Reviewerly/exHarmony huggingface dataset:
Medical Image Classification

Diagnosis of diseases at the right stage with optimal accuracy is a significant requirement in the medical field. Apart from diagnosis from clinical symptoms, diagnosis based on scanned images of both internal and external organs is playing a vital role in understanding the severity of the disease. Classification is a field of study derived from artificial intelligence, and today it is widely used in medical image classification. These techniques are used to classify the different stages of a disease or different variant diseases possible in an organ from different types of input images such as magnetic resonance imaging (MRI), computed tomography (CT), x-ray, fundus images, iris images, etc. Various preprocessing techniques are used to select the relevant features from the input image to form the feature set. The classifiers are trained using the feature set to generate models. The generated models can be optimized to improve the performance. Various metrics such as accuracy, coverage, precision, recall, and FMeasure are used to measure the accuracy.
An Enhanced Clustering Method for Image Segmentation

The findings of image segmentation reflect its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this chapter, the authors have evaluated the performances of three different clustering algorithms used in image segmentation: the classical k-means, its modified k-means++, and proposed enhanced clustering method. Brief explanations of the fundamental working principles implicated in these methods are presented. Thereafter, the performance which affects the outcome of segmentation are evaluated considering two vital quality measures, namely structural content (SC) and root mean square error (RMSE). Experimental result shows that the proposed method gives impressive result for the computed values of SC and RMSE as compared to k-means and k-means++. In addition to this, the output of segmentation using the enhanced technique reduces the overall execution time as compared to the other two approaches irrespective of any image size.
The Improved SIFT Algorithm Based on Rectangular Operator and Its Parallel Implementation

The SIFT algorithm is one of the most widely used algorithm which bases on local feature extraction. But it could not meet the requirement of the real-time application due to the high time complexity and low execution efficiency. In order to improve these drawback, the authors optimized the SIFT algorithm by using the Gaussian convolution scale of adaptive scale space. The authors also provided the executive process of the improved SIFT algorithm on the MapReduce programming model and compared its performance in terms of the stand-alone and cluster environment. The experiment result showed that compared to the traditional algorithm, the improved algorithm had high execution efficiency, good speedup, scalability and is suitable for massive amounts of image data processing.
Status of Electronic Thesis and Dissertations (ETDs) in India

INFLIBNET (information library network) has gradually emerged as the backbone of the higher education sector of India. The remarkable initiatives taken by the INFLIBNET strive for strengthening and supporting information needs of the students, scholars, teachers, scientists, and peers. Scholars all around the world would be thankful to the INFLIBNET for their marvelous and ambitious project in the direction of bringing millions of theses and dissertation under the umbrella of OA. With 166,675 full-text ETD items and growing, it qualifies to be called a massive digital library (MDL). This chapter discusses the salient features, significance, and implications of the Shodhganga. It also discusses Shodhgangotri, a database consisting of approved research proposal submitted by the research scholars. The chapter concludes with discussion on the evaluation report of the Shodhganga website with the web-analyzer software Nibbler.
Decentralized Linear Control Using DSE and ELQR

This chapter presents a control scheme for the decentralized control of power system dynamics. The scheme utilizes dynamic state estimation (DSE) using local measurements and machine parameters and employs the concept of pseudoinputs for decentralization. The method is based on the extended linear quadratic regulator (ELQR) and adapts in real-time to varying operating conditions of the system. The method is also computationally feasible and easily implementable. Using an example test system, it has been shown that the integrated scheme of DSE and ELQR can be utilized for dynamic estimation and control of small-signal dynamics of power systems in a decentralized manner, and it has several advantages over other linear control methods.
Surreal Birthdays and Their Arithmetic

