Reviewerly/exHarmony huggingface dataset:
Energy Trading Between Prosumer and Consumer in P2P Network Using Blockchain

Nowaday’s energy demand and energy production are increasing. Renewable energy resources will play an important role in managing future production of electricity due to an increase in the development of societies. The centralized energy trading system faces a challenge in terms of fair energy distribution. Centralized existing energy trading system totally relies on a central system or third party, because the third party has many drawbacks in the form of record tampering or record altering. The fair transaction is the main issue in the energy trading sector. When the bitcoin is introduced in the market, the trust of Blockchain technology is increased. We proposed a Blockchain based energy trading system in peer to peer networks. Blockchain technology provides trust, security, and transparency for energy trading. In Blockchain technology, there is no necessary need of the third party in the energy supply sector. In our proposed paper, we facilitate the prosumer who produces renewable energy and sells surplus energy to the consumer. We achieved transparency, accuracy, efficiency in our proposed paper. Using a double auction process, we obtain low energy price and acheived consumer trust in energy trading.
Improving Classification Accuracy Using Combined Filter+Wrapper Feature Selection Technique

Large datasets such as medical datasets contain a lot of redundant or irrelevant attributes which will degrade the performance of the classifier. Feature selection methods help to improve the classification accuracy by providing relevant feature subset that best describes the dataset. The generated feature subset will be tendentious to the feature selection approach and the classification accuracy will rely on the relevancy of the features selected. Hence the process of deciding appropriate feature selection algorithm has a crucial role in providing relevant features. Traditional techniques for feature selection are filter methods and wrapper methods. In this paper, a novel hybrid feature selection approach that efficiently incorporates the benefits of both filter and wrapper methods is proposed. The features were first ranked based on the ranking criteria's and then a wrapper algorithm is invoked to generate a subset from the ranked features. A minimal redundancy maximal relevant feature subset is generated through the proposed method.
The Difficulties and Countermeasures of Applying Virtual Reality to Industrial Design Education

The subject of this study is the industrial design which requires physical operation exercises mainly targeted at the drawing techniques for product design courses to introduce virtual reality and to further explore the differences and learning outcomes compared to existing traditional sketch drawing courses. In this study, we constructed the object with the actual size in 3D virtual space according to the requirements of the course. Since virtual reality sketching has a sense of presence and is different from the images on a paper or in the computer, it will not only strengthen the space concept of the students but also make the observation of the interaction between relevant components and people more accurate. However, it is inevitable that there will be differences and requires adaptations in learning due to the differences in operating interface and learning methods. This study adopted Delphi Method to find out the requirements for the drawing for product design of the enterprises through interviews with experts, at the same time, cooperated with the analysis of actual course operations and experiments to find out the result of the expected learning outcomes. We hope to provide suggestions and methods for improvement in the application of virtual reality drawing for product design in industrial education.
A Task-Priority Coordinated Motion Planner Combined with Visual Servo for Mobile Manipulator

Mobile manipulators are increasingly used for material handling in industrial environment. In order to improve efficiency, coordinated motion of the mobile base and manipulator is necessary. However, reasonable motion planning and accurate sensor feedback are required to get the pose of the target object and compensate positioning error of the mobile base. In this paper, we propose a task-priority coordinated motion planner combined with visual servo for mobile manipulator. The trajectory of the end effector is generated by visual servo online and treated as the basic task. Constraint task is the trajectory of the mobile base. Experiments show that both the basic task and constraint task are satisfied well in the motion planner. Compared to strategy that manipulator grasps when the base is fixed, in the proposed resolution, the manipulator can grasp target object while the base moving effectively.
Image Denoising Algorithm Based on Incoherent Dictionary Learning

Aiming at the deficiency of losing texture information in image filtering process, an image filtering algorithm based on uncorrelated dictionary learning is proposed. Firstly, the noisy image is divided into overlapping image blocks, and the image blocks are randomly extracted. The uncorrelated redundant dictionary is obtained by using uncorrelated dictionary learning technology. Finally, the sparse representation coefficients of each image block under the redundant dictionary are obtained by sparse coding algorithm, and the original image is restored by using the sparse representation coefficients. The experimental results show that the irrelevant redundant dictionary has a strong ability to express the texture information of the image. It can keep the details and texture information of the image better and improve the visual effect.
Multiple writer retrieval systems based on language independent dissimilarity learning

