Reviewerly/exHarmony huggingface dataset:
HistoRank: History-Based Ranking of Co-change Candidates

Evolutionary coupling is a well investigated phenomenon during software evolution and maintenance. If two or more program entities co-change (i.e., change together) frequently during evolution, it is expected that the entities are coupled. This type of coupling is called evolutionary coupling or change coupling in the literature. Evolutionary coupling is realized using association rules and two measures: support and confidence. Association rules have been extensively used for predicting co-change candidates for a target program entity (i.e., an entity that a programmer attempts to change). However, association rules often predict a large number of co-change candidates with many false positives. Thus, it is important to rank the predicted co-change candidates so that the true positives get higher priorities. The predicted co-change candidates have always been ranked using the support and confidence measures of the association rules. In our research, we investigate five different ranking mechanisms on thousands of commits of ten diverse subject systems. On the basis of our findings, we propose a history-based ranking approach, HistoRank (History-based Ranking), that analyzes the previous ranking history to dynamically select the most appropriate one from those five ranking mechanisms for ranking co-change candidates of a target program entity. According to our experiment result, HistoRank outperforms each individual ranking mechanism with a significantly better MAP (mean average precision). We investigate different variants of HistoRank and realize that the variant that emphasizes the ranking in the most recent occurrence of co-change in the history performs the best.
A File cloud sharing method based on XOR operation

Abstract To realize the secure storage and sharing of files, a file cloud storage method based on XOR (Exclusive OR) operation is proposed. In this method, a file is encrypted into multiple file slices based on XOR operation, then these file slices are uploaded to different locations of the public cloud platform. These file slices can be downloaded from the cloud platform and restored to the original data file based on XOR operation. In this way, even if a file slice is stolen, the information of the file will not be stolen. This paper gives the XOR-based encryption and restoration algorithms for the two cases where the original data file is not divided or divided into arbitrary secrets. Finally, we give the implementation method of the prototype.
Item-Based Recommender Systems Applying Social-Economic Indicators

Despite the development of online items in the market, Item-based Recommender Systems play an essential role to assist consumers to access their targets rapidly. Research activities show that users (consumers) in various societies have different purchasing behaviors. In the latest studies, the similarity of users was calculated based on different indications such as Cultural Indicators. Nonetheless, they neglect social-economic indicators expected to be more factors that are efficient. In this research, a framework was designed for recommender systems called SEIRS that include several algorithms based on social-economic indicators. In fact, the similarity of items was defined in terms of the similarity between users’ evaluation based on social-economic indicators. The presented method categorizes items and recommends them to users so that they match with users’ favorites more precisely. The results were compared not only with the standard item-based system but also with different mixtures of social-based and economical-based indicators. In addition, the presented idea was used to solve the cold start problem that occurs when a newcomer customer with no information on his previous purchases and evaluations becomes available. Furthermore, it is possible to present suggestions that are more precise by using the SVD algorithm to solve the data sparsity. The present research applied three datasets: first, internet purchase by individuals (isoc_ibuy) dataset, second the related data on social-economic indicators from OECD, and third the actual BookCrossing dataset. The experimental results represent that in comparison with the standard recommender system, by applying social indicators the proposed recommender algorithm improved precision by 17.19%, and economic indicators improved precision by 17.60%, respectively. Applying both social and economic indicators provides 20.91% more accurate recommendations.
Metric entropy and its uses

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Detecting Tampered Region in video using LSTM and U-Net

Recently, we can always shoot digital videos with smartphone and share them with family or friends after editing them. It is necessary to check whether they are tampered by someone or not, if we use these digital videos as forensic evidence in criminal investigation. This paper proposes a method for detecting tampered regions in a video where a specific subject is removed from a scene. In the proposed method, we use LSTM for the time domain analysis and U-Net for the space domain analysis. The detection accuracy achieved 0.98 in terms of F-measure, even though tampered region was deformed or moved in the video. The experimental results show superior performance to detect tampered regions for digital videos.
On global solutions to some non-Markovian quantum kinetic models of Fokker–Planck type

In this paper, global well-posedness of the non-Markovian Unruh–Zurek and Hu–Paz–Zhang master equations with nonlinear electrostatic coupling is demonstrated. They both consist of a Wigner–Poisson like equation subjected to a dissipative Fokker–Planck mechanism with time-dependent coefficients of integral type, which makes necessary to take into account the full history of the open quantum system under consideration to describe its present state. From a mathematical viewpoint this feature makes particularly elaborated the calculation of the propagators that take part of the corresponding mild formulations, as well as produces rather strong decays near the initial time ($$t=0$$) of the magnitudes involved, which would be reflected in the subsequent derivation of a priori estimates and a significant lack of Sobolev regularity when compared with their Markovian counterparts. The existence of local-in-time solutions is deduced from a Banach fixed point argument, while global solvability follows from appropriate kinetic energy estimates.
Why Are Business Schools Generally So Static, and Why Is New Knowledge Needed?

