Reviewerly/exHarmony huggingface dataset:
Optimal Sub-Gaussian Mean Estimation in $\mathbb{R}$

We settle the fundamental problem of estimating the mean of a real-valued distribution in the high probability regime, under the minimal (and essentially necessary) assumption that the distribution has finite but unknown variance: we propose an estimator with convergence tight up to a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 +o(1)$</tex> factor. Crucially, in contrast to prior works, our estimator does not require prior knowledge of the variance, and works across the entire gamut of distributions with finite variance, including those without any higher moments. Parameterized by the sample size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> , the failure probability <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\delta$</tex> , and the variance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma^{2}$</tex> , our estimator has additive accuracy within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma\cdot(1+o(1))\sqrt{\frac{2\log\frac{1}{\delta}}{n}}$</tex> , which is optimal up to the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1+o(1)$</tex> term. This asymptotically matches the convergence of the sample mean for the Gaussian distribution with the same variance. Our estimator construction and analysis gives a framework generalizable to other problems, tightly analyzing a sum of dependent random variables by viewing the sum implicitly as a 2-parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\psi$</tex> -estimator, and constructing bounds using mathematical programming and duality techniques.
Accelerating a Lossy Compression Method with Fine-Grained Parallelism on a GPU

With the rapid increase of data size processed by nowadays applications, high-performance compression methods become important to reduce both the memory consumption and the amount of data transfer. However, unlike the studies on CPU-based compression methods, those on GPU-based compression methods are insufficient. In this work, we target a lossy compression method that reported a high performance on a CPU-centric supercomputer. We propose schemes with fine-grained parallelism to accelerate the CPU-based method on the GPU, solving data dependency and write conflict issues in compression and decompression processes. Experimental results show that the proposed method achieved both higher performance, i.e., 42.0x and 1.9x speedups at best compared to the original method and a state-of-the-art GPU-based lossy compression method, respectively. Moreover, the proposed method achieved at best 1.2x and 1.8x as large as the compression ratio achieved by the original method and the state-of-the-art GPU-based lossy compression method, respectively.
Day-Ahead Solar Generation Prediction from Weather Forecasts in the UK Using LSR-Fuzzy-Markov Chain

Day-ahead forecast is a necessity for optimal management of peer-to-peer energy trading in an &#x201C;energy community&#x201D;, which is proposed as the foundation block of the future energy market. This paper proposes a hybrid technique that consists of &#x0028;1&#x0029; a linear day-ahead PV generation forecast using weather predictions and historical measurements, and &#x0028;2&#x0029; a combined model of fuzzy classifier and Markov Chain to improve the forecast error. The final LSR-Fuzzy-Markov technique shows superiority when tested on two-years data from the Active Office Building in Swansea University, UK.
Pathologist-Level Classification of Melanoma Disease Pathologies Using a Convolutional Neural Network: A Retrospective Study of Chinese

Melanoma is a highly malignant skin tumor which causes nearly half of skin cancer deaths. However, in China, especially in rural area, professional pathologists who can diagnose melanoma early and correctly are insufficient. Therefore, there is a need to develop an objective and quantitative method for melanoma diagnosis. The purpose of this study is to illustrate the potential of deep learning to assist pathologists in assessing the histopathological melanoma diagnosis of Chinese patients. We established a novel convolutional neural network model for melanoma diagnosis. The work was carried out on a histopathology database with 633 digital whole-slide images from 314 patients in China. Then, the model achieved pathologist-level classification of the pathological images, with an accuracy of 0.92. These findings suggest that convolutional neural networks can be an efficient tool to assist pathologists to diagnose melanoma in Chinese patients, with low time cost and high accuracy.
Body Segmentation Using Multi-task Learning

Body segmentation is an important step in many computer vision problems involving human images and one of the key components that affects the performance of all downstream tasks. Several prior works have approached this problem using a multi-task model that exploits correlations between different tasks to improve segmentation performance. Based on the success of such solutions, we present in this paper a novel multitask model for human segmentation/parsing that involves three tasks, i.e., (i) keypoint-based skeleton estimation, (ii) dense pose prediction, and (iii) human-body segmentation. The main idea behind the proposed Segmentation–Pose–DensePose model (or SPD for short) is to learn a better segmentation model by sharing knowledge across different, yet related tasks. SPD is based on a shared deep neural network backbone that branches off into three task-specific model heads and is learned using a multi-task optimization objective. The performance of the model is analysed through rigorous experiments on the LIP and ATR datasets and in comparison to a recent (state-of-the-art) multi-task body-segmentation model. Comprehensive ablation studies are also presented. Our experimental results show that the proposed multi-task (segmentation) model is highly competitive and that the introduction of additional tasks contributes towards a higher overall segmentation performance.
Application of Device-to-Device Communication in Video Streaming for 5G Wireless Networks

