Reviewerly/exHarmony huggingface dataset:
An Econometric Time Series Forecasting Framework for Web Services Recommendation

The convergence of SMAC technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turn out to be a challenging research issue. In the current work, various econometric models are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) value is used as an evaluation metric and their value for the prediction of Response time is found to be 1.7392e+04 and 1.7416e+04 respectively with the ARMA model. The AIC and BIC value for throughput (TP) is found to be 1.5249e+04 and 1.5334e+04 respectively with the ARIMA. The value of variance is 13.2437 and 6.8131 for RT and TP respectively which is also the lowest among other models with t-statistic greater than the p-value. Thus, the experimental results show that the ARMA and ARIMA model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time and throughput respectively.
Book Recommendation System Development Using User-Based Collaborative Filtering

Abstract In general, a library has a Book Search System, both search books by title, author, publisher, and book subject. But with so many book search results displayed by the system, sometimes it makes users (library members) find it difficult to choose books that match their profile. For that, we need a system that can provide recommendations in the book search process to provide recommendations systematically based on previous user searches. This study aims to produce a book search recommendation system in a desktop-based library using the Python programming language and the MySQL database. recommendation system aims to reduce errors in getting the reference books needed. The information that comes out of the search process is right on target with user needs so that it can streamline time in searching for books. The book search recommendation system uses a user-based collaborative filtering method based on the similarity of one member to another member based on the lending pattern which is grouped based on the subject of the book being sought. This system will display the results of book search recommendations by ranking from highest to lowest and provide book title solutions to users according to their profile. The development of the book search recommendation system uses the System Development life cycle (SDLC) method to design a system with a case study research in the TasikmalayaPejuang University library. The first stage of system development is the Identification of Problems that occur in the University of Struggle library following the phenomena that occur in the field. The second stage is the analysis of software requirements, hardware, and information needed in developing a system. The third stage is the design of workflow system design recommendations for book search using Unified Modeling Language (UML), database design and designing the interface. The fourth stage is the implementation of the design results in real form using Python as a programming language and MySql as a database system. The last step is testing the book search recommendation system using the Blackbox testing method by Trial and Error.
Association Rule Mining Algorithms on Large and Small Datasets: A Comparative Study

Association rule mining (ARM) is widely used approach in data mining to discover trends from databases. There is plethora of ARM algorithms available for frequent itemset mining and generating association rules. This paper makes a comparative study of important ARM algorithms such as Apriori, FP-Growth, LCM and FIN. All these algorithms are applied for large dataset and small dataset, those results are drawn,analysed and compared. Out of them FIN is the most recent algorithm and fast in generating frequent item sets. LCM is an award winning algorithm. FPGrowth generates frequent itemsets without candidate generation approach followed by Apriori. Due to their significant differences, these algorithms are chosen for comparative study. This paper provides the details of the algorithms, illustrations to have deeper insights besides comparing them in terms of performance measures like execution time and efficiency.
Clustering adaptive canonical correlations for high-dimensional multi-modal data

Multi-modal canonical correlation analysis (MCCA) is an important joint dimension reduction method and has been widely applied to clustering tasks of multi-modal data. MCCA-based clustering is usually dimension reduction of high-dimensional data followed by clustering of low-dimensional data. However, the two-stage clustering is difficult to ensure the adaptability of dimension reduction and clustering, which will affect the final clustering performance. To solve the issue, we propose a novel clustering adaptive multi-modal canonical correlations (CAMCCs) method, which constructs a unified optimization model of multi-modal correlation learning and clustering. The method not only realizes discriminant learning of correlation projection directions under unsupervised cases, but also is able to directly obtain class labels of multi-modal data. Additionally, the method also realizes out-of-sample extension in class labels. Solutions of CAMCCs are optimized by an iterative way, and we analyze its convergence. Extensive experimental results on various datasets have demonstrated the effectiveness of the method.
A new smoothing algorithm for jump Markov linear systems

