Reviewerly/exHarmony huggingface dataset:
Image Enhancement Using Local and Global Histogram Equalization Technique and Their Comparison

This paper reviews about the different algorithms used for image enhancement. Image processing is a process of matching and overlay more than one image of any scene. This is a method of converting any image into its digital form and performs useful operations by applying some function on it. The purpose for image processing is to observe the objects, for sharpening and restore distorted image. These algorithms are improving the attribute of image and reduce the noise in the image. It has been observed that image enhancement is done before segmentation, detection of object and recognition in vision applications. There are various applications of image processing like remote sensing, tracking of moving objects, in defense field, in biomedical techniques. Current researches under image processing are like Cancer imaging, focusing on Brain imaging, different imaging technologies. This paper also provides a work for understanding that how different type of noises can remove by enhancing the image. Image enhancement basically used in Medical Images.
The security of all private-key bits in isogeny-based schemes

We study the computational hardness of recovering single bits of the private key in the supersingular isogeny Diffie–Hellman (SIDH) key exchange and similar schemes. Our objective is to give a polynomial-time reduction between the problem of computing the private key in SIDH to the problem of computing any of its bits. The parties in the SIDH protocol work over elliptic curve torsion groups of different order N. Our results depend on the parity of N. Our main result shows that if N is odd, then each of the top and lower O(loglogN) bits of the private key is as hard to compute, with any noticeable advantage, as the entire key. A similar, but conditional, result holds for each of the middle bits. This condition can be checked, and heuristically holds almost always. The case of even N is a bit more challenging. We give several results, one of which is similar to the result for an odd N, under the assumption that one always succeeds to recover the designated bit. To achieve these results we extend the solution to the chosen-multiplier hidden number problem, for domains of a prime-power order, by studying the Fourier coefficients of single-bit functions over these domains.
Defining Network Switch Preference by Network Traffic Using Stable Marriage Problem

A network switch is an important network device on a computer network, the role of a switch can determine the good and bad of a network service. In a data center, sometimes it has several switch devices with different capabilities (diversity). For this reason, we need a strategy to carry out the effectiveness of using switch devices on computer networks. One way that can be done to realize this is by choosing the right partner between switches and hosts. The matching theory makes it possible to overcome this problem. In this study, it was simulated using the Stable Marriage Problem (SMP) method. The initial state to determine the ranking of pairs is done by using several attributes that are owned by each switch and host such as interfaces, services on the host, and data traffic on the device. Simulations are performed on mininet using Software-defined networking (SDN).
Outcomes of Introducing a Mobile Interactive Learning Resource in a Large Medical School Course

Mobile devices are an integral part of modern medical education, as convenient platforms for access to online interactive learning resources; students’ use of textbooks has correspondingly declined. We designed an interactive iBook© for pre-clinical students encompassing the content of the pulmonary segment in an organ-based multidisciplinary course. We found, via a survey-based study, that students preferred the iBook to other faculty-supplied materials (PowerPoints and PDFs), mainly due to its interactive images, animations, and study questions. Students’ test performance did not change significantly after introducing the iBook. This study suggests that expanded use of interactive learning resources may enhance students’ engagement with pre-clinical courses.
Medical Image Detection Using Deep Learning

This chapter provides an introduction to deep learning-based systems for object detection and their applications in medical image analysis. First, common deep learning architectures for image detection are briefly explained, including scanning-based methods and end-to-end detection systems. Some considerations about the training scheme and loss functions are also included. Then, an overview of relevant publications in anatomical and pathological structure detection and landmark detection using convolutional neural networks is provided. Finally, some concluding remarks and future directions are presented.
Summarization of Research Publications Using Automatic Extraction

The past two decades have witnessed the significant proliferation of technologies, which has laid a strong foundation for the scientific research in different fields. However, the changes across every field have also created new challenges related to the management of large chunks of data present in the process of converting, storing, searching and providing the user with relevant data. Extracting and transforming the data from one form to another remains as an important task in the current era. It becomes challenging when we focus on the particular extraction instances. Finding the proper research paper from the huge number of papers that includes navigation through the data is not an easy task. It includes huge amount of time search to provide the user with the most appropriate scientific paper of search. This paper concentrates on the extraction of problem statement from one research paper and will be further used to find the related papers. The use of phrases makes the search to considerably reduce the number of search across the Internet and at the same time, it yields a high performance.
Learning from Correlated Events for Equipment Relation Inference in Buildings

