Reviewerly/exHarmony huggingface dataset:
Preface to Special Issue on “Learning to Discover”

International Journal of Modern Physics AVol. 35, No. 33, 2002003 (2020) Special Issue: “Learning to Discover”; Guest Editors: C. Fitzpatrick, V. V. Gligorov and J. AlbrechtNo AccessPreface to Special Issue on “Learning to Discover”https://doi.org/10.1142/S0217751X20020030Cited by:1 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail References 1. N. Braun and T. Kuhr, Int. J. Mod. Phys. A 35, 2043001 (2020). Link, ISI, ADS, Google Scholar2. F. Sergeev, E. Bratkovskaya, I. Kisel and I. Vassiliev, Int. J. Mod. Phys. A 35, 2043002 (2020). Link, ISI, ADS, Google Scholar3. A. Banerjee, I. Kisel and M. Zyzak, Int. J. Mod. Phys. A 35, 2043003 (2020). Link, ISI, ADS, Google Scholar4. T. Ruhe, Int. J. Mod. Phys. A 35, 2043004 (2020). Link, ISI, ADS, Google Scholar5. F. Psihas, M. Groh, C. Tunnell and K. Warburton, Int. J. Mod. Phys. A 35, 2043005 (2020). Link, ISI, ADS, Google Scholar You currently do not have access to the full text article. Recommend the journal to your library today! FiguresReferencesRelatedDetailsCited By 1Universes as big dataYang-Hui He8 November 2021 | International Journal of Modern Physics A, Vol. 36, No. 29 Recommended Vol. 35, No. 33 Metrics History PDF download
Prediction of risk factors for pre-diabetes using a frequent pattern-based outlier detection

Pre-diabetes is the forerunner stage of diabetes. Pre-diabetes develops type-2 diabetes slowly without any predominant symptoms. Hence, pre-diabetes has to be predicted apriori to stay healthier. The risk factors for pre-diabetes are abnormal in nature and are found to be present in a few negative test samples (without diabetes) of Pima Indian Diabetes data. The conventional classifiers will not be able to spot these abnormal samples among the negative samples as a separate group. Hence, we propose algorithm frequent pattern-based outlier detection (FPBOD) to spot such abnormal samples (outliers) as a separate group. FPBOD uses an associative classification technique with few surprising measures like lift, leverage and dependency degree to detect outliers. Among which, lift measure detects more precise outliers that are able to correctly classify the person who did not have diabetes, but just takes the risky chance of being a diabetic patient.
Designing of Autonomous Wheelchair for Movement in Standing Position

Driving a manual wheelchair on any surface is a difficult task and past invented automatic wheelchairs are available at a very high cost in the market. The purpose of this paper, work is to propose an autonomous wheelchair that will reduce human effort while moving on a surface with inclination or declination and is available at a comparatively lower cost. One of the basic problems of user on manual wheelchair is to overcome barriers like kerb, rough surfaces, inaccessible surfaces etc. Though many research have been done in this field, but the question of overcoming these barriers always remains as a topic of discussion for many researchers. In our paper a motor operated &amp; electronically hand controlled autonomous wheelchair concept which can overcome the architectural barriers to a considerable extent has been developed. This paper involves the design of an ergonomically designed battery powered wheelchair for multipurpose use. All the design parameters of wheelchair's are based on the standard design of stair in India. Major part of the paper focuses on the proposed design concept and mechanism and concludes by focusing upon the physical working model developed for the proposed design solution.
Technological Laboratory for Pedagogical Innovation and Learning - (LaTIPA)

This article addresses the theoretical conceptions and purposes of creating the Technological Laboratory of Pedagogical Innovation and Learning (LaTIPA). From a theoretical review of technological innovation concepts in learning processes, the laboratory was systematized to create pedagogical innovations through innovative actions and projects involving applied technology. In this work, we present the theoretical conceptions that substantiate this proposal. The results of this project are expected to come to consolidate the exposed motivations.
Learning Based Resolution Enhancement of Digital Images

Image super-resolution (SR), the process that improves the resolution, has been used in many real world applications. SR is the preprocessing phase of majority of these applications. The improvement in image resolution improves the performance of image analysis process. The SR of digital images take the low resolution images as inputs. In this article, a learning based digital image SR approach is proposed. The proposed approach uses Convolutional Neural Network (CNN) with leaky rectified linear unit (ReLU) for learning and generalization. The experiments with the test dataset from USC-SIPI indicate that the proposed approach increases the quality of the images in terms of the quantitative metric peak signal to noise ratio. Further, it avoided the problem of dying ReLU.
A fast clustering approach for large multidimensional data

