Reviewerly/exHarmony huggingface dataset:
Alzheimer’s Disease Computer Aided Diagnosis Based on Hierarchical Extreme Learning Machine

The usual computer aided diagnosis approaches of Alzheimer’s disease patients based on fMRI often require a lot of manual intervention. By contrast, H-ELM needs only less manual intervention and can extract features by a multi-layer feature representation framework. Therefore, an AD CADx model based on H-ELM is proposed. First, the common spatial pattern is used to extract information from the BOLD signals, and then the features are encoded and trained by H-ELM. H-ELM is used to realize the expression of deep feature of the brain, so as to further improve the diagnostic accuracy. Finally, experimental evaluation proved the effectiveness of the proposed algorithms.
Research in the Wild via Performance

Share on Research in the Wild via Performance: Challenges, Ethics and Opportunities Authors: Robyn Taylor Newcastle University, United Kingdom Newcastle University, United KingdomView Profile , Julie Williamson University of Glasgow, United Kingdom University of Glasgow, United KingdomView Profile , Jocelyn Spence University of Nottingham, United Kingdom University of Nottingham, United KingdomView Profile , Matthew Wood Newcastle University, United Kingdom Newcastle University, United KingdomView Profile , Jonathan Hook University of York, United Kingdom University of York, United KingdomView Profile , Ko-Le Chen Newcastle University, United Kingdom Newcastle University, United KingdomView Profile Authors Info & Claims TVX '19: Proceedings of the 2019 ACM International Conference on Interactive Experiences for TV and Online VideoJune 2019 Pages 279–285https://doi.org/10.1145/3317697.3323348Published:04 June 2019Publication History 0citation84DownloadsMetricsTotal Citations0Total Downloads84Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Profiling Art Forgers

Unlike art thieves, who do not fit a specific criminal profile, art forgers largely do. The vast majority of those who have been found out adhere to a very narrow description, particularly when it comes to their origin story and initial motivation to turn to forgery. This chapter describes this profile, drawing on numerous historical examples of famous forgers to show the remarkable consistency in their biographies. It further describes how a focus on regular forensic testing of objects on the art market would be the best way to curb forgery, since almost no known forgers have bothered to create fraudulent artworks that would fool forensic tests. The fact that the art trade tends to over-rely on connoisseurship and provenance gives the advantage to the forgers, who need to pass off a good story and a reasonably good-looking work of art but one which does not need to do the “heavy lifting” of standing up to scientific tests.
Radiation Patterns Feature Points Identification with Corner Detection Methods

Phased Array Antennas are complex systems that are widely used in many fields. Their development is a long hard process, which requires a lot of testing and analyzing. In exploitation, these systems are repeatedly tested over time and their configuration is adjusted due to the nature of their elements. A big part of development and testing is analyzing results of multiple Radiation Patterns of antenna. The main problem of automating this process is finding feature points of Radiation Pattern to calculate main parameters of antenna. The paper deals with this problem using corner detection methods and comparing their efficiency on Radiation Patterns samples.
Fuzzy Logic Methods in Decision-Making Processes: Application in the Shotgun Sector

This study, carried out in coordination with KOP (Konya Plain Project) Regional Development Administration, aims to create a decision-support matrix for shotgun producers based in Beyşehir, Turkey, into the defense sector. The ecosystem in question accounts for 75% of the total shotgun production in Turkey, which is the world’s second largest shotgun exporter. Empirical data were based on a sample of 17 shotgun producers. Enterprise System Engineering approach was used to create a questionnaire for the collection of data on each of the product life cycle processes (demand collection, design, procurement of materials, production, quality control, sales and marketing, and feedback) of shotgun producers. The questionnaire was applied in two separate semi-structured interviews with each producer. The data collected in the first session of interview was digitalized and verified in the second session. The Fuzzy Analytical Hierarchy Process was used on the verified data to calculate a weighted total ecosystem score and a producer score for each process. Following this, the producers were labelled and categorized according to their individual scores using the Fuzzy C-Means method. A decision-support matrix has been developed according to the category of the producers. The matrix includes road maps that define activities such as training and consultancy for each category in the short, medium and long term. Hence it is possible to enhance the productivity of the producers, such that they may form a sustainable ecosystem and become integrated into the defense sector.
Preschool Children’s Product Design Based on Heart Flow Theory

