Reviewerly/exHarmony huggingface dataset:
FENet

Privacy-preserving machine learning (PPML) has been gaining a lot of attention in recent years, and several techniques have been proposed to achieve PPML. Cryptography-based PPML approaches such as Fully Homomorphic Encryption (FHE) and Secure Multiparty Computation (SMC) have been extensively investigated. However, Functional Encryption (FE), which is a newer paradigm, has not been studied as much, and PPML approaches based on FE are in the early stages. Most of the existing FE-based PPML approaches are focused on privacy-preserving inference, and the research work focused on FE-based privacy-preserving training requires a very high training time. To alleviate this issue, this paper presents a privacy-preserving neural network framework using FE that facilitates both training and inference on encrypted data. Our proposed approach is twofold. First, we use Inner-product Functional Encryption (IPFE) and Function-hiding Inner Product Encryption (FHIPE) schemes to develop secure activation functions. To the best of our knowledge, this is the first work to demonstrate the application of FHIPE in PPML. Second, we develop a PPML framework called FENet using the secure activation functions to perform secure forward propagation and backpropagation. Our experimental results show that our framework can successfully train a neural network on the encrypted MNIST dataset with an overall accuracy of 95%. Our work outperforms the state-of-the-art research work in this area, both in terms of reducing the training time by 28× (for IPFE) and 2× (for FHIPE) and improving security.
An optimized deep supervised hashing model for fast image retrieval

As multimedia data grows exponentially, searching for and retrieving a relevant image is becoming a challenge for researchers. Hashing is a widely adopted method because of its high performance in image retrieval with deep neural networks and multiple convolutional layers. Even so, most hashing methods ignore the computational cost and memory storage consumption. When the deep hashing model size is large, it leads to a slowdown in the response time of the model compared to the small model. Addressing these issues, a novel optimized deep supervised hashing based on a teacher-student approach for swift and precise image retrieval is proposed in this paper. In this work, the small student model is trained using the knowledge distillation from the large teacher model and the information from the one-hot labels. Therefore, a weight allocation loss function based on the teacher and student models is defined. Meanwhile, we apply model pruning to decrease the amount of the student model further to increase the response time. Therefore, knowledge distillation is performed on the pruned model. After that, the remaining weights are quantized to reach the smaller size of the model. Extensive experimental outcomes on two widely used datasets prove the outstanding efficiency of our proposed method.
Natural Disaster Damage Assessment using Semantic Segmentation of UAV Imagery

Numerous natural disasters due to climate change pose major threats to the sustainability of public infrastructure and human lives. For emergency rescue and recovery during a disaster, a rapid and accurate evaluation of disaster damage is essential. In recent years, the Transformer has gained popularity in a number of tasks related to computer vision, which offers tremendous potential for improving the accuracy of disaster damage assessments. Our research aims to determine whether Vision Transformer (ViT) can be used to assess natural disaster damage on high-resolution Unmanned Aerial Vehicle (UAV) data in comparison with conventional deep-learning semantic segmentation techniques. We discuss if Transformer can perform better than CNNs in accurately assessing the damage caused in order to bridge the gap. Detailed performance comparison of state-of-art deep learning semantic segmentation models (UNET, Segnet, PSPNet, Deeplabv3+) and Transformer framework (SegFormer) for damage assessment is presented. The experimentation is performed on both natural disaster damage datasets (RescueNet, FloodNet). The study supported SegFormer as the most appropriate model for estimating disaster damage, with mIoUs of 96% on the RescueNet dataset and 82.22% on the FloodNet dataset, respectively. The Transformer is capable of outperforming conventional segmentation CNNs in understanding the entirety of the scene and assessing the severity of the damage, based on both quantitative evaluation and visual results.
Super-resolution network with dynamic cleanup and temporal–spatial attention for compressed videos

Video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos with pre-defined degradations, and few consider compression. However, the videos on the Internet are compressed to reduce the massive size of video data. Thus, they also contain visual noises and artifacts due to the lossy compression. In this paper, we propose a video super-resolution model specific for compressed videos to suppress artifacts and restore high-resolution contents. We propose a two-stage super-resolution network for compressed videos that contains three new modules: cleanup and local alignment in the first stage, 3D-convolution temporal–spatial attention in the second stage. Specifically, the cleanup module is designed in stage-1 to suppress the artifacts and noises of low-quality inputs. The subsequent local alignment is designed to aggregate adjacent frames to maintain high-frequency details. The 3D-convolution attention is used in stage-2 to refine the weights of the feature maps. In addition, we propose the new CrfVideos dataset to facilitate fair comparisons, containing HEVC/H.265 compressed videos of different compression levels. Extensive experiments substantiate that our method outperforms state-of-the-art methods on the HEVC/H.265 compressed benchmark with medium to high compression rates by 0.3–0.7 dB in terms of PSNR and exceeds state-of-the-art methods on benchmark Vid4 by 0.2–0.8 dB. The code will be released at https://github.com/cvygkhv/decompression.
Estimation of metal and metalloid in crude oil of newly developed oil field after acid digestion/extraction methods using different devices

