Reviewerly/exHarmony huggingface dataset:
Design and Development of Distance Education System Based on Computing System

Distance education is a new form of education which has been evolving in the field of contemporary education. It has been developed vigorously under the promotion of Internet education platform. Distance education also promotes the cooperation and exchange of traditional teaching in different regions. It does not conflict in function, nor does it form commercial competition. It is a good complementary mechanism of education. The purpose of this paper is to design and implement the platform of distance education, so that learners can flexibly arrange their time for education according to their own situation and improve their learning efficiency. This paper analyzes the problems in the development process from the perspective of the practical application of software, describes and evaluates the specific requirements of the three roles of students, teachers and managers, and makes improvement and improvement. In the case of realizing the basic functions of distance education platform, a new evaluation system based on test paper generation algorithm is proposed and applied to the system to realize the function of online random test. Based on the traditional J2EE architecture, the B/S model based on web is adopted. The designer realizes a functional module which takes students, teachers and administrators as the main roles. Finally, after we have realized all the functions of remote sensing distance education platform, we have checked and tested its system function and software performance. It is found that when the system carries out information transmission with no more than 400, the feedback speed of the system or users is not high, usually less than 0.5 s, which indicates that the platform has good performance and meets the teaching and management requirements of distance education.
Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods

This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test.
Feature Extraction Method of Power Grid Load Data Based on STFT-CRNN

Load curve clustering is a fundamental part of smart grid big data analysis, and power load clustering is inseparable from the steps of feature extraction. This paper applies feature extraction methods in the field of voice processing to the processing of load curve data, and proposes a method based on short-time Fourier transform (STFT) and convolutional recurrent neural network (CRNN) to extract load features. Some real power grid load data are used for experiments, and a feature extraction model that conforms to the real label is obtained, and excellent clustering accuracy is achieved on the data of unknown labels, which proves the effectiveness of the proposed feature extraction model.
An Open Source Motion Planning Framework for Autonomous Minimally Invasive Surgical Robots

Planning and execution of autonomous tasks in minimally invasive surgical robotic are significantly more complex with respect to generic manipulators. Narrow abdominal cavities and limited entry points restrain the use of external vision systems and specialized kinematics prevent the straightforward use of standard planning algorithms. In this work, we present a novel implementation of a motion planning framework for minimally invasive surgical robots, composed of two subsystems: An arm-camera registration method only requiring the endoscopic camera and a graspable device, compatible with a 12mm trocar port, and a specialized trajectory planning algorithm, designed to generate smooth, non straight trajectories. The approach is tested on a DaVinci Research Kit obtaining an accuracy of $2.71\pm 0.89$ cm in the arm-camera registration and of $1.30\pm 0.39$ cm during trajectory execution. The code is organised into STORM Motion Library (STOR-MoLib), an open source library, publicly available for the research community.
An Iterative Deviation-based Ranking Method to Evaluate User Reputation in Online Rating Systems✱

With the exponential growth of data scales in the contemporary e-commerce systems, rating items with biased or misleading scores lead to poor performance of recommendation systems. Measures on user reputation are highly preferred to identify those with deliberate biased or random rating spammers. Despite the fact that previous methods are relatively feasible, they are not accurate or robust when the numbers of malicious users have reached a critical value. In this paper, we propose an iterative deviation-based user reputation ranking (IDR) method. It is inspired by the common fact that user with higher ranking usually performs less biased rating scores. Another factor that influences the ranking coming from their rating patterns. High quality rating scores are usually given by users with peaked rating patterns. Experimental results on four real sparse data sets show that the accuracy and robustness of the proposed method are better than the existing state of arts methods.
Cloud Algorithms: A Computational Paradigm for Managing Big Data Analytics on Clouds

