Reviewerly/exHarmony huggingface dataset:
Evaluation of Subject Intention Speed Control Electric Wheelchairs With Myoelectric Signal Control for Users With Disabilities

In doing their daily activities, people with disabilities need wheelchair assistance for their mobility. The research about electric wheelchair with navigation using myoelectric signals from Carpi Radialis muscles with Subject Intention Speed Control has been developed by Elvina, et al in 2020. Therefore, this research focused on evaluating the Subject Intention Speed Control performance of the wheelchair on 5 subjects with Cerebral Palsy. The observation and myoelectric signal intercept were done at the Carpi Radialis muscle while the subjects were doing flexion and extension. Then, the evaluation was carried out on the performance of the adaptive threshold system, signal classification performance for wheelchair movement, and the interaction of disabled users with the system. The subjects were asked to do a series of scenarios that aimed to move the wheelchair. The result showed that the success of wheelchair motion classification for the subject with mild Cerebral Palsy reached 92%. On the other hand, the suitability of the subject’s hand motion with the requested control command reached 90%. Nevertheless, further customization of the wheelchair system must be done, so that the needs of disabled users can be accommodated. The data and conclusions could be used for further development of electric wheelchairs for patients with disabilities.
A Study of the Performance of Solar Cell Powered IoT-based Air Pollution Sensor Node in Indoor Environment

The Internet of Things is boosting indoor solar technologies. It is suitable for low-power applications. However, many low-power communication protocols are not widely used in society, despite the fact that user-friendliness is important for implementation. A sensor node for tracking indoor air pollution is introduced. The sensor module can measure up to 100 ppm of carbon monoxide gas concentration and share real-time data via wifi protocol. One cycle of the carbon monoxide measurement process required 1.093 mWh of total energy capacity over a duration of 33.51s to operate. The system power mode strategy uses a minor current consumption of 31.40 mA, while data transmission uses the most, approximately 113.20 mA. The sensor node could be powered on for 726 cycles with a voltage range of 4.1V-3.3V without solar cells. While including solar cells, the cycles increased to 889 (500 lx) and 1056 (750 lx). It can also be optimized by increasing the luminance and solar cell area. All the carbon monoxide gas concentration data were collected to the IoT server and made online for visualization throughout the testing.
Identification of Poultry Reproductive Behavior Using Faster R-CNN with MobileNet V3 Architecture in Traditional Cage Environment

Muscovy ducks are one of the waterfowl that are widely cultivated because of their relatively higher price compared to chickens and ducks. Determining whether Muscovy ducks are productive (raised to lay eggs and sell the eggs) or unproductive (raised/fattened to sell the meat) is one of the actions that can be taken to increase production. However, monitoring that only focuses on egg-laying behavior and is still done manually by humans is still prone to errors. An automated monitoring system is needed to improve monitoring. In this research, building a Deep Learning model using the Faster RCNN algorithm MobileNetV3 architecture to monitor poultry reproductive behavior. The identified behaviors are mating, non-mating, and egg-laying/swarming behaviors. Reproductive behavior can be used as a reference to determine whether a bird is productive or not. A model was built and tested on traditional cages. The addition of pre-processing techniques to improve image quality was performed. The results obtained were 86% mating behavior, 80% non-mating behavior, and 94% egg-laying. There was a difference in accuracy before using preprocessing techniques, which was 82% mating behavior, 74% non-mating behavior, and 88% egg laying. Adding Preprocessing methods (Image enhancement, ROI, and Blurring) can improve the performance of the Faster R-CNN algorithm to detect objects but impacts blurry image quality. The addition of deblurring techniques after the Faster R-CNN algorithm detection process can be used to restore image quality without affecting accuracy.
Detecting Egg’s Condition by Using Pixy Camera Based on Shell-Color Filtering

Checking the condition of eggs suitable for consumption can be done manually by soaking the eggs in water. In addition, the examination of the condition of the eggs can also be done by looking at a stronger light so that the outside and inside of the egg can be seen. Computer Vision enables computers the ability to see like humans. The objective of egg detection using Computer Vision is to make the process of observing chicken eggs on many breeders which was originally observed by the human eye can be replaced with a camera as an alternative so as to reduce errors due to eyesight tired. Pixycam is one of the tools that helps in image processing. Pixycam can learn to detect the object being taught which is then processed by Arduino. Based on the test results, 100 chicken eggs with a composition of 70 fresh eggs and 30 rotten eggs have a 100% success rate of accuracy.
Anticipating Spectrogram Classification Error With Combinatorial Coverage Metrics

