Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022...
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The paper is devoted to the study of the neural networks inference acceleration using the weights quantization and Intel OpenVINO Toolkit. At the same time, the study considers block architecture convolutional network...
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Effective blood glucose forecasting is crucial for detecting events such as hypo- or hyperglycemia in people with diabetes, yet remains challenging in domains with only small, heterogeneous datasets, such as in the pe...
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Based on the study of cellular aging using the single cell model organism of budding yeast and corroborated by other studies, we propose the Emergent Aging Model (EAM). EAM hypothesizes that aging is an emergent prope...
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Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend o...
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Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. This threat can be prevented by applying blockchain technology to the database that has been used. Therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. The system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. This system was tested using Katalon and Wireshark software to perform throughput testing and man-in-the-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. The system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. The results of the test show that the system is safe from man-in-the-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds.
Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances de...
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In this paper, we investigate the iterative solution of a sequence of graph Laplacian systems by leveraging the (relative) continuity of the generalized inverse operator. Our primary objective is to extend traditional...
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ISBN:
(数字)9798331521165
ISBN:
(纸本)9798331521172
In this paper, we investigate the iterative solution of a sequence of graph Laplacian systems by leveraging the (relative) continuity of the generalized inverse operator. Our primary objective is to extend traditional matrix perturbation techniques specifically to the context of graph Laplacian systems. We achieve this by modeling the evolution of these systems starting from a given initial graph Laplacian and applying a series of systematic updates. We propose a novel framework that formulates an iterative method for solving the targeted graph Laplacian system based on the solutions of simpler, earlier Laplacian systems. By establishing a relationship between the solutions of two Laplacian systems, we derive a recursive formula that enables efficient computation of the updated solutions. This approach not only enhances computational efficiency but also maintains accuracy, as it utilizes the inherent structure of the graph Laplacian, including its positive semi-definiteness and the zero-mean condition of the right-hand side.
Anomaly detection refers to recognition of events different from normal ones for example road accident, fight, robbery, arsenal etc. Anomaly identification in real world surveillance videos is an important application...
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ISBN:
(数字)9798331518578
ISBN:
(纸本)9798331518585
Anomaly detection refers to recognition of events different from normal ones for example road accident, fight, robbery, arsenal etc. Anomaly identification in real world surveillance videos is an important application of computer vision. The work proposed in the paper detects anomalous events in surveillance videos dataset and is based upon semi supervised deep learning model. We trained the model using UCF Crime dataset that consists of 950 normal videos and 950 anomalous videos. The anomaly videos in the dataset consists of 13 different types of anomalies such as Abuse, arrest, explosion, fight etc. that generally occur in real life. The anomaly in the dataset is labelled at video level and not at a specific frame in a video to define the semi supervised nature of learning paradigm. The extracted 3D features from dataset are fed into the multilayered deep learning model. Experimental results show that our approach has significant improvement over state-of-the-art approaches for anomaly detection in surveillance videos. The accuracy of model comes out to be 83.96 percent, that is improvement over other methods.
High power costs and end-use electricity usage are driving energy consumption optimization and data-driven demand-side management. The consumer action and the approach used to energy management have a significant impa...
High power costs and end-use electricity usage are driving energy consumption optimization and data-driven demand-side management. The consumer action and the approach used to energy management have a significant impact on how well these systems work. It is crucial to provide comprehensive energy-consumption information models down to the appliance level to educate the public and encourage them to adopt more efficient energy habits. The Home Energy Management System's (HEMS) primary objective is to cultivate an energy-efficient environment that controls IoT devices via its network. With HEMS, customers may adjust their use to day-to-day price fluctuations, thus reducing energy expenses. expressive data-mining methods to create a numerical model that provides users with data about their home appliances. This data includes the number and length of operations, energy consumption over different periods based on 15-minute monitoring data, and the ability to disaggregate cycles for appliances with cyclic operation. Calibration and validation of the model were performed on two datasets acquired by ENEA via actual monitoring of the Italian home. Subsequent testing across many appliances demonstrated successful analysis of energyingesting decorations. Consequently, it has been included in the ENEA-developed DHOMUS IoT stage, which tracks and analyses residential energy use to raise public participation and knowledge about this issue. Encourage end users to make more ethical and sustainable energy use decisions and meet the current European Council Regulation (EU) 2021/1853 requirements by reducing energy consumption, according to the findings it shows that the model built is sufficiently accurate.
There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advanc...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in India. The dataset constitutes (a) anonymized metadata (gender, age, country, mother tongue) and responses to a questionnaire about how stuttering affects their daily lives, (b) captures both read speech (using the Rainbow Passage) and spontaneous speech (through image description tasks) for each participant and (c) includes detailed annotations of five stutter types: blocks, prolongations, interjections, sound repetitions and word repetitions. We present a comprehensive analysis of the dataset, including the data collection procedure, experience summarization of people who stutter, severity assessment of stuttering events and technical validation of the collected data. The dataset is released as an open access to further speech technology development.
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