SummaryThis paper is about the structure underlying surreal numbers, namely, the birthdays of surreal number forms. The results are intriguing because (i) addition of surreal forms results in simple addition of their birthdays, but (ii) multiplication results in a much more complicated structure of birthdays, given by a kind of two-dimensional Fibonacci sequence. The paper also provides many examples and illustrations designed to help a student become familiar with this interesting set of numbers.MSC: 12J15 AcknowledgmentsI would like to thank David Roberts for helpful comments on this manuscript.Notes1 I'd like to make a surrealist/computer-science joke here, namely `|' is not a pipe, but a conjunction between Magritte and Unix might be considered too obscure even for a paper on surreal numbers.2 Note that the online version of this article has color diagrams.Additional informationFundingAustralian Research Council;Notes on contributorsMatthew RoughanMatthew Roughan, FIEEE, FACM, works in the School of Computer and Mathematical Sciences at the University of Adelaide in South Australia. His research interests range from stochastic modeling to measurement and management of networks like the Internet. He has worked with network operators such as AT&T in the United States, and equipment vendors such as Ericsson. He has many side interests including surreal numbers and their forms.
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In this paper we study the multiplicative function ρk,λ(n) that counts the number of solutions of the equation x12+⋯+xk2≡λ(modn) in (Z/nZ)k. In particular we give closed explicit formulas for ρk,λ(ps). This leads to an algorithm with an arithmetic complexity of constant order that improves previous work by Tóth [10] and completes the quadratic case considered by Li and Ouyang in [8].
MoHoP: A protocol providing for both mobility management and host privacy

The types and numbers of moving objects connected to the Internet are rapidly increasing. Thus, a number of methods have been proposed to provide mobility to moving objects and to ensure host privacy. However, when both methods are applied at the same time without considering their interoperation, either host privacy cannot be provided or packets are delivered to an incorrect location. In this paper, we propose a protocol termed MoHoP which provides both mobility management and host privacy simultaneously. MoHoP obtains the identifier of a mobile node by decoding an anonymized identifier included in a packet. Using the obtained identifier in a distributed hash table, it finds an edge router to which a mobile node is connected, and delivers the packet. We implemented MoHoP in a testbed and proved that it supports mobility. Moreover, the identifier of a mobile node is not exposed in a packet header during the packet transmission process. Our experiment also demonstrated that MoHoP offers low protocol overhead and low handover delay.
Are Third-Party Libraries Secure? A Software Library Checker for Java

Nowadays, there are many software libraries for different purposes that are used by various projects. An application is only as secure as its weakest component; thus if an imported library includes a certain vulnerability, an application could get insecure. Therefore a widespread search for existing security flaws within used libraries is necessary. Big databases like the National Vulnerability Database (NVD) comprise reported security incidents and can be utilized to determine whether a software library is secure or not. This classification is a very time-consuming and exhausting task. We have developed a tool-based automated approach for supporting developers in this complex task through heuristics embedded in an eclipse plugin. Documented vulnerabilities stored in databases will be taken into consideration for the security classification of libraries. Weaknesses do not always entail the same consequences; a scoring that identifies the criticality oriented on their potential consequences is applied. In this paper, a method for the enrichment of knowledge containing vulnerability databases is considered. Our approach is focussing on the scope of software weaknesses, which are library reasoned and documented in vulnerability databases. The Java Library Checker was implemented as eclipse plugin for supporting developers to make potential insecure third-party libraries visible to them.
Usage of Online Electronic Information Resources Among PG Students of Alagappa University

A study was conducted to investigate the accessibility and use of online electronic information resources in Alagappa University PG students. The methodology adopted for the study was survey in which questionnaires used in collection of the data. A total of 80 respondents participated in the study. It was concluded that most of the respondent's frequency of using online electronic information resources search for the author strategies in most useable (35%), the maximum respondents (25%) using for the online resources. This study shows that majority of the respondents (47.5%) have had 1 hour of access in e-resources, and more than 3 hours has been accessed by the respondents (12.5%) using for the electronic resources. Majority of respondents (31.5%) have accessibility of using e-resources.
PPHA-Popularity Prediction Based High Data Availability for Multimedia Data Center

Due to the growth of internet-connected devices and extensive data analysis applications in recent years, cloud computing systems are largely utilized. Because of high utilization of cloud storage systems, the demand for data center management has been increased. There are several crucial requirements of data center management, such as increase data availability, enhance durability, and decrease latency. In previous works, a replication technique is mostly used to answer those needs according to consistency requirements. However, most of the works consider full data, popular data, and geo-distance-based replications by considering storage and replication cost. Moreover, the previous data popularity based-techniques rely on the historical and current data access frequencies for replication. In this article, the authors approach this problem from a distinct aspect while developing replication techniques for a multimedia data center management system which can dynamically adapt servers of a data center by considering popularity prediction in each data access location. Therefore, they first label data objects from one to ten to track access frequencies of data objects. Then, they use those data access frequencies from each location to predict the future access frequencies of data objects to determine the replication levels and locations to replicate the data objects, and store the related data objects to close storage servers. To show the efficiency of the proposed methods, the authors conduct an extensive simulation by using real data. The results show that the proposed method has an advantage over the previous works in terms of data availability and increases the data availability up to 50%. The proposed method and related analysis can assist multimedia service providers to enhance their service qualities.
Visual Semantic Re-ranker for Text Spotting

