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arXiv 2024年

作者： Feng, Yujie Chu, Xu Xu, Yongxin Shi, Guangyuan Liu, Bo Wu, Xiao-Ming Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China Center on Frontiers of Computing Studies Peking University Beijing China

A practical dialogue system requires the capacity for ongoing skill acquisition and adaptability to new tasks while preserving prior knowledge. However, current methods for Continual Dialogue State Tracking (DST), a crucial function of dialogue systems, struggle with the catastrophic forgetting issue and knowledge transfer between tasks. We present TaSL, a novel framework for task skill localization and consolidation that enables effective knowledge transfer without relying on memory replay. TaSL uses a novel group-wise technique to pinpoint task-specific and task-shared areas. Additionally, a fine-grained skill consolidation strategy protects task-specific knowledge from being forgotten while updating shared knowledge for bi-directional knowledge transfer. As a result, TaSL strikes a balance between preserving previous knowledge and excelling at new tasks. Comprehensive experiments on various backbones highlight the significant performance improvements of TaSL over existing state-of-the-art methods. The source code1 is provided for reproducibility. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech recognition

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Retraction Note: Big data analytics for dynamic network slicing in 5G and beyond with dynamic user preferences

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Syamala, Maganti Anusuya, R. Sonkar, Sanjay Kumar Goswami, Chandrashekhar salunkhe, Satish Elangovan, Muniyandy Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering Modern Institute of Technology and Research Center Alwar India Department of Computer Science & Information Systems Shri Ramswaroop Memorial University Barabanki India Department of CSE School of Computing MIT ADT University Pune India Computer Engineering Department Terna Engineering College Navi Mumbai Affiliated to University of Mumbai Navi Mumbai India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of R&D Bond Marine Consultancy London UK

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Abnormal developmental trajectories under multimodal brain networks in ASD

Abnormal developmental trajectories under multimodal brain n...

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IEEE/ICME International Conference on Complex Medical Engineering, CME

作者： Zhifeng Li Yanli Yang Yan Niu Min Guo Department of Information and Computer Brain Science and Intelligence Computing Research Center Taiyuan University of Technology Taiyuan China

Purpose: Emerging evidence suggests structural and functional brain networks are disrupted during the development of ASD patients. As the importance of age in ASD has been firmly established, children and young adults with ASD constitute a high-risk population. Due to the limitations of single-site sample data, this study proposed and applied two methods to investigate the abnormal development of connectome tissue in adolescents with ASD affected by age at multiple *** and Methods: A total of 140 subjects were included in our study, including 76 ASD patients and 64 normal controls. All the data of the subjects were obtained from the public dataset ABIDE II website and contain simultaneously structural DTI data, T1-weighted data, and resting-state fMRI data. In this paper, we first used graph theory to analyze and reconstruct the anatomical structure and functional brain network and then explored the abnormal brain regions of ASD patients with age from two perspectives: SC-Proportion and SC-FC ***: It was found that ASD showed age-related abnormalities in the whole brain compared with the NC group, and SC-FC Coupling values of ASD were significantly higher than those of NC in childhood but decreased significantly with age. We further found that the abnormal changes in whole-brain Coupling values were mainly caused by the abnormal development of RC and Local tissues. In addition, we found abnormal development of RC tissues in ASD at the structural ***: The present study showed that the anatomical RC tissue development in ASD patients was abnormal at the whole brain level. This finding suggests that the brain anatomy of the RC system is affected in high-risk children and adolescents, reflecting one of the connectome characteristics of developmental risk in ASD patients.

