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computer science and Emerging Technologies (CSET), International Conference on

作者： Gyanendra Tiwary Shivani Chauhan Krishan Kumar Goyal Computer Science and Engineering Bhagwant University Ajmer India Computer Application Department Raja Balwant Singh Management Technical Campus Agra India

Millions of people all around the world suffer from depression, which is a common mental health issue. Effective intervention and therapy depend on early and precise identification of depression. Multimodal techniques that incorporate audio and video data have recently demonstrated promising outcomes in the detection of depression. In this article, authors suggest a convolutional neural network (CNN) model for multimodal depression identification based on audio and video. The proposed model makes use of audio and visual elements to gather comprehensive and complementary data about depression. Pitch, intensity, and spectral information, among others, are derived from voice recordings for the audio modality. The video modality uses face landmarks, optical flow, and pose estimation techniques to combine facial expressions, head movements, and body gestures. The parallel branches of the CNN architecture process the audio and visual inputs individually. To learn discriminative audio representations, the audio branch applies three convolutional layers, followed by pooling and dense layers. To extract video-specific information, the video branch uses five convolutional layers with different filter widths and depths, followed by fully connected and pooling layers. Further a late fusion technique has been adopted for multimodal fusion, concatenating learned features from the two modalities and passing them through additional thick layers for depression prediction. Authors also used regularization strategies, including dropout and batch normalization, during training to alleviate the overfitting problem. Authors evaluated the proposed multimodal CNN model on a DAIC-WOZ dataset consisting of audio and video recordings of individuals with and without depression. The proposed model achieved 77% accuracy and hence demonstrate that the proposed multimodal CNN model achieves superior performance compared to unimodal approaches.

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Joint Visual Perception and Linguistic Commonsense for Daily Events Causality Reasoning

Joint Visual Perception and Linguistic Commonsense for Daily...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Ma, Bole Tong, Chao School of Computer Science and Engineering Beihang University China Beihang University State Key Laboratory of Software Development Environment China Yunnan Key Laboratory of Blockchain Application Technology China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Multimodal networks that juxtapose visual and linguistic modalities are currently widely adopted for solving vision-and-language tasks. They perform well in simple and intu-itive tasks, but are prone to mistakes in tasks involving latent or implicit details, due to the difficulty of capturing crucial but imperceptible visual signals in the real world. Perception errors lead to nonsensical results, but can be corrected by commonsense knowledge. To this end, we combine visual perception and linguistic commonsense to solve the challenging daily events causality reasoning task. We propose a novel Object-Aware Reasoning Network to focus on object inter-action while ignoring distracting information to refine visual perception. Further, a language branch with an independent prediction head is supervised to learn causality commonsense to help correct obvious perception errors, resulting in more plausible conclusions. Extensive experiments demonstrate that our method achieves new state-of-the-art results on Vis-Causal dataset. © 2022 IEEE.

关键词： Linguistics

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A Machine-Learning Approach to Time Series Forecasting of Temperature 6

A Machine-Learning Approach to Time Series Forecasting of Te...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Pant, Janmejay Sharma, Rakesh Kumar Juyal, Amit Singh, Devendra Pant, Himanshu Pant, Puspha Graphic Era Hill University Department of Computer Science and Engineering Bhimtal Campus India Pal College of Technology and Management Department of Computer Application Haldwani India Graphic Era Hill University Department of Computer Application Dehradun India M.B. Govt. P.G College Kumaun University Department of Geography Haldwani India

ISBN: (纸本)9781665482714

In the modern world, weather forecasting is an essential application. The forecasts can help us reduce weather- related losses. The need for a massive data and highly computationally intensive parameterization procedure can be eliminated or reduced by the use of machine learning and deep learning algorithms for forecasting. This research work intends to forecast the temperature of three cities (Dehradun, Mukteshwar and Pantnagar) of Uttarakhand using emerging time series model AR/MA (Auto Regressive Integrated Moving Average). The results of this study prove that using the auto ARIMA produces very less MAPE (Mean Absolute Percentage Error) score for all testing data of all three regions. Our used model produces 8.45%,9.65% and 5.64% mean absolute percentage error in temperature data for Dehradun, Mukteshwar and Pantnagar respectively. Hence this paper explains briefly how different parameters can be used to formulate the AR/MA model to predict temperature. MAPE (Mean Absolute Percentage Error) indicates that auto AR/MA model yields excellent results. © 2022 IEEE.

