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Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks

Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Netw...

引用

Quantum computing and Engineering (QCE), IEEE international conference on

作者： Nico Meyer Christian Ufrecht Maniraman Periyasamy Axel Plinge Christopher Mutschler Daniel D. Scherer Andreas Maier Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Nürnberg Germany Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum computer simulation software is an integral tool for the research efforts in the quantum computing community. An important aspect is the efficiency of respective frameworks, especially for training variational quantum algorithms. Focusing on the widely used Qiskit software environment, we develop the qiskit-torch-module. It improves runtime performance by two orders of magnitude over comparable libraries, while facilitating low-overhead integration with existing code-bases. Moreover, the framework provides advanced tools for integrating quantum neural networks with PyTorch. The pipeline is tailored for single-machine compute systems, which constitute a widely employed setup in day-to-day research efforts.

关键词： Training Quantum computing Runtime Quantum algorithm Computer simulation Neural networks Pipelines Focusing software Libraries

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The RSDS: A Current State and Future Plans

The RSDS: A Current State and Future Plans

引用

international Joint conference on Rough Sets (IJCRS)

作者： Suraj, Zbigniew Grochowalski, Piotr Rzeszow Univ Inst Comp Sci Rzeszow Rzeszow Poland

ISBN: (纸本)9783030873349;9783030873332

This paper provides a brief overview of the Rough Set Database System (the RSDS for short) for creating bibliographies on rough sets and related fields, as well as sharing and analysis. The current version of the RSDS includes a number of modifications, extensions and functional improvements compared to the previous versions of this system. The system was made in the client-server technology. Currently, the RSDS contains over 38 540 entries from nearly 42 860 authors. This system works on any computer connected to the Internet and is available at http://***.

关键词： Rough set soft computing Ontology Data mining Knowledge discovery pattern recognition Machine learning Database system

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Shear Stress Estimation in Compound Channel Using M5 Tree and XGBoost soft computing Techniques 28th

Shear Stress Estimation in Compound Channel Using M5 Tree an...

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28th international conference on Hydraulics, Water Resources, River and Coastal Engineering, HYDRO 2023

作者： Tripathi, Neil Sandilya, Shashank Shekhar Das, Bhabani Shankar Devi, Kamalini Khuntia, Jnana Ranjan NIT Patna Patna800005 India CBIT Hyderabad Hyderabad India NIT Warangal Warangal India

ISBN: (纸本)9789819780341

Compound channels, commonly found in natural river systems, typically consist of a main channel alongside one or two floodplains. In prismatic compound channels, water levels remain uniform, whereas non-prismatic ones exhibit a more complex and non-uniform flow pattern. The interaction between the main channel and floodplain generates shear stresses due to momentum transfer, but predicting their distribution along the boundary is challenging as it depends on various factors like velocity distribution, cross-section shape, and boundary roughness. This study aims to develop a model to predict shear stress in non-prismatic compound channels using two machine learning (ML) techniques: M5 tree and extreme gradient boosting (XGBoost). It considers geometric, flow, and roughness parameters such as width ratio, relative flow depth, flow aspect ratio, and bed slope. M5 tree offers transparent formulas that enhance understanding, while XGBoost, known for handling structured data well, is a powerful gradient-boosting algorithm. Both models utilize correlated physical channel characteristic variables to predict shear stress. The predictions from both M5 tree and XGBoost models demonstrate satisfactory performance, with coefficients of determination (R2) exceeding 0.85 and mean absolute percentage errors (MAPE) below 12% for both training and testing datasets. However, the XGBoost model generally outperforms the M5 tree model in predicting shear stress across different sections of non-prismatic compound channels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Shear stress

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Robotics Process Automation Technologies for Data Collection and Analysis

Robotics Process Automation Technologies for Data Collection...

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international conference on computing and Networking Technology (ICCNT)

作者： K Usman Tm Hayath Mohammed Shafiulla Dadapeer Dept. of Computer Science and Engineering Ballari Institute of Technology and Management Ballari Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Data collection and analysis are critical aspects of various business processes. However, these tasks can be time-consuming, prone to errors, and delays employee productivity when done manually, especially when we have to deal with huge data. Robotic Process Automation (RPA) technologies offer a promising solution in form of a BOT, by automating tedious tasks related with data handling. A survey by The Economist corporation believe that automation kick launches digital transformation, robotic process automation (RPA) has evolved from an emerging technology to a must-have digital transformation tool for enterprises to compete globally. An increasing number of companies are now using RPA to automate normal business processes in a non-intrusive and cost-effective way. On the other hand, decision-making, pattern recognition, and many other business-related tasks benefit greatly from the use of big data and analytics. This paper explores the application of RPA in data collection and analysis, examining its functionalities, benefits, and limitations. In practical, Bot produces error-free work and takes 94.44% less time for data collection compared to a manual process, which encourages all firms to adopt to RPA technologies.