Many current state-of-the-art methods for text recognition are based on purely local information and ignore the semantic correlation between text and its surrounding visual context. In this paper, we propose a post-processing approach to improve the accuracy of text spotting by using the semantic relation between the text and the scene. We initially rely on an off-the-shelf deep neural network that provides a series of text hypotheses for each input image. These text hypotheses are then re-ranked using the semantic relatedness with the object in the image. As a result of this combination, the performance of the original network is boosted with a very low computational cost. The proposed framework can be used as a drop-in complement for any text-spotting algorithm that outputs a ranking of word hypotheses. We validate our approach on ICDAR’17 shared task dataset.
A Multi-Objective Evolutionary Approach for Preprocessing Imbalanced Microarray Datasets

Microarray datasets are usually imbalanced with a huge number of features and few samples. Most of the existing methods consider classification accuracy as the performance measure while converting the imbalanced datasets into a balanced one and selecting the optimal feature subset, resulting in overfitting. To address the above-mentioned issue, in this paper, a multi-objective genetic algorithm approach is proposed that utilizes a combination of evaluation metrics for both imbalanced data processing and feature selection. The proposed methodology is implemented in seven real datasets taken from public repositories. The cross-validation results prove that the proposed methodology outperforms existing methods in all seven datasets. The proposed methodology serves as a simple and efficient method for preprocessing microarray datasets and helps clinical practitioners in making strategic decisions with the help of a few features. It will also greatly reduce the time spent on processing unnecessary features.
Research on Resource Allocation Strategy of PaaS Platform

In order to allocate elastic resource to the application of PaaS platform, the authors analyze the key technologies and the particularity of resource scheduling in PaaS platform, and design an application-oriented resource allocation model and heuristic scheduling algorithm based on an ant colony algorithm. Different from the existing resource allocation methods based on virtual machines in IaaS, the scheduling strategy is based on Application in PaaS platform. According to the analysis of the application layout, the heuristic algorithm is used to minimize the number of application migration and reduce the waiting time of the task. In order to avoid falling into the loop or local optimal solution, the authors also used a tabu search technique. The results of comparative experiments show that, this strategy has higher resource utilization and shorter task waiting time.
Object-oriented lexical encoding of multiword expressions: Short and sweet

Multiword expressions (MWEs) exhibit both regular and idiosyncratic properties. Their idiosyncrasy requires lexical encoding in parallel with their component words. Their (at times intricate) regularity, on the other hand, calls for means of flexible factorization to avoid redundant descriptions of shared properties. However, so far, non-redundant general-purpose lexical encoding of MWEs has not received a satisfactory solution. We offer a proof of concept that this challenge might be effectively addressed within eXtensible MetaGrammar (XMG), an object-oriented metagrammar framework. We first make an existing metagrammatical resource, the FrenchTAG grammar, MWE-aware. We then evaluate the factorization gain during incremental implementation with XMG on a dataset extracted from an MWE-annotated reference corpus.
Bottom-Up Attention, Models of

In this review, we examine the recent progress in saliency prediction and proposed several avenues for future research. In spite of tremendous efforts and huge progress, there is still room for improvement in terms finer-grained analysis of deep saliency models, evaluation measures, datasets, annotation methods, cognitive studies, and new applications. This chapter will appear in Encyclopedia of Computational Neuroscience.
A Term Matching Algorithm and Substitution Generality

We revisit a simple non-deterministic algorithm for term matching and employ it for deciding substitution generality as well, using witness term technique. The technique alleviates the need for ad-hoc proofs involving generality of substitutions.
A Quality-Level Selection for Adaptive Video Streaming with Scalable Video Coding

In this paper, we propose a quality-level selection method for adaptive video streaming with scalable video coding (SVC). The proposed method works on the client with the dynamic adaptive streaming over HTTP (DASH) with SVC. The proposed method consists of two components: introducing segment group and a buffer-aware layer selection algorithm. In general, quality of experience (QoE) performance degrades due to stalling (playback buffer underflow), low playback quality, frequent quality-level switching, and extreme-down quality switching. The proposed algorithm focuses on reducing the frequent quality-level switching, and extreme-down quality switching without increasing stalling and degrading playback quality. In the proposed method, a SVC-DASH client selects a layer every G segments, called a segment group to prevent frequent quality-level switching. In addition, the proposed method selects the quality of a layer based on a playback buffer in a layer selection algorithm for preventing extreme-down switching. We implement the proposed method on a real SVC-DASH system and evaluate its performance by subjective evaluations of multiple users. As a result, we confirm that the proposed algorithm can obtain better mean opinion score (MOS) value than a conventional SVC-DASH, and confirm that the proposed algorithm is effective to improve QoE performance in SVC-DASH.
PLSA-Based Personalized Information Retrieval with Network Regularization