This paper presents a method for calculating the smoothed state distribution for Jump Markov Linear Systems. More specifically, the paper details a novel two-filter smoother that provides closed-form expressions for the smoothed hybrid state distribution. This distribution can be expressed as a Gaussian mixture with a known, but exponentially increasing, number of Gaussian components as the time index increases. This is accompanied by exponential growth in memory and computational requirements, which rapidly becomes intractable. To ameliorate this, we limit the number of allowed mixture terms by employing a Gaussian likelihood mixture reduction strategy, which results in a computationally tractable, but approximate smoothed distribution. The approximation error can be balanced against computational complexity in order to provide an accurate and practical smoothing algorithm that compares favourably to existing state-of-the-art approaches.
Joint TDOA and FDOA Estimation for Interleaved Pulse Trains From Multiple Pulse Radiation Sources

In this article, we address the problem of estimating with high precision the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) for interleaved pulse trains radiated from multiple sources. The pulse signal parameters of multiple sources are indistinguishable and as a result conventional de-interleaving techniques fail to separate the pulses emitted from each source. By noting that cross-correlating pulses from the same source at their true TDOA and FDOA produces consistent phases, a high-precision joint TDOA-FDOA estimator is proposed. Within the developed technique, a histogram-based clustering procedure is introduced to obtain initial TDOA and FDOA estimates. Then, an expectation maximization (EM)-based method is utilized to refine their estimates, where the unknown source labels of pulse signals are hidden variables and they are estimated together with TDOA and FDOA iteratively. Simulation results reveal the superior performance of the proposed algorithm compared to existing techniques, at the cost of moderate computational complexity increment.
NLPExplorer: Exploring the Universe of NLP Papers

Understanding the current research trends, problems, and their innovative solutions remains a bottleneck due to the ever-increasing volume of scientific articles. In this paper, we propose NLPExplorer, a completely automatic portal for indexing, searching, and visualizing Natural Language Processing (NLP) research volume. NLPExplorer presents interesting insights from papers, authors, venues, and topics. In contrast to previous topic modelling based approaches, we manually curate five course-grained non-exclusive topical categories namely Linguistic Target (Syntax, Discourse, etc.), Tasks (Tagging, Summarization, etc.), Approaches (unsupervised, supervised, etc.), Languages (English, Chinese,etc.) and Dataset types (news, clinical notes, etc.). Some of the novel features include a list of young popular authors, popular URLs, and datasets, a list of topically diverse papers and recent popular papers. Also, it provides temporal statistics such as yearwise popularity of topics, datasets, and seminal papers. To facilitate future research and system development, we make all the processed datasets accessible through API calls. The current system is available at http://lingo.iitgn.ac.in:5001/
K-RMS Algorithm

Clustering is an unsupervised learning problem in the domain of machine learning and data science, where information about data instances may or may not be given. K-Means algorithm is one such clustering algorithms, the use of which is widespread. But, at the same time K-Means suffers from a few disadvantages such as low accuracy and high number of iterations. In order to rectify such problems, a modified K-Means algorithm has been demonstrated, named as K-RMS clustering algorithm in the present work. The modifications have been done so that the accuracy increases albeit with less number of iterations and specially performs well for decimal data compared to K-Means. The modified algorithm has been tested on 12 datasets obtained from UCI web archive, and the results gathered are very promising.
Bangla Sign Digits Recognition Using HOG Feature Based Multi-Class Support Vector Machine

Sign language or gesture language is used by the people who are unable to hear for communicating among themselves as well as with other persons. It is not as simple as the natural speaking language. It is a fully developed natural language having its grammar and lexicon. Gesture Languages are represented via the manual sign-stream or alphabet-stream together with non-manual components. As the spoken languages have various types of sounds, letters, digits, etc. Similarly, sign languages have their grammar, letter, digits, etc. In this paper, the main focus is sign digits as the sign numerical digits are a major part of a sign or gesture language which are not familiar to general people. So the goal is to make it understandable to the general people. For this purpose, an easily understandable model has been constructed with computer vision features and machine learning methods to recognize Bangla finger numerical digits. The histogram of oriented gradient features of images has been applied to train the classifier, here a multiple-class support vector machine has been employed to classify the images. In this paper, the proposed multiple-class Support Vector Machine is trained with the HOG features of the images with the training images dataset to achieve the goal. The classifier is trained and tested for 900 training pictures and 100 test pictures of ten-digit classes respectively. After training and tests, the proposed classifier model has gained about 95 percent accuracy on the perception of Bangla sign numerical digits.
Survey Says…?: Assessing Interlibrary Loan/Document Delivery Use and Awareness for Responsive Service Delivery