We present one of the key-enabling technologies in 5G wireless networks, i.e., Device-to-Device (D2D) communication. Furthermore, we discus some of our work in utilizing D2D communication in one of the most bandwidth-demanding applications in 5G networks, i.e., video streaming. D2D communication, introduced by the LTE-Advanced (LTE-A) standard, allows direct communication between devices in cellular networks. We review some of the work in the literature on D2D communication and its applications in video streaming. We also discus an architecture that provides Dynamic Adaptive Streaming over HTTP (DASH)—based Peer-to-Peer (P2P) video streaming in cellular networks. The architecture employs Base-Station (BS)—assisted D2D video transmission in cellular networks for direct exchange of video contents among users. We evaluate the performance of the proposed architecture in terms of many video streaming Quality-of-Experience (QoE) metrics.
Hybrid Neuro-Fuzzy Learning Models for Classification of Motion Sickness Levels Using Biosignals

Motion sickness is all around as long as there is existence of humans and motion. This sickness has been common in numerous people and due to which it has become the focus area of neurological, psychological and physiological researchers. Most common group of this motion sickness pertains to the category of visual sensitivity; also called visual dependence, wherein people become sick due to visual motion. In this research paper, classification of the levels of motion sickness is done by developing classifiers: (1) k-Nearest neighbour (kNN) classifier (2) Fuzzy c-means classifier (3) ELMAN neural classifier (4) Fuzzy-Wavelet neural network classifier. All the developed classifier models are based on variants of machine learning approaches and are designed to overcome the limitation of the conventional binary classification approach. In this work, electroencephalogram (EEG) data, centre of pressure and trajectories of head and waist motion data of 20 people were recorded and the developed classifier models were applied over them to attain the classification accuracy. Features of these multiple biosignals are denoised and extracted over which the classifier models were tested. The proposed technique is simulated in MATLAB simulation environment for the considered candidate data samples. Numerical simulation was carried out and the results prove the superiority and effectiveness of the developed classifiers over the various existing classifier models.
Action Detection Based on Transfer Learning of Human Pose Estimation

The purpose of action detection is to detect whether the specified action behavior occurs in the video, and find out the time when the action occurs. Predecessors introduced pose information into the field of action detection through RNN and GCN, but because of the gradient disappearance problem of recurrent neural network (RNN) and the problem of graph convolution (GCN) calculation efficiency, they encountered bottlenecks. In this paper, we propose to use convolutional neural network (CNN) to introduce pose information into behavior detection by means of transfer learning. We use CenterNet to obtain high-semantic information to improve the accuracy of behavior detection. In order to generate a more suitable pose heatmap of the application scenario, our CenterNet pose estimation network participates in the training, and constantly updates the parameters of the pose estimation network according to the new dataset. We conducted a large number of ablation experiments on the Ur fall detection dataset and RWF-2000 dataset. On the RWF-2000 data set, we added a pose estimation network to generate a pose heatmap and input it into the subsequent network, the accuracy increased from 79.0 to 83.75%.
Slang and verbal aggression

Abstract While lexical and discourse strategies of hate speech have widely been studied hitherto, there is limited research devoted to the contribution of grammatical and morphological aspects to verbal aggression. This paper provides a corpus-assisted analysis of slang morphological means used in verbal aggression. The focus is on four compound families ( X-ass , X-brain , X-face , X-head ), which are often used in slang to form compound words referring to specific groups, such as homosexuals, fools, or ineffectual people. The paper adopts a morphopragmatic approach to investigate three pragmatic meanings/functions – namely, derisive, critical, and offensive – of slang words in situations of conflict. The combination of quantitative and qualitative analyses of data drawn from the Corpus of Contemporary American English (COCA) shows the frequency of the morphological processes, their privileged genres and contexts, as well as their negative potential and face-threatening power.
Two-Policy Cooperative Transfer for Alleviation of Sim-to-Real Gap