Modern buildings produce thousands of data streams, and the ability to automatically infer the physical context of such data is the key to enabling building analytics at scale. As acquiring this contextual information is currently a time-consuming and error-prone manual process, in this study we make the first attempt at automatically inferring one important contextual aspect of the equipment in buildings --- how each equipment is functionally connected with another. The main insight behind our solution is that functionally connected equipment is exposed to the same events in the physical world, creating correlated changes in the time series data of both equipment. Because events are of indeterminate length in time series, however, identifying them requires solving a non-polynomial combinatorial data segmentation problem. We present a solution that first extracts latent events from the sensory time series data, and then sifts out coincident events with a customized correlation procedure to identify the relationship between equipment. We evaluated our approach on data collected from over 1,000 pieces of equipment from 5 commercial buildings of various sizes located in different geographical regions in the US. Results show that this approach achieves 94.38% accuracy in relation inference, compared to 85.49% by the best baseline.
Towards a Gesture-Based Story Authoring System: Design Implications from Feature Analysis of Iconic Gestures During Storytelling

Current systems that use gestures to enable storytelling tend to mostly rely on a pre-scripted set of gestures or the use of manipulative gestures with respect to tangibles. Our research aims to inform the design of gesture recognition systems for storytelling with implications derived from a feature-based analysis of iconic gestures that occur during naturalistic oral storytelling. We collected story retellings of a collection of cartoon stimuli from 20 study participants, and a gesture analysis was performed on videos of the story retellings focusing on iconic gestures. Iconic gestures are a type of representational gesture that provides information about objects such as their shape, location, or movement. The form features of the iconic gestures were analyzed with respect to the concepts that they portrayed. Patterns between the two were identified and used to create recommendations for patterns in gesture form a system could be primed to recognize.
A Note on Hall’s Sextic Residue Sequence: Correlation Measure of Order $k$ and Related Measures of Pseudorandomness

It is known that Hall's sextic residue sequence has some desirable features of pseudorandomness: an ideal two-level autocorrelation and linear complexity of the order of magnitude of its period p. Here we study its correlation measure of order k and show that it is, up to a constant depending on k and some logarithmic factor, of order of magnitude p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> , which is close to the expected value for a random sequence of length p. Moreover, we derive from this bound a lower bound on the Nth maximum order complexity of order of magnitude log p, which is the expected order of magnitude for a random sequence of length p.
Minimum Delay Object Detection From Video

We consider the problem of detecting objects, as they come into view, from videos in an online fashion. We provide the first real-time solution that is guaranteed to minimize the delay, i.e., the time between when the object comes in view and the declared detection time, subject to acceptable levels of detection accuracy. The method leverages modern CNN-based object detectors that operate on a single frame, to aggregate detection results over frames to provide reliable detection at a rate, specified by the user, in guaranteed minimal delay. To do this, we formulate the problem as a Quickest Detection problem, which provides the aforementioned guarantees. We derive our algorithms from this theory. We show in experiments, that with an overhead of just 50 fps, we can increase the number of correct detections and decrease the overall computational cost compared to running a modern single-frame detector.
Challenges, Approaches and Solutions in Data Integration for Research and Innovation

In order to be implemented by policy makers, science, technology, and innovation ( science, technology, and innovation (STI) ) policies and indicator building need data. Whenever we need data, we need a method for data management, and in the era of big data big data , a crucial role is played by data integration big data integration . Therefore, STI policies and indicator development need data integration. Two main approaches to data integration exist, namely procedural and declarative. In this chapter, we follow the latter approach and focus our attention on the ontology-based data integration ( ontology-based data integration (OBDI) ) paradigm. The main principles of OBDI are: We introduce the main challenges of data integration for research and innovation ( research and innovation (R&I) ) and show that reasoning over an ontology connected to data may be very helpful for the study of R&I. We also provide examples by using Sapientia, an ontology specifically defined for multidimensional research assessment.
CORONARY HEART DISEASE DETECTION USING NONLINEAR FEATURES AND ONLINE SEQUENTIAL EXTREME LEARNING MACHINE