Density-based clustering is a strong family of clustering methods. The strength of this family is its ability to classify data of arbitrary shapes and to omit the noise. Among them density-based clustering (DENCLUE), which is one of the well-known powerful density-based clustering methods. DENCLUE is based on the concept of the hill climbing algorithm. In order to find the clusters, DENCLUE has to reach a set of points called density attractors. Despite the advantages of DENCLUE, it remains sensitive to the growth of the size of data and of the dimensionality, in the fact that the density attractors are calculated of each point in the input data. In this paper, in the aim to overcome the DENCLUE shortcoming, we propose an efficient approach. This approach replaces the concept of the density attractor by a new concept which is 'the hyper-cube representative'. The experimental results, provided from several datasets, prove that our approach finds a trade-off between the performance of clustering and the fast response time. In this way, the proposed clustering methods work efficiently for large of multidimensional data.
Optimal ECC-based signcryption algorithm for secured video compression process in H.264 encoder

Combination of cryptography method and video technology is a video encryption. Video encryption process is a total and demonstrable security of video data. For the purpose of protecting the video sequence, we have intended to recommend a video compression procedure along with its encryption for providing secured video compression framework. In this document we have suggested a method for ECC-based signcryption algorithm for secured video compression process. At first, encryption process will be used on the motion vector by applying elliptic curve cryptography (ECC)-based signcryption algorithm. The suggested method uses ECC method for the generation of public and private key. At the point when contrasted with the other encryption algorithms like small key, more security, increased velocity, little storage space and low data transfer capacity ECC has particular preferences. The suggested method employs the improved artificial bee colony (IABC) algorithm in order to optimise the private key. Next, the optimal selection of private key applied to encrypt the motion vector. By means of different security attacks such as man in middle (MiM) attack, brute force and denial of service (DoS) attacks, the security of the suggested method will be examined.
Cables, Trains and Types

Many concepts of computing science can be illustrated in ways that do not require programming. CS Unplugged is a well-known resource for that purpose. However, the examples in CS Unplugged and elsewhere focus on topics such as algorithmics, cryptography, logic and data representation, to the neglect of topics in programming language foundations, such as semantics and type theory. This paper begins to redress the balance by illustrating the principles of static type systems in two non-programming scenarios where there are physical constraints on forming connections between components. The first scenario involves serial cables and the ways in which they can be connected. The second example involves model railway layouts and the ways in which they can be constructed from individual pieces of track. In both cases, the physical constraints can be viewed as a type system, such that typable systems satisfy desirable semantic properties.
Analysis of the influence of multimedia network hybrid teaching on college students English learning ability

In order to improve college students' English learning ability, this paper puts forward a model to analyse the influence of multimedia network mixed teaching on college students' English learning ability. Based on the logistic regression analysis method, the multimedia hybrid network is designed, and the network node model of multimedia network hybrid teaching is constructed. This paper constructs an information acquisition model adapted to college students' English learning ability, and uses an improved extreme learning machine model to integrate multimedia network teaching information with college students' English learning ability information. The simulation results show that the method has high accuracy in analysing the effect of multimedia network mixed teaching on college students' English learning ability, and reduces the network transmission delay, improves the throughput, and promotes college students' English learning ability.
Fuzzy Optimization Model for post harvest selection process of Pecan (Carya illinoinensis)

The quality of food is associated with a set of properties and characteristics that can be considered from its chemical and nutritional physical composition that have the ability to meet the needs of the consumer. The paper aims to evaluate the application of fuzzy logic in the evaluation and classification of the selection of pecans in the post harvest process using tests and instruments that determine their best quality. Fuzzy logic has proven to be very effective with matlab/simulink to develop and simulate the entire system, through an appropriate choice of rules and membership functions and applying the Mamdani method to defuzzification the results considering they are positive.
Stargan-based camera style transfer for person retrieval

Person retrieval is also known as person re-identification (ReID) aiming to match person among cross cameras. Although the results of the person ReID have performed well in small datasets, the issues of the large number of identities in real scenarios or with more cameras have not been fully investigated. Being an image retrieval task under cross multi-cameras of intelligent video security, person ReID is influenced by the image style change caused by different camera illumination and view angles. The number of cameras in the latest datasets is increasing and more camera transfer models need to be trained. Traditional methods of generative adversarial network (GAN) can only handle transfer of two domains. To facilitate the research towards solving these problems, we use star generative adversarial networks (StarGAN) to transfer the image from one camera to another camera in the latest large benchmark datasets. We train multiple transfer models simultaneously, minimising the bias among different cameras. Label smooth regularisation (LSR) algorithm is utilised to mitigate the effects of noise in the model. We learn part-based descriptors from pedestrian samples to generate robust feature representation. Our work is competitive compared to the state-of-the-art.
Error in Author Affiliations

