Reviewerly/exHarmony huggingface dataset:
Phase congruency-based filtering approach combined with a convolutional network for lung CT image analysis

Computed tomography (CT) imaging was confirmed as one of the important evolving radiological features of disease diagnosis. In this work, we propose an image filtering method for CT images that fuses the spatial and the transform domain. Filters in the transform domain work well in restoring low-contrast details that are usually smoothed by spatial domain filters. The new filter is based on a modified anisotropic diffusion approach combined with the phase congruency (PC) feature. The PC feature is a scale-invariant compared to the classical gradient, where weak edges are usually omitted and undetected with gradient-based feature detectors. This feature is incorporated in the diffusion function to enhance image edges while eliminating noise and texture background. In a further step, a U-net convolutional network is used to segment the lung-infected area. Comparative tests and results show the efficiency of our method, which permits to enhance image features while removing noise perfectly.
Mobile Instant Messaging for Reference Queries Post COVID-19

The study investigated whether students would prefer mobile instant messaging versus in-person enquiries for reference queries post-COVID-19. A descriptive survey research design was utilized in the study. The population consisted of 245 Adeleke University undergraduates that partook in library orientation programme. A descriptive statistic was employed to analyze the data. Findings indicated that the most preferred mobile instant messaging for reference queries by students are WhatsApp, Instagram, Telegram, default text messenger and Facebook Messenger. However, the most significant problem of using mobile instant messaging for reference queries were discovered to be internet access issues. The study concluded that Adeleke University undergraduate students prefer to utilize mobile instant messaging to make reference queries rather than in-person reference queries post-COVID-19, hence reference librarians should enhance their use of mobile instant messaging to receive reference queries.
Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a fine-grained sentiment analysis task that consists of two types of subtasks: aspect term extraction and aspect sentiment classification. In the aspect term extraction task, current methods suffer from the lack of fine-grained information in aspect term extraction and difficulty in identifying aspect term boundaries. In the aspect sentiment classification task, the current aspect sentiment classifier cannot adapt itself to the text and determine the local context. To address these two challenges, this work proposes an adaptive semantic relative distance approach based on dependent syntactic analysis, which uses adaptive semantic relative distance to determine the appropriate local context for each text and increase the accuracy of sentiment analysis. Meanwhile, the study also predicts the current word labels by combining local information features extracted by local convolutional neural networks and global information features to precisely locate the word labels. In two subtasks, our proposed model improves accuracy and F1 scores on the SemEval-2014 Task 4 Restaurant and Laptop datasets compared to the state-to-the-art approaches, especially in the aspect sentiment classification subtask.
Enhancement of Photovoltaic Power Potential in China from 2010 to 2020: The Contribution of Air Pollution Control Policies

China is expected to have a total installed photovoltaic capacity of 1300 GW in 2050, accounting for 39% of the national electricity consumption. However, air pollutants consisting of gases and particulates have attenuation effects on the solar radiation reaching the photovoltaic panels. This work purports to assess the influence of air pollutants on the photovoltaic power potential. We calculated the hourly point-of-array irradiance (POAI) in China for 2010–2020 with a spatial resolution of 0.1° × 0.1° using the PV_LIB model and assessed the effect of air pollutants on POAI. The results indicated that the annual average POAI in China for 2010–2020 ranged from 118 to 286 Wm−2. The Air Pollution Control Action Plan (APPCAP) has played a certain role in photovoltaic power potential, and POAI has increased in areas where surface concentrations of air pollutants have declined. Especially in North China, the surface concentrations of CO, NO2, PM10, PM2.5, and SO2 decreased throughout the APPCAP period with −0.446, −4.985, −35.610, −30.700, and −26.251 μgm−3, respectively, corresponding to an increase in POAI of up to 4.917 Wm−2. The surface concentrations of CO, NO2, PM10, PM2.5, and SO2 were negatively correlated with POAI, with correlation coefficients of −0.764, −0.854, −0.204, −0.110, and −0.664, respectively. Surface concentrations of air pollutants (CO, NO2, PM10, PM2.5, and SO2) and clear-sky POAI in 2018 showed a High-Low clustering in Northeast China and North China. This study demonstrates the role of China’s air pollution control policy in enhancing photovoltaic power potential.
DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%.
Mobile real-time gesture detection application for sign language learning