The object of this study is to assess Texas A&M University (TAMU) students/faculty/staff members' use and awareness of the Libraries' free document delivery and interlibrary loan service, branded as "Get It For Me." Since 2010, we have continued to see a decline in new user registrations each year. Previous Get It For Me user surveys conducted in 2003 and 2011 indicated that the overall satisfaction of registered users was high and customer feedback gleaned from these surveys was used to initiate a number of service improvements. But what about the large percentage of the campus community who do not use the Get It For Me service? Are they aware of what this service has to offer? If so, why might they choose not to use it? If they are not aware of the service, might they use it if they knew more about how it can assist them in their scholarly endeavors? Instead of only surveying those customers who have used this service, we invited everyone on TAMU College Station campus to participate in the survey. Promoting the service is a vital component of service delivery.
Energy Consumption Analysis of XRP Validator

The XRP Ledger is an open-source payment system with a primary focus on providing an efficient cross-border payment network. The advancement of the XRP Ledger relies on the agreement of validator nodes which propose candidate transaction sets to be included in the next ledger through an iterative consensus process. Unlike Bitcoin miners, there is no reward scheme to incentivize volunteers to run an XRP validator node. In this article, the energy consumption of an XRP validator is studied to quantify the cost to run this node. Various parameters in the configuration settings are also modified to study the change in the energy consumed. Using these results, estimated annual electricity costs are compared.
Sentiment Analysis of Yelp Reviews by Machine Learning

Sentiment analysis is a process of analyzing a piece of text written by a writer to identify and classify the opinions buried in that text and to determine whether the views of the writer about the topic is positive, negative, or neutral. Yelp is a review forum which provides reviews on local businesses. Users from anywhere in the world can post reviews and rate any business in this social networking site. In this paper, the textual yelp reviews of businesses are analyzed to assign a probability for the review as having positive or negative sentiment. The data considered for the sentiment analysis are the reviews on restaurants about food, service, price and ambience. Machine learning algorithms in the nltk library of python can prove to be very useful in any such research on Natural Language Processing and the library has been used extensively in this work. Each algorithm used has been analyzed and has been compared on the basis of their efficiency (confidence).
The Application of the Multi-Agent Coverage and Self-Healing Control Based on a Swarm Intelligence SONM and Potential Function Approach

Self-organizing network will probably be a priority choice for the multi-agent systems because of its good features of configuration, management, optimization and self-healing. In this paper, a swarm intelligence coverage and self-healing method based on the Self-Organizing Network Model (SONM) and potential function is proposed for our multi-agent system. The SNOM is presented with a hierarchical network structure, which mainly includes the link layer design, route establishment and route maintenance. Based on the SNOM, the multi-agent coverage and self-healing control law is devised, and the potential function is designed to realize the collision avoidance, coverage and self-healing control for the multi-agent systems. The proposed approaches are implemented and evaluated by extensive simulations and experiments, and the results confirm that it is feasibility and efficiency in multi-agent coverage and self-healing control.
Improving Text Encoding for Retro-Remote Sensing

A recent work on retro-remote sensing (converting ancient text descriptions into images) was proposed using a multilabel encoding scheme in which an input text description is represented by a binary vector indicating the presence or absence of specific objects. However, this kind of encoding disregards information such as object attributes and spatial relationship between multiple objects in a description, resulting in images that do not semantically (fully) conform to the input description. In this letter, we propose an improved text-encoding mechanism that takes into account different levels of information available from an input text. The encoded text is then used as conditional information to guide the image synthesis process using generative adversarial networks (GANs). Besides, we present a modified GAN architecture intending to improve the semantic content of the generated images. Both the qualitative and quantitative results obtained indicate that the proposed method is particularly promising.
Hybrid fuzzy-genetic algorithm to automated discovery of prediction rules

