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arXiv 2024年

作者： AlSobeh, Anas Mohammad Ramadan Department of Information Systems Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan Program of Information Technology School of Computing College of Engineering Computing Technology and Mathematics Southern Illinois University CarbondaleIL United States

In the intricate domain of software systems verification, dynamically model checking multifaceted system characteristics remains paramount, yet challenging. This research proposes the advanced observe-based statistical model-checking (OSM) framework, devised to craft executable formal models directly from foundational system code. Leveraging model checking predicates, the framework melds seamlessly with aspect-oriented programming paradigms, yielding a potent method for the analytical verification of varied behavioral attributes. Exploiting the transformative capacity of OSM framework, primary system code undergoes a systematic metamorphosis into multifaceted analysis constructs. This not only simplifies the model verification process but also orchestrates feature interactions using an innovative observing join point abstraction mechanism. Within this framework, components encompassing parsing, formal verification, computational analytics, and rigorous validation are intrinsically interwoven. Marrying the principles of model checking with aspect-oriented (AO) modularization, OSM framework stands as a paragon, proficiently scrutinizing and affirming system specifications. This ensures the unyielding performance of electronic health record systems amidst shifting preconditions. OSM framework offers runtime verification of both object-oriented and AO deployments, positioning itself as an indispensable open-source resource, poised to automate the enhancement of system performance and scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Aspect oriented programming

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CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models

arXiv

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arXiv 2023年

作者： Li, Jiazheng Sun, Zhaoyue Liang, Bin Gui, Lin He, Yulan Department of Informatics King’s College London United Kingdom Department of Computer Science University of Warwick United Kingdom Joint Lab of HITSZ-CMS Harbin Institute of Technology Shenzhen China The Alan Turing Institute United Kingdom

Text classifiers built on Pre-trained Language Models (PLMs) have achieved remarkable progress in various tasks including sentiment analysis, natural language inference, and question-answering. However, the occurrence of uncertain predictions by these classifiers poses a challenge to their reliability when deployed in practical applications. Much effort has been devoted to designing various probes in order to understand what PLMs capture. But few studies have delved into factors influencing PLM-based classifiers’ predictive uncertainty. In this paper, we propose a novel framework, called CUE, which aims to interpret uncertainties inherent in the predictions of PLM-based models. In particular, we first map PLM-encoded representations to a latent space via a variational auto-encoder. We then generate text representations by perturbing the latent space which causes fluctuation in predictive uncertainty. By comparing the difference in predictive uncertainty between the perturbed and the original text representations, we are able to identify the latent dimensions responsible for uncertainty and subsequently trace back to the input features that contribute to such uncertainty. Our extensive experiments on four benchmark datasets encompassing linguistic acceptability classification, emotion classification, and natural language inference show the feasibility of our proposed framework. Our source code is available at https://***/lijiazheng99/CUE. © 2023, CC BY.

关键词： Sentiment analysis

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A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features

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Computational Intelligence and Neuroscience 2022年 2022卷 4942637页

作者： Zareen, Syeda Shamaila Guangmin, Sun Li, Yu Kundi, Mahwish Qadri, Salman Qadri, Syed Furqan Ahmad, Mubashir Khan, Ali Haider Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Informatics University of Leicester Leicester United Kingdom Department of Computer Science Muhammad Nawaz Shareef University of Agriculture Multan66000 Pakistan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China Faculty of Computer Science and Technology University of Lahore Sargodha Pakistan Department of Computer Science School of Systems & Technology University of Management and Technology Lahore Pakistan

The main purpose of this study is to observe the importance of machine vision (MV) approach for the identification of five types of skin cancers, namely, actinic-keratosis, benign, solar-lentigo, malignant, and nevus. The 1000 (200 × 5) benchmark image datasets of skin cancers are collected from the International Skin Imaging Collaboration (ISIC). The acquired ISIC image datasets were transformed into texture feature dataset that was a combination of first-order histogram and gray level co-occurrence matrix (GLCM) features. For the skin cancer image, a total of 137,400 (229 × 3 x 200) texture features were acquired on three nonover-lapping regions of interest (ROIs). Principal component analysis (PCA) clustering approach was employed for reducing the dimension of feature dataset. Each image acquired twenty most discriminate features based on two different approaches of statistical features such as average correlation coefficient plus probability of error (ACC + POE) and Fisher (Fis). Furthermore, a correlation-based feature selection (CFS) approach was employed for feature reduction, and optimized 12 features were acquired. Furthermore, a classification algorithm naive bayes (NB), Bayes Net (BN), LMT Tree, and multilayer perception (MLP) using 10 K-fold cross-validation approach were employed on optimized feature datasets and the overall accuracy achieved by MLP is 97.1333%. © 2022 Syeda Shamaila Zareen et al.