The contamination of the environment by metals and metalloids occurs due to oil exploration, owing to combustion via automobiles or different industrial activities. The crude oil obtained from two newly developed oil fields in Tando Allahyar, Pakistan, were analyzed for the physical characteristics of crude oil. Moreover, arsenic (As) and cadmium (Cd) concentrations in crude oil were measured by three digestion/extraction methods, conventional acid digestion (CAD), microwave-assisted acid digestion (MAD), and ultrasonic-assisted extraction (UAE). The inductively coupled plasma emission spectrometry was applied to determined the As and Cd in digests and extracts of crude oil samples. The standard addition method was applied to evaluate the accuracy of all three methodologies at two concentration levels of As and Cd in a composite crude oil sample. Under the optimum operating conditions, the limits of detection obtained from the standard addition curves were lower in the MAD method, whereas higher value was observed for the CAD method. The LOD for As obtained after MAD, CAD, and UAE were observed as (0.072, 0.083, and 0.078) and Cd (0.062, 0.087, and 0.092) µg L−1, respectively. The respective recoveries of As and Cd obtained with MAD were optimum as compared to those obtained by CAD and UAE. However, the recovery of As was lower from the conventional heating system as compared to UAE. The UAE method was easy, efficient, and environmental friendly. The results obtained are comparable with conventional acid heating systems and about 1% lower than those values obtained by MAD. However, the difference in% recoveries between the three digestion/extraction methods of both elements was insignificant (p>0.05).
Development of feedback system by customized UI to improve UX of web page

Abstract In recent years, the number of websites has been increasing, and with it, the number of similar services. As a result, UX is becoming more and more important to measure the differentiation of a website from its competitors, and UI plays a major role in improving UX. Website developers implement a development process called HCD to improve UI. However, in HCD, there is a gap between the perception of the evaluator and the developer, resulting in a lot of man‐hours. In this paper, we propose a method for evaluating web pages using GUI operations to solve this problem. We let the evaluator of the web page edit the web page by mouse operation from the browser, and feedback the result directly to the developer. As a result of the experiment, we were able to discover problems with the web pages that could not be obtained by the conventional questionnaire. It also made it easier to design improvement plans, leading to a reduction in man‐hours.
Unsupervised learning of healthy anatomy for anomaly detection in brain CT scans

Automatic detection of abnormalities to assist radiologists in acute and screening scenarios has become a particular focus in medical imaging research. Various approaches have been proposed for the detection of anomalies in magnetic resonance (MR) data, but very little work has been done for computed tomography (CT). As far as we know, there is no satisfactory approach for anomaly detection in CT brain images. We present a novel unsupervised deep learning approach to generate a normal representation (without anomalies) of CT head scans that we use to discriminate between healthy and abnormal images. In the first step, we train a GAN with 1000 healthy CT scans to generate normal head images. Subsequently, we attach an encoder to the generator and train the auto encoder network to reconstruct healthy anatomy from new input images. The auto encoder is pre-trained with generated images using a perceptual loss function. When applied to abnormal scans, the reconstructed healthy output is then used to detect anomalies by computing the Mean Squared Error between input and output image. We evaluate our slice-wise anomaly detection on 250 test images including hemorrhages and tumors. Our approach achieves an area under receiver operating characteristic curve (AUC) of 0.90 with 85.8% sensitivity and 85.5% precision without requiring large training data sets or labeled anomaly data. Therefore, our method discriminates between normal and abnormal CT scans with good accuracy.
Thangka Sketch Colorization Based on Multi-Level Adaptive-Instance-Normalized Color Fusion and Skip Connection Attention