The main difficulty of sim-to-real is the reality gap between the source domain and the target domain. In order to solve it, various methods where domain randomization is the mainstream have been emerged, whose essence is to make the single policy more robust. In contrast, we propose a novel transfer method, namely two-policy cooperative transfer, whose core is that one policy (task policy) is used to complete the task and another policy (gap policy) aims to assist the former to cover the gap, hence we can focus on the training of task and the overcoming of gap respectively. Based on this method, the setting of the learning objective of gap policy depends on the transfer situation of deploying task policy into real system, besides how to conduct the cooperation of the both lies in the threshold reflecting gap and the coupling of output actions of two policies. For the typical contact-rich gap in the dynamics field, we design an adaptive object pushing experiment based on UR3 robot, and verify the effectiveness of the proposed method.
Convolution Padding in Recurrent Neural Networks for Image Denoising with Limited Data

Recurrent neural networks are widely used in applications for time sequence prediction, such as speech recognition, text prediction, and target tracking. These networks are not popular for image restoration tasks due to the fact that there is no time dependency on images. In this paper, we repurpose a recurrent neural network to recover images from noisy observations and investigate convolutional padding to improve the results. Our proposed method artificially creates a time dependency between the image reconstructions at different iterations of the algorithm, allowing us to use a recurrent neural network. In addition, we do not train the network over the true images. Rather, we only utilize the noisy image and the structure of the network to perform image denoising tasks. We test our method using images from the CIFAR-10 dataset and present our results using the structural similarity index.
Exploring and Eliciting Needs and Preferences for Wikidata

Wikidata is an open knowledge graph created, managed, and maintained collaboratively by a global community of volunteers. As it continues to grow, it faces substantial editor engagementchallenges, including acquiring new editors to tackle an increasing workload and retaining existing editors. Experiences from other online communities and peer-production systems,including Wikipedia, suggest that recommending tasks to editors could help with both. Our aim with this paper is to understand how a Wikidata recommender system could look like.We conduct a mixed-methods study with a thematic analysis of in-depth interviews with 31 Wikidata editors and three Wikimedia managers, complemented by a quantitative analysis ofedit records of 3, 740 Wikidata editors. The insights gained from the study help us outline design requirements for our recommender system, WikidataRec. We briefly introduce the system,which demonstrates that the design requirements can be feasibly implemented to produce useful recommendations for editors. We conclude with a discussion of directions for future work.
A Case Study of Container Behavior Analysis with Cloud Resource Profile

In the field of cloud computing, a container is a lightweight abstraction of an operating system and the software application running for a particular service. To select a machine to deploy the container, the scheduling component of a cloud computing system usually considers the behavior of the container for decision. Several recent studies have shown that the prediction of the future behavior of a container can provide helpful guidance for scheduling. In this paper, we exploit the Alibaba cloud scheduling trace data which was generated on the year 2018 and named cluster-trace-v2018, to analyse containers behavior. For cluster-trace-v2018, we report our method for the prepossessing, container behavior feature engineering, and behavior classification. Specifically, we developed a processing procedure to filter out incomplete records. We designed a method for building behavior feature vector for containers on 12-hours long and 24-hours long periods. And, based on these features, we utilize the K-Means method to cluster the containers into a specified number of clusters. Experimenters on varying length of container feature vector show that the containers fits well into 10 clusters.
User interest representation intelligent session recommendation model based on multi-attention mechanism

To solve the problem that it is difficult to take account of user behavior diversity and item features inherent in a limited sequence of user conversation behavior, and a single model cannot make full use of favorable feature information for recommendation matching, In this paper, we propose a User Interest Representation (UIR) session recommendation model (SR-UIAM) integrating Attention Mechanism (AM). Using project information, studies project representation from a multi-dimensional and fine-grained perspective and excavates deeper relationships between users and projects. Finally, some experiments are carried out on two real open benchmark data sets, and the experimental results show that the proposed method can improve the expressiveness of the model and improve the recommendation performance to some extent.
Semantic Embedding Graph Convolutional Networks for Multi-label Video Segment Classification

Video classification is a challenging problem, video segment labels are sparse and expensive to get, and it is important to leverage as much available information as possible from labeled datasets. There have been several ways to capture video frame information but none of them have utilized the information hidden in labels correlation to increase classification accuracy. This work proposed a framework called Graph Convolution Semantic Network of aggregated descriptors (GCSN) which can extract neighboring information of related segment labels to increase video segmentation classification accuracy. Label relation graph was built by thresholding on cosine similarity computed from mutual embedding similarities, word embeddings were generated by Deep Bidirectional Transformers. The testing accuracy on Youtube-8m video segments classification dataset shows that our proposed GCSN outperforms NeXtVLAD baseline by considering additional labels relation information.
Facial Expression Study Based on 3D Facial Emotion Recognition

