Reviewerly/exHarmony huggingface dataset:
Evolution Based Single Camera Resectioning Based on Distance Maps of a Known Geometry for Squash Sports

Nowadays, video recordings of sport events is standard practice for a variety of applications, ranging from entertainment to competition analysis.Beside that, analysis of athletes while exercising is of particular interest for their coaches in order to gain insight into training quality and allow for training control.To bring together video recordings and the desire for analysis we present the implementation of a genetic algorithm (GA) for the important step of camera calibration.Our implementation can be used not only in a prospective but also in a retrospective manner for the squash sport.We do not rely on directly or manually provided image-world coordinates, but rather only on the playing field as known geometric object, present in the physical camera's captured image.To find the best GA configuration, we evaluate all combinations of 2 initialization-, 2 fitness-, 3 selection-, 4 crossover-, and 2 mutation strategies.We apply and evaluate the GA's accuracy on synthetic, artificial renderings, and real world data as well as comparing it to other standard optimization algorithms.Our results reveal the importance of correct camera placement and show sufficient accuracy for our goal of athlete movement analysis.The results will serve for a automatic athlete movement analysis tool to support squash specific training procedures.
Monitoring Time Series with Missing Values: A Deep Probabilistic Approach

Systems are commonly monitored for health and security through collection and streaming of multivariate time series. Advances in time series forecasting due to adoption of multilayer recurrent neural network architectures make it possible to forecast in high-dimensional time series, and identify and classify novelties early, based on subtle changes in the trends. However, mainstream approaches to multi-variate time series predictions do not handle well cases when the ongoing forecasts must include uncertainty, nor they are robust to missing data. We introduce a new architecture for time series monitoring based on combination of state-of-the-art methods of forecasting in high-dimensional time series with full probabilistic handling of uncertainty. We demonstrate advantage of the architecture for time series forecasting and novelty detection, in particular with partially missing data, and empirically evaluate and compare the architecture to state-of-the-art approaches on a real-world data set.
Integrating Speech Technology into the iSTART-Early Intelligent Tutoring System

Speech technology (automated speech recognition – ASR and text-to-speech) offers great promise in the field of automated literacy and reading tutors for children. Students in third and fourth grades struggle with generating longer strings of text on a QWERTY keyboard because they still “hunt and peck” for the letters and symbols rather than typing fluently. Thus, in addition to reading comprehension, students’ performance is limited by their ability to translate their ideas into language and then transcribe those words into written text. Fourth grade students produce fewer words and recall less when typing or writing a response relative to speaking. Hence, speech technology is a crucial component in the development of iSTART-Early, an intelligent tutoring system that aims to provide online, automated reading strategy instruction and practice to improve deep comprehension for third and fourth graders. This paper discusses the key components and features of the speech technology incorporated into iSTART-Early. We also discuss some initial findings from pilot studies conducted with adults and youth for this ASR integrated tutoring system. Finally, we discuss considerations for future development of speech technology and integration with intelligent tutoring systems.
Iivrs: An Intelligent Image and Video Rating System to Provide Scenario-Based Content for Different Users

Visual documents, such as images and videos, are exploding on the Internet as they are the most effective methods to share information. However, the harmful contents contained in the images and videos are overwhelming due to the lack of effective evaluation mechanisms. To address this problem, this research designed and developed an intelligent rating system for visual information called Intelligent Image and Video Rating System (IIVRS). With the developed IIVRS prototype, this research further tested its reliability and efficiency by several designed tests. It further indicated that IIVRS’s intelligent rating results are perfectly consistent with human experts’ manual rating results. Besides, IIVRS showed a significant advantage in rating efficiency. Furthermore, IIVRS’s abilities on small sample learning and background context(s) differentiation were also demonstrated, revealing additional innovative advantages of the IIVRS.
Question Answering Over Knowledge Graphs Using Bert Based Relation Mapping

A Knowledge Graph (KG) is a structured form of knowledge describing real-worldentities, properties, and relationships as a graph. Question Answeringover Knowledge Graphs (KGQA) allows people to ask questions in natural languageand extract answers from KG accurately and more quickly. The maintask of a KGQA is to convert a natural language query to the correspondingstructured query form like SPARQL. However, generating the precise SPARQLquery from a question is a challenging task and is highly error-prone. Here wepropose a question answering framework that uses Knowledge Graph to answersimple questions without using SPARQL. Question classification, dependencyparsing, entity linking, BERT-based relation finding, and answer extraction arethe main modules of the approach. We used the DBpedia as the knowledgegraph and tested the end-to-end system with a subset of QALD-4, LC-QuAD,and SimpleQuestions datasets.
Stable Subgraph Isomorphism Search in Temporal Networks

