Reviewerly/exHarmony huggingface dataset:
Prognosis of multiple sclerosis disease using data mining approaches random forest and support vector machine based on genetic algorithm

Background: Multiple sclerosis (MS) is a degenerative inflammatory disease which is most commonly diagnosed by magnetic resonance imaging (MRI). But, since the MRI device uses of a magnetic field, if there are metal objects in the patient’s body, it can disrupt the health of the patient, the functioning of the MRI, and distortion in the images. Due to limitations of using MRI device, screening seems necessary for those patients who have metal objects in their bodies. Therefore, this study is carried out to compare two models: support vector machine and random forest. Methods: This analytical-modelling research was implemented on MS data collection, the specifications of which are recorded in health registry system in School of Public Health, Kermanshah University of Medical Sciences, Iran, from May 2017 to August 2018. For the purpose of this study, a total of 317 subjects were selected as a sample; 188 subjects were diagnosed with MS and 128 subjects showed no symptoms of MS. In order to fit the support vector machine (SVM) model, radial basis kernel function was used. The parameters of this machine were optimized with genetic algorithm. After this step, the support vector machine and random forest (RF) were compared with respect to three factors: accuracy, sensitivity, and specificity. Results: Based upon the obtained results of study, accuracy, sensitivity, and specificity of SVM were 0.79, 0.80, and 0.78, respectively. In comparison, accuracy, sensitivity, and specificity of RF were found to be 0.76, 0.81, and 0.70, respectively. Conclusion: In general, both models which were compared in current study showed desirable performance; however, in term of accuracy, as an important criteria for performance comparison in this area of research, it can be argued that support vector machine can do better than random forest in diagnosing multiple sclerosis. © 2019, Tehran University of Medical Sciences. All rights reserved.
Interactive Video Retrieval in the Age of Deep Learning

We present a tutorial focusing on video retrieval tasks, where state-of-the-art deep learning approaches still benefit from interactive decisions of users. The tutorial covers general introduction to the interactive video retrieval research area, state-of-the-art video retrieval systems, evaluation campaigns and recently observed results. Moreover, a significant part of the tutorial is dedicated to a practical exercise with three selected state-of-the-art systems in the form of an interactive video retrieval competition. Participants of this tutorial will gain a practical experience and also a general insight of the interactive video retrieval topic, which is a good start to focus their research on unsolved challenges in this area.
Optimal allocation of cloud multi-tenant platform infrastructure resources

Infrastructure resources optimisation is a significant challenge in multi-tenant cloud environment. A system and associated algorithm and processes to enable a multi-tenant platform operator or administrator to make more optimal decisions with regards to the allocation of platform infrastructure resources (such as computational capabilities, data storage, etc.) among one or more tenants or accounts are provided. In particular, we construct a data 'signature' for a set of identified users, accounts, or tenants, where the signature contains data regarding the user, account, or tenant's 'consumption' of platform infrastructure resources. Later tenants 'signatures' are being used to accomplish cloud infrastructure resources optimisation in multi-tenant environment. An innovative algorithm for cloud infrastructure resources optimisation in multi-tenant environment is introduced in current paper.
Clothing Information Collection Based on Theme Web Crawler

With the rapid development of big data technology, many 'sleeping' data can be utilized, but the source of data is the key point.The previous methods of obtaining data can no longer meet the demand.This article uses python web crawler to down jacket of Alibaba International Station.Information (shell material, structure type, fill material, process information,
A Novel Real-Time Online Payment Platform for E-commerce Website Running on Local Machine

This paper outlines a novel approach to the integration of a real time payment platform on an ecommerce website hosted locally.A local ecommerce website; Publication Marketing System (PMS) was developed to enable sales and marketing of electronic publications.It was integrated with an easy-to-use payment platform; Voguepay using tightly coupled PhP codes.NGrok was incorporated to achieve a localhost to real time tunneling through the internet to the online payment Platform.This provides an easy and less expensive way of achieving a real-time online payment for e-commerce website and all other websites involving online payments running on local machines in Nigeria.
The effects of age and language on paragraph recall performance: Findings from a preliminary cross-sctional study