关键词： Time series

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Analytical Evaluation of Web Performance Testing Tools: Apache JMeter and SoapUI

Analytical Evaluation of Web Performance Testing Tools: Apac...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Vatsya Tiwari Sachin Upadhyay Jayati Krishna Goswami Sanjiv Agrawal Department of Computer Science & Applications BSA College of Engineering & Technology Mathura Deparmtnent of Computer Engineering & Application GLAUniv. Mathura Computer Sciecnce & Engineering G L BAJAJ Group of Institutions Mathura

Computing on the Web is based on distributed system applications that use the client-server architecture. Since web-based applications have been beta-tested, the quality criteria cannot be fixed. Whenever a website is launched, the question is always raised as to how many HTTP requests of users can the website handle at once. As part of this paper, various tools for performance testing will be used, including Apache SoapUI, JMeter, etc., and selection tools for load testing will be discussed.

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Classification of Online Sentiment Data tweets using Various Approaches in Machine Learning

Classification of Online Sentiment Data tweets using Various...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Vandana Rawat Paritosh Bisht Suryakant Shruti Kothiyal Neelam Singh Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering IFP Energies Nouvelles (IFPEN) France

Online data is the data that is present on an online platform such as social media. Social media contains various information through which it can analyze users' attitudes & behavior. Sentimental analysis enables the automatic analysis of expression whether it is positive or negative. It can be about places, people, events, organizations, brands, products, etc. By using tools provided by machine learning & natural language processing with different approaches sentiment analysis can be made possible. The ubiquitous use of the Online World has given people a new method for expressing their emotions. It also functions as a platform with a shedload of knowledge where viewers may examine other users' thoughts, which are categorized into so many sentiment classifications and have become more and more essential in judgment. The representation of the number of comments that contain complex multidimensional sentiments that are either good or bad or anything in between the two can really be analyzed by means of this paper's approach to sentiment classification for consumer review classification. In this research, sentiment contribution in the field of online data tweets are collected, and using various machine learning algorithms results are identified as collective actions using different accuracy.

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Text-Guided Image Generation Method Based on Cross-Modal Fashion Style Learning

Text-Guided Image Generation Method Based on Cross-Modal Fas...

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Electronic engineering and Signal Processing (EESP), International Conference on

作者： Wanhui Zhang Li Liu Xiaodong Fu Lijun Liu Wei Peng Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China

ISBN: (数字)9798331520632

ISBN: (纸本)9798331520649

In the interdisciplinary field spanning computer vision and art, text-guided image generation techniques have garnered significant attention, particularly in the realms of virtual try-ons and fashion design. Current approaches to text-guided image generation primarily utilize latent space encoding and alignment of text-image feature spaces, which prove effective in general scenarios. However, when tasked with generating images possessing specific fashion attributes, these methods often result in semantic inconsistencies and missing details in the generated clothing images. To address this challenge, this work presents an effective text-guided image generation method based on cross-modal fashion style learning. First, the fashion text encoding module is constructed to represent the input text description as global-local text descriptors to obtain fashion features. Then, a clothing image style representation module is designed to extract coarse-grained, fine-grained, and hierarchical latent features from the input texture patterns and clothing images. Finally, a cross-modal fashion style learning block, integrating the fashion mapping network and the generative adversarial network, is utilized to reinforce semantic consistency and yield clothing images with intricate details. Extensive experiments conducted on the Viton-HD dataset validate that the proposed method significantly outperforms other state-of-the-art approaches, achieving an initial IS score of 5.22 and a Fréchet Inception Distance (FID) of 16.35. Ablation studies and comparative analysis further demonstrate the potential application of our method, specifically in improving the semantic consistency between fashion texts and generated images, as well as enhancing fashion details in the generated images.