Thangka is an important intangible cultural heritage of Tibet. Due to the complexity, and time-consuming nature of the Thangka painting technique, this technique is currently facing the risk of being lost. It is important to preserve the art of Thangka through digital painting methods. Machine learning-based auto-sketch colorization is one of the vital steps for digital Thangka painting. However, existing learning-based sketch colorization methods face two challenges in solving the problem of colorizing Thangka: (1) the extremely rich colors of the Thangka make it difficult to color accurately with existing algorithms, and (2) the line density of the Thangka brings extreme challenges for algorithms to define what semantic information the lines imply. To resolve these problems, we propose a Thangka sketch colorization method based on multi-level adaptive-instance-normalized color fusion (MACF) and skip connection attention (SCA). The proposed method consists of two parts: (1) a multi-level adaptive-instance-normalized color fusion (MACF) to fuse sketch feature and color feature; and (2) a skip connection attention (SCA) mechanism to distinguish the semantic information implied by the sketch lines. Experiments on colorizing Thangka sketches show that our method works well on two small datasets—the Danbooru 2019 dataset and the Thangka dataset. Our approach can generate exquisite Thangka.
Residue representations - The rank one case

With each resonance of the Laplacian acting on the compactly supported sections of a homogeneous vector bundle over a Riemannian symmetric space of the non-compact type, One can associate a residue representation. The purpose of this paper is to study them. The symmetric space is assumed to have rank-one but the irreducible representation {\tau} of K defining the vector bundle is arbitrary. We give an algorithm that aims at determining if these representations are irreducible, finding their Langlands parameters, their Gelfand-Kirillov dimensions and wave front sets. As an example, we apply this algorithm to the Laplacian of the p-forms in the cases of all the classical real rank-one Lie groups.
Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages

Although low-resource relation extraction is vital in knowledge construction and characterization, more research is needed on the generalization of unknown relation types. To fill the gap in the study of low-resource (Uyghur) relation extraction methods, we created a zero-shot with a quick relation extraction task setup. Each triplet extracted from an input phrase consists of the subject, relation type, and object. This paper suggests generating structured texts by urging language models to provide related instances. Our model consists of two modules: relation generator and relation and triplet extractor. We use the Uyghur relation prompt in the relation generator stage to generate new synthetic data. In the relation and triple extraction stage, we use the new data to extract the relation triplets in the sentence. We use multi-language model prompts and structured text techniques to offer a structured relation prompt template. This method is the first research that extends relation triplet extraction to a zero-shot setting for Uyghur datasets. Experimental results show that our method achieves a maximum weighted average F1 score of 47.39%.
School Sports Club in South Korea: Supporting Middle School Students’ Physical Activity Engagement and Social-Emotional Development in Schools

Adolescents in South Korea have been facing physical (physical inactivity) and social-emotional (e.g., bullying and suicide) challenges in the last few decades. To address these issues, the Ministry of Education in South Korea implemented School Sports Club as a part of the school curriculum. This article introduces the overview, the goals and structure, the status quo, and the future directions of the School Sports Club. School Sports Club is offered during the regular school time and a required curriculum. The primary goal of the School Sports Club is to increase physical activity opportunities, strengthen humanistic education, prevent bullying, and ultimately develop a positive attitude in their school life among students. In School Sports Club, teachers are allowed to select the content that would best meet their students’ needs and school contexts. While there are some challenges, the students have positive experiences in School Sports Club. In summary, School Sports Club is one way to support students’ physical activity engagement and social-emotional learning in school settings.
State-dependent Trotter limits and their approximations

This paper gives sufficient conditions to conclude the validity of digital simulations involving the Trotter product formula for finite discretizations of continuous operators. Essentially, it depends on the state-dependent Trotter error, for which it establishes explicit bounds that are also of independent interest.
Three-stage training and orthogonality regularization for spoken language recognition

Abstract Spoken language recognition has made significant progress in recent years, for which automatic speech recognition has been used as a parallel branch to extract phonetic features. However, there is still a lack of a better training strategy for such architectures of two individual branches. In this paper, we analyze the mostly used two-stage training strategies and reveal a trade-off between the recognition accuracy and the generalization ability. Based on the analysis, we propose a three-stage training strategy and an orthogonality regularization method. The former adds a multi-task learning stage to the traditional two-stage training strategy to extract hybrid-level and noiseless features, which can improve the recognition accuracy on the basis of maintaining the generalization ability, while the latter constrains the orthogonality of base vectors and introduces prior knowledge to improve the recognition accuracy. Experiments on the Oriental Language Recognition (OLR) dataset indicate that these two proposed methods can improve both the language recognition accuracy and the generalization ability, especially in complex challenge tasks, such as cross-channel or noisy conditions. Also, our model, which combines these two proposed methods, performs better than the top three teams in the OLR20 challenge.
Path planning of indoor mobile robot based on improved A* algorithm incorporating RRT and JPS