An educational gesture detection system for real-time sign language learning was developed. The applied 3-step approach consists of continuous image gathering, hand detection using the MediaPipe pipeline from Single-Shot Detector BlazePalm method and Hand Landmark Model, pretrained on a combination of synthetic and real images, for hand key points defining, and simple gesture classification. Its primary purpose is to provide an opportunity for people with hearing or speech disorders to improve their sign language skills with the help of a real-time gesture detection educational system. The process of sign recognition is divided into several phases: gathering an image from the camera, detecting a palm, setting key points, and defining a letter.
Path Planning Method of Robot Arm Based on Improved RRT* Algorithm

Aiming at the problems of long planning time and long path distance in the path planning of the robotic arm, a path planning strategy based on the improved RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (rapid random search tree) algorithm was proposed. First, in order to reduce the randomness of the traditional RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> path search, adding the node gravitational field function could reduce the generation of redundant nodes, thereby improving the path convergence rate; Secondly, in order to change the folding problem in the traditional RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm path, the B-spline function was imported to smooth the planned path, which could reduce the vibration of the joint axis and prolong the service life of the robotic arm. Simulations in MATLAB showed that the average planning time of the improved RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm was shortened by 76%, the average path distance was shortened by 14%, and the total number of nodes was reduced by 31.9%. After improving the algorithm, the lines convergence quickly and the line -seeking efficiency is high. The algorithm has good feasibility and effectiveness, and has reference value for the planning of robotic arm path.
Simulation of Liquid Level Cascade Control System Based on Genetic Fuzzy PID

The model design and basic principle of liquid level control system are described. In order to improve the dynamic response of liquid level control, the overshoot and steady-state error accuracy of the system are improved. In this paper, we apply the conventional PID controller to the single-loop system and the cascade system respectively, and introduce the fuzzy control algorithm based on the conventional PID control algorithm to design a cascade parameter self-adjusting fuzzy PID controller, Then use genetic algorithm to optimize fuzzy control rules to generate genetic fuzzy PID controller. The MATLAB/Simulink simulation results show that, compared with the traditional PID control, the genetic fuzzy PID control has better control effect and anti-interference ability, and has good control performance.
Research on Camera-Based Target Detection Enhancement Method in Complex Environment

A target detection method based on convolutional neural network for multiple complex environments is proposed to address the problem that current target detection methods are prone to miss detection and false detection under complex environment conditions, resulting in reduced detection accuracy. First, an environment recognition architecture is constructed based on convolutional neural networks to classify the images acquired in different environments. Then, the images in different environments are enhanced separately using an image enhancement algorithm. Finally, the enhanced images under different environments are trained based on the YOLOX detection algorithm to achieve real-time detection of the images, and the NUSCENES dataset is used for experimental evaluation. The results show that the proposed detection method improves the average accuracy AP for target detection in sunny, rainy, cloudy and night environments by 6.5%, 13.56%, 6.52% and 6.82%, respectively, improving the detection of small targets.
Symbolic Math Reasoning with Language Models

The emergence of large language models (LLMs) such as OpenAI’s GPT-3, Google’s LaMDA, Meta’s OPT [2, 3, 7, 10] etc. have revolutionized the field of natural language processing (NLP). These models with upwards of hundreds of billions of parameters are trained on large unlabeled text corpora and can subsequently solve downstream tasks with little to no labeled data. While these models are increasingly versatile in their abilities, e.g., solving math word problems, the larger question of their ability to reason remains. Using and modifying the SVAMP dataset, we find that GPT-3’s davinci-002 model, in addition to having good performance on numerical math word problems, also performs well on the potentially harder symbolic version of the same problems. Furthermore, adopting a two-step approach (solve symbolically and then substitute numerical values) leads to better accuracy on the numerical test set in the zero-shot regime. Additionally, we find that the use of specific prompting techniques pushes the model, in many cases, to actively describe its thought process and aid in the final answer output when faced with a complex, multi-step problem, aligning with recent observations.
Costa Rican Sign Language Recognition using MediaPipe

