of 0.694, placing 2nd in the competition. Our results are a promising example of how deep learning approaches can effectively utilize imaging and clinical data for medical outcome prediction in HNSCC, but further work in optimizing these processes is needed. © 2022, Springer Nature Switzerland AG.

关键词： Image enhancement

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE international conference on Big data and Cloud computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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BERT-based models’ impact on machine reading comprehension in Hindi and Tamil

BERT-based models’ impact on machine reading comprehension ...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Shailender Kumar Deepak Department of Computer Science Delhi Technological University New Delhi India

ISBN: (数字)9781665474368

ISBN: (纸本)9781665474375

"Multilingual Machine Comprehension" is a QA sub-task that comprises citing an answer to a question from a context, even if that answer written in a separate language from the excerpt itself. A lot of models have been trained to answer the question from a given short context which is a limitation of MRC, few models are considering this problem and adapting to handle the large input context to make the MRC more accessible and applicable to open domain scenarios. In this study, we examine Multilingual Representations for Indian Languages (MuRIL), rebalanced multilingual BERT (RemBERT), and XLM-RoBERTa, which are all BERT-based deep learning models. We trained these models to work on multilingual MRC particularly for two of the most used Indian languages Hindi and Tamil The datasets utilized in this study are freely available. The results of our research reveal that RemBERT outperformed other BERT-based deep learning models. For the dataset employed, the model received an F1 score of 84.58, an Exact Match of 74.05, and a Jaccard Index of 0.81.

关键词： Deep learning Adaptation models Computational modeling Bit error rate Transformers data models Context modeling

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