In this paper, we study a new problem of seeking stable subgraph isomorphisms for a query graph in a temporal graph. To solve our problem, we first develop a pruning-based search algorithm using several new pruning tricks to prune the unpromising matching results during the search procedure. To further improve the efficiency, we propose a novel index structure called BCCIndex, based on an idea of bi-connected component decomposition of the query graph, which can efficiently support the stable subgraph isomorphism search. Equipped with the BCCIndex, we present an efficient query processing algorithm based on a carefully designed tree join technique. We conduct extensive experiments to evaluate our algorithms on four large real-life datasets, and the results demonstrate the efficiency and effectiveness of our algorithms.
Towards Simplification of ME-Maps

As knowledge increases tremendously each and every day, there is a need for various means to manage and organize it, so to utilize it when needed. For example, for finding solutions to technical and engineering problems. An alternative for achieving these goals is through knowledge mapping that aims at indexing the knowledge. Recently, we devised an approach called ME-MAP for mapping out know-how, so to facilitate knowledge indexing. However, the challenge of handling large maps still exists. In this paper, we address this challenge by proposing a simplification mechanism for ME-maps. In particular, we define the meaning of simplification in ME-MAP, set simplification rules, and develop an algorithm for simplifying ME-maps. In addition, we confirm the correctness of the algorithm and its scalability, as well as its support for understanding domain maps through a preliminary user study.
A Novel Flexible Feature Extraction Algorithm for Spanish Tweet Sentiment Analysis Based on the Context of Words

A tweet polarity classifier is presented with four categories: positive, neutral, negative and no opinion. A grouping genetic algorithm performs feature extraction on the reviews. The feature definition is based on entropy and context semantics described as the relative positions between words. The feature selection is flexible because it is customized to each word studied in the reviews. The algorithm has been applied with two corpus written in Spanish, of sizes 3413 tweets and more than 63000 tweets, to classify an evaluation set of 1899 reviews. The results were evaluated by the metrics M-F1 and accuracy. The algorithm has improved the results of both metrics and on both corpus compared to the previous work in literature, achieving an M-F1 of 0.640 and an accuracy of 0.689. The flexibility property in feature extraction has been the major qualitative improvement of the classifier.
The Impact of Table and Index Compression on Data Access Time and CPU Costs

The amount of stored data is growing rapidly, and it brings considerable challenges. The enormous growth in the volume of data makes storage one of the biggest cost elements. For this purpose, relational databases are used very often. Fast access to data is becoming increasingly important and great emphasis is placed on its improvement. This paper deals with the impact of table and index compression on data access time and CPU costs. It was compared in eight different scenarios of different combinations of compressed and uncompressed indexes over compressed and uncompressed tables with non-unique data of various row numbers.
Sequential Spatiotemporal Bias Compensation and Data Fusion for Maneuvering Target Tracking

In multisensor tracking systems, bias compensation is essential for accurate data fusion. It is challenging to obtain correct fusion results in the presence of both spatial and temporal biases, as well as target maneuver. In this article, a sequential spatiotemporal bias compensation and data fusion method is proposed for maneuvering target tracking with asynchronous multisensor measurements. The spatiotemporal biases of the sensors are augmented into the state vector to be estimated. The state equations are presented to formulate the nearly coordinated turn motion and the nearly constant acceleration motion, respectively, without exactly known measurement interval. In each target motion, the measurements are formulated as functions of both spatiotemporal bias and target state based on the time difference between the measurements and the states to be estimated. Furthermore, the interacting multiple model estimator is incorporated with the unscented Kalman filter to achieve simultaneous sequential estimation of spatiotemporal biases and target states in the presence of target maneuvers. Finally, the posterior Cramer–Rao lower bound for spatiotemporal bias and state estimation is provided. Simulation experiments are performed to demonstrate the effectiveness of the proposed method.
Research on user demand evolution model based on online product community

Aiming at the problem of how to accurately mine the dynamic evolution of customer requirements from online product community user reviews, an optimized Kano model customer requirement hierarchy evolution design method is proposed. By introducing domain-related lexicons through conditional random fields(CRFs), the multi-feature fusion of evaluation objects and words is performed and a core emergence-based customer requirement evolution analysis model is established. The model is used to analyze the evolution characteristics of the demand network hierarchy and achieve dynamic tracking of the requirements’ evolution process, with the aim of providing guidance for product improvement iterations. The validity of the method was verified using experimental data from a vertical website of a certain model.
Automatic Web Data API Creation via Cross-Lingual Neural Pagination Recognition