In the age of the industry 4.0, we stand at the cusp of a technological era that has challenged the way sapiens manage data. When the pipeline of data breached its limits, big data analytics became the obvious answer to manage this rapid data flow. When the bubble of big data analytics sprouted across all verticals, we resorted to cloud computing. But this is just the start and not the end of this saga. Data is now deemed to be the currency, oil, and oxygen of the twenty-first century. As such, it is difficult to say whether the erstwhile data lakes would be sufficient to accommodate the reproductive volumes of data over the next few decades. Having said that, cloud computing remains the unanimous solution for sapiens to bank upon. Cloud computing offers a blend of utilities ranging from software-as-a-service (SaaS) to platform-as-a-service (PaaS). Other utilities include infrastructure-as-a-service (IaaS) and computing-as-a-service (CaaS). This may be extended to data-as-a-service (DaaS). However, the contours of the cloud continue to expand to the extent that our existing computational paradigms need to be revisited. This paper proposes to deploy the concept of Johnson’s algorithm in the cloud ecosystem for effective management of big data. With the help of Johnson’s algorithm, we can allocate and synchronize processes between clouds without much time lag. The aim is to not only monitor, but manage and moderate activities between clouds. This would eventually culminate into effective data management, efficient resource allocation, and timely data delivery.
Low-overhead multi-language dynamic taint analysis on managed runtimes through speculative optimization

Dynamic taint analysis (DTA) is a popular program analysis technique with applications to diverse fields such as software vulnerability detection and reverse engineering. It consists of marking sensitive data as tainted and tracking its propagation at runtime. While DTA has been implemented on top of many different analysis platforms, these implementations generally incur significant slowdown from taint propagation. Since a purely dynamic analysis cannot predict which instructions will operate on tainted values at runtime, programs have to be fully instrumented for taint propagation even when they never actually observe tainted values. We propose leveraging speculative optimizations to reduce slowdown on the peak performance of programs instrumented for DTA on a managed runtime capable of dynamic compilation.
Efficient Time Series Predicting with Feature Selection and Temporal Convolutional Network

TCN (Temporal Convolutional Network) has been proved that it outperforms canonical recurrent networks across different tasks and demonstrates longer effective memory. Besides, it speeds up the prediction with parallel calculation. The Granger causality test is used to detect temporal causality between variables to select the most influential features. In this paper, we adopt the Granger causality test and introduce TCN with two-dimensional convolution kernel to predict the temperature of superheater in thermal power plants. To reduce the noise of dataset, we use the Granger causality test to select features which have the greatest impact on the temperature prediction. The challenge of efficiency is addressed by applying the TCN with two-dimensional convolution kernel. To evaluate our method, we conducted experiments on time series predicting with the real-sensor data from thermal power plant. Compared with canonical recurrent network, our method has improved the predicting ability in terms of accuracy and efficiency.
Intelligent Translation Strategy Based on Human Machine Coupling

No matter what kind of machine translation is based on, the quality of translation has always been concerned, and the computer is facing some difficulties in natural language processing in the process of translation. In the face of the above problems, this paper proposes an intelligent translation model based on human-computer coupling (HMCIT). The core strategy of HMCIT is to use natural language understanding to analyze the grammar and semantics of the text, and then use human-computer coupling intelligent translation technology to generate text summary after information fusion. Firstly, the Markov random field is used to map it to three tuples. Then, the semantic distance between the image and the sentence is calculated by similarity. Finally, the sentence with the closest semantics is selected from the sentence pool to generate the image description. In this paper, we compare HMCIT with the classical alignment model in terms of confusion. The results show that the confusion degree of the translation results of this model is 43% lower than that of the alignment based model, which indicates that the effect of HMCIT is better. HMCIT is to extract one sentence or several sentences from a document or document set to form a summary. Its advantages are simple and practical, and it is not easy to deviate from the main idea of the article. However, it may have some disadvantages, such as incoherent summary, poor word control, and unclear target sentence. The quality of the summary depends on the original text.
Q-Mixing Network for Multi-agent Pathfinding in Partially Observable Grid Environments

In this paper, we consider the problem of multi-agent navigation in partially observable grid environments. This problem is challenging for centralized planning approaches as they typically rely on full knowledge of the environment. To this end, we suggest utilizing the reinforcement learning approach when the agents first learn the policies that map observations to actions and then follow these policies to reach their goals. To tackle the challenge associated with learning cooperative behavior, i.e. in many cases agents need to yield to each other to accomplish a mission, we use a mixing Q-network that complements learning individual policies. In the experimental evaluation, we show that such approach leads to plausible results and scales well to a large number of agents.
Validating New Method for Measuring Cohesion in Object-Oriented Projects