Retrieval based on query images supports interesting applications in handwritten document analysis, such as checking manuscripts originality, and authorship. In this respect, writer retrieval systems aim to automatically find all manuscripts belonging to the same author. Presently, we propose a new combination scheme for multiple writer retrieval systems that employ different features and dissimilarities. The proposed combination is founded on writer-independent, SVM dissimilarity learning. For experimental evaluation, three individual systems are proposed each of which, has its specific features. To develop the first system, we propose the Multiscale Histogram Of Templates (M-HOT). For the second system, we introduce the so-called Multi-Gradient Elongated Quinary Pattern (MG-EQP) as new descriptor for handwriting characterization. The third system uses the well-known Run Length Features. Retrieval tests are performed on CVL, ICDAR-2011, ICDAR-2013 and ICDAR-2017 datasets. Furthermore, to highlight the language-independence aspect, experiments are performed on KHATT dataset that contains Arabic handwritten documents. Results obtained evince the effectiveness of the proposed features as well as the combination scheme, which outperforms both individual systems and the state of the art.
Skeleton-based Action Recognition with Lie Group and Deep Neural Networks

Skeleton-based action recognition has always been an important research topic of computer vision since the skeleton data is more robust to illumination and rotation. Traditional action recognition methods mainly rely on manual features. Among those methods, the skeleton feature representation modeled on Lie group can effectively describe the three-dimensional geometric relationship between joints. In recent years, deep learning methods such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory Networks (LSTM) have also achieved good performance in action recognition. In order to obtain more spatio-temporal information, we combine manual features and deep learning methods to propose a deep neural network LS-LieNet with CNN and Bi-direction LSTM (Bi-LSTM) based on the LieNet [1] network. First, the LS-LieNet network inputs the extracted Lie group representation of skeleton into a special CNN network which is designed for Lie group. Second, the transformed Lie algebra features are fed into the Bi-LSTM network before the fully connected layer of CNN. Then, the predicted labels and scores of the two network softmax layers are merged to effectively recognize the action. The experiment results on the standard 3D human action dataset show that the proposed LS-LieNet can efficiently improve the accuracy of action recognition.
Detection and Content Retrieval of Object in an Image using YOLO

Abstract It is easy for human beings to identify the object that is in an image. Even if the task is complex, human beings require only a minimal effort. Since computer vision is actually replicating human visual system, the same thing can be achieved in computers when they are trained with large amount of data, faster GPUs and many advanced algorithms. In general terms, Object detection can be defined as a technology that detects instances of object in images and videos by mimicking the human visual system functionalities. The motivation of the project is making the search process easier for the user i.e., if the object is very new for the user and he has no idea about it, he can upload a picture of that object and the algorithm will detect the object and gives a description about it. The objective of the project is to detect the object in an image, once the object is detected, the label i.e., the name of the detected object is searched in Wikipedia and few lines of description about that object is retrieved and printed. Also, the label is searched in google and the URL of the top pages with content related to the label are also displayed. The detection of object in an image is done using YOLO (You Only Look Once) algorithm with pre-trained weights. Previous methods for object detection, like R-CNN and its variations, used a pipeline to perform this task in multiple steps. This can take some time for execution, complex optimization may be involved because individual training of components is required. YOLO, does it all fastly with a single neural network. Hence, YOLO is preferred.
Thoth: A Web-based Tool to Support Systematic Reviews

Background: Systematic Reviews (SR) are crucial for collecting evidence related with a particular field or area. Hence, there are different protocols for conducting SR in software engineering. There is also several tools designed to provide support for these protocols. Aims: We understand that there is still a lack of a web-tool, fully integrating the features considered important for SR. Implementing such a tool would be a great contribution to the field. Method: We analyzed several studies discussing features for tools supporting SR. We defined design decisions based on these features. Results: We created Thoth, a web-based tool for SR. In this study we present a first version, containing a set of features. Before moving forward with the implementation, we conducted a quasi-experiment for evaluating and comparing Thoth with a similar tool. Conclusion: Despite being in the right direction for achieving our goal, the results of the quasi-experiment provided us with important evidence for improving Thoth.
Tree-based generational feature selection in medical applications

In many knowledge discovery experiments feature selection is obvious initial part. In the paper, some attempt to tree-based generational feature selection applications in medical data analysis is presented. This approach devotes to application of classification tree algorithm to estimate importance of attributes extracted from structure of the tree with recursive application of generational feature selection. This method apply removing of selected features from dataset and then creates next generation of important feature set. The process goes until the most important feature will be a random value. Implemented method were applied on three artificial and real-world medical datasets and the results of selection and classification are presented. They were mostly more efficient after selection than using original datasets.
Underwater Image Enhancement by Gaussian Curvature Filter

