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arXiv 2021年

作者： Qin, Rongjun Department of Civil Environmental and Geodetic Engineering Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio State University 2036 Neil Avenue ColumbusOH United States

The geodatabase (vectorized data) nowadays becomes a rather standard digital city infrastructure;however, updating geodatabase efficiently and economically remains a fundamental and practical issue in the geospatial industry. The cost of building a geodatabase is extremely high and labor intensive, and very often the maps we use have several months and even years of latency. One solution is to develop more automated methods for (vectorized) geospatial data generation, which has been proven a difficult task in the past decades. An alternative solution is to first detect the differences between the new data and the existing geospatial data, and then only update the area identified as changes. The second approach is becoming more favored due to its high practicality and flexibility. A highly relevant technique is change detection. This article aims to provide an overview the state-of-the-art change detection methods in the field of Remote Sensing and Geomatics to support the task of updating geodatabases. data used for change detection are highly disparate, we therefore structure our review intuitively based on the dimension of the data, being 1) change detection with 2D data;2) change detection with 3D data. Conclusions will be drawn based on the reviewed efforts in the field, and we will share our outlooks of the topic of updating geodatabases. . Copyright © 2021, The Authors. All rights reserved.

关键词： Remote sensing

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Planning with Diffusion Models for Target-Oriented Dialogue Systems

arXiv

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arXiv 2025年

作者： Du, Hanwen Peng, Bo Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States

Target-Oriented Dialogue (TOD) remains a significant challenge in the LLM era, where strategic dialogue planning is crucial for directing conversations toward specific targets. However, existing dialogue planning methods generate dialogue plans in a step-by-step sequential manner, and may suffer from compounding errors and myopic actions. To address these limitations, we introduce a novel dialogue planning framework, DiffTOD, which leverages diffusion models to enable non-sequential dialogue planning. DiffTOD formulates dialogue planning as a trajectory generation problem with conditional guidance, and leverages a diffusion language model to estimate the likelihood of the dialogue trajectory. To optimize the dialogue action strategies, DiffTOD introduces three tailored guidance mechanisms for different target types, offering flexible guidance towards diverse TOD targets at test time. Extensive experiments across three diverse TOD settings show that DiffTOD can effectively perform non-myopic lookahead exploration and optimize action strategies over a long horizon through non-sequential dialogue planning, and demonstrates strong flexibility across complex and diverse dialogue scenarios. Our code and data are accessible through https://***/r/DiffTOD. © 2025, CC BY.

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An Industrial IoT Driven Approach for Sewage Gas Identification and Reporting System using Intelligent Sensors with Logical Controller

An Industrial IoT Driven Approach for Sewage Gas Identificat...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： J. Rajasubha R Ramya A. Hency Juliet Janakiraman V S. Rukmani Devi Karuppaiah S Department of Computer Science and Engineering Dhanalakshmi College of Engineering and Technology Chennai Tamil Nadu India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Based on a real-time sewage gas level detection and monitoring system, this system seeks to offer intelligent solutions for the monitoring of toxic sewage gases. An alarm is issued to the observer who is looking at the circumstances from a distance whenever a specific threshold is crossed. Following this, the data is transmitted together with different gas ppm levels, indicating if the worker can clean or operate securely in that region. The suggested solution incorporates both the IoT platform and the remotely located IoT monitoring devices. This necessitates finding the appropriate threshold values for septic plants and facilities, as well as calibrating gas sensors for use in industry. The gear is meant to provide the sewage worker advance notification whenever the concentration of toxic gaseous substances grows over time, ensuring their safety. The presence of gas, temperature, and other factors in sewage may be detected using a variety of sensors. By notifying sewage workers as well as cleaners via SMS whenever the threshold value drops below the observed levels, this technology helps save people from harmful infections, which is a social good. A number of harmful gas concentrations are graphed out for real-time monitoring, and this is how it accomplishes its job. In order to assess the efficacy of the suggested model, this paper cross-validates it with the Conventional Sewage Gas Detection (CSGD) based on GPRS functioning. The goal of the cross-validation is to create an IoT based Sewage Gas Detection (IoTSGD) system that can monitor and analyze toxic gas levels in real-time and notify workers. Firebase receives the measured gas levels. If the levels rise over a certain point, it will notify the authorized individuals working remotely through their linked mobile devices. An SMS message can alert the authorities if the sewage system is likely to overflow. For the purpose of monitoring, an android app was created and connected with the planned system.