This chapter provides a main argument for why business schools may be facing needs to fundamentally change perhaps more today than before. “New” pedagogy is available to make effective change happen, above all, so-called blended learning. But indeed several factors seem to slow down business schools’ capabilities and willingness to change, including a typically too strong focus on achieving favorable so-called ranking. It is the changing need of business itself, the key “customer”, that provides the main inputs for change, and the chapter concludes with a discussion of 10 such factors.
Wavelet Based Compression of Acne Face Images with Automatic Selection and Lossless Compression of Acne Affected Region

Medical image compression is inevitable part of medical research centers and hospitals. In this paper, compression of acne face images is considered by exploiting the fact that such images normally have some region of interest (ROI) space that contains acne and other space (without acne) as non region of interest. For proper diagnosis and treatment of acne, compression should be performed in such a way that no information loss results for the acne affected region. This paper proposes a new algorithm, in which the acne affected region is automatically selected using K-means clustering and then compressed minimally whereas relatively higher compression is applied on the non region of interest using wavelet transform in RGB colour space. Using this algorithm good Compression Ratio (CR) upto 8-14 is achieved without degradation in image quality of the acne affected region.
Recommendation systems

column Share on Recommendation systems: principles and practice Authors: Chittaranjan Tripathy Duke University Duke UniversityView Profile , Yannis Pavlidis Carnegie Mellon University Carnegie Mellon UniversityView Profile Authors Info & Claims XRDS: Crossroads, The ACM Magazine for StudentsVolume 26Issue 3Spring 2020 pp 54–56https://doi.org/10.1145/3383396Published:05 April 2020Publication History 0citation356DownloadsMetricsTotal Citations0Total Downloads356Last 12 Months25Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Learning labeling functions in distantly supervised relation extraction

Distant supervision has become the leading method for training large-scale information extractors. It could be encoded in the form of labeling functions, which employ knowledge bases to provide labels for the data. However, most previous works use only simple labeling functions, resulting in too mu ch noise in the training data, and the knowledge bases are far from well-explored. In this paper, in order to improve the labeling quality of the training data for distant supervision relation extraction, we propose to make use of existing knowledge bases to effectively learn labeling functions. Specifically, labeling functions are represented as Markov Logic, which can integrate various resources into a unified model naturally. Experimental results show that the training data produced by the learned labeling functions is significantly improved in quality. Different distantly supervised relation extraction models trained on the produced training data can also achieve better performances.
Adaptive streaming algorithm based on reinforcement learning

Abstract As video streaming occupies a large part of the Internet traffic, and DASH gradually becomes the dominant standard for video transmission recent years, a lot of researches about client-side bitrate adaptive algorithms are constantly emerging.This paper mainly adopts the DRL(Deep Reinforcement learning) algorithm that combines Deep Learning and Reinforcement Learning techniques to optimize the Quality of Experience (QoE) of DASH.This method learns to make decisions by observing the network environment and adjust the strategy to get more reward based on feedback,which doesn’t rely on pre-programmed models or assumptions about the environment. After experiments on the real network trace datasets, we can find that the algorithm this paper adopted can obtain higher quality of user experience than the existing researches.Moreover, the experimental results show that less buffer underflow occurs and smoother video playback is guaranteed.
Segmentation-Free Word Spotting in Handwritten Documents Using Scale Space Co-HoG Feature Descriptors

In this chapter, the author present a segmentation-free-based word spotting method for handwritten documents using Scale Space co-occurrence histograms of oriented gradients (Co-HOG) feature descriptor. The chapter begin with introduction to word spotting, its challenges, and applications. It is followed by review of the existing techniques for word spotting in handwritten documents. The literature survey reveals that segmentation-based word spotting methods usually need a layout analysis step for word segmentation, and any segmentation errors can affect the subsequent word representations and matching steps. Hence, in order to overcome the drawbacks of segmentation-based methods, the author proposed segmentation-free word spotting using Scale Space Co-HOG feature descriptor. The proposed method is evaluated using mean Average Precision (mAP) through experimentation conducted on popular datasets such as GW and IAM. The performance of the proposed method is compared with existing state-of-the-segmentation and segmentation-free methods, and there is a considerable increase in accuracy.
The feeder outage prediction model based on Optimization and Improvement of Random Forests Algorithm