We develop a new Retinex-based variational model for enhancing single underwater image by imposing Gaussian curvature priors on the illumination and the reflection. Gaussian curvature filters are employed to estimate the illumination and the reflection efficiently for solving the proposed variational model. And Gaussian curvature filter can capture better underwater image details and prevent image over-enhancement. In addition, Gaussian curvature filter can reduce the runtime of underwater image enhancement without calculating both partial derivative operations and the gradient of overall energy functional. Ultimately, we provide numerous experiments to demonstrate the effectiveness of the proposed method, which outperforms several underwater enhancement algorithms in terms of enhancement performance, visual improvements and runtime.
Automatic Door Opening using Quantitative Gait Algorithm

Abstract Quantitative Gait algorithm provides a system to identify a person from a distance. This project identifies three types of systems : Detection, Recognition and Control to open the Door.The initial process is extracting the forefront facial (human) images are converted into ordinary binary silhouette, it is captured from each frame in burst mode. The images are converted into values and stored in the database. In the process at real time, the facial recognition is done by comparing with the database. From the results it can also be observed that the person identified by raspberry pi camera and is done mainly for security purpose. This proposed system is more efficacious, consistently good in quality, and it utilize very less amount of data and power that is compared to the other existing systems. This project indicates that very better sensitivity, specificity and also the execution time of Quantitative Gait algorithms which is implemented in ARM processor therefore the results are obtained using MATLAB software.
CBIR using Multi-level DWT

In the Content Based Image Retrieval (CBIR) technique requires features such as color, texture, shape and hybrid features. In this work texture based feature is used to retrieve the images from the database. In the first instance, intensity component is separated from the color image. DWT is applied on intensity matrix. Maximum valued coefficients from LH, HL and HH are considered for feature vector. LL band is further decomposed to next three levels. Fourth level decomposed LL coefficients also be taken to construct the feature. Hence, feature vector contains all the resolutions of the image. Retrieval of the image is based on the distance between query and data base images. Experiments are performed on the Corel 500 Image Database. Finally, experimental results are evaluated by the recall, precision, and F-Score.
Research on Target Detection and Recognition Algorithm Based on Deep Learning

With the rapid development of robotics, people have higher requirements for detection and recognition of targets. The traditional target recognition algorithm can identify the type of the target, but can not identify the position information of the target, and the recognition rate of the traditional target recognition algorithm is low when the same target appears in the image. To this end, this paper proposes a target detection and recognition algorithm based on deep learning technology using convolutional neural network and region segmentation. The algorithm uses the region proposal to segment the image in the recognition process, which can recognize the type of target and precisely calibrate the position of the target in the image. The simulation results show that the algorithm has a high recognition rate and is sensitive to small target recognition.
Real-Time Sentiment Analysis of Tweets: A Case Study of Punjab Elections

Twitter, a micro-blogging site, is a huge repository of public opinions expressed towards various people, services, organizations, products etc. Sentiment analysis is the process of analyzing those public opinions. Sentiment analysis when combined with twitter gives useful insights into what is expressed on Twitter. It has applications in a number of domains like in stock market prediction, election results prediction, movie revenues, product reviews etc. This paper discusses about the different machine learning and deep learning models trained on a dataset of tweets collected from online github directory. The proposed system has been validated using a case study of election results prediction of February 2017 Punjab elections by analyzing public sentiments as expressed on twitter towards various political leaders. The developed SA system performs real-time sentiment analysis presenting analysis results simultaneously as tweets occur on twitter. A dashboard has been designed for presenting results to the user. The display updates every one minute, bringing tweets in real time and presenting the results graphically. Tweets are also getting stored in a CSV file for final results prediction. The system gave very encouraging results with minimal diversion when compared with actual results.
Speckle Noise Removal in Ultrasound Images with Stationary Wavelet Transform and Canny Operator

Speckle noise is random multiplicative noise which inhibits the segmentation and recognition of fine details or textures in ultrasound images. In this paper, a method for despeckling of medical ultrasound images is proposed, which is based on stationary wavelet thresholding. Canny edge detection is applied to the low frequency sub-bands to adjust the denoising strategy in the vicinity strong edges. An adaptive NeighShrink algorithm is used for thresholding the wavelet coefficients of the high frequency sub-bands of an image decomposed by stationary wavelet transform. The combination of Canny edge detection and the adaptive NeighShrink effectively preserve edges and removes noise. The method is compared against some popular methods on de-noising the synthetically speckled images and clinical ultrasound images. The experimental results show that our proposed method results in better performance under most conditions over comparator algorithms in terms of Peak Signal to Noise Ratio, Edge Preservation Factor and 2D cross correlation, and is consistently superior by these metrics at higher noise levels.
An Enhanced V-BM3D Algorithm for VideoSAR Denoising Combined with Temporal Information