Through the study of the theory and results of flow, this paper puts forward the method suitable for preschool children’s product design, and seeks for a new way of development and research for preschool children’s product design. Based on the flow theory, through on-the-spot investigation, put forward on the premise of preschool children’s needs and behavior characteristics, combined with the use of environment and the function of product features, establish a preschool children product innovation design method, including a specific goal, timely and accurate feedback, the unity of behavior and consciousness, potential thinking guide, subjective emotional experience and other elements, and is verified through a case. Based on the innovative design concept of flow theory, this paper deeply understands the needs of preschool children, so as to provide preschool children with emotional experience and psychological space of entertaining through lively education through design, promote the healthy development of children’s body and mind, and provide new ideas for the design of children’s furniture. The innovative design concept based on flow theory is a new design method from unilateral functional needs to spiritual needs.
Offering a Combined Approach to The analysis of News database, using the software Rapid Mine Case study: News agency in Persian

One of the most widely used approaches in the management of Web-based systems, use the online agency and Data related to them.Online web agency, like the heart of a community and is one of the most critical information resources in a society.The importance of this topic in the news military organizations will be doubled.So having a right way and a unique conceptual approach to analyze and categorize the resources, Can provide numerous benefits and assistance to decision-makers within the organization, especially military organizations in carrying out decisions.In this study, firstly we contribute to the study and understanding of the concepts of data mining methods.Secondly, analysis of different methods of text processing must be done from different perspectives So as to identify the positive strategies and strengths of a variety of methods and The Finally, the analysis of news database, so as to uncover information hidden in the data and how to use it.
Students’ Independent Learning Activity as an Effective Method of Acquiring Professional Competencies

The article represents the model of the organization of independent out-of-class students’ learning activities. The main purpose of the model is to stimulate students’ cognitive activity while transferring accent from the academic knowledge to making independent research and practical skills application. The model is based on the results of students’ psychological characteristics diagnostics that had been carried out by authors in the period 2016–2018 in order to define the features of personal motivational sphere. The model implies an application of project-oriented educational technologies based on the interaction scheme “teacher-student” and “student-student”. It is described the experience of model integration in the courses “Marketing” and “Mathematics in Management” for bachelor-students of the polytechnic university. Within the presented model, the training tasks are designed so that all members of the group are interconnected but at the same time independent in making research and solving problems.
PREPARATION OF TEACHING STAFF TO USE HTML5 TECHNOLOGIES FOR THE DEVELOPMENT OF ELECTRONIC EDUCATIONAL RESOURCES

The article discusses the organization of training teaching staff to use HTML5 technologies for the development of electronic educational resources in the format of web apps. The relevance of using multimedia interactive electronic educational resources in modern school is noted and the effectiveness of developing such electronic educational resources in the form of both offline and online web apps is justified. The additional professional development program “Modern web technologies in education” for educators of the Southeast School District of the Moscow region is described: the goal of the program, improving professional competencies of students, planned learning outcomes, category of students, the form and the period of education, forms of students’ work, the timetable with both the thematic plan and the forms and the distribution of hours by the topics and the types of work. The content of the program and materials for intermediate and final certification are given.
Energy-Efficient Virtual Machines Dynamic Integration for Robotics

The rapid development of cloud computing technology has brought a lot of energy consumption. However, the utilization rate of resources such as data center CPUs is often less than half. Therefore, if the virtual machines in operation are centrally integrated into some servers, and idle servers are switched to low-power modes, the power consumption of data centers can be greatly reduced. The consumption. The traditional research on the integration of virtual machines is mainly based on the current load of the host to set a high-load threshold or periodically perform the migration. At present, research based on time-series prediction faces the problem of low prediction accuracy. In order to solve these problems, this paper synthetically considers the influence of multi-order Markov model and CPU state at different times, and proposes a new K-order mixed Markov model for CPU load prediction of the host for a period of time in the future. By conducting large-scale data experiments on the CloudSim simulation platform, the host load forecasting method proposed in this paper is compared with traditional load detection methods, and the proposed model is greatly reduced in the number of virtual machine migrations and data center energy consumption. And the violation of the SLA is also at an acceptable level.
Cryptanalysis on CCA2-Secured LRPC-Kronecker Cryptosystem