关键词： Couplings Pediatrics Brain Sociology Anatomical structure Functional magnetic resonance imaging Graph theory

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Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems

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Informatics in Medicine Unlocked 2024年 49卷

作者： Nabi, Shaik Abdul Kalpana, Ponugoti Chandra, N. Subhash Smitha, L. Naresh, K. Ezugwu, Absalom E. Abualigah, Laith Department of Computer Science and Engineering AVN Institute of Engineering and Technology Department of Computer Science and Engineering CVR College of Engineering Department of Information Technology G Narayanamma Institute of Technology and Science Department of Computer Science and Engineering TKR College of Engineering & Technology Unit for Data Science and Computing North-West University Computer Science Department Al Al-Bayt University MEU Research Unit Middle East University Applied Science Research Center Applied Science Private University Jadara Research Center Jadara University Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk School of Engineering and Technology Sunway University Malaysia

In the field of cognitive healthcare Internet of Things (CH-IoT), there is a strong demand for reliable and minimally intrusive smart gadgets that consistently acquire, analyse, and obtain the confidential health details of the individual. In fact, CH-IoT is empowered with artificial intelligence (AI) to transmute a fewer operational inputs into actionable, intelligent actions through the digitization of medical healthcare data. However, these systems consume more network complexity, interaction, and overhead costs, while inducing a blend of susceptibility and confidentiality issues. In support of this complexity, these cognitive systems need centralised data collection and to be gathered and analysed, which affects scalability issues and adds fuel to privacy and security breaches. Even though it possesses greater intricacy in its potential application, a substantial factor is maintaining the private preservation of healthcare data against the growing attacks. Thus, this paper presents a distributed privacy-preserving, chaotic encryption-based framework that can be deployed for CH-IoT systems to safeguard sensitive data against message modification, denial of service (DoS), and man-in-the-middle attacks (MIM), guaranteeing privacy and data integrity. The proposed framework integrated the federated learning layered hybrid chaotic encryption strategies by investigating through examination the learning infrastructure of convolutional neural networks (CNN). In the examination, the complete framework was carried out in the Tensorflow Federated Learning Libraries (FLL), and numerous performance metrics such as accuracy, precision, recall, f1-score, transmission efficiency, and overhead ratio were measured and contrasted with the various existing frameworks. For the intensive analysis, formal and informal security experiments were also conducted by NIST (National Institute of science and Technology). The analytical results illustrate the importance of the proposed framewor

关键词： Cognitive health care Distributed learning Federated learning libraries Internet of things NIST Privacy and security breaches

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Differentially Private Kernel Density Estimation

arXiv

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arXiv 2024年

作者： Liu, Erzhi Hu, Jerry Yao-Chieh Reneau, Alex Song, Zhao Liu, Han Center for Foundation Models and Generative AI Department of Computer Science Northwestern University EvanstonIL60208 United States Ensemble AI San FranciscoCA94133 United States Simons Institute for the Theory of Computing UC Berkeley BerkeleyCA94720 United States Center for Foundation Models and Generative AI Department of Computer Science Department of Statistics and Data Science Northwestern University EvanstonIL60208 United States

We introduce a refined differentially private (DP) data structure for kernel density estimation (KDE) with 1, 2 and pp kernels. This new DP data structure offers not only improved privacy-utility tradeoff but also better query efficiency over prior results. Specifically, we study the mathematical problem: given a similarity function f (or DP KDE) and a private dataset X ⊂ d, our goal is to preprocess X so that for any query y ∈ d, we approximate ∑ x∈X f(x, y) in a differentially private fashion. The best previous algorithm for f(x, y) = x − y1 is the node-contaminated balanced binary tree by [Backurs, Lin, Mahabadi, Silwal, and Tarnawski, ICLR 2024]. Their algorithm requires O(nd) space and time for preprocessing with n = |X|. For any query point, the query time is α−1dlog2 n, with an error guarantee of (1 + α)-approximation and ϵ−1α−0.5d1.5Rlog1.5 n. In this paper, we use the same space and pre-processing time, improve the best previous result [Backurs, Lin, Mahabadi, Silwal, and Tarnawski, ICLR 2024] in three aspects • We reduce query time by α−1 log n factor • We improve the approximation ratio from α to 1 • We reduce the error dependence by a factor of α−0.5 From a technical perspective, our method of constructing the search tree differs from previous work [Backurs, Lin, Mahabadi, Silwal, and Tarnawski, ICLR 2024]. In prior work, for each query, the answer is split into α−1 log n numbers, each derived from the summation of log n values in interval tree countings. In contrast, we construct the tree differently, splitting the answer into log n numbers, where each is a smart combination of two distance values, two counting values, and y itself. We believe our tree structure may be of independent interest. © 2024, CC BY-NC-SA.