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Detection of threatening user accounts on Twitter social media database

The freedom of social media platforms to post and share daily activities is being misused by threatening users as they post the suspicious and fake content on social media for personal or organisational advantage. This demands to generate a system that can detect suspicious content and their respective user accounts. In this paper, an ant colony optimisation based system for threatening account detection (ACOTAD) is proposed. The connections among the different Twitter users are determined by the pheromone substance secreted by ants on the edges of the path travelled. Better the quality of pheromone indicates the strong connection of one user with another. This research work considers the experimentation on Twitter based Social Honeypot Database. The evaluated results in terms of precision, recall, f-measure, true positive rate, and false positive rate indicate the superiority of the proposed concept in comparison with existing techniques.
An integrative approach for path planning and tracking of a shape-aware mobile robot in structured environment using vision sensor

A shape-aware path planning algorithm is necessary for real-time execution of a task by a mobile robot whereas path planning algorithms available in literature consider mobile robot as a point object. Current work proposes a shape-aware A* path planning approach with a heuristic function to accommodate the shape of mobile robot. In this paper, the detection, tracking and control of the robot has been carried out for the mobile robot while performing a task in a structured environment. For the implementation and validation, an overhead camera is used to capture the images of obstacles in the task space and published in ROS platform. The captured images are also processed using OpenCV software for detection and tracking using Kanade-Lucas-Tomasi (KLT) and Kalman filter algorithms for different test scenarios. The proposed approach accurately detects and tracks the shape aware mobile robot with percentage error ranging from 6%-10% in different cases.
Perception of the acquisition of competences for university professors in a context of problem-based learning methodology

This research studies the impact of a training course for University professors based on active methodologies, such as Problem-Based Learning (PBL), on their perception of the acquisition of some relevant specific and generic competences. To that end, we prepare a survey about competences and compare the perception for the teaching staff before and after carrying out the PBL activity, by analysing if there are statistically significant differences in mean (ANOVA-F test), median (Kruskal-Wallis test) and variance (Levene test). Our results may confirm that the perception would improve in competences related to the appropriate use of ICTs and problem-solving skills, among others, showing statistically significant differences. Finally, university professors would positively evaluate the practical use of the training course (4.5 out of 5), remarking the need to apply active methodologies such as PBL in the training of teachers.
Secure and Optimized Data Sharing Model Group in Healthcare Cloud Environment

The cloud computing provides convenient ondemand access of the data. Sharing of data in the cloud computing will enable several users to easily handle the data that is being shared. The medical-field finds more advantages by the cloud-computing technology as the data can be accessed anywhere and anytime by the patients as well as this data can be shared with other medical-practitioners. This alarms for the security issues as the huge amount of sensitive data is being shared. The data must not be available to malicious-attackers. In this paper, we propose the block-design based key agreement protocol in order to share the data securely and the design provides fault-detection and fault-tolerance. The group-data model PSM is given with the block-based design, which decides how the sharing of the data is done by grouping and giving positions to users in particular blocks and the column. The design is proposed in our paper, which gives the technique for positioning of the users. The encryption and decryption of data is done and their times cost according to file size is found. The comparison of the time-cost for our model and existing models is compared with respect to different number of simulations.
Unique challenges facing Linked Data implementation for National Educational Television

Implementing Linked Data involves a costly process of converting metadata into an exchange format substantially different from traditional library "records-based" exchange. To achieve full implementation, it is necessary to navigate a complex process of data modelling, crosswalking, and publishing. This paper documents the transition of a data set of National Educational Television (NET) collection records to a "data-based" exchange environment of Linked Data by discussing challenges faced during the conversion. These challenges include silos like the Library's media asset management system Merged Audio-Visual Information System (MAVIS), aligning PBCore with the bibliographic Linked Data model BIBFRAME, modelling differences in works between archival moving image cataloguing and other domains using Entertainment Identifier Registry IDs (EIDR IDs), and possible alignments with EBUCore (the European Broadcasting Union Linked Data model) to address gaps between PBCore and BIBFRAME.
Web Objects Opinion through Sentiment Engineering