The launch of Google MediaPipe as a real-time platform for capturing and tracking the human body has allowed the removal of technological barriers in hand recognition for the construction of sign language translation software. It is of interest to understand how this platform can be used to encode Costa Rican Sign Language videos in low-dimensional structures to establish the basis for sign classification using machine learning. The experiment proposed in this paper targets the encoding of videos into one-dimensional vectors, the augmentation of video data to increase recognition capabilities, and the use of different machine learning and distance-based methods for sign recognition. The data for this experiment consists of 117 videos of one-second length (on average) from Costa Rican Sign Language (LESCO) emergency situations. This data format makes the encoding more challenging than performing finger spelling recognition, as each video is composed of many frames, and multiple hands can be outlined in each frame. The proposed approach allows the compression of all this information from a single video into a labeled vector added to a CSV file. The approach under evaluation recognized unary signs with an accuracy of 87.9% for the Extra Trees Classifier and 92.0% by using the Manhattan Distance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization

Neural abstractive summarization has been widely studied and achieved great success with large-scale corpora. However, the considerable cost of annotating data motivates the need for learning strategies under low-resource settings. In this paper, we investigate the problems of learning summarizers with only few examples and propose corresponding methods for improvements. First, typical transfer learning methods are prone to be affected by data properties and learning objectives in the pretext tasks. Therefore, based on pretrained language models, we further present a meta learning framework to transfer few-shot learning processes from source corpora to the target corpus. Second, previous methods learn from training examples without decomposing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">content</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">preference</i> . The generated summaries could therefore be constrained by the preference bias in the training set, especially under low-resource settings. As such, we propose decomposing the contents and preferences during learning through the parameter modulation, which enables control over preferences during inference. Third, given a target application, specifying required preferences could be non-trivial because the preferences may be difficult to derive through observations. Therefore, we propose a novel decoding method to automatically estimate suitable preferences and generate corresponding summary candidates from the few training examples. Extensive experiments demonstrate that our methods achieve state-of-the-art performance on six diverse corpora with 30.11%/33.95%/27.51% and 26.74%/31.14%/24.48% average improvements on ROUGE-1/2/L under 10- and 100-example settings.
Blockchain Storage With Sharing of Internet of Things Data in Textile Production Supply Chains

Internet of things (IoT) technology is poised to change the flow of data handling for tangible and intangible assets in textile production and utilization industries. It is reinventing global textile manufacturing operations, product distribution, and how manufacturers exchange value. For example, IoT technology helps gather operational data, store it, process it, and improve business efficiency. However, IoT technology-based textile production and its supply chain's information systems are highly vulnerable to security, privacy, and trust-related issues. This chapter presents the basic design and operation constraints that intelligent textile industries expect to experience within modern wireless data communication networks (e.g., 4G, 5G) with IoT technology and how blockchain technology can mitigate these constraints (e.g., privacy and security). The advantages of blockchain-based computing are its ability to scale rapidly, store data remotely, and provide service in a dynamic environment. Finally, this chapter presents a hybrid (i.e., IoT, blockchain, service-oriented computing) data processing architecture for the textile industries.
Real-time light-field generation based on the visual hull for the 3D light-field display with free-viewpoint texture mapping

Real-time dense view synthesis based on three-dimensional (3D) reconstruction of real scenes is still a challenge for 3D light-field display. It’s time-consuming to reconstruct an entire model, and then the target views are synthesized afterward based on volume rendering. To address this issue, Light-field Visual Hull (LVH) is presented with free-viewpoint texture mapping for 3D light-field display, which can directly produce synthetic images with the 3D reconstruction of real scenes in real-time based on forty free-viewpoint RGB cameras. An end-to-end subpixel calculation procedure of the synthetic image is demonstrated, which defines a rendering ray for each subpixel based on light-field image coding. In the ray propagation process, only the essential spatial point of the target model is located for the corresponding subpixel by projecting the frontmost point of the ray to all the free-viewpoints, and the color of each subpixel is identified in one pass. A dynamic free-viewpoint texture mapping method is proposed to solve the correct graphic texture considering the free-viewpoint cameras. To improve the efficiency, only the visible 3D position and texture that contributes to the synthetic image are calculated based on backward ray tracing rather than computing the entire 3D model and generating all elemental images. In addition, an incremental calibration method by dividing camera groups is proposed to satisfy the accuracy. Experimental results show the validity of our method. All the rendered views are analyzed for justifying the texture mapping method, and the PSNR is improved by an average of 11.88dB. Finally, LVH can achieve a natural and smooth viewing effect at 4K resolution and the frame rate of 25 ∼ 30fps with a large viewing angle.
AI Benchmarking for Science: Efforts from the MLCommons Science Working Group