关键词： Intelligent automation Surveys Productivity Digital transformation Decision making Manuals Chatbots pattern recognition Delays Business

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Gait pattern recognition via Fusing Static and Dynamic Features in Single Stream Network

Gait Pattern Recognition via Fusing Static and Dynamic Featu...

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international conference on computing and Networking Technology (ICCNT)

作者： Isha Jain Navya Dharini Reddy Vijay Kumar Pandey Tushar Sandhan EE PIL IIT-K Kanpur INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Gait refers to the walking and motion characteristics of an individual. In this paper, we suggest a unique method for analyzing human gait patterns using static as well as dynamic features passing through the same convolutional neural network. Generally, the gaits are processed using contour detection and segmentation to perform time series analysis with extracted features. Furthermore, machine learning models such as decision trees, support vector machine are also utilized for the gait classification. We initially trained our model on both cropped average images and optical flow of each gait independently. Subsequently, we fused the average image of each gait (building static information) with its corresponding optical flow (dynamic information). This novel fusion approach facilitated the training of a single stream convolutional neural network model. Our findings reveal a notable accuracy of ${9 8. 7 5 \%}$ in successfully recognizing individuals based on their gait patterns. Simplicity of the model and succinct fusion of static and dynamic features produce improved results with very less computational time.

关键词： Training Support vector machines Computational modeling Motion segmentation Time series analysis Streaming media Feature extraction pattern recognition Convolutional neural networks Optical flow

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Sequential pattern Analysis of Emotion in Speech With LSTM

Sequential Pattern Analysis of Emotion in Speech With LSTM

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Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： Deepak Upadhyay Ananya Tiwari Himanshi Niathani Dept.of CSE Graphic Era Hill University Dehradun India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

Automatic speech emotion recognition poses great challenges due to the large differences between acoustic properties and human perception. its performance relies on a variety of acoustic discriminations suited to specific tasks. it makes things difficult when people share different opinions. speech emotions have different strengths with different sounds at different points in the middle phase. therefore, speech recognition has become a difficult task in computer processing. this article introduces deep learning techniques and discusses some recent literature applying these techniques to speech recognition. in this context, the recurrent neural network (rnn) algorithm has become the basis of speech recognition. the classifers used to detect emotions like happiness, surprise, anger, neutral state, sadness, etc. the foundation of the speech emotion recognition system lies in a database comprising emotional speech samples. extracted from these samples are key features such as energy, pitch, and mel frequency cepstrum coefficient (mfcc). the evaluation of classification performance is contingent on the analysis of these extracted features. it includes different models for emotion recognition that use separate components to distinguish emotions. materials needed to train emotional speech may include speech patterns. the librosa package helps remove obtrusive features from the speech model. the key to classification is based on the quality of extracted features. finally, through this process we can identify and analyze the emotional content embedded in a speech signal.

关键词： Emotion recognition Analytical models Recurrent neural networks Cepstrum Speech recognition Feature extraction Speech processing

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A Comparative Study of Datasets for Cyber-attacks Detection in Wireless Sensor Networks

A Comparative Study of Datasets for Cyber-attacks Detection ...

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computing and Machine Intelligence (ICMI), international conference on

作者： Shereen Ismail Diana W. Dawoud Hassan Reza School of Electrical Engineering and Computer Science University of North Dakota ND USA College of Engineering and Information Technology University of Dubai Dubai UAE

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Machine Learning (ML) is one of the effective security approaches to build cyber-attacks detection systems in Wireless Sensor Networks (WSNs). ML leverages the power of data analysis and pattern recognition to detect and classify various types of cyber-attacks to enhance the security of WSN s. A well-constructed dataset is one of the key factors that significantly impact the performance and generalization capabilities of any ML classifier trained on it. In this paper, we evaluate the effectiveness of two datasets: WSN-DS and WSN-BFSF which are specialized for Denial-of-Service (DoS) attacks targeting WSNs. We compare the two datasets in terms of their key characteristics, dataset quality, and ML classification performance. Mutual Information (MI) and Recursive Feature Elimination (RFE) are used for feature selection. The dataset quality is measured using statistical information calculation. The ML classification performance is investigated for three supervised ensemble techniques: LightGBM, bagging, and stacking using evaluation metrics including probability of detection, probability of false alarm, probability of misdetection, classification accuracy, model size, and processing time.