Teaching evaluation is an indispensable key link in the modern education model. Its purpose is to promote learners' cognitive and non-cognitive development, especially emotional development. However, today's education has increasingly neglected the emotional process of learners' learning. Therefore, a method of using machines to analyze the emotional changes of learners during learning has been proposed. At present, most of the existing emotion recognition algorithms use the extraction of two-dimensional facial features from images to perform emotion prediction. Through research, it is found that the recognition rate of 2D facial feature extraction is not optimal, so this paper proposes an effective the algorithm obtains a single two-dimensional image from the input end and constructs a three-dimensional face model from the output end, thereby using 3D facial information to estimate the continuous emotion of the dimensional space and applying this method to an online learning system. Experimental results show that the algorithm has strong robustness and recognition ability.
Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

The Chinese language is a nation’s symbol, the accumulation of a country’s magnificent culture, and the pearl precipitated in the long history’s washing. The Chinese language is rich and complex, and there are still many topics and issues that merit repeated exchanges and discussions in academic circles. This study proposes a classification method of emotion polarity based on reliability analysis in order to identify the tendency of literary emotion in Chinese language. Support vector machine (SVM), class center, and KNN (K-nearest neighbor) are included in the combined classifier, which effectively improves the accuracy and efficiency of emotion polarity classification. A Chinese literary emotion analysis model based on the method of UKNNC (unbalanced K-nearest neighbor classification) is proposed at the same time by analysing the characteristics of text structure and emotion expression. The experimental results show that, when compared to traditional machine methods, the UKNNC method can analyse text sentiment in fine-grained and multilevel ways, while also improving the accuracy of Chinese literary sentiment analysis.
QoE Models for Online Video Streaming

With the rising popularity of video streaming services, Quality of Experience (QoE) acts as the crucial role in improving user's experience. Distinguished from Quality of Service (QoS), which mainly weigh the video streaming quality with network transmission performance, QoE focuses on the subjective and user-oriented assessment when users views the videos online. It is a vital issue for online streaming to ensure a user-satisfied transmission under dynamic network. This highlights the need for an accurate and feasible QoE model to balance the user experience and available transmission bandwidth. This paper summaries and analyzes existing explorations on QoE models for online video streaming, especially with the advancement on emerged learning-based models, to promote the development of this research field.
Research on the Application of Computer Big Data and Web-based Intelligent Information System in Classroom Network Video Course

At present, the classroom teaching part of the Web-based learning management system has the problems of insufficient expression of teaching resources, difficulty in synchronization of courseware, lack of teaching scenarios and interactivity. The article designs and develops a Web-based classroom teaching video analysis and diagnosis system. The system can embed cue points in classroom teaching videos, mark the videos in segments and corresponding editing processing, and automatically generate analysis reports and diagnostic results according to relevant classroom analysis models and diagnostic criteria, and establish keywords and segmented videos at the same time Index, and can quickly find the required videos, which is conducive to improving teachers’ comprehensive skills and classroom teaching efficiency.
A New Gpu Parallel Method for Image Classification Based on Deep Learning

To solve the problem of low transmission efficiency after multi-GPU parallel in the scene of image classification based on deep learning, firstly, an improved Ring All Reduce algorithm with lower time complexity is proposed, which optimizes the data transmission process through the principle of interval pairing of nodes, and is used to alleviate the bandwidth loss of parallel structure in traditional parameter server. Secondly, aiming at the problem that data parallelism is difficult to support the acceleration delay caused by large-scale network parameters, taking advantage of the characteristics of deep learning backbone network that the weight parameters are smaller than that of the full connection layer, the synchronization overhead is small, and the gradient transmission overhead of the full connection layer is too high, a GPU hybrid parallel optimization algorithm is proposed . It perfoms data parallel ism in the backbone network and model parallelism in the full connection layer. The improved Ring All Reduce algorithm is used to realize the post-parallel data communication among nodes, which is used in image classification based on deep learning model. In order to verify the effectiveness of the proposed method, experiments are carried out on three datasets, namely, Cifar10, mini ImageNet and fish4knowledge. The results show that the proposed method can achieve better acceleration effect while keeping the same classification accuracy, and can achieve nearly 45% improvement effect compared with data parallelism.