关键词： computer vision

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Purpose Driven Disputation Modeling, Analysis and Resolution Based on DIKWP Graphs

Purpose Driven Disputation Modeling, Analysis and Resolution...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yingtian Mei Yucong Duan Liang Chen Zaiwen Feng Lei Yu Zhendong Guo School of Computer Science and Technology Hainan University Haikou China Hainan Meteorological Service Center Haikou China College of Informatics Huazhong Agricultural University Wuhan China Department of Computer Science Inner Mongolia University Hohhot China

Inconsistency, incompleteness, inaccuracy and inexpressibility(4-No problem) in input and outcome of content processing has become a new form requirement in energy efficient computation, storage and communication. To cope this challenge, we propose to map content with 4-No problem to typed Data, Information, Knowledge, Wisdom and Pur- pose(DIKWP) in the form of DIKWP(content) Graphs. We adopt a purpose driven approach to model subjective content related to stakeholders cognition, as subjective DIKWP Graphs. We combine objective DIKWP(content) Graphs and subjective DIKWP(stakeholder) Graphs to construct DIKWP semantic Graphs. Through traversing the DIKWP semantic Graphs under a purpose driven strategy, we expect to locate the content difference and explain the disputation or misunderstanding. We have also propose optimization on the processing in term of reduces cognitive distance and reasoning gaps. We showcase the feasibility of our approach in a lawsuit disputation resolution scenario.

关键词： Uncertainty Law Semantics Information sharing Cognition Energy efficient computing Stakeholders

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Self-attention transfer networks for speech emotion recognition

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Virtual Reality & Intelligent Hardware 2021年第1期3卷 43-54页

作者： Ziping ZHAO Keru Wang Zhongtian BAO Zixing ZHANG Nicholas CUMMINS Shihuang SUN Haishuai WANG Jianhua TAO Björn WSCHULLER College of Computer and Information Engineering Tianjin Normal UniversityTianjin 300387China GLAM-Group on Language Audio&MusicImperial College LondonSW72AZUK Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg86159Germany Department of Biostatistics and Health Informatics IoPPNKing's College LondonLondonSE58AFUK Department of Computer Science and Engineering Fairfield University 06824USA National Laboratory of Pattern Recognition CASIABeijing 100190China

Background A crucial element of human-machine interaction,the automatic detection of emotional states from human speech has long been regarded as a challenging task for machine learning *** vital challenge in speech emotion recognition(SER)is learning robust and discriminative representations from *** machine learning methods have been widely applied in SER research,the inadequate amount of available annotated data has become a bottleneck impeding the extended application of such techniques(e.g.,deep neural networks).To address this issue,we present a deep learning method that combines knowledge transfer and self-attention for SER ***,we apply the log-Mel spectrogram with deltas and delta-deltas as ***,given that emotions are time dependent,we apply temporal convolutional neural networks to model the variations in *** further introduce an attention transfer mechanism,which is based on a self-attention algorithm to learn long-term *** self-attention transfer network(SATN)in our proposed approach takes advantage of attention transfer to learn attention from speech recognition,followed by transferring this knowledge into *** evaluation built on Interactive Emotional Dyadic Motion Capture(IEMOCAP)dataset demonstrates the effectiveness of the proposed model.

关键词： Speech emotion recognition Attention transfer Self-attention Temporal convolutional neural networks(TCNs)

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Social Media Analytics through Big Data Using Hadoop Framework

Social Media Analytics through Big Data Using Hadoop Framewo...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Rasha Almajed Bilal Shoaib Khan Ayman Bassam Nassoura Muhammad Sohail Irshad Muhammad Amjad Muhammad Hassan Shahid Maqbool Manas Pradhan College of Computer and Information Technology American University in Dubai United Arab Emirates Department of Informatin Technology Akhuwat College University Lahore Pakistan Management Information Systems Department City University Ajman National College of Business Administration and Economics Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan National University of Science & Technology School of Information Technology Skyline University College Sharjah UAE

In the current decades, data from social networks has increased dramatically. Since outmoded tools and techniques are not appropriate for working with such a large amount of data, a good structure is needed to accumulate these vast volumes of data and provide the opportunity for finale users to analyze them. In this research work, we use data analysis techniques to support businessmen for the development of new strategies. We use Apache Hadoop framework and apache flume platform for obtaining social media analytics. Apache flume works as a local host for the Hadoop data file system (HDFS). Iron man 3 movie reviews on twitter have been executed and analyzed by using apache flume and hive. By observing the table created by apache hive from twitter tweets it is shown that only 2 movie viewers observe that it is negative, some of them observe the movie not of standard while giving a neutral opinion or negative option, but most of them like the movie by giving positive remarks.

关键词： Text analysis Social networking (online) Blogs Big Data Media Motion pictures Security

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SEE-MCAM: Scalable Multi-Bit FeFET Content Addressable Memories for Energy Efficient Associative Search

SEE-MCAM: Scalable Multi-Bit FeFET Content Addressable Memor...