THE ABSTRACT IS AVAILABLE IN THE PDF
Populating architectural design: Introducing scenario-based design in residential care projects

Despite the very aim to design living environments for people, in architectural design processes the perspectives of end users are underrepresented. Architects are expected to address the challenges of a diverse and ageing society but, due to increasingly complex design processes, they often have limited access to the perspectives of those they are designing for. This study aims to bring people’s spatial experience to the foreground in architects’ design processes, by turning to techniques developed by related design disciplines. More precisely, it analyses the potential of scenario-based design, a family of techniques for exploring user experience in design, which architects are largely unfamiliar with. Based on elements like personas, scenarios and user journeys, a scenario-based design approach tailored to architectural design’s particularities was developed. Test workshops were conducted in two architecture firms involved in designing residential care projects, and findings were discussed with an expert panel. Findings illustrate how these workshops offered architects insight into user profiles and themes, facilitated exploring and diversifying potential futures during design development, and supported communication with team members and the client. Additional opportunities and challenges are identified, which can advance the development of an integrated approach to support architects in designing human-centred environments.
Reasoning Methods in Fuzzy Rule-based Classification Systems for Big Data Problems

The analysis with a very high number of examples is a subject of growing interest that needs new algorithms and procedures.In this case, we study how the massive use of data affects the reasoning processes for classification problems that make use of fuzzy rule-based systems.First, we describe the standard reasoning model and the operations associated with its use, and once it is verified that these calculations may be inefficient in some cases we propose a new model to perform such calculations.Basically, the proposal eliminates the need to review all the rules in every inference process, generating the rule that best adapts to the particular example, which does not have to be part of the set of rules, and from it explore only the rules that have some effect on the example.We make an experimental study that shows the interest of the proposal presented.
Design Science Research for the Humanities – The Case of Prosopography

The humanities focus on understanding human beings and cultures. They include such disciplines as history, literature, and the arts. Digital humanities, defined as the application of IT to research and teaching in the humanities, is well established as a field in its own right. More particularly, research in the humanities is in demand of innovative and useful IT artifacts. This makes it a relevant application area for design science research (DSR). This also raises specific challenges to DSR researchers, due to the specific stakeholders and knowledge domains that come into play in the digital humanities. This paper focuses on prosopography, a branch of digital humanities that represents and interprets historical data, sourced from texts describing historical person’s life. Starting from typical issues addressed by prosopographical researchers, we identify relevant IT artifacts to address these issues, making DSR relevant for prosopographical research. We adapt and instantiate Hevner’s DSR framework to the specific case of prosopography, as a first step towards defining a DSR framework for the humanities more generally. Based on this adapted and instantiated DSR framework, we propose two artifacts: requirements and a methodology for prosopography. We demonstrate the methodology on a prosopography scenario.
PRAGMATIC APPROACH FOR DIGITAL DATA CLUSTERING USING I-DELEGATE ALGORITHM

Now a day, we use all new scientific and technical process in digital world to create huge digital documents so, the analysis of such huge set of document is really difficult and more important work.Document clustering should be an automatic process where documents are partition into clusters having high likeness based on input term.It is a popularly studied problem in text classification but generally the study of analogy measure for document clustering is not based on keywords normally domain based clustering is done.Our main aim is to improve the accessibility, scalability and usability of text mining for various applications.So, to do text document analysis within a stipulated time is a key factor.So it's not an easy work for examiner to do such analysis in quick period of time.That's why to do the digital document analysis within less period of time, requires particular techniques to make such difficult task in a simpler way.Such special technique called document clustering.So, clustering algorithms are of great advantage.Here we proposed a I-Delegate algorithm which uses Jaccard distance measure for computing the most dissimilar k documents as centroids for k clusters.Our pragmatic experimental results display that our implemented I-delegate algorithm with Jaccard distance measure for computing the centroid improves the clustering performance of the simple K-means algorithm.The accuracy of clustering of documents has been improved by means of this I-delegate approach due to synonym identification and delegates that synonym to clustering process along with near index approach for better result.
iLEAP: A HUMAN-AI TEAMING BASED MOBILE LANGUAGE LEARNING SOLUTION FOR DUAL LANGUAGE LEARNERS IN EARLY AND SPECIAL EDUCATIONS