Recently, a new rank metric code, namely LRPC-Kronecker Product codes was proposed in APKC 2018 Workshop, and adapted into a construction of a new cryptosystem, namely the LRPC-Kronecker cryptosystem. The LRPC-Kronecker cryptosystem has compact key size, with their parameters achieve 256-bit security with key size (9,768 bits) smaller than the RSA's key size (15,360 bits). It was also shown that the LRPC-Kronecker cryptosystem is CCA2-secured via the Kobara-Imai conversion. In this paper, we point out some errors in the original LRPC-Kronecker cryptosystem and suggest a reparation for the errors. We show that the LRPC-Kronecker cryptosystem in fact is equivalent to the LRPC cryptosystem. With this equivalence shown, we suggest alternative encryption and decryption, namely AKron for the LRPC-Kronecker cryptosystem. Furthermore, we show that there exists design weakness in the LRPC-Kronecker cryptosystem. We exploit this weakness and successfully cryptanalyze all the suggested parameters for $$k_1 = n_1$$ . We are able to recover secret key for all the proposed parameters within the claimed security level.
Visual Annotations for Hybrid Graph-based User Model

Structured user model data not only allow system personalization, but also may be of interest as a source for analysis: in particular, for the study of general trends and for the detection of anomalies in preferences and mutually-referenced features among different user models. Such sources are multidimensional and interrelated, and recently started to be represented as graph-based datasets. Among the most effective ways of studying such data is visual exploration based on data-driven graph drawing approaches: in particular, node-link and node-link-group diagrams. The paper provides an overview of advanced approaches to the graphical representation of multidimensional data derived from user modeling and presents a proposal for developing flexible and scalable user interfaces for the hypergraph-based visual exploration of relations within a user model (UM). Then, we propose these principles in the visualization of an existing adaptive system.
Combined kNN Classifier for Classification of Incomplete Data

Common problem in data classification is the incompleteness of the data, and not always it is possible to re-acquire the missing values. Another approach is to fill-in missing values using some statistical methods. This however distracts the original data and may lead to over-fit the classifier to the artificially generated values, and in consequence to overestimate the classifier accuracy in Cross Validation tests. In this paper we propose a solution where, for a reference data consisting of complete and incomplete records, complete records serve as a reference data for a standard classifier, while the whole set serves as a reference data for single feature subspaced classifier.
FEDUS : A comprehensive algorithm for web usage mining

With the growing use of the Internet, users are using the Internet for varied purposes like online shopping etc. At the same time browsing history of users which includes the pages visited by the user and the time spent on each page stored in log file can be used for gathering useful insights. Web mining is one such technique that is applied to these log files to mine navigational patterns. In this paper, we have proposed a unique algorithm called FEDUS: Field Extraction, Data Cleaning, User and Session Identification. To the best of our knowledge, there is no such comprehensive algorithm that considers all these stages of pre-processing. The proposed algorithm is compared with existing approaches and the results indicate 77% reduction in the overall size of the log file after pre-processing. Also, the number of sessions identified using the proposed algorithm was more as compared to the existing techniques.
Bayesian Estimation for Fast Sequential Diffeomorphic Image Variability

In this paper, we analyze the diffeomorphic image variability using a Bayesian method to estimate the low-dimensional feature space in a series of images. We first develop a fast sequential diffeomorphic image registration for atlas building (FSDAB) to reduce the computation time. To analyze image variability, we propose a fast Bayesian version of the principal geodesic analysis (PGA) model that avoids the trivial expectation maximization (EM) framework. The sparsity BPGA model can automatically select the relevant dimensions by driving unnecessary principal geodesics to zero. To show the applicability of our model, we use 2D synthetic data and the 3D MRIs. Our results indicate that the automatically selected dimensions from our model can reconstruct unobserved testing images with lower error, and our model can show the shape deformations that corresponds to an increase of time.
Grover’s Algorithm for Unstructured Search

Grover’s algorithm is used to search for an element on an unsorted list with quadratic speed-up when compared to the best possible classical algorithm. This quantum algorithm has an enormous historical importance, and is also a fundamental building block in quantum computing.
A Theoretical Study on the Generation and Propagation of Traveling Waves in Strings

The goal of this work is to understand the generation and propagation of one-dimensional steady state traveling waves in a finite medium with a two-force excitation methodology. The solution to the second order partial differential equation describing the equation of motion for a string is theoretically solved considering a fixed-fixed boundary condition. The parameters that affect the generation and propagation of waves should be well understood to control and manipulate the desired system’s response. The string equation is solved by rearranging it based on linear wave components and phase difference components needed to generate steady-state traveling waves in a string. Two excitation forces are applied to a string near the boundaries to understand the generation and propagation of traveling waves at various frequencies. Determining the quality of the traveling waves and understanding the parameters on the wave propagation of a string can lead to further understand and leverage various engineering disciplines such as mechanical actuation mechanisms, propulsion of flagella, and the basilar membrane in the ear’s cochlea.
A Comparative Analysis and Performance Evaluation of Web Application Protection Techniques against Injection Attacks

