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2020 National Conference on Emerging Trends on Sustainable Technology and engineering Applications, NCETSTEA 2020

作者： Ghosh, Debjyoti Roy, Soumen Roy, Utpal Sinha, D.D. Visva-Bharati Department of Computer and System Sciences Santiniketan India University of Calcutta Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9781728143620

A strong and convenient identity verification method is always demanded. Nowadays, the advanced sensing features like gyroscope, accelerometer and rotation information are easily available through a smartphone. These prominent and hidden features (orientation and force) created simultaneously against any activities on a smartphone that can be used in user identity verification instead of considering morphological features (video and image). Two similarity measure algorithms (Euclidean and Manhattan) have been used on the collected dataset from 63 users in 5 sessions with 4 repetitions and we got 8.7% of EER (Equal Error Rate). This study also explains the data collection tools, data capture procedure and anomaly detection for further development in this topic area. © 2020 IEEE.

关键词： Smartphones

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Predicting Transcription Factor Binding Sites using Transformer based Capsule Network

arXiv

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arXiv 2023年

作者： Ghosh, Nimisha Santoni, Daniele Saha, Indrajit Felici, Giovanni Odisha Bhubaneswar751030 India Institute for System Analysis and Computer Science "Antonio Ruberti" National Research Council of Italy Via dei Taurini Rome00185 Italy Department of Computer Science and Engineering National Institute of Technical Teachers' Training and Research West Bengal Kolkata700106 India

Motivation: Prediction of binding sites for transcription factors is important to understand how they regulate gene expression and how this regulation can be modulated for therapeutic purposes. Although in the past few years there are significant works addressing this issue, there is still space for improvement. In this regard, a transformer based capsule network viz. DNABERT-Cap is proposed in this work to predict transcription factor binding sites mining ChIPseq datasets. DNABERT-Cap is a bidirectional encoder pre-trained with large number of genomic DNA sequences, empowered with a capsule layer responsible for the final prediction. Results: The proposed model builds a predictor for transcription factor binding sites using the joint optimisation of features encompassing both bidirectional encoder and capsule layer, along with convolutional and bidirectional long-short term memory layers. To evaluate the efficiency of the proposed approach, we use a benchmark ChIP-seq datasets of five cell lines viz. A549, GM12878, Hep-G2, H1-hESC and Hela, available in the ENCODE repository. The results show that the average area under the receiver operating characteristic curve score exceeds 0.91 for all such five cell lines. DNABERT-Cap is also compared with existing state-of-the-art deep learning based predictors viz. DeepARC, DeepTF, CNN-Zeng and DeepBind, and is seen to outperform them. Availability and implementation: The datasets used in this work along with code are available at https://***/NimishaGhosh/DNABERT-Cap/tree/main. © 2023, CC BY.

关键词： DNA sequences

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Algorithmic Trading Model for Stock Price Forecasting Integrating Forester with Golden Ratio Strategy

Algorithmic Trading Model for Stock Price Forecasting Integr...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Niranjana Govindan Bharathi Baskaran Azizul Azizan Fitri Yakub Inge Dhamanti C Dhaya Department of Computing Technologies Faculty of Engineering & Technology SRMIST Kattankulathur Chennai India Cyberphysical Research Group (Embedded AI & ML Science Lab) Faculty of Intelligence UTM Kuala Lumpur Malaysia Electronic System Engineering Department Malaysia-Japan International Institute of Technology UTM Kuala Lumpur Malaysia Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Department of Computer Science and Technology Adhiparasakthi Engineering College Melmaruvathur India

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model.

关键词： Adaptation models Machine learning algorithms Soft sensors Forestry Reinforcement learning Predictive models Feature extraction Data models Forecasting Testing

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Fully integrated hybrid microwave photonic receiver

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Photonics Research 2022年第6期10卷 1472-1483页