关键词： Visualization Technological innovation Image segmentation Image synthesis Clothing Semantics Text to image Signal processing Feature extraction Encoding

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A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry

A Machine Learning Approach to Compare Causal Inference Mode...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sanskriti Sanjay Kumar Singh Apoorv Kumar Sinha Trilok Nath Pandey Biswa Mohan Acharya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Application ITER SOA University Bhubaneswar India

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

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A multi-view comparative learning method for spatial transcriptomics data clustering

A multi-view comparative learning method for spatial transcr...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Wei Zhang, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350337488

Clustering individual cells or spots based on their gene expression profiles in a spatial context is a powerful approach to uncovering the underlying biological diversity and relationships among cells. The intricate information within spatial transcriptomics data demands sophisticated algorithms that effectively integrate gene expression, cell position, and tissue image data for accurate cell or spot clustering. This work proposes a Multi-View Comparative Learning method for clustering Spatial Transcriptomics data (MVCLST). MVCLST first builds on two data views using gene expression profiles, cell space coordinates, and image features. Then it employs four different encoders to capture the common and private features of the two views. The model employs a contrastive learning loss to encourage effective interaction between the two views and ensure feature consistency. The shared and private features from both views are fused using corresponding decoders. Finally, the model employs the Leiden algorithm for downstream clustering of the learned features. We test the MVCLST method on a human dorsolateral prefrontal cortex dataset. The results show that MVCLST outperforms other state-of-the-art methods in most cases. Additionally, the clusters identified by MVCLST align closely with manual annotations and established neuroscience definitions. © 2023 IEEE.

关键词： contrast learning multiview learning spatial transcriptome data clustering

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Visualization and visual analysis of vessel trajectory data: A survey

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Visual Informatics 2021年第4期5卷 1-10页

作者： Haiyan Liu Xiaohui Chen Yidi Wang Bing Zhang Yunpeng Chen Ying Zhao Fangfang Zhou Institute of Data and Target Engineering Information Engineering UniversityZhengzhouChina School of Computer Science and Engineering Central South UniversityChangshaChina Rail Data Research and Application Key Laboratory of Hunan Province ChangshaChina

Maritime transports play a critical role in international trade and *** vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel *** and understanding these patterns are valuable for maritime traffic surveillance and *** essential techniques in complex data analysis and understanding,visualization and visual analysis have been widely used in vessel trajectory data *** paper presents a literature review on the visualization and visual analysis of vessel trajectory ***,we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data ***,we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in ***,we expound on the prospects of the remaining challenges and directions for future research.

关键词： Maritime traffic Vessel trajectory data Automatic identification system Visualization and visual analysis

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A Note On Lookahead In Real Life And Computing

arXiv

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arXiv 2024年

作者： Sharma, Burle Mohanty, Rakesh Panda, Sucheta Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla India Department of Computer Application Veer Surendra Sai University of Technology Odisha Burla India

Past, Present and Future are considered to be three temporal and logical concepts which are well defined by human beings for their existence and growth. We, as human beings, have the privilege of using our intelligence to mentally execute an activity before physical occurrence of the same in the real world. Knowledge of the past, aplomb of present and visualisation for the future correspond to three concepts such as look-back, look-at and look-ahead respectively in real life as well as in diversified domains of computing. Look-Ahead(LA) deals with the future prediction of information and processing of input to produce the output in advance. In this article, our main objective is to learn, understand and explore the concept of LA and design novel models as solution for real world problems. We present three well known algorithmic frameworks used in practice based on availability of input information such as offline, online and semi-online. We introduce interesting real life applications and well known computing problems where LA plays a significant role for making a process, system or algorithm efficient. We define new types of LA and propose a taxonomy for LA based on literature review for designing novel LA models in future. Using the concept of LA, We identify and present many interesting and non-trivial research challenges as future potential research directions. Intuitively, we observe that LA can be used as a powerful tool and framework for future researchers in design of efficient computational models and algorithms for solving non-trivial and challenging optimization problems. © 2024, CC BY.

关键词： Computational efficiency

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