关键词： Differential privacy

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Depression Detection in Tweets from Urban Cities of Malaysia using Deep Learning 7

Depression Detection in Tweets from Urban Cities of Malaysia...

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7th International Conference on Research and Innovation in Information Systems, ICRIIS 2021

作者： Priya Sri, E.K. Savita, K.S. Zaffar, Maryam Universiti Teknologi PETRONAS Department of Computer and Information Sciences Perak Malaysia Positive Computing Center Universiti Teknologi PETRONAS Department of Computer and Information Sciences Perak Malaysia University of Lahore Department of Computer Science and Information Technology Islamabad44000 Pakistan

ISBN: (纸本)9781665408059

This document was inspired by how the usage of social media platforms in Malaysia such as Twitter have drastically increased ever since the recent Covid-19 pandemic. While practicing social distancing and other pandemic regulations was for the betterment and prevention of physical health, mental health of most was affected negatively. People generally revolve around with having interactions with other humans and once the physical form of it was cut, people tend to turn to social media. A twitter sentiment analysis approach was used to find the casual link between social media and mental health. This project aims to utilise the broaden scope of social media-based mental health measures since research proves the evidence of a link between depression and specific linguistic features as well. Therefore, the research entails on how the problem statement of this project on developing an algorithm that can predict text- based depression symptoms using deep learning and Natural Language Processing (NLP) can be achieved. The objective of the project is to identify depressive tweets using NLP and Deep Learning in the urban cities of Malaysia within the beginning of the Covid-19 period to enable individuals, their caregivers, parents, and even medical professionals to identify the linguistic clues that point towards to signs of mental health deterioration. Additionally, this paper also researches to make the proposed system to identify words that represent depression and categorize them accordingly as well as improve the accuracy of the system in identifying tweets that display the depression related words based on its specific location. This objective will be achieved following the methodology using the Deep Learning approach and Natural Language Processing technique. A recurrent neural network approach was implemented in this project known as the Long-Term Short Memory, which is a form of advanced RNN, that allows information to be preserved. Conducting an analysis on the li

关键词： Sentiment analysis

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∂H: Differentiable Holography

Research Square

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Research Square 2023年

作者： Chen, Ni Wang, Congli Heidrich, Wolfgang Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Department of Electrical Engineering & Computer Sciences University of California BerkeleyCA94720 United States Visual Computing Center King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Over the past decade, the field of holography has gained significant ground due to advances in computational imaging. However, the utilization of computational tools is hampered by the mismatch between experimental setups and the conceptual model. We present differentiable holography (∂H), a novel framework for automatically self-calibrating experimental imperfections in inverse holographic imaging. The technique is demonstrated on auto-focused complex field imaging from a single intensity-only inline hologram. © 2023, CC BY.

关键词： Inverse problems

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The Impact of Language Expressions and visual Exploration on Neurodivergent Artist Quotes in Educational Setting

The Impact of Language Expressions and Visual Exploration on...

