Electroencephalography (EEG) data presents complex and high-dimensional signals, offering great potential for applications in various fields such as neurofeedback, clinical diagnostics, cognitive neuroscience, human-c...
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In distributed learning, a network of agents co-operate for solving a common task, like training a particular neural network. The devices usually adopt an iterative procedure with two steps, namely, they, first, perfo...
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ISBN:
(数字)9789464593617
ISBN:
(纸本)9798331519773
In distributed learning, a network of agents co-operate for solving a common task, like training a particular neural network. The devices usually adopt an iterative procedure with two steps, namely, they, first, perform local optimization, using, e.g., stochastic gradient descent, and, then, they exchange information among them in order to achieve consensus on the final solution. In current literature, the proposed distributed algorithms achieve consensus using averaging rules over the received information at each agent. Here, the paper departs from this paradigm and focuses on “single agent” cooperation strategies in which each agent selects a particular neighbor at each iteration and uses only that information during the local optimization step. Three selection rules are designed and it is shown that they inherit the convergence properties of commonly used averaging rules. Moreover, their effectiveness is demonstrated experimentally in classification tasks over other algorithms using well-known datasets for a wide range of scenarios, capturing factors like non-IID datasets, network size, and AI model size.
Text semantic similarity computation is a fundamental problem in the field of natural language processing. In recent years, text semantic similarity algorithms based on deep learning have become the mainstream researc...
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In the past few years, a few game studios have already developed mid-core games. In this kind of game, U is one of the most crucial and complex parts. Game companies have to develop their products quickly and effectiv...
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Melanoma is one of the most deadly types of skin cancer. Until now, the diagnosis of melanoma skin cancer is still using the biopsy method, which is the procedure of taking a small portion of tissue from the patient&#...
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EEPIS Robot Soccer On Wheeled (ERSOW) has a goalkeeper robot used as a defense to prevent the opposing team from scoring goals. The ability to detect the ball is one of the main abilities that a goalkeeper robot must-...
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Audio datasets support the training and validation of Machine Learning algorithms in audio classification problems. Such datasets include different, arbitrarily chosen audio classes. We initially investigate a unifyin...
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Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to large feature sizes, printed classifiers...
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ISBN:
(数字)9783982674100
ISBN:
(纸本)9798331534646
Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to large feature sizes, printed classifiers are limited by high power, area, and energy overheads, which restricts the realization of battery-powered systems. In this work, we design sequential printed bespoke Support Vector Machine (SVM) circuits that adhere to the power constraints of existing printed batteries while minimizing energy consumption, thereby boosting battery life. Our results show 6.5x energy savings while maintaining higher accuracy compared to the state of the art.
The Diffie-Hellman Key Exchange Protocol (DHKE) is a fundamental element of modern cryptographic systems, enabling secure key exchange over unsecured channels. The present research work aims to provide a comprehensive...
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Our study examines how formatted text influences reading behavior concerning the smartphone screen regions users utilize on social media comments. Aiming to research visual attention in scrollable content, we investig...
ISBN:
(纸本)9798400716263
Our study examines how formatted text influences reading behavior concerning the smartphone screen regions users utilize on social media comments. Aiming to research visual attention in scrollable content, we investigate the differences in the distribution of visual attention in screen regions between social media comment sections and the users’ preferred reading regions across all cases examined. An experiment was conducted with participants (n = 47) engaging in reading activities on the comment section of four social media -Twitter, YouTube (in two versions), Facebook, and Instagram- chosen due to their popularity and the users’ familiarity with them. Results showed that users’ visual attention is distributed differently between social media. Different reading styles were observed, with users not utilizing the entirety of the screen but instead focusing on specific screen regions that varied between users.
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