作者： JIACHEN LI SIGANG YANG HONGWEI CHEN XINGJUN WANG MINGHUA CHEN WEIWEN ZOU Beijing National Research Center for Information Science and Technology(BNRist) Department of Electronic EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Microwave photonic receivers are a promising candidate in breaking the bandwidth limitation of traditional radio-frequency(RF)*** further balance the performance superiority with the requirements regarding size,weight,and power consumption(SWa P),the implementation of integrated microwave photonic microsystems has been considered an upgrade ***,up to now,to the best of our knowledge,chip-scale fully integrated microwave photonic receivers have not been reported due to the limitation of material *** this paper,we report a fully integrated hybrid microwave photonic receiver(FIH-MWPR)obtained by comprising the indium phosphide(In P)laser chip and the monolithic silicon-on-insulator(SOI)photonic circuit into the same substrate based on the low-coupling-loss micro-optics *** from the integration of all optoelectronic components,the packaged FIH-MWPR exhibits a compact volume of 6 cmand low power consumption of 1.2 *** FIH-MWPR supports a wide operation bandwidth from 2 to 18 ***,its RF-link performance to down-convert the RF signals to the intermediate frequency is experimentally characterized by measuring the link gain,the noise figure,and the spurious-free dynamic range metrics across the whole operation frequency ***,we have utilized it as a de-chirp receiver to process the broadband linear frequencymodulated(LFM)radar echo signals at different frequency bands(S-,C-,X-,and Ku-bands)and successfully demonstrated its high-resolution-ranging *** the best of our knowledge,this is the first realization of a chip-scale broadband fully integrated microwave photonic receiver,which is expected to be an important step in demonstrating the feasibility of all-integrated microwave photonic microsystems oriented to miniaturized application scenarios.

关键词： method microwave bands

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Select Informative Samples for Night-Time Vehicle Detection Benchmark in Urban Scenes

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Remote Sensing 2023年第17期15卷 4310-4310页

作者： Wang, Xiao Tu, Xingyue Al-Hassani, Baraa Lin, Chia-Wen Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China School of Computer Science Wuhan University Wuhan430081 China Department of Electrical Engineering Industrial Technology Research Institute National Tsing Hua University Hsinchu30013 Taiwan

Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-Time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset. © 2023 by the authors.

关键词： Vehicles

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CoMiX: Cross-Modal Fusion with Deformable Convolutions for HSI-X Semantic Segmentation

arXiv

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arXiv 2024年

作者： Zhang, Xuming Gu, Xingfa Tian, Qingjiu Bruzzone, Lorenzo International Institute for Earth System Science Nanjing University Nanjing210023 China Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Department of Information Engineering and Computer Science University of Trento Trento38050 Italy

Pixel-wise semantic segmentation of hyperspectral images (HSIs) enables detailed and accurate analysis of complex scenes, benefiting various applications. Improving HSI semantic segmentation by exploiting complementary information from a supplementary data type (here denoted as X-modality) is promising but challenging due to differences in imaging sensors, image content, and resolution. Current cross-model fusion techniques have limitations in enhancing modality-specific and modality-shared information, as well as in capturing dynamic information interactions and fusion between different modalities. In response, this study proposes CoMiX, a cross-modal fusion framework with deformable convolutions (DCNs) for pixel-wise HSI-X semantic segmentation. CoMiX is an asymmetric encoder-decoder architecture designed to extract, calibrate, and fuse information from HSI and X modalities. Its pipeline includes an encoder with two parallel and interacting backbones and a lightweight all-multilayer perceptron (ALL-MLP) decoder. Within the encoder, four stages are deployed, each incorporating 2D DCN blocks for the X-model to adapt to geometric variations, and 3D DCN blocks for HSIs to adaptively aggregate spatial-spectral features. Additionally, each stage includes a Cross-Modality Feature enhancement and eXchange (CMFeX) module and a feature fusion module (FFM). CMFeX is designed to exploit spatial and spectral correlations from both modalities to recalibrate and enhance modality-specific and modality-shared features while adaptively exchanging complementary information between them. Outputs from CMFeX are fed into the FFM for fusion and passed to the next stage for further information learning. Finally, the outputs from each FFM are integrated by the ALL-MLP decoder for final prediction. Extensive experiments demonstrate that our CoMiX achieves superior performance and generalizes well to various multimodal recognition tasks. The CoMiX code will be released. Copyright © 2024, Th

关键词： Semantic Segmentation

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An Effective Random Forest based Long-Term Wind Power Forecasting Technique

An Effective Random Forest based Long-Term Wind Power Foreca...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Jannet Jamii Mohamed Trabelsi Majdi Mansouri Mohamed Faouzi Mimouni Laboratory of Automatic Electrical System and Enviroment (LAS2E) National Engineering School Monastir Monastir Tunisia Engineering Department Electronic and Communications Kuwait College of Science and Technology Kuwait City Kuwait Electrical and Computer Engineering Department Texas A&M University at Qatar Doha Qatar Electrical Engineering Department Laboratory of Automatic Electrical System and Enviroment (LAS2E) National Engineering School Monastir Monastir Tunisia