In this paper, we propose an automated approach that combines the generalized discriminant analysis (GDA) as feature reduction scheme with radial basis function (RBF) kernel and the online sequential extreme learning machine (OSELM) having Sigmoid, Hardlim, RBF and Sine activation function as binary classifier for detection of congestive heart failure (CHF) and coronary artery disease (CAD). For this analysis, 13 nonlinear features as Correlation Dimension (CD), Detrended Fluctuation Analysis (DFA) as DFA-[Formula: see text]1 and DFA-[Formula: see text]2, Bubble Entropy (BBEn), Sample Entropy (SampEn), Dispersion Entropy (DISEn), Lempel–Ziv Complexity (LZ), Sinai Entropy (SIEn), Improved Multiscale Permutation Entropy (IMPE), Hurst Exponent (HE), Permutation Entropy (PE), Approximate Entropy (ApEn) and Standard Deviation (SD1/SD2) were extracted from Heart Rate Variability (HRV) signals. For validation of proposed method, HRV data were obtained from standard database of normal sinus rhythm (NSR), CHF and CAD subjects. Numerical experiments were done on the combination of database sets such as NSR-CAD, CHF-CAD and NSR-CHF subjects. The simulation results show a clear difference in combination of database sets by using GDA having RBF, Gaussian kernel function and OSELM binary classifier having Sigmoid, RBF and Sine activation function and achieved an accuracy of 98.17% for NSR-CAD, 100% for NSR-CHF and CAD-CHF subjects.
Lesion Detection from the Ultrasound Images Using K-Means Algorithm

It is well known that, early diagnosis is also very important for cancer patients. One of the imaging techniques used in the diagnosis of breast cancer is ultrasonography. In this study, a system that helps the doctor to detect the lesion in the breast has been suggested. We used K-Means clustering algorithm to detect the lesion in the images. The effects of three different filters (Median, Laplace, Sobel) have been examined in the study. Also, different partitioning effects are considered. According to the accuracy rates we obtained, it was concluded that the accuracy increased as the number of partitions increased. In addition, the Median filter was the best filter compared to other filters.
Integrating Data Protection into the Software Life Cycle

Data protection has become increasingly important in recent years, partly due to the EU General Data Protection Regulation (GDPR) and similar legislations in other countries, but also because of various privacy scandals which led to bad press for the affected companies. Since most of the processing of the relevant personal data is performed by software, data protection needs to be addressed in the development of software. This paper therefore investigates how to incorporate data protection in the software life cycle. Based on a simple default life cycle model, the main questions to ask and issues to address in the various phases are summarized. These questions and issues are independent of the exact life cycle model used, whether plan-driven, agile or some hybrid, and can therefore easily be mapped to some other model. Not surprisingly, data protection mainly affects the analysis and design of software systems (“privacy by design”) when the data to be processed and stored as well as the form of processing and the protection mechanisms to be used are defined. Nevertheless, to some extent the entire life cycle down to withdrawal is affected.
Research on Web Service Clustering Method Based on Word Embedding and Topic Model

Because of the short length of documents describing Web services on the Internet, the traditional modeling method is not ideal, which affects the clustering effect of Web services. To this end, we propose a word-embedded topic model, which can effectively solve the problem of data sparsity. A method based on embedded words and topic models. Firstly, Wikipedia is used as an external corpus to extend API service document, and LF-LDA model is used to model its topic distribution. The corpus data is extracted from Wikipedia by wikiextractor, and the corpus is trained with Word2vec tool. The data comes from its word vector model. Use the trained Wikipedia Word Vector Model to extend API service description documents. In the extended API service documents, LF-LDA topic modeling technology is used to mine the implicit topic information and determine the optimal number of topics, so as to accurately measure the semantic similarity between API service documents. Finally, K-means algorithm is used to cluster Web services, and the validity of this method is verified by the real data of API services on the basis of programmability. The experimental results show that this method has better accuracy, recall rate and F value than the traditional Web service clustering method based on topic model algorithm.
A Robust Roll Angle Estimation Algorithm Based on Gradient Descent

This paper introduces a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense subpixel disparity map by minimizing a global energy using golden section search algorithm. In this paper, to achieve greater computational efficiency, we utilize gradient descent to optimize the aforementioned global energy. The experimental results illustrate that the presented roll angle estimation method takes fewer iterations to achieve the same precision as the previous method.
Implement 3D video call using cloud computing infrastructure

Cloud-based technologies are driving positive changes in the way organizations can communicate. In running a global business, the need for travel and being available in meetings is a must. However, with expensive travel costs, an alternative solution to overcome this problem is required. This paper presents, a new approach that enhances current 2D video calls to 3D video calls benefiting from the unlimited features of the cloud-computing infrastructure. Three technologies were implemented, OpenStack cloud, webRTC call and 3D anaglyph effect to achieve the sense of 3D video. Experiments proved that our approach has better CPU performance by 47% compared to 65% for traditional peer-to-peer 3D video call. In addition, our implementation has better PSNR value by 0.991 dB compared to 3D video call on peer-to-peer. Survey results stated, 60% of participants found our anaglyph video has a good effect, and 45% found that our video is 75% similar to YouTube video.
Extended Reality Experiences Prediction using Collaborative Filtering