关键词： Temperature sensors Temperature measurement Gases Temperature Sewage treatment Message services Real-time systems

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Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance

arXiv

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arXiv 2025年

作者： Chen, Ziqi Peng, Bo Zhai, Tianhua Adu-Ampratwum, Daniel Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Medicinal Chemistry and Pharmacognosy The Ohio State University ColumbusOH43210 United States Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Drug development is a critical but notoriously resource- and time-consuming process. In this manuscript, we develop a novel generative artificial intelligence (genAI) method DiffSMol to facilitate drug development. DiffSMol generates 3D binding molecules based on the shapes of known ligands. DiffSMol encapsulates geometric details of ligand shapes within pre-trained, expressive shape embeddings and then generates new binding molecules through a diffusion model. DiffSMol further modifies the generated 3D structures iteratively via shape guidance to better resemble the ligand shapes. It also tailors the generated molecules toward optimal binding affinities under the guidance of protein pockets. Here, we show that DiffSMol outperforms the state-of-the-art methods on benchmark datasets. When generating binding molecules resembling ligand shapes, DiffSMol with shape guidance achieves a success rate 61.4%, substantially outperforming the best baseline (11.2%), meanwhile producing molecules with novel molecular graph structures. DiffSMol with pocket guidance also outperforms the best baseline in binding affinities by 13.2%, and even by 17.7% when combined with shape guidance. Case studies for two critical drug targets demonstrate very favorable physicochemical and pharmacokinetic properties of the generated molecules, thus, the potential of DiffSMol in developing promising drug candidates. © 2025, CC BY-NC-SA.

关键词： Ligands

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Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text 13

Findings of the Sentiment Analysis of Dravidian Languages in...

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Working Notes of FIRE - 13th Forum for Information Retrieval Evaluation, FIRE-WN 2021

作者： Chakravarthi, Bharathi Raja Priyadharshini, Ruba Thavareesan, Sajeetha Chinnappa, Dhivya Thenmozhi, Durairaj Sherly, Elizabeth McCrae, John P. Hande, Adeep Ponnusamy, Rahul Banerjee, Shubhanker Vasantharajan, Charangan Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Ireland ULTRA Arts and Science College Tamil Nadu Madurai India Eastern University Sri Lanka Thomson Reuters United States Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu India Indian Institute of Information Technology and Management Kerala India Indian Institute of Information Technology Tiruchirappalli India ADAPT Centre Data Science Institute National University of Ireland Galway Ireland Department of Computer Science and Engineering University of Moratuwa Sri Lanka

We present the results of the Dravidian-CodeMix shared task1 held at FIRE 2021, a track on sentiment analysis for Dravidian Languages in Code-Mixed Text. We describe the task, its organization, and the submitted systems. This shared task is the continuation of last year’s Dravidian-CodeMix shared task2 held at FIRE 2020. This year’s tasks included code-mixing at the intra-token and inter-token levels. Additionally, apart from Tamil and Malayalam, Kannada was also introduced. We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English. The top system for Tamil-English, Malayalam-English and Kannada-English scored weighted average F1-score of 0.711, 0.804, and 0.630, respectively. In summary, the quality and quantity of the submission show that there is great interest in Dravidian languages in code-mixed setting and state of the art in this domain still needs more improvement. © 2021 Copyright for this paper by its authors.

关键词： Sentiment analysis

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Urban Sensing Based on Mobile Phone data:Approaches,Applications,and Challenges

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 627-637页

作者： Mohammadhossein Ghahramani MengChu Zhou Gang Wang Insight Centre for Data Analytics University College DublinDublin 4Ireland IEEE Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyMacao 999078China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China China Telecom at Macao Macao 999078China

data volume grows explosively with the proliferation of powerful smartphones and innovative mobile *** ability to accurately and extensively monitor and analyze these data is *** concern in cellular data analysis is related to human beings and their *** to the potential value that lies behind these massive data,there have been different proposed approaches for understanding corresponding *** that end,analyzing people's activities,e.g.,counting them at fixed locations and tracking them by generating origindestination matrices is *** former can be used to determine the utilization of assets like roads and city *** latter is valuable when planning transport *** insights allow a government to predict the adoption of new roads,new public transport routes,modification of existing infrastructure,and detection of congestion zones,resulting in more efficient designs and *** data exploration can help research in various fields,e.g.,urban planning,transportation,health care,and business *** can also help organizations in decision making,policy implementation,monitoring,and evaluation at all *** work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone *** classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.