Information extraction, transformation and loading (abbreviated as ETL) tools are important for big data analysis and value-added applications on the Web. Typical Web scraping systems such as “Dexi.io” or “Import.io” allow users to specify where to fetch the page and what information or data to be extracted from the page. Although these commercial services already provide a graphical user interface to guide the system to the target pages for each data source, such systems are not scalable because users have to create crawlers one by one. In this paper we consider the problem of pagination recognition, which aims to automate the process of finding similar pages by locating the next page link and the list of page links from any starting URL. We propose a neural sequence model that will label each clickable link in a page as either “NEXT”, “PAGE” or “Other”, where the first two could guide the system to find similar pages of the seed URL. To have multilingual support, we have exploited the attribute contents in the links as well as Language-Agnostic SEntence Representations (LASER) for anchor text embedding. The experimental results show that the proposed model, achieves an average of micro 0.834 and macro 0.818 F1 score on pagination recognition. In terms of practical deployment, we are able to automatically create 1,060 (MDR) and 153 (DCADE) data APIs from 392 event source pages within 62 min.
Cross Virtual Machine Security: A Survey

Cloud computing security has emerged as the most pressing problem on the globe. Moreover, hosting several machines in a single physical environment offer more flexibility and performance, especially with the emergence of modern concepts such as smart cities, IoT, and robotics. The benefits and advantages of virtualization technology are enormous. On the other hand, sharing resources such as network, CPU, and RAM by several tenants cause security vulnerability related to virtualization components or side-channel attacks that challenge those gains. The present paper highlights the virtualization technologies and their security issues and survey the critical vulnerabilities related to side-channel attacks.
Semantic-Based Image Retrieval Using Hierarchical Clustering and Neighbor Graph

In this paper, a data partitioning method was built and applied to a model of semantic-based image retrieval. An improvement of data partitioning based on the hierarchical structure is proposed in which data regions were created to store images; called the growth partition tree (GP-Tree). Based on this, the neighbor cluster graph was built in order to increase the performance of retrieving similar images. The k-NN algorithm was applied to classify an input query image; then, a SPARQL statement structure was generated and executed on the ontology to extract the semantics of an input image as well as the semantics of the image class. From there, the semantic-based image retrieval model was proposed and experimented on the image datasets ImageCLEF, and Stanford Dogs. The experimental results evaluated and compared with the recently published works on the same datasets and demonstrate that the proposed method improves the retrieval accuracy and efficiency.
An Improved Similarity Measure Based on Collaborative Filtering for Sparsity Problem in Recommender Systems

In this era, with the huge increase in user engagement on the online platforms day by day, the more is the necessity in providing relevant search results for the people. Recommender systems (RSs) play a significant role in generating accurate recommendations. Among the different types of RS, collaborative filtering-based (CF) RS is the most used due to its ability to produce recommendations that can fit the user’s varying preferences over time. CF works on the methodology that similarity in users/items can be discovered by a user-item rating matrix. From the generated resultant similarity, prediction on new or unknown users is made by means of a prediction formula. The most common similarity measures are cosine, Jaccard, Pearson correlation, mean square difference, etc., based on which research is going on to improve the accuracy as well as to confront the drawbacks of each of these measures. This project proposes a hybrid similarity measure more precisely a linear combination of similarity measures that utilize the advantage of each measure as well as by taking user rating preferences into consideration, hence able to achieve the least RMSE, MAE score for the datasets taken. The datasets chosen for this experiment (FilmTrust, CiaoDVD) are 99% sparse, so hybrid similarity can give better recommendations by handling the sparsity problem based on the results obtained. An average of 0.33 reduction by value in RMSE and MAE is observed when proposed similarity was compared with other similarity measures used in the experiment on two different datasets.
Assessment of Image Quality Metrics by Means of Various Preprocessing Filters for Lung CT Scan Images

Lung cancer is the most predominant and lethal growth around the world. Lung Computerized Tomography (CT) scan images have been contributing enormously to clinical research and diagnosis. Although due to the presence of artifacts, noise and blurring effects, the CT images produce a degraded output of the actual part under diagnosis. Hence, application of preprocessing filters on CT images proves crucial to reduce the noise and improve the quality of image. In this paper, we evaluate the image quality by exploring various parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Speckle reduction and Mean Preservation Index (SMPI) and Speckle Suppression Index (SSI), thereby measuring the performance of different filters.
Elliptic Curve Cryptography (ECC) for a Lightweight Public Key Infrastructure (PKI)