Sentiment Analysis is the analysis of thoughts, feelings and qualities of people towards an object. Automatically recognizing user-generated content views is of great help for commercial and political use. Sentiment Analysis / Opinion Mining lets us gather information about the positive and negative characteristics of any given object / product, and we recommend the favorable and highly scoring views on the object / product to the user. Although researchers have contributed a lot towards objects review through sentiment analysis, still there are open issues needs to be addressed such as Negation Handling, Domain Generalization and Detection and Removal of Fake Reviews. This paper presents a review on the various algorithms used for Negation Handling, Domain Generalization and Detection and Removal of Fake Reviews along with a comparative study against performance metrics along with their limitations.
Phishing Scam Detection using Machine Learning

As a wrongdoing of utilizing specialized intends to take sensitive data of clients and users in the internet, phishing is as of now an advanced risk confronting the Internet, and misfortunes due to phishing are developing consistently. Recognition of these phishing scams is a very testing issue on the grounds that phishing is predominantly a semantics based assault, which particularly manhandles human vulnerabilities, anyway not system or framework vulnerabilities. Phishing costs. As a product discovery plot, two primary methodologies are generally utilized: blacklists/whitelists and machine learning approaches. Every phishing technique has different parameters and type of attack. Using decision tree algorithm we find out whether the attack is legitimate or a scam. We measure this by grouping them with diverse parameters and features, thereby assisting the machine learning algorithm to edify.
Feature Based Identification of Web Page Noise through K-Means Clustering

Web pages has pieces of information which are of unequal importance like navigational bar, copyright notice, links, advertisement etc. and these are considered as noise or insignificant items of web page for web mining. Web page informative content is only useful for performing effective web mining task and presence of noise on web page can hamper the result of this task. Web page has several features including information location, occupied area and its contents. Content data in different portions of an internet web page has dissimilar significance weights according to its location, occupied location and content that are considered to be features of the web page. The position of contents and importance of contents play a vital role in identification of noise in web pages for removal. In this paper web page feature based method is proposed for identification of noise from web pages. K-means clustering technique is applied to classify main content information and noise content information into two clusters of web pages based on these features. For performance evaluation of clustering method, accuracy, precision, f-measure, and recall are calculated.
Artificial Bee Colony Algorithm

Artificial Bee Colony (ABC) algorithm is a powerful optimization algorithm that simulates the task division and self-organization abilities of honey bees in their foraging activity. ABC algorithm has attracted the attention of researchers and has been applied to many problems in various fields successfully. ABC algorithm was originally proposed to solve numeric optimization problems and researchers have made some arrangements in the algorithm to solve combinatorial, constrained, multi-objective problems, etc. In this chapter, a tutorial on the basic concepts of the ABC algorithm is given including the explanations of each phase and its control parameters. Source-codes of the ABC algorithm in Matlab and C++ programming languages are provided for the readers who want to apply the algorithm to solve the problems in their research fields. Search behaviour of the ABC algorithm balancing the exploration and exploitation is analysed by showing step-by-step problem solving procedure on a numeric test problem.
A Machine Learning-based Online Social Network Analysis for 360-degree User Profiling

This paper aims to analyse the online social network for reconnaissance of people for finding their potentiality. The work considers one of the famous social networking sites, Twitter, where people express their thoughts and ideas, through which the people in the site knowingly or unknowingly reveal the information about themselves such as personal interests, likes and dislikes. The Machine Learning technique facilitates the work to mine the tweet data of a person to get his/her 360-degree profiling. This profiling is helpful to identify the personality type of a person, which is essential for the Government to identify the people involved in spreading the fake news in Twitter.
P.437 Common and specific behavioural markers of dependence on five different classes of drugs identified by machine learning

Continuous microseismic monitoring of hydraulic fracturing is commonly used in many engineering, environmental, mining, and petroleum applications. Microseismic signals recorded at the surface, suffer from excessive noise that complicates first-break picking and subsequent data processing and analysis. This study presents a new first-break picking algorithm that employs concepts from seismic interferometry and time-frequency (TF) analysis. The algorithm first uses a TF plot to manually pick a reference first-break and then iterates the steps of cross-correlation, alignment, and stacking to enhance the signal-to-noise ratio of the relative first breaks. The reference first-break is subsequently used to calculate final first breaks from the relative ones. Testing on synthetic and real data sets at high levels of additive noise shows that the algorithm enhances the first-break picking considerably. Furthermore, results show that only two iterations are needed to converge to the true first breaks. Indeed, iterating more can have detrimental effects on the algorithm due to increasing correlation of random noise.