Indoor mobile robots are widely used in modern industry. Traditional motion control methods for robots suffer from discontinuous path curvature, low planning efficiency, and insufficient verification of theoretical algorithms. Therefore, a motion control system for an intelligent indoor robot was designed. By optimizing the radar map detecting and positioning, path planning, and chassis motion control, the performance of the system has been improved. First, a map of the warehouse environment is established, and the number of resampling particles interval is set for the Gmapping building process to improve the efficiency of map construction. Second, an improved A* algorithm is proposed, which converts the path solution with obstacles between two points into the path solution without obstacles between multiple points based on the Rapidly expanding Random Trees and Jump Point Search algorithms and further improves the pathfinding speed and efficiency of the A* algorithm by screening the necessary expansion nodes. The Dynamic Window Approach (DWA) algorithm based on the dynamic window is used to smooth the path, and the target velocity is reasonably assigned according to the kinematic model of the robot to ensure the smooth motion of the chassis. By establishing raster map models of different sizes, the traditional and improved A* pathfinding algorithms are compared and validated. The results illustrate that the improved pathfinding algorithm reduces the computing time by 67% and increases the pathfinding speed by 47% compared with the A* algorithm. Compared with the traditional method, the speed and effect are greatly improved, and the motion control system can meet the requirements of autonomous operation of mobile robots in indoor storage.
How to explain a machine learning model: HbA1c classification example

Aim: Machine learning tools have various applications in healthcare. However, the implementation of developed models is still limited because of various challenges. One of the most important problems is the lack of explainability of machine learning models. Explainability refers to the capacity to reveal the reasoning and logic behind the decisions made by AI systems, making it straightforward for human users to understand the process and how the system arrived at a specific outcome. The study aimed to compare the performance of different model-agnostic explanation methods using two different ML models created for HbA1c classification.&#x0D; Material and Method: The H2O AutoML engine was used for the development of two ML models (Gradient boosting machine (GBM) and default random forests (DRF)) using 3,036 records from NHANES open data set. Both global and local model-agnostic explanation methods, including performance metrics, feature important analysis and Partial dependence, Breakdown and Shapley additive explanation plots were utilized for the developed models. &#x0D; Results: While both GBM and DRF models have similar performance metrics, such as mean per class error and area under the receiver operating characteristic curve, they had slightly different variable importance. Local explainability methods also showed different contributions to the features. &#x0D; Conclusion: This study evaluated the significance of explainable machine learning techniques for comprehending complicated models and their role in incorporating AI in healthcare. The results indicate that although there are limitations to current explainability methods, particularly for clinical use, both global and local explanation models offer a glimpse into evaluating the model and can be used to enhance or compare models.
Similarity matching of medical question based on Siamese network

With the rapid development of the medical industry and the gradual increase in people's awareness of their health, the use of the Internet for medical question and answer, to obtain more accurate medical answers. It is necessary to first calculate the similarity of the questions asked by users, which further matches professional medical answers. Improving the efficiency of online medical question and answer sessions will not only reduce the burden on doctors, but also enhance the patient's experience of online medical diagnosis.This paper focuses on building a bidirectional gated recurrent unit(BiGRU) deep learning model based on Siamese network for medical interrogative similarity matching, using Word2Vec word embedding tool for word vector processing of ethnic-medical corpus, and introducing an attention mechanism and convolutional neural network. Bidirectional gated recurrent unit extracts contextual semantic information and long-distance dependency features of interrogative sentences; Similar ethnic medicine interrogatives vary in length and structure, and the key information in the interrogative is crucial to similarity identification. By introducing an attention mechanism higher weight can be given to the keywords in the question, further improving the recognition of similar words in the question. Convolutional neural network takes into account the local information of interrogative sentences and can capture local position invariance, allowing feature extraction for words of different granularity through convolutional operations; By comparing the Euclidean distance, cosine distance and Manhattan distance to calculate the spatial distance of medical interrogatives, the Manhattan distance produced the best similarity result.Based on the ethnic medical question dataset constructed in this paper, the accuracy and F1-score reached 97.24% and 97.98%, which is a significant improvement compared to several other models.By comparing with other models, the model proposed in this paper has better performance and achieve accurate matching of similar semantic question data of ethnic medicine.