New immersive technologies are providing added value to many areas of our lives. A wide spectrum of hardware and software, including sensory interfaces, applications, and infrastructures are some of the technical drivers of these new opportunities. In particular, Extended Reality applications and simulators are increasingly becoming popular in business, but are often limited by their predictability and moreover, they lack personalization in the selection of the scenarios.
Representation Learning of Knowledge Graphs with Multi-scale Capsule Network

Representation learning of knowledge graphs has gained wide attention in the field of natural language processing. Most existing knowledge representation models for knowledge graphs embed triples into a continuous low-dimensional vector space through a simple linear transformation. In spite of high computation efficiency, the fitting ability of these models is suboptimal. In this paper, we propose a multi-scale capsule network to model relations between embedding vectors from a deep perspective. We use convolution kernels with different sizes of windows in the convolutional layer inside a Capsule network to extract semantic features of entities and relations in triples. These semantic features are then represented as a continuous vector through a routing process algorithm in the capsule layer. The modulus of this vector is used as the score of confidence of correctness of a triple. Experiments show that the proposed model obtains better performance than state-of-the-art embedding models for the task of knowledge graph completion over two benchmarks, WN18RR and FB15k-237.
Implementation of IDS Within a Crew Using ID3Algorithm in Wireless Sensor Local Area Network

Mobile and Pervasive Computing was introduced by the technological vision of Mark Weiser. With the ideology of Urban Development, it is considered that the world will be composed of interconnected devices and system models of networks, which allows the accessibility of information present across the globe. A U-City characterized by the information and computing technologies is gradually becoming indistinguishable and unviable from daily life. In that regard, this article provides an in-depth evaluation of Mobile and Pervasive Computing considered as an evolutionary framework applicable in electric motors, which are invisible hence forming a pervasive environment. Mobile technology will play an essential role in urban development. This signifies that the mobility initiatives are applicable for many telecommunication aspects used in our daily lives. The article starts by illustrating the significant mobile technologies in the urban areas, thereby elaborating on the planning format of pervasive computing, which is meant for urban development. To effective plan for the establishment or expansion of urban centers, the article calls for planners to concentrate on the formation of pervasive computing areas that define the correlations between people, places, objects, buildings and infrastructure. Mobile Crowd Sourcing Technologies for smart environments calls for planners to concentrate on revolutionizing the globe by aligning technological functions and enhance the integration and coordination of various services involved in technological intelligence.
Sign consistency for the linear programming discriminant rule

Linear discriminant analysis (LDA) is an important conventional model for data classification. Classical theory shows that LDA is Bayes consistent for a fixed data dimensionality p and a large training sample size n. However, in high-dimensional settings when p ≫ n, LDA is difficult due to the inconsistent estimation of the covariance matrix and the mean vectors of populations. Recently, a linear programming discriminant (LPD) rule was proposed for high-dimensional linear discriminant analysis, based on the sparsity assumption over the discriminant function. It is shown that the LPD rule is Bayes consistent in high-dimensional settings. In this paper, we further show that the LPD rule is sign consistent under the sparsity assumption. Such sign consistency ensures the LPD rule to select the optimal discriminative features for high-dimensional data classification problems. Evaluations on both synthetic and real data validate our result on the sign consistency of the LPD rule.
Idempotent Anti-unification

In this article, we address two problems related to idempotent anti-unification. First, we show that there exists an anti-unification problem with a single idempotent symbol that has an infinite minimal complete set of generalizations. It means that anti-unification with a single idempotent symbol has infinitary or nullary generalization type, similar to anti-unification with two idempotent symbols, shown earlier by Loïc Pottier. Next, we develop an algorithm that takes an arbitrary idempotent anti-unification problem and computes a representation of its solution set in the form of a regular tree grammar. The algorithm does not depend on the number of idempotent function symbols in the input terms. The language generated by the grammar is the minimal complete set of generalizations of the given anti-unification problem, which implies that idempotent anti-unification is infinitary.
Blending into the White Box of the Art Museum

Mixed reality applications can enrich museum exhibits and make them more attractive to an audience of adolescents. However, in the design of such applications, we face a myriad of possibilities and little guidance on how to choose between (early) alternatives. In this paper, we explore the notion of experience blend - which could act as an aesthetic governing the design of mixed reality experiences. We present an effort to operationalize experience blend and illustrate its use in the design and evaluation of an application for an art museum. Stakeholders in the project assumed that in order to reach out to adolescents an exciting experience was needed, deviating from education and breaking with the hidden rules of the art-museum, our user study showed that adolescents favored a blended experience. This suggests experience blend may be a helpful aesthetic in the design of other mixed reality experiences.