Class cohesion, understood as the degree of how tightly are bound or related its internal elements to one another, is often a critically important for quality assurance (QA) of object-oriented software. Typically, a low cohesive class contains disparate and/or isolated members; therefore, cohesion level is useful for detecting of poorly designed classes and ensuring faster and better system reconfiguration. There are over thirty different metrics to measure cohesion, based on class member analysis in terms of number and structure of attributes and methods. Utilizing class cohesion metrics can promote Java code static analysis quality, improve object-oriented programming practices, and suggest advanced and efficient ones. The jPeek tool with its library for inspecting objects in Java was developed by Huawei Technologies Co. Ltd. to achieve these goals [1]. The idea of code quality assessment is well known for a long time; class connectivity metrics were proposed by community several years ago and have not become generally applicable practice in industrial programming. The main goal of this study is to make a methodology for using cohesion metrics to analyze code that allows users to estimate their projects in the most appropriate way. During the study, each of the metrics was studied in detail, the influence of programming patterns on the result of the metrics was evaluated, and as a result a methodology for using the metrics to obtain the most reliable estimate was formulated.
MR Image Denoising by FGMM Clustering of Image Patches

The general procedure of image denoising is solving an inverse problem with some statistic prior information as the regularization. We propose a novel noise removal method for Magnetic Resonance (MR) images based on the Fuzzy Gaussian Mixture Model (FGMM) Clustering of image patches. In this method, the FGMM, which is an extension of the Gaussian Mixture Model (GMM), has been trained using overlapping clean patches randomly selected from the image database firstly. Then the objective function, which is the sum of the image corruption model and the prior model, is constructed. We optimize the objective using the “Half Quadratic Splitting” method and obtain an expression of iteration to restore the whole image. Finally, we use the proposed method to denoise Magnetic Resonance (MR) images. The experimental results show that the proposed method achieves good performance in MR image denoising.
Services Extraction for Integration in Software Projects via an Agent-Based Negotiation System

The great development of the internet and all its associated systems has led to the growth of multiple and diverse capabilities in the field of software development. One such development was the emergence of code repositories that allow developers to share their projects, as well as allowing other developers to contribute to the growth and improvement of those projects. However, there has been such a growth in the use of these systems that there are multiple works with very similar names and themes that it is not easy to find a repository that completely adapts to the developer’s needs quickly. This process of searching and researching for repositories that fit the initial needs has become a complex task. Due to the complexity of this process, developers need tools that allow them to process a large amount of information that can be downloaded and analysed programmatically. This complexity can be solved by approaches such as big data and scraping. This paper presents the design of a data ingestion system for libraries, components and repositories in a multi-agent programming environment.
Underwater Image Quality Database Towards Fish Detection

Marine biosphere monitoring is one of the crucial links to the understanding and protection of the marine environment. Moreover, underwater images play an important role in marine biosphere monitoring. Since the capture and transmission conditions are extremely poor in complicated underwater environments, images will suffer typical types of distortions significantly. Pre-evaluation of the image quality to facilitate subsequent processing becomes particularly important. Traditional Image Quality Assessment (IQA) methods are normally developed based on perceptual quality. Nevertheless, images are captured for understanding and analysis to achieve the purpose of intelligent monitoring. There are barriers between traditional IQA and IQA in marine biosphere monitoring. To address this issue, this paper focused on the fish detection task which is a vital task in marine biosphere monitoring. An Underwater Image quality database for Fish Detection (UIFD) is proposed based on the characteristics of the underwater environment. This database can be utilized as a benchmark to develop and evaluate underwater IQA. In addition, it can also be used to provide guidance for improving the performance of underwater fish detection algorithms.
An Efficient Method for Boosting Human Pose Estimation

Most existing human pose estimation approaches fall into designing new network architectures or tend to apply deeper layers. Most of the methods are time-consuming, and lightweight plug-in for improving human pose estimation to the existing network gets little investigation. In this paper, we propose a lightweight plug-in module to boost the performance of human pose estimation named PoseReNed. PoseReNet is a network with three branches that are designed to tackle the attenuation caused by occluded keypoints or different scales of the keypoints. Generally, small-scale keypoints are more difficult to detect, and we observe that different channels of the output feature map have different attributes to the performance of estimation. We apply a channel attention mechanism to re-weight the channel to trade-off among different scales of the keypoints. By aggregating multiscale output feature maps, the pose estimation performance can be improved. Serving as a model-agnostic plug-in, PoseReNet brings about significant performance boost to existing human pose estimation models. Extensive experiments show that PoseReNet can effectively improve precision on COCO and MPII.
Car Damage Detection and Cost Evaluation Using MASK R-CNN