Abstract The accurate prediction of feeder monthly fault level is the key to distribution network operation and maintenance. Aiming at the problems of low prediction accuracy, a method based on improved random forest to predict feeder monthly fault level is presented. Compared with the similarity calculation method of original random forest sample, this one increases the measurement of leaf node path distance, and applies the improved sample similarity to classification problem. Through the experimental comparison in the fault database of distribution network, the improved method can achieve a better classification effect than the original one, thus proving the effectiveness of the improved method.
Maximum Likelihood-Based Fuzzy Adaptive Kalman Filter Applied to State Estimation of Permanent Magnet Synchronous Motors

In this chapter, a novel fuzzy adaptive Kalman filter for state estimation of a permanent magnet synchronous motor is proposed. The fuzzy set theory is used as a tool to perform on-line modification of the covariance matrices, adjusting the EKF and UKF parameters according to estimation reliability of the currents in the two windings of the rotor, position, and velocity for a two-phase permanent magnet synchronous motor. Also, the methodology uses the maximum likelihood technique, where the difference between the theoretical covariance and the measured covariance is defined as an approximation considering the average of a moving estimation window. This difference is performed continually and used to dynamically update the covariance matrices, aiming to obtain an efficient estimation. The membership functions are optimized to adjust the covariance matrices so that the error variation is minimal. Simulation results illustrate the efficiency and applicability of the proposed methodology.
Region Based Hierarchical Modelling for Effective Shadow Removal in Natural Images

Shadow removal from natural images is always a challenging but intriguing issue. In this paper, we develop a novel region based hierarchical model to address this problem. Firstly, an automatic shadow detection method is applied to obtain the shadow region and then roughly decompose it into umbra and penumbra areas. A hierarchical framework is then adopted to relight umbra and penumbra separately. For umbra arrears, each pixel is recovered via a lighting model on the basis of the characteristics of shadow-free regions. Afterwards, we present an improved brute force filling image in-painting strategy to relight complex penumbra areas using the information of neighboring patches which are selected from recovered umbra and lit regions according to their similarities in texture, gradient and distance. The comparing experimental results with three canonical and other state-of-the-art approaches have demonstrated the superiority and versatility of our method in generating high quality shadow-free images with consistent illumination.
Analysis of a New Fractional Variational Model for Image Noise Removal

In this paper, a new variational model based on fractional-order partial differential is proposed for removing noise. We analyse the mathematical property of the proposed model, and prove our model exists the unique weak solution on fractional-order bounded variation space. Numerical analysis and Matlab simulation are also implemented, the experimental results show that the proposed model doesn't only have better denoising ability than some existing integer-order and fractional-order variational models, but also preserved better the textural details and eliminated obviously the staircase effect.
Performance Evaluation of XPath Routing Protocol in Data Center Network using NS3-Simulator

Data Center Network (DCN) is a popular approach to build a big and scalable network, which processed big data. One of the common topology used for DCN is Fat Tree, it is consists of a core switch, aggregation switch, and edge switch interconnecting with each other in a hierarchical manner. Each DCN topology includes Fat Tree, uses routing topology to make data transmission between each node in the network. DCN is a big network and so it is a good way to simulating some topology scheme in a simulator before implementing it to the real world. NS3 Simulator is the most widely used Network Simulator, well known for its discrete-event flexibility. In the paper, we present a framework for Fat Tree topology with XPath routing protocol in Network Simulator 3 (NS3). The information we provide includes performance evaluation of XPath routing protocol in Fat Tree DCN and tools available in NS3 to study routing protocol performance, the result shown that XPath routing protocol is better than default routing protocol for DCN running in NS3 simulator.
Token Distribution and Regular Expressions

This chapter explains how to use the positions of words in a vector to create distribution plots showing where words occur across a narrative. We introduce the grep function and show how to use regular expressions for more nuanced pattern matching.
Parameters of a class of complex continuous wavelet family

In this paper, we have established the relationship between the wavelet scale and equivalent Fourier wavelength for the family of the new complex continuous wavelet. The same relation is used to relate the maximum of the Scalogram of each coefficient obtained from the wavelet transform of the signal to its amplitude. The relationship was applied to a synthetic signal and the simulated results show the effectiveness of using the proper wavelet transform definition to this proposed family of wavelets.
ILL-Dataset: A Dataset Under Different Illumination Conditions for Face Recognition

