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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Saha, Sourav Roy, Dwaipayan Goud, B. Yuvaraj Reddy, Chethan S. Basu, Tanmay Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Computational and Data Sciences Indian Institute of Science Education and Research Kolkata India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India

CLEF SimpleText 2022 lab focuses on developing effective systems to identify relevant passages from a given set of scientific articles. The lab has organized three tasks this year. Task 1 is focused on passage retrieval from the given data for a query text. These passages can be complex and hence require further simplification to be carried out in tasks 2 and 3. The BioNLP research group at the Indian Institute of science Education and Research Bhopal (IISERB) in collaboration with two different information retrieval research groups at IISER Kolkata and ISI Kolkata participated only in Task 1 of this challenge and submitted three runs using three different retrieval models. The paper explores the performance of these retrieval models for the given task. We used a standard BM25 model as our first run to identify 1000 relevant passages for each query. Moreover, the passages for each query were ranked based on their similarity scores generated by the BM25 model. For our second run, we used a BERT (Bidirectional Encoder Representations from Transformers) based re-ranking method, called as Mono-BERT to further rank the 1000 passages retrieved by our first run for each query. A pre-trained sequence to sequence model based re-ranking method, called MonoT5 was used as our third run to reorder the 1000 passages retrieved by the Mono-BERT model for each query. As the official results of this task are not yet announced, we cannot explore the performance of our submissions. However, we have manually checked the retrieved results of many queries for each run, which indicate that the performance improved from run 1 to run 2 and further to run 3. © 2022 Copyright for this paper by its authors.

关键词： Information retrieval

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The Prediction of Micro Plasma Impacts of Farm Fresh Vegetables Using Machine Learning

The Prediction of Micro Plasma Impacts of Farm Fresh Vegetab...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Maragatham, G. Sundaramoorthy, K. Maheswari, K. Sajana, T. Shiba, C. Mary Kumar, Ashok Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Tamil Nadu Chennai600100 India Department of Computer Science and Engineering Cmr Technical Campus Kandlakoya Telangana Hyderabad501401 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Business Systems R. M. K Engineering College Tamil Nadu Chennai601206 India Department of Computer Science Banasthali Vidyapith Rajasthan304022 India

ISBN: (纸本)9798350334357

In general, the innovative foods produced on fruit and vegetable based farms are always high quality and healthy. Indicators of fruit and vegetable consumption include plasma vitamin C and arytenoids, which are plant pigments discovered in blood samples. The researchers decided to utilize blood samples rather than the more common food frequency questionnaire in their investigation. to assess the amount of food consumed in order to forestall measuring errors and to establish dependencies. Because vitamin C and arytenoids may be found in a wide variety of fruits and vegetables, we can use them as objective measures of our consumption of these food groups. The fact that individuals who do not consume a diet that is abundant in fruits and vegetables do not consume significant quantities of vitamin C and arytenoids is reflected in the plasma levels of these individuals. In this paper a smart machine learning algorithm was proposed to predict the micro plasma impacts. This monitors the regular shape and harvesting of different farm fresh products and predicts the impacts of it. This will helpful for farmers to enhance the harvesting. © 2023 IEEE.

关键词： Machine learning

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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

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Mental Workload Classification from fNIRS Signals by Leveraging Machine Learning

Mental Workload Classification from fNIRS Signals by Leverag...

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2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023

作者： Hasan, M. Mahmud, M. Poudel, S. Donthula, K. Poudel, K. University of Memphis Department of Electrical and Computer Engineering TN United States Senior Software Engineer Optum Inc MurfreesboroTN United States Middle Tennessee State University Computational and Data Science MurfreesboroTN United States Middle Tennessee State University Computer Science MurfreesboroTN United States

ISBN: (纸本)9798350341256

Mental workload (MWL) identification is vital to know human cognitive functioning, performance, and well-being. In this work, we develop models for identifying low vs. high MWL using different genres of machine learning classifiers. We used non-invasive functional near-infrared spectroscopy (fNIRS) signals while participants classified the low vs. high levels of MWL tasks. Our analysis shows the low vs. high MWL can be identified best from the whole brain data. The k-nearest neighbors classifier showed the best performance with an accuracy of 98.8%, an area under the curve (AUC) of 98.8%, F1 score, precision, and recall of 98.0% from the whole brain data without overlapping signals. A separate hemisphere analysis using left hemisphere (LH) and right hemisphere (RH) activity showed that the LH activity has better classification ability than the RH activity. We also examined the classification with the top six features that could identify the low vs. high MWL with an accuracy of 97.4%, (AUC) 97.4%, F1 score, precision, and recall of 97.0%. These findings would be useful for developing more intuitive and user-friendly interfaces in the human-computer interface. © 2023 IEEE.