关键词： Measurement Wireless sensor networks Time division multiple access Stacking Probability Feature extraction pattern recognition

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Enhancing 3D Referential Grounding by Learning Coarse Spatial Relationships 27th

Enhancing 3D Referential Grounding by Learning Coarse Spati...

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27th international conference on pattern recognition, ICPR 2024

作者： Joshi, Soham Tripathi, Aditay Gopalakrishnan, Viswanath Chakraborty, Anirban International Institute of Information Technology Bangalore India Indian Institute of Science Bengaluru India

ISBN: (纸本)9783031781124

Large-scale pre-training is commonly used in 2D referential grounding tasks owing to the easy availability of a large number of image-text pairs with corresponding bounding box annotations. However, for 3D referential grounding, the unavailability of high-quality 3D scene-text pairs with annotations poses a significant challenge. To address this issue, we leverage the large corpus of 3D scenes with bounding box annotations of object instances and design an automated strategy to synthesize scene-text data for pre-training by utilising the coarse spatial relationships between the objects in the scene without any human supervision. The proposed strategy first clusters the 3D bounding boxes and then uses these clusters to create pairwise and triplet relations between the objects in the 3D scene. We achieved improved results consistently across various top-performing methods in 3D referential grounding, when the proposed pre-training strategy is deployed. In addition to pre-training with the samples containing coarse spatial relations, we also encode semantic relationships between the bounding boxes conditioned on the language utterance, using a compatibility measure between the box features and the language utterance. To evaluate the performance of our proposed techniques, we conduct experiments on large-scale publicly available datasets, namely ScanRefer and ReferIt3D (SR3D and NR3D). Our proposed techniques can be seamlessly integrated with any off-the-shelf 3D referential grounding method. Specifically, when integrated with BUTD-DETR, we observed notable improvements of 2.2% and 1% in performance on the SR3D and NR3D datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Food Nutritional Detection Personalized Dietary Recommendations for Health Monitoring using Image Processing with Machine Learning

Food Nutritional Detection Personalized Dietary Recommendati...

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international conference on ent computing and Control Systems (ICICCS)

作者： M. Venkateswarlu M. Vinay M. Likhitha G. Shivaramu Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

A good nutrition monitoring that involves promoting healthy lifestyle and dietary challenges. In this paper, we propose an AI driven nutrition monitoring system utilizing deep learning, computer vision, and natural language processing to handle dish detection, freshness analysis and ingredient recognition. The proposed system takes into account the accuracy of food classification using a fine-tuned convolutional neural network, OpenCV based techniques to measure ingredient quality, and NLP to pull out detailed nutritional information. The system applies real time feedback and actionable insights to the limitations of manual logging and traditional dietary tracking. Built with a modular architecture, it is adaptable to different cuisines, and dietary needs, and can be used by individuals as well as healthcare professionals. One of the innovations of such an approach is to make nutrition management easier, to promote healthier eating habits, and to create a launchpad for future personalized health monitoring advances.

关键词： Deep learning Computer vision Technological innovation Shape Medical services Manuals Natural language processing Real-time systems pattern recognition Monitoring

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A resultant of Local Binary pattern Histogram (LBPH) and Speeded up Robust Feature (SURF) algorithm for face recognition 12

A resultant of Local Binary Pattern Histogram (LBPH) and Spe...

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12th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2021

作者： Pandey, Durgesh Kushwaha, Nitin Srivastava, Kamal Department of Computer Engineering Ambalika Institute of Management and Technology Lucknow India

In the time of increasing crime Face recognition is very significant with regards to computer vision, security, monitoring, reconnaissance, pattern checking, neural network, real-time video processing, etc. Face is non- interfering solid Biometrics for distinguishing people and subsequently goons consistently attempt to cover their faces by various fake methods, for example, plastic surgery, mask, etc. The accessibility of an exhaustive face database is vital to test the working of these algorithms of face recognition. Though, we-tested freely accessible face databases as well as self-created one containing face pictures with a wide assortment of postures, light, signals and face impediments. The contribution of this research paper is Comparing Local Binary pattern Histogram (LBPH) and Speeded Up Robust Feature (SURF) algorithms and finding the best among them. © Grenze Scientific Society, 2021.

关键词： Local binary pattern

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