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Proceedings of the 51st International Conference on Parallel Processing

作者： Yuhan Wu Zhuochen Fan Qilong Shi Yixin Zhang Tong Yang Cheng Chen Zheng Zhong Junnan Li Ariel Shtul Yaofeng Tu School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China and PCL Research Center of Networks and Communications Peng Cheng Laboratory China School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China National University of Defense Technology China Redis Labs Israel ZTE Corporation China

ISBN: (纸本)9781450397339

1Data stream mining over a sliding window is a fundamental problem in many applications, such as financial data trackers, intrusion detection and QoS. To meet the demand for high throughput of high speed data streams, sliding window algorithms turn to hardware platforms including FPGA/ASIC and programmable switches. These hardware platforms have three constraints for algorithms running on, which are 1) small memory usage 2) single stage memory access and 3) limited concurrent memory access. Algorithms perfectly fit in with these constraints will enable a highest utilization of these hardware platforms. However, no existing sliding window algorithm is specifically designed for hardware platforms. In this paper, we propose the Sliding Hardware Estimator (SHE), which is a generic framework that extends existing fixed window algorithms to sliding windows on hardware platforms. The key idea of SHE is that, during insertions we approximately delete out-dated information with little time and space overhead, while during queries we design sophisticated techniques to minimize error. We have fully implemented our SHE on FPGA, achieving a throughput of 544 Mips. We apply SHE to four typical data stream mining tasks. Experimental results show that, when compared with the state-of-the-art which cannot be implemented in hardware, SHE reduces the error by up to 100 times in membership queries. All related source codes are released at Github.

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Evaluating the Impact of Different Features on the Efficiency of Emotion Recognition Algorithms in Text

Evaluating the Impact of Different Features on the Efficienc...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Sunil Sharma G. Ezhilarasan Rajkumari Ghosh Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electrical and Electronics Engineering School of Engineering and Technology AIN (Deemed to be University) Bangalore Karnataka India Department of English ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

The effectiveness today's emotion recognition algorithms in texts has become increasingly more critical in current years today's its programs in supporting users gauge actual-time reactions from text-based totally conversations. On this paper, we advise a fixed state-of-the-art experiments to assess the effect that different functions, along with syntactic and semantic functions, have at the efficiency contemporary emotion reputation algorithms in textual content. We use a manually annotated dataset from the automatic have an effect on recognition in text (AART) task to create characteristic units consultant of various ranges latest functions. Those feature units are then used to assess the overall performance latest emotion recognition algorithms – Recursive Neural Networks and Convolutional Neural Networks. The results display that incorporating syntactic and semantic functions can improve the accuracy present day emotion popularity algorithms at a charge ultra-modern 6-10% while as compared with the baseline overall performance. From the findings modern this test, we conclude that incorporating syntactic and semantic features can drastically enhance the overall performance trendy emotion popularity algorithms in text-based totally conversations.

关键词： Emotion recognition Text recognition Semantics Neural networks Medical services Machine learning Syntactics

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Dct image compression implemented on raspberry pi to compress image captured by cmos image sensor 11th

Dct image compression implemented on raspberry pi to compres...

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11th National Technical Symposium on Unmanned System Technology, NUSYS 2019

作者： Mohsin, Ibrahim Saad Ahmad, Muhammad Imran Salman, Saad M. Al-Dabagh, Mustafa Zuhaer Nayef Isa, Mohd Nazrin Md Ahmad, Raja Abdullah Raja School of Computer and Communication Engineering Universiti Malaysia Perlis Kampus Pauh Putra Perlis02600 Malaysia Department of Mathematical and Computer Application College of Science Al-Nahrain University Baghdad Iraq School of Microelectronic Engineering Universiti Malaysia Perlis Kampus Pauh Putra Perlis02600 Malaysia Department of Computer Science College of Science Knowledge University Kurdistan Region Erbil Iraq

ISBN: (纸本)9789811552809

The purpose of compression is to reduce the amount of data at the same time maintain the quality of image and signal for the other purpose. Discrete Cosine Transform (DCT) is a family of image compression where the raw image is transformed to the other domain to produce smaller size of data. DCT transform has low computational complexity and fast processing algorithm. In this project, DCT transform will be implemented using PI camera and Raspberry Pi SBC development board running on an ARM based processor. The raspberry Pi board has an advantage of image processing implementation due to the existing software development tool offered a rich feature for image processing such as OPENCV. The result of applying DCT compression algorithm on images with six compression rate level which are 10, 20, 50, 100, 170 and 200. The best performance can be achieved with compression rate level 200. However, on reducing the quality level of compression rate, the error measurements start becoming worse until a point is reached, where the perceptual difference from the original image can be easily noted. © Springer Nature Singapore Pte Ltd 2021.