With the rapid development of the Internet, the information retrieval model based on the keywords matching algorithm has not met the requirements of users, because people with various query history always have different retrieval intentions. User query history often implies their interests. Therefore, it is of great importance to enhance the recall ratio and the precision ratio by applying query history into the judgment of retrieval intentions. For this sake, this article does research on user query history and proposes a method to construct user interest model utilizing query history. Coordinately, the authors design a model called PLSA-based Personalized Information Retrieval with Network Regularization. Finally, the model is applied into academic information retrieval and the authors compare it with Baidu Scholar and the personalized information retrieval model based on the probabilistic latent semantic analysis topic model. The experiment results prove that this model can effectively extract topics and retrieves back results more satisfied for users' requirements. Also, this model improves the effect of retrieval results apparently. In addition, the retrieval model can be utilized not only in the academic information retrieval, but also in the personalized information retrieval on microblog search, associate recommendation, etc.
A Survey on Implementation Methods and Applications of Sentiment Analysis

Sentiment analysis also called opinion mining, and it studies opinions of people towards products and services. Opinions are very important as the organizations always want to know the public opinions about their products and services. People give their opinions via social media. With the advent of social media like Twitter, Facebook, blogs, forums, etc. sentiment analysis has become important in every field like automobile, medical, film, fashion, stock market, mobile phones, insurance, etc. Analyzing the opinions and predicting the opinion is called sentiment analysis. Sentiment analysis is done using opinion words by classification methods or by sentiment lexicons. This chapter compares different methods of solving sentiment analysis problem, algorithms, its merits and demerits, applications, and also investigates different research problems in sentiment analysis.
Study of a New Digital Text Watermarking Algorithm

As information technology develops rapidly and smart devices become popular, the traditional publishing industry is changing form. It has become very common for newspapers, books, magazines and pictures to transmit on the network. Because the network is easy to copy and spread text resources, the lawful interests of owners are liable to violate. In order to solve this problem, copyright protection technology represented by the text watermark has been invented. In this paper, a novel digital text watermarking algorithm utilizing word processing software is researched, which can ignore some faults in the documents to some extent. By means of this new algorithm, users can keep the copyright information in the reserved words of files and properly use the documents. The embedded watermark can be extracted and the copyright can be verified.
Errata to: A 3D polar-radius-moment invariant as a shape circularity measure

In this paper we point out the theoretical results from [2] that were established and discussed earlier. All these statements were a part of results proven in [1], and do overlap partially with the results established in [3] and [4]. Several mistakes in [2] are pointed out, as well.
General Construction for Extended Visual Cryptography Scheme Using QR Codes

This article describes how a visual cryptography scheme, with one prominent feature—decrypting simply, has attracted much research attention since it was first proposed. However, meaningless shares remain a continuing challenge in the development of VCS. In this article, an extended visual cryptography scheme (EVCS) based on XOR operation is proposed, in which QR codes are utilized as the cover images of shares. By designation, all the shares generated in the scheme can be decoded by standard QR code readers with specific meaning. In addition, to achieve high sharing efficiency, a method of simultaneously sharing a secret QR code among multiple subsets is presented. Also, sufficient and necessary conditions of the method are analyzed with an integer programming model, providing a general construction approach for EVCS under arbitrary access structures.
Deep Learning for Big Data Analytics

Traditional approaches like artificial neural networks, in spite of their intelligent support such as learning from large amount of data, are not useful for big data analytics for many reasons. The chapter discusses the difficulties while analyzing big data and introduces deep learning as a solution. This chapter discusses various deep learning techniques and models for big data analytics. The chapter presents necessary fundamentals of an artificial neural network, deep learning, and big data analytics. Different deep models such as autoencoders, deep belief nets, convolutional neural networks, recurrent neural networks, reinforcement learning neural networks, multi model approach, parallelization, and cognitive computing are discussed here, with the latest research and applications. The chapter concludes with discussion on future research and application areas.