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International Conference on Business and Industrial Research (ICBIR)

作者： Desi Indrawati Yuda Suryasa Sjaerodji Aris Darisman Imaniar Rizki Waridha Computer Science Department Language Center Faculty of Humanities Bina Nusantara University Bandung West Java Indonesia Visual Communication Design Study Program School of Design Bina Nusantara University Bandung West Java Indonesia

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

In recent years, neurodiversity has gained popularity among those proponents of people's inclusion, dignity, and rights. Demonstrations and images help them learn more effectively. Give written instructions, visual clues, and brief spoken instructions when feasible. Furthermore, the researchers included seventeen student populations, eleven officers and staff, and twelve lecturer populations. For two days in a row, they attended the interactive quotation display by neurodivergent artists, and in response, they contributed drawings that reflected their interpretations of the quotes. An interview served as the second method of gathering data. The researchers also conducted a structured interview with four students, four officers, and three lecturers acting as spokespeople. Quotes from the works of Neurodivergent Artists such as Nintendo, Love, Amsterdam, Suzanne, Kacamata (Glasses), Teman (Friends), Mudik (Homecoming), Menjahit (Sewing), and Iggle can be found in the Interactive Installation activity. Using a mixed-methods approach, the researchers collected and examined the data (Creswell, 2014). It is an approach that blends qualitative and quantitative research techniques. According to the findings, the Amsterdam quote is liked by lecturers, staff, and students alike. In addition, the Love quote sparks curiosity in both students and instructors, indicating a mutual admiration for its meaning.

关键词： visualization Glass Semiotics Sparks Interviews Business

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A Multi-Stream Scrambling and DNA Encoding Method Based Image Encryption

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computer Systems science & Engineering 2023年第11期47卷 1321-1347页

作者： Nashat Salih Abdulkarim Alsandi Dilovan Asaad Zebari Adel Al-Zebari Falah Y.H.Ahmed Mazin Abed Mohammed Marwan Albahar Abdulaziz Ali Albahr Information Technology Duhok Private Technical InstituteDuhokIraq Department of Computer Science College of ScienceNawroz UniversityDuhok42001Iraq Department of Information Technology Technical College of Informatics AkreDuhok Polytechnic UniversityDuhokIraq Faculty of Computing and Information Technology Sohar UniversityOman College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Science Umm Al Qura UniversityP.O.Box 715MeccaSaudi Arabia College of Applied Medical Sciences King Saud Bin Abdulaziz University for Health SciencesAl Ahsa31982Saudi Arabia King Abdullah International Medical Research Center Al-Ahsa36276Saudi Arabia

Information security has emerged as a key problem in encryption because of the rapid evolution of the internet and ***,the progress of image encryption techniques is becoming an increasingly serious issue and considerable *** space of the key,encryption-based low confidentiality,low key sensitivity,and easily exploitable existing image encryption techniques integrating chaotic system and DNA computing are purposing the main problems to propose a new encryption technique in this *** our proposed scheme,a three-dimensional Chen’s map and a one-dimensional Logistic map are employed to construct a double-layer image encryption *** the confusion stage,different scrambling operations related to the original plain image pixels are designed using Chen’s map.A stream pixel scrambling operation related to the plain image is ***,a block scrambling-based image encryption-related stream pixel scrambled image is *** the diffusion stage,two rounds of pixel diffusion are generated related to the confusing image for intra-image ***’s map,logistic map,and DNA computing are employed to construct diffusion operations.A reverse complementary rule is applied to obtain a new form of DNA.A Chen’s map is used to produce a pseudorandom DNA sequence,and then another DNA form is constructed from a reverse pseudorandom DNA ***,the XOR operation is performed multiple times to obtain the encrypted *** to the simulation of experiments and security analysis,this approach extends the key space,has great sensitivity,and is able to withstand various typical *** adequate encryption effect is achieved by the proposed algorithm,which can simultaneously decrease the correlation between adjacent pixels by making it near zero,also the information entropy is *** number of pixels changing rate(NPCR)and the unified average change intensity(UACI)both are very near to optimal values.

关键词： Grayscale image encryption stream scrambling-confusion DNA encoding XOR operation chaotic systems

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MCCNet: Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images 25th

MCCNet: Multi-Color Cascade Network with Weight Transfer for...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Frisky, Aufaclav Zatu Kusuma Putranto, Andi Zambanini, Sebastian Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Vienna Austria Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Faculty of Cultural Science Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9783030687861

Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. Thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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