In recent years, the information access, use, and control have become progressively an interesting area of research. The increase in the number of cyberattacks makes network and information security situation bleak. In this chapter, we have presented an overview of cryptography that complements strongly security, we describe and compare symmetric encryption systems and asymmetric encryption systems. Therefore, we give a brief introduction to the elliptic curve cryptography (ECC), performance, characteristics, advantages, and challenges. Also, we investigate the PKI (Public Key Infrastructure) technology for certificate revocation. Then we propose a new approach for a lightweight PKI based in elliptic curve cryptography (ECC); furthermore, we present a public key infrastructure model for certificate revocation, as well we elaborate the deployment of the presented Model.
A Novel Fish Migration Optimization with QUasi-Affine TRansformation Evolutionary for Numerical Optimization and Application

Fish migration optimization (FMO) is an efficient swarm intelligence algorithm inspired by the migration process of fish. This paper proposes a new optimized structure—QUasi-Affine TRansformation Evolutionary for the Fish Migration Optimization (QTFMO). The new algorithm introduces the evolution matrix of the QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm. Simultaneously, it also increases the disturbance of the individual optimal position to the search agent position in the fecundity stage. By improving the update of the strategy of individual positions, the particle movement process is more scientific and the search space is broader. It largely avoids premature convergence of FMO. We compared the new algorithm with five other intelligent algorithms through 23 different types of test functions and applied the new algorithm to the Capacitated Vehicle Routing Problem (CVRP). Experimental results reveal that the newly proposed QTFMO has powerful competitiveness and can achieve better path planning.
A Surrogate-Assisted Ensemble Particle Swarm Optimizer for Feature Selection Problems

For feature selection problems on high-dimensional data, this paper proposes a surrogate-assisted ensemble particle swarm feature selection algorithm, by combining the global search ability of evolutionary algorithm with the fast search ability of filter method. A space partition method based on K-nearest neighbors is proposed to select represent samples as surrogate. The proposed ensemble algorithm is applied to several datasets. Experimental results show that the proposed algorithm can obtain feature subsets with higher classification accuracy in less computing time.
Securing of Identification System Data Transmission Using Deep Autoencoders and Data Hiding

The reliability of any identification systems depends on the level of security provided to the storage of sensitive data. These identification systems provide information about people working in any organization which is intended to help the organization keep track and monitor their activity. Databases of such systems need to be fast, reliable and highly secured. Apart from providing authorization layer to the databases the data itself can be encoded and stored in a simple as well as easy form such that the data itself is secure and does not go through lossy transformation. In this paper, the data is encoded with the help of DAE (Deep Autoencoder) models and these encoded data are then merged with the images of respective people using RDH (Reversible Data Hiding) which is then stored as a simple image data. The data is then retrieved when required and decoded using the same autoencoder mode and checked for loss in the data. This helps us to easily store and send the data as a simple image and improves the security of the system. The models perform good on all datasets with simple autoencoders gives the best result with a loss of only 1.5% loss in data as compared to 3% and 7% by deep autoencoder and convolutional autoencoder respectively Keywords: Identification system, authorization, security, Deep Autoencoder, Reversible Data hiding, storage, transformation.
Transfer Machine Learning Algorithms

In this chapter we explore and understand the concept of transfer machine learning algorithms, which deals with the ability to utilize the existing knowledge and transfer it to a specific target task effectively and efficiently. We perceive the scope, significance and various types of transfer learning and traverse through its emergence along with some prevailing applications in recent times. This chapter also includes several benefits and popular techniques of transfer learning that are prevalent. Transfer Learning methods have already been successfully applied to various domains and applications, we strive to comprehend its utilization in different applications using numerous use case solutions. We also uncover the associated challenges and limitations of transfer learning in order to make use of it in the best possible way. The usage of transfer learning is well-known to boost the algorithm performance, and we discuss its applicability more in the future direction to research. We conclude our chapter by summarizing what we have learned so far with some main key takeaways in order to use transfer learning in real-world applications.
Fuzzy Set-Based Frequent Itemset Mining: An Alternative Approach to Study Consumer Behaviour

The application of fuzzy logic in consumer behaviour using fuzzy sets can give a more realistic understanding for firms, marketing research agencies and the policy makers. In this paper, an algorithm fuzzy set-based frequent itemset mining (FSFIM) has been proposed to mine frequent patterns in the form of itemsets and association rules which are expressed in the form of categories of item. Itemsets in the source database are classified into low, medium and high based upon quantity of the item purchased in a transaction. The classification of an item is done using membership of the item to the fuzzy set where each category is represented as a fuzzy set. The number of patterns of interest generated using FSFIM is comparatively less as compared to the traditional methods.