Recently, combinatorial interaction testing (CIT) has been applied to machine learning. Recent results demonstrate that combinatorial coverage metrics can correlate with classification error. However, these methods have not been applied to the area of cognitive communications. In this paper, we show that combinatorial coverage metrics can be used to anticipate spectrogram classification error for convolutional neural networks (CNNs) for batch sizes of 100-500 spectrograms. We propose a method that combines deep autoencoders, metric learning, and k-means clustering to learn a low-dimensional, discrete representation of spectrograms for coverage analysis. Our results show strong correlations which suggest that combinatorial coverage metrics can be used to anticipate spectrogram classification error. This finding identifies coverage as a new class of solution methods for monitoring and testing cognitive radios.
An Iterative Meta-Testing Framework for Few-Shot Segmentation

Substantial Few-Shot Segmentation (FSS) methods adopt iterative manner to boost segmentation performance. Specifically, the traditional meta-testing can be turned into an iterative mode and the prediction result is iteratively refined. However, the abovementioned method shares the problem of Random Harmful Expansion (RHE). In the study, we propose a novel framework named Iterative Meta-Testing (IterMeta-Testing) to solve the RHE problem. In IterMeta-Testing, a Back-Off Module (BOM) is designed. The BOM can conduct a "back-off" operation to intelligently reject harmful expansion prediction results. Extensive experiments on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> proved that the proposed IterMeta-Testing outperformed the baseline method. The proposed method can be integrated in an automated system of a robot for target detection and segmentation when sufficient fully-labeled image data are not available.
Texture Mapping for Voxel Models Using SOM

In this article, we propose an innovative algorithm for texture-mapping voxel-based models. Voxel-based models are composed of voxels. Their surfaces are digitalized and basic geometrical information, like normal and tangent vectors, are absent from their representations. Relying on connectivity and geometrical information to parametrize the surface of a voxel-based model is impossible. Instead, we derive an automatic mapping procedure, based on Self-Organizing Map (SOM), to parametrize its surface voxels. First, we use an unsupervised training to convert the SOM lattice into an approximation surface of the model by using the surface voxels as input data. Then, another unsupervised training is triggered to parameterize the nodes of the SOM lattice by using the texels of the texture as input data. In the $3^{rd}$ stage, the surface voxels are textured, based on the relations established in the two training processes. As a result, the mapping task is efficiently accomplished without too much human interference.
Hydraulic System Failure Prediction Method with Limited Failure Data

Hydraulic systems in production equipment are relatively complex, involving various time series sensor data, such as operating sound frequencies and hydraulic pressure. However, fault prediction generally faces the bottleneck of insufficient fault data, leading to a severe imbalance between normal and fault data ratios. This imbalance makes it difficult for prediction models to effectively learn fault trends. In this paper, we propose a synthesis method for time series data to address this issue. The experimental results demonstrate that the synthesized data lead to significant improvements.
Bipolar Sentiment Analysis of Japanese Social Media Posts: A Semantic Similarity Based Approach

Social media generates a colossal amount of data round the clock. Many social entities including governments, business organizations, and researchers harness insights from these social-media data. With the recent advancement in algorithms based on machine learning, deep learning, and language models, automatic extraction of sentiments from textual social-media posts has become highly effective. These algorithms, however, are often need huge amount of labeled data for training, making them largely inapplicable when such data datasets do not exist-as is the case with Japanese social-media posts. Hence, in this paper, we propose an alternative approach that combines machine-learning-based word embedding and polarity dictionaries for classifying a social-media post written in Japanese as either positive or negative. Our experiments have demonstrated promising results with this approach which does not require any labeled dataset.
Integrating Rational Unified Process and Design Thinking Approach to Develop A Tangible User Interface for Mobile VR Immersive Scenarios Authoring Prototype