关键词： Big data analysis human mobility origin destination matrices smart infrastructure urban planning

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Reproducibility in Joint Blind Source Separation: Application to fMRI Analysis

Reproducibility in Joint Blind Source Separation: Applicatio...

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Asilomar Conference on Signals, Systems & computers

作者： Francisco Laport Trung Vu Hanlu Yang Vince Calhoun Tulay Adali Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD USA CITIC Research Center University of A Coruna A Coruña Spain Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA

Joint blind source separation (JBSS) techniques have been successfully applied for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. However, convergence in JBSS can be only guaranteed to a local optimum, since typically cost functions are non-convex. Also, iterative methods are usually implemented with random initialization for best performance, resulting in high variability, especially for more flexible solutions. Yet, the assessment of the reproducibility of JBSS has been limited in the literature, even though it has been demonstrated that when not taken into account, the solutions can be highly suboptimal. In this work, we propose a framework for the evaluation of the reproducibility of independent vector analysis, an important JBSS solution. We introduce a mechanism for selecting the model complexity that offers the most consistent and accurate solution, and demonstrate results to underline its importance using resting state fMRI data.

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Study of deep learning techniques for medical image analysis: A review

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Materials Today: Proceedings 2022年 56卷 209-214页

作者： Ayush Singhal Manu Phogat Deepak Kumar Ajay Kumar Mamta Dahiya Virendra Kumar Shrivastava Department of Computer Science Engineering Faculty of Engineering and Technology Shree Guru Gobind Singh Tricentenary University Gurugram 122505 Haryana India Department of Mechanical Engineering Faculty of Engineering & Technology Shree Guru Gobind Singh Tricentenary University Gurugram 122505 Haryana India Department of Big Data Analytics Adani Institute of Digital Technology Management Gandhinagar Gujarat 382423 India

Now, there is a lot of research going on in the field of medical image analysis by using deep convolutional networks. Deep learning uses various models to extract the information from the images provided to deep learning model. The deep learning is now widely used in the field of medical to detect and diagnose the disease and after diagnosing classifying it into particular category of the disease. The most widely model used for medical image analysis is Convolutional neural network. So, this review paper focusses on how deep learning uses deep networks to detect the disease by retrieving or extracting the information from the images provided to the network and also give information about the clinical applications in the medical fields and the limitations of deep learning in image analysis process is also highlighted.

关键词： Deep learning Medical image analysis Convolutional neural network

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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Hybrid genetic-discretized algorithm to handle data uncertai...

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作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U Rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

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Intrusion Detection and Prevention at the End-User Level in Smart Grid Cyber-Physical Systems

Intrusion Detection and Prevention at the End-User Level in ...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： N. V. Balaji Sachin Mittal K.K. Senthilkumar Gourav Sood Manjunatha R D.S. Wankhede Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Data Analytics and Mathematical Sciences JAIN Deemed to be University Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

In order to keep the electric power system running smoothly and reliably, it is constantly monitored and controlled by an intelligent cyber layer that consists of data processing algorithms and a wide network of sensors. Cyberattacks on this data processing and collecting system might halt operations and have catastrophic physical consequences like system breakdowns. A common kind of attack is the FDIA. To avoid detection by the state estimator's inherent anomaly detection capabilities, an attacker tries to trick the grid's underlying control system into producing disturbances. On top of that, there are major issues with customer-end security. For instance, criminals might intercept, alter, or replay wireless transmissions of power consumption signals sent by consumers to their utility provider. Consequently, smart grid security and privacy are major apprehensions. In order to prevent cyberattacks on the vital infrastructures of the electrical system, this thesis helps. New detection systems for the generating and transmission side, as well as the end-user customer side infrastructure, are the primary focus of the study. To protect the generating and transmission sides from cyber-attack, we provide a system based on intelligent sensor weights and an optimization strategy based on sophisticated grey wolves. In addition, to safeguard end-user customer data from cyberattacks, we provide a gaussian mixture model–based privacy protection strategy and an artificial neural structure–based intelligent loop detection method.

关键词： Wireless communication Privacy Wireless sensor networks Technological innovation data processing Smart meters Sensor systems Smart grids Security Intelligent sensors

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