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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Ashish Sharma Piyansh Agrawal Krishan Bhisham Sharma Imed Ben Dhaou Department of Computer Engineering & Applications GLA University Mathura UP India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms.

关键词： Resistance Technological innovation Road accidents Social networking (online) Smart cities Face recognition Roads

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A Multi-Modal Behavior Quantitative Analysis Model for Autism Early Screening

A Multi-Modal Behavior Quantitative Analysis Model for Autis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lei, Jiayi Zhang, E. She, Yingying Wang, Xin Liao, Yuhan Hu, Bin Wu, Hang Yang, Minqiang Tian, Jiajia Wang, Yong School of Informatics Xiamen University China University of Kansas Medical Center Department of Occupational Therapy Education United States School of Film National Institute for Data Science in Health and Medicine Xiamen University China School of Computer Science and Technology Beijing Institute of Technology China Together Education Institute China School of Information Science and Engineering Lanzhou University China Fujian Medical University Union Hospital Pediatric Department China

ISBN: (纸本)9798350337020

Human-computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance. © 2023 IEEE.

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A Comparison of Regression Techniques for Prediction of Air Quality in Smart Cities

A Comparison of Regression Techniques for Prediction of Air ...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Kamal Deep Garg Manik Gupta Bhisham Sharma Imed Ben Dhaou Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

The expansion of the internet of things (IoT) and Big Data are two factors that have contributed to the rise in popularity of smart cities. The ability to anticipate the quality of the air in an area with precision and efficiency is one of the fundamental building blocks of a smart city. The amount of polluted air found in smart cities throughout the world has been gradually growing. Because of this, there has been a rise in the concentration of several air pollutants in the environment, including particulate matter (PM 10), sulphur dioxide (SO2), and PM 2.5, amongst others. Because of the possibility of uncontrollable repercussions, such as an increase in the severity of asthma and cardiovascular disease, this situation poses a risk to the country and to the people who live in it. Heavy industry and vehicle exhaust have been major contributors to the growth of air pollution in smart cities such as New Delhi, Bombay, Chandigarh, and Bengaluru in India. The purpose of this investigation is to compare and contrast the efficiency of a variety of machine learning methods in order to assess the precision of the air quality index (AQI) projection of PM 2.5 in Chandigarh, India. Models for predicting AQI are trained and tested using a variety of statistical techniques like Linear regression, Lasso regression, KNN regression, and Random Forest regression This Root Mean Square Error (RMSE) found for Linear regression, Lasso regression, KNN regression, and Random Forest regression are 31.01, 29,45, 37.09 and 28.3. From all four models, random forest regression was more accurate than the other three regression models in estimating PM 2.5 levels in India’s smart city.

关键词： Deep learning Technological innovation Smart cities Atmospheric modeling Linear regression Air pollution Prediction algorithms

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Maximum-Width Rainbow-Bisecting Empty Annulus∗

SSRN

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SSRN 2023年

作者： Banerjee, Sandip Baral, Arpita Mahapatra, Priya Ranjan Sinha Bae, Sang Won Yoon, Sang Duk Division of Computer Science and Engineering Kyonggi University Korea Republic of Institute of Informatics University of Wroclaw Poland Department of Computer Science and Engineering University of Kalyani India Department of Service and Design Engineering Sungshin Women's University April 4 2023 Korea Republic of

Given a set of $n$ colored points with $k$ colors in the plane,we study the problem of computing a maximum-width rainbow-bisecting empty annulus (of objects specifically axis-parallel square, axis-parallel rectangle and circle) problem. We call a region \emph{rainbow} if it containsat least one point of each color. The maximum-width rainbow-bisecting emptyannulus problem asks to find an annulus $A$ of a particular shape with maximum possible width such that $A$ does not contain any input points and it bisects the input point set into two parts, each of which is a \emph{rainbow}.We compute a maximum-width rainbow-bisecting empty axis-parallel square, axis-parallel rectangular and circular annulus in $O(n^3)$ time using $O(n)$ space, in $O(k^2n^2\log n)$ time using $O(n\log n)$ spaceand in $O(n^3)$ time using $O(n^2)$ space respectively. © 2023, The Authors. All rights reserved.

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Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition

Patch-NetVLAD+: Learned patch descriptor and weighted matchi...

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2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2022

作者： Cai, Yingfeng Zhao, Junqiao Cui, Jiafeng Zhang, Fenglin Feng, Tiantian Ye, Chen Tongji University School of Electronics and Information Engineering Department of Computer Science and Technology Shanghai201804 China Tongji University Institute of Intelligent Vehicle Shanghai201804 China Tongji University School of Surveying and Geo-Informatics Shanghai200092 China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge. Existing VPR methods using global descriptors have difficulty capturing local specific region (LSR) in the scene and are therefore prone to localization confusion in such scenarios. As a result, finding the LSRs that are critical for location recognition becomes key. To address this challenge, we introduced Patch-NetVLAD+, which was inspired by patch-based VPR researches. Our method proposed a fine-tuning strategy with triplet loss to make NetVLAD suitable for extracting patch-level descriptors. Moreover, unlike existing methods that treat all patches in an image equally, our method extracts patches of LSR, which present less frequently throughout the dataset, and makes them play an important role in VPR by assigning proper weights to them. Experiments on Pittsburgh30k and Tokyo247 datasets show that our approach achieved up to 9.3% performance improvement than existing patch-based methods. © 2022 IEEE.

关键词： Location awareness Visualization Aggregates Semantics Intelligent systems IEEE Regions

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning 3

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia

ISBN: (纸本)9798350306231

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%;Naive Bayes: 83%. © 2023 IEEE.