Virtual reality (VR) technology has advanced significantly in recent years. However, effective VR scene design remains challenging as it is both laborious and time-consuming. Although studies on immersive 3D scene editing have been conducted, it is currently limited to laboratory-specific designs or professional VR equipment. This study aims to develop immersive authoring systems that provide portability, accessibility, reusability, sustainability, cost-effectiveness, epidemic safety, mobility, immersion, and intuition not present in current commercial or lab-specific VR. To develop such a system, it is necessary to consider both the physical and software designs of the interaction, as VR-TUI is not only a computer device and software program but also a headset and an interactive physical controller. To satisfy the said research goal, the researchers of this study proposed an approach that integrates a rational unified process with design thinking to address the entire VR-TUI development and design. This approach can be used to develop human-centered products or systems that require integrating various software designs, hardware designs, physical designs, and other product or system design areas, providing an alternative reference development process and methodology for cross-disciplinary technology product development.
Adaptive Segmentation of Basketball Game Video Based on Markov Random Fields

To solve the problem of low coverage of some adaptive segmentation methods for basketball game videos, this paper proposes an adaptive segmentation method for basketball game videos based on Markov random field. Obtain the average value of pixel values of all frames at a specific position from the shot, extract the keyframes of the game video, and appropriately increase or reduce the segmentation threshold to resist illumination. The background model with texture information is constructed to obtain the smooth trajectory of basketball movement. The spatial position of each point in the video background is found in different frames. After obtaining a more accurate closed edge of the target, the pixel points are filled into it, and the adaptive segmentation process is optimized based on Markov random field. Experimental results show that the proposed adaptive segmentation method achieves an average coverage of 85.50% for basketball game videos, indicating its effectiveness after introducing Markov random field.
Adversarial Attacks on Regression Systems via Gradient Optimization

Adversarial attack can fabricate imperceptible fake samples to cheat a well-trained artificial intelligence (AI) model, and it has shown strong destructive power in many classification tasks. In real-world AI applications, there is another popular kind of machine learning paradigm—regression. The threats of adversarial attack may also exist in the regression scenario, however, the research on the adversarial vulnerability of the regression model has been basically neglected. This article first systematically explores the adversarial attack on regression problems. Starting from analyzing the difference between the attacking classification models and regression systems, we show the existing attack framework of classification problems is unsuitable for attacking regression systems. Then, we discuss the essence of regression tasks and design an appropriate attack objective for regression problems. After that, we propose two algorithms with different properties based on gradient optimization to achieve the attack objective. The proposed attack methods are evaluated on three real-world regression cases, and the results show that our attacks can successfully make the prediction deviate a lot from its original value by only exerting a tiny perturbation on the inputs. Finally, we conduct further experiments and analyses to discuss the effectiveness and characteristics of the proposed methods from various perspectives.
Optimal Design of Color Laparoscopic Super-Resolution Image Quality Based on Generative Adversarial Networks

The Generative Adversarial Networks (GAN) is unsupervised learning enabled to transform according to data characteristics, though this generate unreal data by learning characteristics from data. As past our study, we discussed from the viewpoint of image quality for super-resolution of color laparoscopic image including SRCNN (Super-Resolution Convolutional Neural Network). However, it was not enough to compare to other neural network methods in our discussion. We consider that it is possible to support the medical image diagnosis by measuring whether the difference of both neural network method and image contents is affected or not for image quality. In this paper, first we carried out the objective image quality assessment by designing optimally of color laparoscopic super-resolution image using Generative Adversarial Networks (GAN). And then, we discussed for performance between methods comparing to result of SRCNN. On the other hand, from a view of information science, we consider that we need to verify experimentally for affect between network learning effect and generated image, and then to improve method. Therefore, we also discussed relationship between image quality and learning effect in color laparoscopic image generation using SRGAN.
Using Monocular Depth Estimation for Distance Estimation in a Moving Vehicle

Accompanying the increase in demand for autonomous systems and robotic solutions is the increase in the relevance of various depth estimation technologies. Monocular Depth Estimation (MDE) is used to predict distances by generating depth maps using only a single RGB camera. However, without out-of-the-box calibration or ground truth reference for generated depth values from MDE models its use case in practical applications is limited. This research introduces a method of actualizing generated depth map values for different applications. The proposed system involves the utilization of machine vision using YOLO for object detection, followed by the computation of the lens optic algorithms to calculate the distance. Results demonstrated a real-time environment detection and depth estimation solution with more than 90% accuracy for measuring object depth in static environments. Furthermore, the system was also successfully tested in a moving vehicle to provide an estimated distance of surrounding vehicles. In the future, further tests will be done to improve the accuracy and calculation speed for use in car safety.
Application of Spectrum State Prediction Method Based on CNN-LSTM Network in Communication Interference