With machine learning (ML) becoming a transformative tool for science, the scientific community needs a clear catalogue of ML techniques, and their relative benefits on various scientific problems, if they were to make significant advances in science using AI. Although this comes under the purview of benchmarking, conventional benchmarking initiatives are focused on performance, and as such, science, often becomes a secondary criteria. In this paper, we describe a community effort from a working group, namely, MLCommons Science Working Group, in developing science-specific AI benchmarking for the international scientific community. Since the inception of the working group in 2020, the group has worked very collaboratively with a number of national laboratories, academic institutions and industries, across the world, and has developed four science-specific AI benchmarks. We will describe the overall process, the resulting benchmarks along with some initial results. We foresee that this initiative is likely to be very transformative for the AI for Science, and for performance-focused communities.
Graphical Tools for Increasing the Effectiveness of Gage Repeatability and Reproducibility Analysis

The article deals with the quality of measured data, which is necessary for effective quality management and successful implementation of the concept Industry 4.0 and the related concept Quality 4.0. The quality of the measured data is determined by the properties of the used measurement system, which are evaluated by measurement system analysis (MSA). Attention is paid to increasing the effectiveness of the repeatability and reproducibility analysis often used in practice. The importance of graphical tools of analysis, which are often neglected in practice, is emphasized in this regard, and new or modified graphical tools are proposed. The proposed graphical tools allow more detailed analysis of the data collected for the study and reveal the causes of the measurement system variability. The information obtained by applying these graphical tools is a valuable basis for proposals of appropriate actions to improve the measurement system. The use of the proposed graphical tools is presented in a real study of the repeatability and reproducibility of the measurement system.
A Novel Hand Gesture Recognition for Aphonic People Using Convolutional Neural Network

Hand gesture is a form of nonverbal correspondence wherein apparent activities are utilized to impart significant messages. Sign language is the most natural and effective way for communication between normal human beings and hearing impaired and aphonic people. Existing hand gesture recognition systems used k-means clustering algorithm and support vector machine algorithm that suffered from challenges such as poor edge detection and required trained persons to avoid diagnostic errors. The proposed architecture uses convolutional neural network for the recognition of hand gestures. Gesture sign is recorded and subjected to binarization where the image is separated into foreground and background. Contours are detected from the binarized image. Feature extraction is done using the Scale Invariant Feature Transform (SIFT) algorithm. The extracted features are given to the convolutional neural network classifier to recognize the hand gestures. The recognized hand gesture is given as output in the text format. The proposed architecture shows improved performance in terms of varying background and illumination conditions.
A dual branch and fine-grained enhancement network for pancreatic tumor segmentation in contrast enhanced CT images

Segmentation of pancreatic tumors in CT images is important for clinical diagnosis and treatment, but it faces challenges of small size, low contrast, and large position difference. To address these issues, the abnormal pancreas is first segmented based on a dual branch coding network (DB-Net) using a coarse-to-fine segmentation strategy. In the encoder part, one branch extracts the semantic features of the pancreas and its surroundings, and the other branch captures the complex pancreas through wide-channel convolution and few down-sampling operations. An aggregation layer is used to fuse the different feature maps obtained by the two branches, and a U-Net decoder is used to segment the abnormal pancreas in CT images with pancreatic tumors. DB-Net is further trained to obtain the accurate pancreatic segmentation. Then, pancreatic tumors are segmented in the pancreas based on the fine-grained enhancement network (FE-Net). The FE-Net integrates a contrast enhancement block with a reverse attention block to extract detailed features and excavate effective information from the feature maps of the encoder and decoder to segment pancreatic tumors. In order to segment the tumor more accurately, the pancreatic tumor is segmented in the cropped pancreas. Experiments on 116 contrast-enhanced abdominal CT volumes of pancreatic cancer and 42 contrast-enhanced abdominal CT volumes of normal pancreas verify the effectiveness of the proposed framework in pancreatic tumor segmentation by using the two-fold cross-validation strategy. Compared to state-of-the-art deep learning segmentation network, the proposed method can achieve better segmentation of pancreas and pancreatic tumors.