Detecting the damage on a car is an image-based processing method with enormous scope for automation. This concept of automated detection of the extent of exterior damage on a car and subsequent quantification of the damage severity would benefit car insurers, car rentals and repair services. In this paper, we propose employing convolution neural networks to build a Mask R-CNN model that can detect the area of damage on a car. The dataset used consists of images of damaged vehicles with a single class named scratch. The images are precisely annotated with the area of damage. The model is trained using the base weights of Mask R-CNN COCO dataset. The images are processed for 21 epochs. After processing, the final result is visualized using a color splash technique, wherein the area of damage is highlighted. This model would help in reducing the cost of processing insurance claims and lead to greater customer satisfaction. Car dealers can eliminate the manual process of damage assessment and the labor cost accompanied by it. Accuracy and transparency in pricing cars and their potential repairs will be made more prevalent. Fraudulent vehicle insurance claims can also be diminished. On implementing our model, we achieved an overall loss of 0.3888.
A Study on Using Emojis in a Shoulder Surfing Resistant Authentication Method

While images or emojis offer good memorability when used in an authentication method, inherently graphical data are highly susceptible to shoulder surfing attacks. An authentication system incorporating emojis was proposed and designed, which offers resistance to shoulder surfing attacks. The proposed system implements emojis in place of numerics in the reference method, DragPIN, and adds cue questions. The methods are compared in terms of performance and memorability, through user testing. The proposed authentication system was found to be successful at resisting shoulder surfing attacks. After 4–6 weeks, memorability was also higher in the proposed method compared to the reference method.
Network Design and Implementation Based on Azure PAAS

With the popularization of information education, the development and design of network courses has become an important field of modern educational technology research. Based on the analysis of the characteristics and development principles of music education network course, the architecture of music education network course based on azure PAAS is designed. Through the steps of development environment configuration, creation and testing, deployment to Azure Platform, the construction of music education network course applying cloud computing technology is realized, and the high scalability and openness of network course are achieved, It effectively meets the needs of learners to realize distributed online learning.
A LSTM-cBiGAN based hybrid sampling method for time series customer classification

Accurate customer classification is an important prerequisite for successful customer relationship management. With the development of the Internet, companies have obtained more time series data on consumers. Time series customer classification can extract the potential characteristics of customers and classify them accurately. One of the thorny problems facing the customer classification problem is class imbalance. However, there are few existing studies that address the issue of class imbalance in time series classification. So we introduce Generative Adversarial Networks (GAN), a deep learning model with strong performance on general data, to time series classification. A novel LSTM-cBiGAN based hybrid sampling method was proposed. In the oversampling part we use LSTM-cBiGAN to generate the data. In the undersampling part we proposed Time Series Features based Nearest Neighbor (TSFNN) undersampling algorithm. Experimental results on real-world time series customer classification show that our proposed hybrid sampling method outperforms other benchmark sampling methods. It provides an important solution to the class imbalance problem in time series customer classification.
A Generalized Meta-loss Function for Distillation Based Learning Using Privileged Information for Classification and Regression

Learning using privileged information (LUPI) is a powerful heterogeneous feature space machine learning framework that allows models to learn from highly informative (privileged) features which are available during training only. These models then generate test predictions using input space features which are available during both training and testing. LUPI can significantly improve prediction performance in a variety of machine learning problems. However, existing large margin and neural network implementations of learning using privileged information are mostly designed for classification tasks. In this work, we propose a simple yet effective formulation that allows general application of LUPI to classification, regression, and other related problems. We have verified the correctness, applicability, and effectiveness of our method on regression and classification problems over different synthetic and real-world problems. To test the usefulness of the proposed model in real-world problems, we have further evaluated our method on the problem of protein binding affinity prediction where the proposed scheme has shown to outperform the current state-of-the-art predictor.