Automated language translation involving low-resource language has gained wide interest from many research communities in the past decade. One lesson learned from the past COVID-19 pandemic, particularly in Indonesia,...
详细信息
Automated language translation involving low-resource language has gained wide interest from many research communities in the past decade. One lesson learned from the past COVID-19 pandemic, particularly in Indonesia, is that many local Governments have to release regular public announcements to keep people following health protocol especially when they are in public areas. Many studies showed some evidence that rural people in Indonesia which covers a large proportion of Indonesia’s population, feel more convenience receiving official announcements in their local language. However, translating official announcement from the national language to many local languages in Indonesia require many experienced bilingual translators and time. This paper presents exploration results in developing an automated language translator model to translate texts in Bahasa Indonesia to the Sundanese language. In particular, this study aims to explore the effect of ReLU, Sigmoid, and Tanh activation functions of the Vanilla Transformer Model on its translation performance. The experiment results showed that the activation function under study gives similar training accuracy (0.98). However, ReLU achieves better performance than Tanh in terms of validation accuracy, training loss, and validation loss.
We present an algorithm for computing the minimum-rank positive semidefinite completion of a sparse matrix with a chordal sparsity pattern. This problem is tractable, in contrast to the minimumrank positive semidefini...
详细信息
This paper presents a thorough exploration of Cryptoprocessors in the context of the Internet of Things (IoT). The focus is on hardware support, cryptographic processors, and energy-efficient designs to fortify the se...
详细信息
ISBN:
(数字)9798350369441
ISBN:
(纸本)9798350369458
This paper presents a thorough exploration of Cryptoprocessors in the context of the Internet of Things (IoT). The focus is on hardware support, cryptographic processors, and energy-efficient designs to fortify the security of IoT ecosystems. Leveraging insights from recent research papers, the survey examines various aspects of Cryptoprocessor implementations, ranging from lightweight countermeasures to advanced lattice-based post-quantum cryptography. The proposed paper amalgamates findings from studies on hardware architectures, energy-efficient designs, and innovative cryptographic algorithms to provide a holistic overview of state-of-the-art advancements in IoT security. The synthesis of this survey’s exploration aims to contribute to the ongoing discourse on securing IoT devices through robust hardware-backed cryptographic solutions.
The Eyring-Powell fluid flow between two micro-parallel plates in the context of electro-magneto-hydrodynamic is the focus of the article. The Lorentz force, which is generated by the interactions of a vertical magnet...
详细信息
This work investigates cyber attacks targeting cyber-physical systems in the framework of discrete event systems. From intruders’ perspective, we propose a concept called k-step attackability to explore attack scenar...
详细信息
The quality of the cornea endothelial microscopy image is critical for clinical analysis. Although the noncontact specular microscope is more user-friendly than the contact confocal microscope, the imaging quality of ...
The quality of the cornea endothelial microscopy image is critical for clinical analysis. Although the noncontact specular microscope is more user-friendly than the contact confocal microscope, the imaging quality of the specular microscope is lower. The modality transfer is a promising solution for image quality enhancement. This paper proposes a Structure Consistent Generative Adversarial Network (SC-GAN) to transfer the imaging style from the specular microscope to the confocal microscope. Specifically, we use the Fourier frequency domain consistency to preserve cell structure and propose a multi-scale perception discriminator to improve model robustness under cell size variation. Experiment results prove the effectiveness of our method.
In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a tw...
详细信息
In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.
In this paper, we report on new findings about the results of an empirical study which aims to investigate how the COVID-19 pandemic has been shaping nomadic work practices and also challenging the lifestyles of digit...
详细信息
Biometric technologies are being considered lately for student identity management in Higher Education Institutions, as they provide several advantages over the traditional knowledge-based and token-based authenticati...
详细信息
this paper proposes novel deep-learning models that can generate Muslim names. A recurrent neural network (RNN) approach is used as the machine learning model. Generate new names, using the character-level language mo...
详细信息
this paper proposes novel deep-learning models that can generate Muslim names. A recurrent neural network (RNN) approach is used as the machine learning model. Generate new names, using the character-level language model. This technique has learned the different name patterns and generates new names. In order to achieve this objective, thousands of Muslim names have been collected from popular domains and then trimmed for the data set. After processing the whole dataset, 1000 pure and most common names are selected for a dataset. The deployed system is using some important functions provided by neural network libraries. Here, the argument also reveals that if a huge data set is used to employ this deep learning technique, more accurate results are expected. These outcomes will be better when compared with the traditional machine learning algorithms. The efficacy of the proposed model is proved by the similarity of artificial names with real names.
暂无评论