Moving target in video Synthetic Aperture Radar (videoSAR) can be detected via shadow. Thus the inherent speckle noise and thermal noise in videoSAR may bring in a great mass of false alarms. V-BM3D (Video Block-Matching 3D) algorithm has proved its excellent effect in optical video denoising. Though its denoising performance may drop dramatically in high noisy environment. In this work, we introduce V-BM3D to suppress various kinds of noise in videoSAR and evaluate its denoising effect. Temporal information hidden in SAR video is exploited to enhance the denoising effect. Experiments on a real videoSAR footage show that the enhanced V-BM3D algorithm can effectively suppress the noise in videoSAR and bring in dramatic improvements in videoSAR moving target detection.
A Blockchain Framework for Proptech: Success Model Through Disintermediation and Self-regulation

Land records are traditionally recorded via physical documentation method, where the documents are isolated with different stakeholders in order to ensure their integrity. Of late, governments across the world are ushering exponential changes to transform Property Technology (PropTech) i.e. a facet of digital transformation in the property industry. Can government itself be a part of the blockchain ecosystem by paving the way for disintermediation? Is self-regulation a way forward for blockchain in PropTech. This question remains unanswered in the present-day scenario. Therefore, this paper explores the possibility of regulating PropTech through Blockchain, and presents a framework for Blockchain adoption in PropTech & the role, the Government can play within this ecosystem.
A modified Whale Optimization Algorithm based on Chaos Initialization and Regulation Operation

A rising meta-heuristic swarm intelligent computation algorithm, the Whale optimization Algorithm (WOA), which has been proved competitive with other intelligent optimization algorithms in solving different optimization problems. To accelerate the convergence rate of WOA, this paper has proposed a new improved whale optimization algorithm, called Chaos Regulation Whale optimization Algorithm (CRWOA). The chaotic map generator composed with three kinds of chaotic maps has been used to initialize the population. Furthermore, Cauchy distribution and Gauss distribution are introduced to regulate the position in each iteration. To analyze the superiority of CRWOA further, ten single-objective benchmark test functions are chosen. Through the comparison and analysis of the optimal results, CRWOA is demonstrated superior in general. It has faster convergence speed and better results than the other algorithms when less iteration number, less population quantity and wider ranges of variables are set.
Constructing Artificial Data for Fine-Tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation

Biomedical text tagging systems are plagued by the dearth of labeled training data. There have been recent attempts at using pre-trained encoders to deal with this issue. Pre-trained encoder provides representation of the input text which is then fed to task-specific layers for classification. The entire network is fine-tuned on the labeled data from the target task. Unfortunately, a low-resource biomedical task often has too few labeled instances for satisfactory fine-tuning. Also, if the label space is large, it contains few or no labeled instances for majority of the labels. Most biomedical tagging systems treat labels as indexes, ignoring the fact that these labels are often concepts expressed in natural language e.g. ‘Appearance of lesion on brain imaging’. To address these issues, we propose constructing extra labeled instances using label-text (i.e. label’s name) as input for the corresponding label-index (i.e. label’s index). In fact, we propose a number of strategies for manufacturing multiple artificial labeled instances from a single label. The network is then fine-tuned on a combination of real and these newly constructed artificial labeled instances. We evaluate the proposed approach on an important low-resource biomedical task called PICO annotation, which requires tagging raw text describing clinical trials with labels corresponding to different aspects of the trial i.e. PICO (Population, Intervention/Control, Outcome) characteristics of the trial. Our empirical results show that the proposed method achieves a new state-of-the-art performance for PICO annotation with very significant improvements over competitive baselines.
Strafing Gain: A Novel Redirected Walking Technique

Redirected walking enables natural locomotion in virtual environments that are larger than the user's real world space. However, in complex setups with physical obstacles, existing redirection techniques that were originally designed for empty spaces may be sub-optimal. This poster presents strafing gains, a novel redirected walking technique that can be used to shift the user laterally away from obstacles without disrupting their current orientation. In the future, we plan to conduct a study to identify perceptual detection thresholds and investigate new algorithms that can use strafing gains in combination with other existing redirection techniques to achieve superior obstacle avoidance in complex physical spaces.