Nowadays, most animation activities are based on internet-enabled applications. But, the majority of web developers have ignored the privacy and security aspects of each application, turning them into attractive targets for security issues and therefore increasing the attacker's concerning. Structured query language injection attack (SQLIA) is the prevalent and dominant type of severe web application attacks. This paper provides a comparative study for web application protection techniques and evaluates their performance against SQLIA by conducting a detailed review of various SQLIA previously detected and prevented by protection techniques, a summary and analysis of a critical review of the defensive techniques that were done to address such attack, performance comparison of the different protective approaches through an evaluation using performance metrics to identify efficient and high-performance techniques. Finally, the paper highlights and focuses on the critical and vital directions or protection approaches that require more studies by future research.
Fast Chosen-Key Distinguish Attacks on Round-Reduced AES-192

The open-key attack is a very popular research topic in the symmetric-key community recently. In this paper, we focus on the security of AES-192 in one of its settings, namely the chosen-key setting. First, thanks to the linear relations between most of AES-192 subkeys, we construct an 8-round chosen-key distinguishers for it using the meet-in-the-middle idea and the SuperSbox technique. Then we turn this distinguisher into a key-recovery attack with a time complexity of one 8-round AES-192 encryption. Using the same approaches and with more efforts on exploiting the weak key schedule of this variant, 9-round chosen-key distinguishers is constructed and the master key is recovered afterwards at the cost of one 9-round AES-192 encryption. These results have been experimentally confirmed and two examples can be found in the appendix. While our work may not pose a threat to the security of AES-192 in a traditional way as those single-key recovery attacks do, we believe it do prove a non-trivial weakness in its key schedule to some extent and thus undermines its expectation as an ideal building block for hash functions.
Application of Intelligent Algorithms in System Integration Design

System integration has been the fastest growing industry in the international information service industry. The essence of system integration is optimum integrated design with a large-scale integrated computer network system. System integration includes the integration of computer software, hardware, operating system technology, database technology, network communication technology, as well as the integration of product selection, collocation and system integration of different manufacturers. The goal-overall performance is the best, that is, all components can work together, and the whole system is a low-cost, efficient, symmetrical, scalable and maintainable system. In order to achieve this goal, the merits and demerits of system integrators are crucial. However, the traditional models can not provide sufficient considerations on the system integration, so the actual computation has a larger error and reliability of system integration formula is greatly limited. In this paper, a new method applied to system integration is proposed combining Grey Relational Analysis (GRA) and BP Neural Network Technology (BPNN).
Modularization Challenges in Prolog: What to Divide and Conquer in AI

Although Prolog is considered to be the language of choice for decision support systems and most of the other fields of applied artificial intelligence, developing large projects with Prolog can become a complicated task. A programming language with adequate support for modularization can facilitate easier handling and reuse of different forms of control such as fuzzy logic. It is our view that this support can be provided without modifying the original logical programming paradigm. This paper aims to present an initial approach to this challenge by presenting an analysis of the requirements of modularization that originate from the artificial intelligence domain and the peculiarities of the Prolog language itself.
Measuring Audience Appreciation via Viewing Pattern Analysis

Accurately quantifying audience appreciation poses significant technical challenges, privacy concerns and difficulties in scaling the results to realistic audience sizes. This paper presents a new approach to appreciation measurement based on the analysis of BBC iPlayer on-demand viewing pattern data, such as the timeline of the user’s interactions with the play button, combined with appreciation scores from traditional feedback surveys. This methodology infers implicit viewer appreciation automatically, without adding significant cost or time overheads and without requiring additional input from the participant or the use of intrusive methods, such as facial recognition. The results obtained, based on data from a sample of over 27,000 iPlayer users, show accuracy scores above 90% for predictions generated using computationally efficient models, including Decision Trees and Random Forests. The analysis suggests that the user’s appreciation of a programme can be predicted based on their online viewing behaviour, potentially improving our understanding of the audience.
An Improved ORB Image Matching Algorithm Based on Compressed Sensing

Aiming at the problems such as large amount of computation, high complexity and slow speed in feature extraction of the existing algorithms, this paper presents an improved ORB image matching algorithm based on compressed sensing. Firstly, compressed sensing is used to compress the target image and the matched image, and obtain sparse matrices of wavelet coefficient respectively. Secondly, the ORB algorithm is used to extract the feature points of the image. Finally, the KNN algorithm is used as a matching strategy to perform image matching. Experimental results show that the algorithm realizes fast image matching and guarantees the matching accuracy.