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IEEE International Conference on computer-Aided Design

作者： Shengxi Shou Che-Kai Liu Sanggeon Yun Zishen Wan Kai Ni Mohsen Imani X. Sharon Hu Jianyi Yang Cheng Zhuo Xunzhao Yin College of Information Science and Electronic Engineering Zhejiang University China Department of Information and Computer Science University of California Irvine USA School of Electrical and Computer Engineering Georgia Institute of Technology USA Department of Electrical and Microelectronic Engineering Rochester Institute of Technology USA Department of Computer Science and Engineering University of Notre Dame USA School of Micro-Nano Electronics. Zhejiang University China Key Laboratory of Collaborative Sensing and Autonomous Unmaned Systems of Zhejiang Province China

Artificial intelligence has made remarkable advancements in recent years, leading to the development of algorithms and models capable of handling ever-increasing amounts of data. The computational demands of these algorithms necessitate circuit and architecture designs that go beyond the von-Neumann paradigm. Content addressable memories (CAMs), which implement parallel associative search functionality within memory blocks to overcome the memory wall bottleneck, have proven to be effective for data-intensive tasks. While current CAM designs have achieved higher storage density and energy efficiency than their CMOS-based counterparts by leveraging emerging non-volatile memories (NVM), most of these implementations are limited to binary storage cells. In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi-level-cell characteristics of FeFETs, our proposed SEE-MCAM designs enable multi-bit associative search functions and achieve better energy efficiency and performance than existing FeFET-based CAM designs. We validated the functionality of our proposed designs by achieving 3 bits per cell CAM functionality, resulting in 3x improvement in storage density. The area per bit of the proposed SEE-MCAM cell is 8% of the conventional CMOS CAM. We thoroughly investigated the scalability and robustness of the proposed design. Evaluation results suggest that the proposed 2FeFET-1 T SEE-MCAM achieves 9.8× more energy efficiency and 1.6× less search latency compared to the CMOS CAM, respectively. When compared to existing MCAM designs, the proposed SEE-MCAM can achieve 8.7× and 4.9× more energy efficiency than ReRAM-based and FeFET-based MCAMs, respectively. Benchmarking results show that our approach provides up to 3 orders of magnitude improvement in speedup and energy efficiency over a GPU implementation in accelerating a novel quantized hyperdimensiona

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A ZERO-SHOT METHOD FOR 3D MEDICAL IMAGE SEGMENTATION

A ZERO-SHOT METHOD FOR 3D MEDICAL IMAGE SEGMENTATION

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Ma, Shiqiang Li, Xuejian Tang, Jijun Guo, Fei School of Computer Science and Technology College of Intelligence and Computing Tianjin University Tianjin 300350 China Department of Computer Science and Engineering University of South Carolina ColumbiaSC29208 United States Key Laboratory of Systems Bioengineering Ministry of Education Tianjin University Tianjin 300072 China

ISBN: (纸本)9781665438643

Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, existing methods based on outstanding 2.5D and 3D segmentation strategies are time-consumption and hardware-consumption while ensuring high accuracy. In order to reduce the high demand for automatic segmentation of tumor images and avoid the noise interference in a single input image, we propose an end-to-end zero-shot CNN segmentation method. Our method only utilizes two adjacent images, instead of the target image, as the input data of deep neural network to predict the brain tumor area in the target image. Avoiding noise interference in the target image, this method makes full use of the spatial context feature between adjacent slices in order to obtain accurate zero-shot segmentation results. We compare with the state-of-the-art segmentation frameworks on the same benchmark and notice that our method has strong competitiveness. © 2021 IEEE

关键词： Diagnosis

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Communication-Efficient Stochastic Distributed Learning

arXiv

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arXiv 2025年

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multipliers (ADMM) to address the challenges of high communication costs, and large datasets. Our design tackles these challenges i) by enabling the agents to perform multiple local training steps between each round of communications;and ii) by allowing the agents to employ stochastic gradients while carrying out local computations. We show that the proposed algorithm converges to a neighborhood of a stationary point, for nonconvex problems, and of an optimal point, for convex problems. We also propose a variant of the algorithm to incorporate variance reduction thus achieving exact convergence. We show that the resulting algorithm indeed converges to a stationary (or optimal) point, and moreover that local training accelerates convergence. We thoroughly compare the proposed algorithms with the state of the art, both theoretically and through numerical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and Machine Learning

Detailed review on Integrated Healthcare Prediction System U...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Deepa Parasar Arif Ali N. Muthuvairavan Pillai Archana Shahi Badria Sulaiman Alfurhood Kumud Pant CSE Amity University Maharashtra Department of CSE Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Computer Science and Business Systems R.M.D Engineering College Kavarapettai Chitkara Business School Chitkara University India Department of Computer Sciences College of Computer and Information Sciences Princess Nourahbint Abdulrahman University Saudi Arabia Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology’s function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

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