关键词： Support vector machines

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Speculative Vulnerability: Uncovering the Temporalities of Vulnerability in People s Experiences of the Pandemic

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Proceedings of the ACM on Human-computer Interaction 2022年第CSCW2期6卷 1-27页

作者： Seberger, John S. Obi, Ike Loukil, Mariem Liao, William Wild, David J. Patil, Sameer Department of Information Science College of Computing and Informatics Drexel University 3675 Market Street PhiladelphiaPA19104 United States Department of Computer Graphics Technology Purdue Polytechnic Institute Purdue University 401 N. Grant St. West LafayetteIN47907 United States Mediterranean Institute of Technology Les Jardins du Lac 2 Tunis1053 Tunisia Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University Bloomington 901 E. Tenth Street BloomingtonIN48104 United States School of Computing University of Utah 50 Central Campus Dr. Salt Lake CityUT84112 United States

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-computer Interaction (HCI) and for the oversight of app-based public health surveillance. © 2022 Owner/Author.

关键词： Public health

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ViPRA-Haplo: De Novo Reconstruction of Viral Populations Using Paired End Sequencing Data

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年第3期21卷 492-500页

作者： Li, Weiling Malhotra, Raunaq Wu, Steven Jha, Manjari Rodrigo, Allen Poss, Mary Acharya, Raj Weill Cornell Medical College Institute for Computational Biomedicine New YorkNY10021 United States ScienceIO New YorkNY10100 United States National Taiwan University Department of Agronomy Taipei10617 Taiwan Pennsylvania State University School of Electrical Engineering and Computer Science University ParkPA16802 United States University of Auckland School of Biological Sciences Private Bag 92019 New Zealand University of Virginia Department of Hematology and Oncology School of Medicine CharlottesvilleVA22908 United States Indiana University Bloomington Luddy School of Informatics Computing and Engineering BloomingtonIN47405 United States

We present ViPRA-Haplo, a de novo strain-specific assembly workflow for reconstructing viral haplotypes in a viral population from paired-end next generation sequencing (NGS) data. The proposed Viral Path Reconstruction Algorithm (ViPRA) generates a subset of paths from a De Bruijn graph of reads using the pairing information of reads. The paths generated by ViPRA are an over-estimation of the true contigs. We propose two refinement methods to obtain an optimal set of contigs representing viral haplotypes. The first method clusters paths reconstructed by ViPRA using VSEARCH Deorowicz et al. 2015 based on sequence similarity, while the second method, MLEHaplo, generates a maximum likelihood estimate of viral populations. We evaluated our pipeline on both simulated and real viral quasispecies data from HIV (and real data from SARS-COV-2). Experimental results show that ViPRA-Haplo, although still an overestimation in the number of true contigs, outperforms the existing tool, PEHaplo, providing up to 9% better genome coverage on HIV real data. In addition, ViPRA-Haplo also retains higher diversity of the viral population as demonstrated by the presence of a higher percentage of contigs less than 1000 base pairs (bps), which also contain k-mers with counts less than 100 (representing rarer sequences), which are absent in PEHaplo. For SARS-CoV-2 sequencing data, ViPRA-Haplo reconstructs contigs that cover more than 90% of the reference genome and were able to validate known SARS-CoV-2 strains in the sequencing data. © 2004-2012 IEEE.

关键词： Coronavirus

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Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery

arXiv

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arXiv 2024年

作者： Chang, Ching-Chun Gao, Kai Xu, Shuying Kordoni, Anastasia Leckie, Christopher Echizen, Isao National Institute of Informatics Chiyoda Tokyo Japan The Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan The Department of Psychology Lancaster University Lancaster United Kingdom The Department of Computing and Information Systems University of Melbourne MelbourneVIC Australia The Information and Society Research Division National Institute of Informatics The Department of Informatics Graduate University for Advanced Studies The Department of Information and Communication Engineering University of Tokyo Chiyoda Tokyo Japan

Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject's subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoors threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine's behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern being the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability. © 2024, CC BY.

关键词： Reverse engineering

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Blockchain-based Security Framework for Metaverse: A Decentralized Approach 15

Blockchain-based Security Framework for Metaverse: A Decentr...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Rajawat, Anand Singh Goyal, S.B. Solanki, RamKumar Raboaca, Maria Simona Mihaltan, Traian Candin Illes, Zoltan Verma, Chaman School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Icsi Energy Department National Valcea Romania Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca40033 Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary

ISBN: (纸本)9798350321388

People can act out fake social exchanges in the Metaverse, which is an online community. Security has become a problem in the Metaverse because there are so many online transactions and digital assets. In this thesis, a Blockchain-based security system for the Metaverse is proposed. This would be a decentralised way to keep the virtual world safe. Our system uses Blockchain's autonomous structure to make sure that all financial transactions in the Metaverse are safe, anonymous, and true. We suggest a new consensus process that combines Proof of Stake (PoS) and Proof of Authority (PoA) to make networks safer and allow them to grow. We also have a user-focused, decentralised identity management system that keeps their information safe. We also suggest a way to process Metaverse transactions safely that is based on smart contracts. Our method makes sure that transactional proof, recording, and validation are done in a safe and reliable way. We also show a reputation system that benefits people who follow the law and punishes people who break the law. Lastly, we test how well our suggested structure works with simulations and real-world tests. The results show that our way works to reduce possible security risks in the Metaverse and create a safe, scalable environment for online business. Overall, our Blockchain-based Security Framework for Metaverse: A Decentralised Approach is a good way to keep the security and privacy of digital activities in Metaverse. © 2023 IEEE.

关键词： Blockchain

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