In this research paper, we present an AR-and AI-based mobile learning tool that provides: 1.) automatic and accurate intelligibility analysis at various levels: letter, word, phrase and sentences, 2.) immediate feedback and multimodal coaching on how to correct pronunciation, and 3.) evidence-based dynamic training curriculum tailored to each individual's learning patterns and needs, e.g., retention of corrected pronunciation and typical pronunciation errors.The use of visible and interactive virtual expert technology capable of intuitive AR-based interactions will greatly increase student's acceptance and retention of a virtual coach.In school or at home, it will readily resemble an expert reading specialist to effectively guide and assist a student in practicing reading and speaking by him-/herself independently, which is particularly important for dual language learners (DLL) whose first language (L1) is not English as many of their parents don't speak English fluently and cannot offer the necessary help.Our human-AI teaming based solution overcomes the shortfall of conventional computer-based language learning tools and serve as a supportive and team-based learning platform that is critical for optimizing the learning outcomes.
Decentralised Planning for Multi-Agent Programming Platforms

Extensive research has been done on planning for single-agent problems, but multi-agent planning has not as yet been thoroughly explored in agent development platforms. These platforms typically provide various mechanisms for runtime coordination, which are often useful in online planning. In this context decentralised multi-agent planning can be efficient as well as effective, especially in loosely-coupled domains, whilst also ensuring important properties in agent systems such as privacy and autonomy. In this paper, we describe the DOMAP planning framework and its integration with a multi-agent programming platform to support the achievement of social goals. Our planning framework has separate phases for goal allocation and individual HTN planning, whilst relying on available runtime coordination. Experiments on three different multi-agent systems implemented in JaCaMo show that DOMAP outperforms four other state-of-the-art multi-agent planners with regards to both planning and execution time.
Instance-Based Enrichment of Sentiment Ontology

Sentiment ontology is usually realized as triplets of objects, aspects, and sentiment of a product. Such ontology can effectively support coreference resolution in textual documents. To develop a sentiment ontology, one can start from a preliminary comprehensive knowledge base and gradually enrich it. This paper proposes a method to a sentiment ontology, which is based on the instance relationship captured from collected data. Our approach is a hybrid one based on rules, dependence grammar, and existing knowledge bases, such as WordNet and Opinion Lexicon. The application of a dependency grammar is to find relationships between components in the ontology and WordNet is used to identify semantic classes for unknown words and phrases. We have experimented our approach with real datasets and the preliminary results are quite promising.
A Study on Social Support, Participation Motivation and Learning Satisfaction of Senior Learners

This study focuses on the status and differences between senior learners’ social support, participation motivation and learning satisfaction, and explores their relevance using the structural equation model. Taking Senior Learners of Senior University as the research object, after the questionnaires were collected, statistical analysis was conducted for confirmatory factor analysis, independent sample test, single factor variance analysis, snowflake comparison method, and structural equation model. The results of the study found that: 1. “Information Support” level 3, and the rest are all above 4; 2. Different living areas will affect social support, participation motivation and learning satisfaction; 3. Social support positively influences participation motivation. Social support positively influences learning satisfaction, and participation motivation positively influences learning satisfaction.
BCDP: A Blockchain-based Credible Data Publishing System

With the advent of the era of big data, how to publish electronic data in a manner of credible become a challenge. Traditional data publishing schemes either require a trusted third party (TTP) that is responsible to collect and publish data, or cannot support similar data duplicate detection. The result is that they are vulnerable to distributed denial of service (DDoS) attacks and suffer from an unnecessary waste of storage space. To address the above issue, in this paper, we propose a blockchain-based credible data publishing model (BCDP), which enjoys the properties of decentralized, tamper resistant and anti-DDoS attacks. Then, we construct a two-layer near duplicate detection algorithm with counting bloom filter (CBF) that can detect near duplicate data based on BCDP. In addition, we exploit an academic publishing system based on BCDP, which introduces an academic scoring mechanism to achieve reward and punishment for academic publishing.
A Synchronization Scheme Based on Moving Average for Robust Audio Watermarking