Genetic algorithm is one of data mining classification techniques and it has been applied successfully in a wide range of applications. However, the performance of Genetic algorithm fluctuates significantly. This research work combines Genetic algorithm with fuzzy logic to adapt dynamically crossover and mutation parameters of Genetic algorithm. Two different datasets are taken during the experiment. Several experiments have been performed to prove the effectiveness of the proposed algorithm. Results show that the rules generated from a proposed algorithm are significantly better with high fitness and more efficient as compared to a normal Genetic algorithm.
A Method for 3D Reconstruction of Net Undulation for Fluid Structure Interaction of Fractal Induced Turbulence

Fractal generated turbulence is highly anisotropy and inhomogeneous, particularly, within its unique turbulence production regime. This study aims to develop an experimental-based detection method which allows the details of fluid-structure-interaction (FSI) via grid-net interplays to better express the strength of fractal grid induced flow dynamics. Hence, a novel approach is initiated to perform nets photogrammetry for 3D-reconstruction through an innovative means that permits the historically difficult task of reconstructing nets to be achieved. A keypoint extraction algorithm designed specifically for nets dubbed Neighborhood OCCupancy (NOCC), and a complementary stereo matching algorithm named Propagative Stereo Matching (PSM) are presented. The former identifies the center of intersections in the net by determining the percentage of an ellipse filled at each pixel. PSM works on the NOCC keypoints via matching pairs close to a known match and using the newly matched pairs as reference for subsequent matches in a way reminiscence to how humans would perform the stereo matching task manually. The effectiveness, accuracy and robustness of the detection are carefully validated using three different net geometries, which are individually subjected to three synthetic configurations. Consequently, the system repeatability, utilisation, stereo matching accuracy and reconstruction accuracy, of which, with the mean of all cases outstandingly deduced to 99.0%, 87.0%, 100% and 0.12mm, respectively. The presented method is then employed to elucidate the windward flow straightener influence on grid-net fluid flow interplays. Overall, the current framework may be an enabling factor in providing critical FSI-features to realize the mechanical strength of fractal generated turbulence.
High-Speed Privacy Amplification Scheme Using GMP in Quantum Key Distribution

Privacy amplification (PA) is the art of distilling a highly secret key from a partially secure string by public discussion. It is a vital procedure in quantum key distribution (QKD) to produce a theoretically unconditional secure key. The throughput of PA has become the bottleneck of most high-speed discrete variable QKD (DV-QKD) systems. Although some Toeplitz-hash PA schemes can meet the demand of throughput, their high throughput extremely depends on the high cost platform, such as MIC or GPU. From the comprehensive view of development cost, integration level and power consumption, CPU is a general low cost platform. However, the throughput of CPU based PA scheme is not satisfactory so far, mainly due to the conflict between the intrinsic serial characteristic of CPU and the parallel requirement of high throughput Toeplitz-hash PA scheme. In this paper, a high throughput modular arithmetic hash PA scheme using GNU multiple precision arithmetic library (GMP) based on CPU platform is proposed. The experimental results show that the throughput of our scheme is nearly an order of magnitude higher than the comparative scheme on the similar CPU platform, which is 135 Mbps and 69 Mbps at the block sizes of 106 and 108 on Intel i3-2120 CPU respectively. Moreover, our scheme can provide the best throughput among DV-QKD PA schemes, which is 260 Mbps and 140 Mbps at the block sizes of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> on Intel i9-9900k CPU respectively.
A survey and classification of XML based attacks on web applications

XML based attacks are executed in web applications through crafted XML document that forces XML parser to process un-validated documents. This leads to disclosure of sensitive information, malicious code execution and disruption of services. OWASP has included XML based attacks at number four in its top 10 list of vulnerabilities published in 2017. Most of the vulnerabilities reported using the XML document range from high to critical and require to be addressed immediately. As per the National Vulnerability Database, 152 vulnerabilities have already been reported in the first five months of the year 2019. A varied number of XML vulnerabilities and their classification exist but are limited to a specific vulnerability. In this paper, the authors have proposed a classification of XML based vulnerabilities based on exhaustive literature survey. The approach/strategies to mitigate these vulnerabilities are also presented. The work will help the web developers for proposing secure parsers that will thwart such attacks.