Face recognition has achieved extraordinary success recently due to the advancement of algorithms, technology, and hardware. Also, this is partly due to the richness of the face dataset. However, face recognition in complicated environment such as illumination, occlusion, pose is still a challenging task. Among them, illumination problems are still tough challenges in the field of face recognition. The datasets play an important role in deep learning, while there are few datasets specially for different illumination conditions and the currently existing face data sets have limitations. In this paper, we collect and set up a dataset called ILL-Dataset specifically for different illumination conditions. We also provide the detected dataset which is processed by using the face detection method. After it established, it can provide image data and promote the face recognition accuracy under various illumination conditions. The experiment results indicate the ILL-Dataset is a challenge dataset on illumination face recognition.
A Generalized Overview of the Biomedical Image Processing From the Big Data Perspective

Computer-aided biomedical data and image analysis is one of the inevitable parts for today's world. A huge dependency can be observed on the computer-aided diagnostic systems to detect and diagnose a disease accurately and within the stipulated amount of time. Big data analysis strategies involve several advanced methods to process big data, such as biomedical images, efficiently and fast. In this work biomedical image analysis techniques from the perception of the big data analytics are studied. Big data and machine learning-based biomedical image analysis is helpful to achieve high accuracy results by maintaining the time constraints. It is also helpful in telemedicine and remote diagnostics where the physical distance of the patient and the domain experts is not a problem. This work can also be helpful in future developments in this domain and also helpful in improving present techniques for biomedical data analysis.
A Ranking Learning Training Method Based on Singular Value Decomposition

With the development of artificial intelligence, the use of machine learning technology to sort search results has become a very popular research field in recent years. In this paper, the correlation annotation dataset and the non-annotated dataset are combined with the singular value decomposition, and the new feature set is added to the training set, then the non-labeled data information is introduced in the training set. The differences in the ranked models trained by the new feature set before and after the experiment were compared. Experiments show that the feature set selected by SVD (singular value decomposition) helps to improve the accuracy of ranking effect.
A Bacterial Foraging Framework for Agent Based Modeling

Swarm optimization algorithms and agent based modeling (ABM) are two closely related research areas, parts of the multi agent system field, but they are traditionally not combined. Swarm optimization, in this case the bacterial foraging optimization (BFO), searches for an optimal solution while the ABM searches for a conclusion which resembles the real world, and it can be far from optimal. To bridge the gap, the overall goal this paper is to propose a new paradigm in the form of an architecture and operation procedures, thus creating a BFO-ABM hybrid. The other goal is to create a method which enables 3D visualization of the BFO algorithm. Firstly, an environment is created together with bacteria which physically perform all operators of the BFO. Secondly, a way of seamlessly embedding the bacteria from the BFO into the ABM environment is described. The bacteria are then manipulated and motivated with food and toxicity to act in a certain agent-like way. Simulation results prove that the agents can be effectively used as an ABM tool to present agents of all sizes and behaviors resembling numerous things, from companies, vehicles to people.
[Papers] Method of Multiview Video Switching for Soccer Game Analysis in Large Scale Space

Research on tactical and performance analysis utilizes videos of dynamic sports scenes. An effective multiview video switching method can support the analysis. Bullet-time video is a multiview video browsing approach. Because the image is presented almost as it is, it is suitable for high-quality observations of the subject from multiple directions. This paper proposes a multiview image switching method for understanding dynamic scenes in large-scale spaces such as soccer games. We develop a prediction model for the camerawork for shooting Bullet-time videos. The model using deep neural network, which can estimate a suitable viewpoint to observe the target scene from the position information of the soccer players, ball, and goals.
An Approach to Data Flow Optimization for False Invoice Emergency Management on Cloud

As the cloud computing has become popular, there is a tendency to develop the service-oriented workflow on cloud. On false invoice emergency management, service-oriented workflow on cloud is a good choice. However, during the workflow execution, large amounts of intermediate data need be stored effectively, otherwise causing unnecessary data transfers, so that become a bottleneck to performance. In this paper, a data scheduling strategy is proposed, which offers additional efficiencies by allowing services of workflow to execution with less data transfers by scheduling the data among data centers based on the data collaboration relations. Our data scheduling strategy not only makes the data transfer time reduced obviously, but also makes the running cost of data scheduling reduced obviously. Simulations demonstrate that the new architecture brings about at least 65% and 47% improvements than existing architecture in process execution time and throughput.