Accurate Wind Power Forecasting (WPF) has become an essential task for grid operators due to its ability to minimize the effect of wind power intermittency and the need of energy storage deployment. In this paper, a Random Forest (RF)-based long-term WPF technique is proposed. The model inputs consist of some meteorological parameters such as wind speed, temperature, and pressure. The input data are selected from the data NOAA’S Surface Radiation (SURFRAD) network at Bondville station (USA). The RF parameters (number of trees, number of leaves) are optimized to get the best prediction accuracy. The normalized RMSE and MAE metrics are computed to assess the performance of the proposed RF model.

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Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

Memory-Based Contrastive Learning with Optimized Sampling fo...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

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A structural equation modeling framework for exploring the industry 5.0 and sustainable supply chain determinants

Supply Chain Analytics

引用

Supply Chain Analytics 2024年 6卷

作者： Jamil, Md. Asfaq Mustofa, Ridwan Hossain, Niamat Ullah Ibne Rahman, S.M. Atikur Chowdhury, Sudipta Department of Industrial Engineering and Management Khulna University of Engineering & Technology Khulna 9203 Bangladesh Engineering Management Department College of Engineering and Computer Science Arkansas State University 72467 AR United States Department of Industrial Manufacturing and System Engineering University of Texas at El Paso 79968 TX United States Department of Mechanical and Industrial Engineering Marshall University Huntington 25755 WV United States

Sustainable Supply Chain and Industry 5.0 are two important concepts reshaping how businesses operate in the modern world. Together, these two concepts drive the advancement of a highly sustainable and robust worldwide economy. Companies are now becoming more sustainable in supply chain management, using technologies like blockchain and co-bots to track the origin of goods, ensure ethical and sustainable sourcing, and work with humans safely and effectively. This study develops a theoretical model highlighting the determinants of Industry 5.0, Sustainable Supply Chain Practices, by combining theoretical frameworks from the manufacturing, supply chain, and information systems literature. The study's analytic sample comprises 342 responses collected from professionals working in the electronics industry's supply chain. Hypotheses were constructed employing deductive reasoning, leveraging insights gleaned from prior research. The study is conducted utilizing the Structural Equation Modeling (SEM) to substantiate the presumed connections among various constructs, namely, Industry 5.0 innovations, Sustainable Supply Chain Practices (SSCP), Sustainable Supply Chain Performance (SCP), and Supply Chain Risks (SCR). The Structural Equation Modeling analysis results show a direct impact of Industry 5.0 technologies through Sustainable Supply Chain Practices can enhance Supply Chain Performance and mitigate Supply Chain Risks. Combining the two paradigms can foster the development of new business models that prioritize sustainability and contribute to a more equitable and environmentally friendly economy that brings positive change for both businesses and society. © 2024 The Authors

关键词： Industry 5.0 Statistical Analysis Structural Equation Modelling Supply Chain Performance Supply Chain Risks Sustainable Supply Chain

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Chatbot Persona Selection Methodology for Emotional Support

Chatbot Persona Selection Methodology for Emotional Support

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SICE Annual Conference

作者： Akihiro Yorita Simon Egerton Carina Chan Naoyuki Kubota Department of Engineering Kwansei Gakuin University Hyogo Japan Department of Computer science & IT La Trobe University VIC Australia Department of Psychology Counselling and Therapy La Trobe University VIC Australia Department of System Design Tokyo Metropolitan University Tokyo Japan

Self-help groups are one way to cope with mental illness. Here, people in similar circumstances come together to provide peer support and help each other. However, some people are not comfortable using such groups. Therefore, it is necessary to investigate the possibility of using robots or other means of support. There are two types of identity: social identity and personal identity. Personal identity is defined as a kind of personality. Social identity is like a persona, a profile, such as a job or position. So far, we have suggested implementing personalities as chatbot identities and adapting the communication method to the user. However, using personalities alone would result in bland content. Therefore, we implement social identity in chatbots and use the chatbot’s personal information to enable the chatbot to speak in a way that is sympathetic to the user.

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