关键词： Image compression

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Enhancing Rule-Based Methods for Pulmonary Effusion Diagnosis

Enhancing Rule-Based Methods for Pulmonary Effusion Diagnosi...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Vikas Kumar Kharbas N Raghu Priyabati Choudhury Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electrical and Electronics Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The diagnosis of pulmonary effusion is a difficult assignment for scientific practitioners because of the excessive variance in the presentation and manifestation of the circumstance. Maximum current diagnosis structures rely upon the use of either sensory/visible remark of chest radiographs (CRs) by radiologists or handcrafting statistical features through area experts with the use of computer-aided analysis (CAD) tools. However, those methods regularly need better generalization and are constrained via their reliance on conventional heuristic algorithms, as well as the mistake-inclined technique of guide identification of features. This paper explores the capacity of the usage of rule-based total methods as better diagnostic structures for pulmonary effusion. Particularly, a rule-based total device primarily based on supervised mastering-based total fashions is proposed as an alternative to traditional feature identification and model development strategies. The proposed device includes an output layer and an enter layer, which includes photograph features and radiological descriptors. Experiments have been conducted on the LIDC-IDRI dataset, and the proposed system outperformed traditional feature-based approaches in terms of both accuracy and specificity. Furthermore, the use of rule-based methods for diagnosis of pulmonary effusion provides a more reliable diagnosis process, which may reduce the radiological review times and improve the accuracy of non-radiological diagnosis.

关键词： Solid modeling Accuracy Design automation Reviews Heuristic algorithms Lung Sensitivity and specificity Reliability Object recognition Diagnostic radiography

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NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs

arXiv

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arXiv 2022年

作者： Luo, Haoran Haihong, E. Yang, Yuhao Zhou, Gengxian Guo, Yikai Yao, Tianyu Tang, Zichen Lin, Xueyuan Wan, Kaiyang School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Institute of Computer Technology and Application Beijing China

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n≥2) containing more than two entities, which are more prevalent in the real world. Moreover, previous CQA methods can only make predictions for a few given types of queries and cannot be flexibly extended to more complex logical queries, which significantly limits their applications. To overcome these challenges, in this work, we propose a novel Nary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs), which include massive n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries, including existential quantifiers (∃), conjunction (∧), disjunction (∨), and negation (¬). We also propose a parallel processing algorithm that can train or predict arbitrary n-ary FOL queries in a single batch, regardless of the kind of each query, with good flexibility and extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and other standard CQA datasets show that NQE is the state-of-the-art CQA method over HKGs with good generalization capability. Our code and dataset are publicly available. © 2022, CC BY-NC-SA.

关键词： Embeddings

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AI applications and Simulation-Based Learning Integrating Future of Nursing Education

AI Applications and Simulation-Based Learning Integrating Fu...

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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Deepak Suwalka Deepak Pandita Snehal Godse Rani Ramrao Patil Abdul Salam Khan Alok Kumar Department of Mental Health Nursing Rama University Kanpur Uttar Pradesh Department of Computer Application (MCA) School of Engineering and Technology Pimpri Chinchwad University Sate Maval Pune Maharashtra India Department of MCA JSPM’s Rajarshi Shahu College of Engineering Tathawade Department of Computer Application Dr.D.Y. Patil School of Science and Technology Pune Maharashtra India Amity School of Fine Arts Amity University Noida UP India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Nurse educators are modifying their practices with the help of artificial intelligence. artificial intelligence helps nursing students gain their Goal facility arsenic good arsenic their important Althought skills. The purpose of this research is to investigate the advantages disadvantages and results of using artificial intelligence to power Imitation-based learning for nursing school instructors and students. amp mixed-methods access was old inch rate to analyse the memory attending employment and science control of ii c nursing students. The Information utilized included both quantitative and qualitative information. observations such as arsenic the score percentage gain inch attending rates and the forty-five head v head v percentage gain inch important Althought lots point that thither was amp important advance inch pupil interest atonement and Goal competency. Even if significant advancements have been made there is still a need for expensive equipment and insufficient Teaching for faculty members. it is light from these results that Imitations powerful away ersatz word bear the prospective to translate nursing pedagogy and break school prospective nurses for amp active healthcare surround.