关键词： Infrared devices

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Invertibility of Discrete-Time Linear Systems with Sparse Inputs

Invertibility of Discrete-Time Linear Systems with Sparse In...

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IEEE Conference on Decision and Control

作者： Kyle Poe Enrique Mallada René Vidal Applied Mathematics and Computational Science Group University of Pennsylvania PA Mallada is with the Department of Electrical and Computer Engineering Johns Hopkins University MD Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control.

关键词： Linear systems Geometry Sufficient conditions Systematics Heuristic algorithms Standards

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Convolutional Neural Network-based image tamper detection with Error Level Analysis

Convolutional Neural Network-based image tamper detection wi...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Manjunatha S Swetha M D Rashmi S Ananda Kumar Subramanian Vinoth Kumar V Mallikarjuna Swamy S Department of Information Science and Engineering Global Academy of Technology Bengaluru India Department of Computer Science and Engineering BNM Institute of Technology Bengaluru India Department of Computer Science and Engineering (Data Science) Dayananda Sagar College of Engineering Bengaluru India Dept of Computational Intelligence School of Computer Science Engineering VIT – University Vellore India School of Computer Science Engineering and Information Systems VIT – University Vellore India Dept of Electronics and Communication Engineering JSS Academy of Technical Education Bengaluru India

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution.

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Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model

arXiv

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arXiv 2024年

作者： Sinhababu, Nilanjan Parry, Andrew Ganguly, Debasis Samanta, Debasis Mitra, Pabitra Centre for Computational and Data Sciences IIT Kharagpur India School of Computing Science University of Glasgow United Kingdom Department of Computer Science and Engineering IIT Kharagpur India

A supervised ranking model, despite its advantage of being effective, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning. This has motivated researchers to explore simpler pipelines leveraging large language models (LLMs) that are capable of working in a zero-shot manner. However, since zero-shot inference does not make use of a training set of pairs of queries and their relevant documents, its performance is mostly worse than that of supervised models, which are trained on such example pairs. Motivated by the existing findings that training examples generally improve zero-shot performance, in our work, we explore if this also applies to ranking models. More specifically, given a query and a pair of documents, the preference prediction task is improved by augmenting examples of preferences for similar queries from a training set. Our proposed pairwise few-shot ranker demonstrates consistent improvements over the zero-shot baseline on both in-domain (TREC DL) and out-domain (BEIR subset) retrieval benchmarks. Our method also achieves a close performance to that of a supervised model without requiring any complex training pipeline. © 2024, CC BY.

关键词： Zero-shot learning

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Online Affinity of Instructing Methods for Using Personal-Efficacy and Reading Capacious During Covid-19 5th

Online Affinity of Instructing Methods for Using Personal-Ef...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： PremaLatha, V. Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Bhoi, Akash Kumar Ghosh, Uttam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Guntur Vaddeswaram522502 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Data Science Meharry School of Computational Sciences Nashville NashvilleTN United States

ISBN: (纸本)9789811920035

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be "more expensive, difficult, and time-consuming". Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes "comprehension reading work" is treated as fundamental in the proposal of disclosures. In case of any, "the programme appeared to positively affect contributing students". Techniques of classification like "decision tree and Mixed Model database Miner (MMDBM)" are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive lice

关键词： Students

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Autonomous Robot Navigation in Unknown Terrains using Generative Adversarial Networks

Autonomous Robot Navigation in Unknown Terrains using Genera...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： P Chitra K Sridar Sudha Govindan Britto Raj S Akshya. J Mani Deepak Choudhry M. Sundarrajan Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology Chennai Department of Computer Science and Engineering Veerammal Engineering College Dindigul Department of Computer Science and Engineering Sona College of Technology Salem Department of Computer Science and Engineering RRASE College Of Engineering Chennai Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. These motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. This may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. This study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. This approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems.

关键词： Navigation Statistical analysis Machine learning Generative adversarial networks Path planning Real-time systems Generators Safety computational efficiency Autonomous robots

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Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video dataset

arXiv

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arXiv 2025年

作者： Panchal, Aneesh Khan, Kainat Katarya, Rahul Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore560012 India Big Data Analytics and Web Intelligence Laboratory Department of Computer Science & Engineering Delhi Technological University Delhi110042 India

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remains undiagnosed. In this paper, we worked on a methodology for detection of ASD from 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and using nature-inspired optimization algorithms for feature extraction from dataset. The proposed methodology involve the classification of ASD using supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients for finding the initial leading particle. This selection of particle significantly reduces the computation time and hence, improving the total efficiency and accuracy for the ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combination of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of 100% using random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academia fields, providing a foundation for future research and advancements in ASD diagnosis. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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