The continuous development of communication technology and various deep learning models has led to the invention and application of many anti-interference technologies in the field of communication countermeasures. The existing communication interference models have defects such as low anti-interference rate and low accuracy in communication spectrum prediction. To solve these problems, this study attempts to construct a Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and apply it to the communication jamming system for spectrum state prediction. Firstly, the framework of the communication interference system using the USRP RIO radio platform software was designed, and based on it, the communication interference channel was optimized using reinforcement learning Q-learning algorithm. Next, to further predict the signal spectrum state during the communication process, neural networks are utilized to construct a communication spectrum state prediction model.According to the optimization effect of communication interference channel and network spectrum prediction effect tested, the communication model under the Q-learning algorithm can achieve a 100% effective interference probability in fixed communication strategies. The Convolutional Neural Networks-1 Long-Short Term Memory-2 model has a prediction accuracy of 95.2% and can accurately predict changes in the communication spectrum. In summary, the Convolutional Neural Networks-1 Long-Short Term Memory-2 network constructed by this research can provide new solutions and achieve goodresultsfor communication spectrum prediction.
Determining region color by using maximum colorfulness

Color naming has been widely used as an object attribute or an image region feature. However, estimating appropriate color names is difficult even for single-color objects because the lightness and colorfulness of pixels typically vary depending on the highlight and shade. This study proposes a simple and robust method to estimate the color name of a given region. The proposed algorithm uses the color distribution of the image region. Specifically, this method first estimates the primary hue of the region in the CIECAM16 color space. Next, the colorfulness distribution of pixels with the primary hue is examined. It is used to determine the representative colorfulness and lightness. Finally, categorical color naming is performed in the CIECAM16 color space. Compared with the mean color of the region, the proposed method can produce more saturated colors. This method can be applied to various applications such as image retrieval, image indexing, object attribute recognition, non-photorealistic rendering, and color transformation.
AdaDS: Adaptive data selection for accelerating pre-trained language model knowledge distillation

Knowledge distillation (KD) is a widely used method for transferring knowledge from large teacher models to computationally efficient student models. Unfortunately, the computational cost of KD becomes unaffordable as pre-trained language models (PLMs) grow larger. Computing KD loss on only part of the training set is a promising way to accelerate KD. However, existing works heuristically leverage only one static data selection strategy during the KD process, demonstrating inconsistent improvements across different distillation scenarios. In this work, we conduct a thorough study on various typical data selection strategies for KD, and show that this problem is due to the fact that the best data selection strategy is specific to various factors, including task, selected data size, and training stage. To automatically adapt to these factors, we propose a framework named AdaDS to learn to choose the data selection strategy adaptively during the KD process. Experimental results show that our proposed method is effective for various tasks and selected data sizes under both fine-tuning and pre-training stages, achieving comparable performance to DistilBERT with only 10% amount of queries to the teacher model.
Research on Automatic Text Summarization Based on Extractive Method

With the rapid growth of text data, automatic text summarization can save a lot of time and energy to quickly obtain the important information of text. Aiming at the application of text feature representation and different abstracts extraction methods, this paper summarizes the common techniques and characteristics of summarization from three stages: text vector representation, summary method selection and sentence extraction. According to the inevitable problems of semantic logic and redundancy in extractive summarization, this paper summarizes the existing methods to deal with redundancy. Finally, the common evaluation methods are introduced.
Retraction Notice: English-Chinese Corpus Collection and Machine Intelligent Translation Methods based on Big Data and Pattern Analysis Model

First, it analyzes the development trend of artificial intelligence in the machine translation industry, and explains the specific application methods; second, based on the national conditions, explore the development advantages of China in the artificial intelligence and machine translation industry, and automatically align the results with the artificial Comparison of manual alignment results, thinking about the problems encountered in the process of building the database, including the selection of sentence identification, the alignment and matching of many-to-many types of sentences, and on this basis, this paper proposes a design framework for machine translation systems. Including the design method of the electronic dictionary, the storage structure of the electronic dictionary.