关键词： Technological innovation Arsenic Education Employment Medical services Learning (artificial intelligence) Organizations Educational technology Electronic healthcare Pupils

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HyperCalm Sketch: One-Pass Mining Periodic Batches in Data Streams

HyperCalm Sketch: One-Pass Mining Periodic Batches in Data S...

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International Conference on Data engineering

作者： Zirui Liu Chaozhe Kong Kaicheng Yang Tong Yang Ruijie Miao Qizhi Chen Yikai Zhao Yaofeng Tu Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China ZTE Corporation

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced.

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Developing Adaptive Deep Learning Strategies for 5G Network Management

Developing Adaptive Deep Learning Strategies for 5G Network ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Shrinivas Patil Viranshu Kumar Manish Kumar Goyal School of Mangement - PG JAIN Deemed to be University Bangalore Karnataka India Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper affords a unique method for growing deep mastering strategies for 5G community control. It leverages state-of-the-art deep neural community architectures and superior technologies inclusive of hierarchical learning, reminiscence networks, and differentiable programming to allow the machine to operate more efficaciously in an ever-changing surrounding. The gadget includes two principal components: an offline schooling community and a web auxiliary network. Each additive is related, permitting the net network to refine the educated parameters to adapt to changing occasions. The gadget is designed to deal with some of the maximum hard issues in 5G community management, along with excessive degree choice-making, resource optimization, and exceptional service enforcement. Experiments and simulations are used to evaluate the performance enhancements finished via applying the proposed technique. The consequences display that the proposed method should improve the general overall performance of 5G networks drastically. The proposed method may be used in a selection of network management packages, which include network virtualization, visitor engineering, and self-organizing networks.

关键词： Deep learning Knowledge engineering Adaptive systems 5G mobile communication Quality of service Self-organizing networks Security Virtualization Programming profession Optimization

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Applying Deep Neural Networks to Sentiment Analysis in Social Media

Applying Deep Neural Networks to Sentiment Analysis in Socia...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Meena Y R Amit Kumar Shrivastav Vikas Kumar Kharbas Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Registrar Office of Registrar ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

Deep Neural Networks (DNNs) have currently been applied to sentiment evaluation responsibilities in social media, attaining advanced overall performance compared to conventional machine mastering techniques. That is due to the truth that DNNs are capable of analyzing representations via non-linear function maps, permitting them to seize the complex semantic relationships in textual content correctly. DNNs are also perfect for natural language processing (NLP) obligations because they can generalize from sparse records and use distributed representations in huge-scale datasets. The maximum commonly used DNN architectures for sentiment evaluation in social media are recurrent neural networks (RNNs) and convolutional neural networks (CNNs). RNNs are especially nicely applicable to this project due to the fact that they are able to capture temporal and contextual data and capture the dynamics of language use. RNNs are also extra sturdy to noisy or unstructured entry, which is commonplace in social media information. Alternatively, CNNs are nicely suited to seize neighborhood patterns in textual content, consequently permitting them to study neighborhoods, which means from phrases or phrases. In order to observe DNNs to sentiment analysis in social media, these networks ought to be taught with relevant and correct labels. It requires the development of an excessively satisfactory categorized dataset for training and validation purposes, which can be quite exertion-intensive. Additionally, DNNs are required to enter statistics in a form that is suitable for education and can frequently be non-trivial to extract from social media resources.

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Region-aware Image-based Human Action Retrieval with Transformers

arXiv

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arXiv 2024年

作者： Wang, Hongsong Gui, Jie Department of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Engineering Research Center of Blockchain Application Supervision and Management Nanjing China

Human action understanding is a fundamental and challenging task in computer vision. Although there exists tremendous research on this area, most works focus on action recognition, while action retrieval has received less attention. In this paper, we focus on the neglected but important task of image-based action retrieval which aims to find images that depict the same action as a query image. We establish benchmarks for this task and set up important baseline methods for fair comparison. We present an end-to-end model that learns rich action representations from three aspects: the anchored person, contextual regions, and the global image. A novel fusion transformer module is designed to model the relationships among different features and effectively fuses them into an action representation. Experiments on the Stanford-40 and PASCAL VOC 2012 Action datasets show that the proposed method significantly outperforms previous approaches for image-based action retrieval. © 2024, CC BY.

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