The synchronization scheme based on moving average is robust and suitable for the same rule to be adopted in embedding watermark and synchronization code, but the imperceptibility and search efficiency is seldom reported. The study aims to improve the original scheme for robust audio watermarking. Firstly, the survival of the algorithm from desynchronization attacks is improved. Secondly, the scheme is improved in inaudibility. Objective difference grade (ODG) of the marked audio is significantly changed. Thirdly, the imperceptibility of the scheme is analyzed and the derived result is close to experimental result. Fourthly, the selection of parameters is optimized based on experimental data. Fifthly, the search efficiency of the scheme is compared with those of other synchronization code schemes. The experimental results show that the proposed watermarking scheme allows the high audio quality and is robust to common attacks such as additive white Gaussian noise, requantization, resampling, low-pass filtering, random cropping, MP3 compression, jitter attack, and time scale modification. Moreover, the algorithm has the high search efficiency and low false alarm rate.
Exploring the Current State of Quantum Computing

Quantum computing promises to help us someday reach impressive computing speed. Explains briefly what quantum computing is and discusses future applications for its use.
Managing technical debt through software metrics, refactoring and traceability

Technical Debt (TD) is a concept borrowed from the financial domain to express extra maintenance costs caused by short-term solutions that compromise internal quality in order to meet urgent business demands. TD can occur throughout the life cycle of software development, and it can be related to different artifacts. Thus, different TD types exist, among which Code TD, Design TD and Documentation TD are the most prevalent ones. To efficiently manage TD there are several activities that can be performed, like the identification and prioritisation of TD instances, activities aiming at TD repayment, as well as activities aiming to prevent the further accumulation of TD. The research problem addressed in this thesis concerns the management of TD with respect to the three aforementioned TD types (i.e., Code, Design and Documentation TD). Specifically, in terms of Code TD it concerns the lack of high accuracy in tooling that supports the identification, prioritisation and resolution of bad smells. In terms of Design TD, it concerns the lack of systematic support for identifying incorrectly instantiated design patterns, as well as the lack of guidance on how to refactor the design. In terms of Documentation TD, it concerns the lack of tools for preventing the occurrence of insufficient, incomplete or outdated requirements documentation. The overall solution consists of the application of software metrics, as well as refactoring and traceability techniques to alleviate these shortcomings.
Pengelompokkan Profil Work Readiness Mahasiswa Teknik Industri Universitas Telkom Menggunakan Data Mining Berbasis Algoritma K-Means Clustering

This study aims to formulate a group of student work readiness profiles. Based on this profile, students' readiness to work will be identified. Grouping is done using data mining based on the k-means clustering algorithm. The work readiness profile group consists of student skills or abilities based on Critical Thinking/Problem Solving, Oral Communications, Written Communications, Teamwork/Collaboration, Diversity, Information Technology Application, Leadership, Lifelong Learning/Self Direction, and Professionalism / Work Ethics. The case study of grouping the profile of work readiness of students is carried out in the Industrial Engineering Study Program of Telkom University with the number of respondents 191 students in the class of 2014 who are currently taking level IV lectures. Based on the results of grouping by dividing into 9 clusters, it was identified that students who met the criteria as students were ready to succeed in work in cluster 7. The number of students in cluster 7 was 50 students with good academic skills, but less active in student activities in the field of reasoning and culture, while the work readiness tends to be very good. The highest work readiness score for cluster 7 is in Lifelong Learning/Self Direction and Teamwork/Collaboration capabilities. Oral Communications, and Leadership which are the benchmarks of student readiness in a career. Further research can be done by identifying groups of work readiness profiles for students at various levels so that they can be used as a reference for designing programs to improve work readiness that varies for each level.
Deep learning for image segmentation-a short survey

Deep learning works as a discrete non-linear mapping function and has achieved great success as a powerful classification tool. However, is deep learning omnipotent? This paper gives a short survey of the accuracy achieved by deep learning so far in image segmentation. Compared to the close to 100% classification accuracy achieved by deep learning, the image segmentation accuracy achieved by deep learning is only about 80%. We analyze the possible reasons why deep learning could not achieve acceptable accuracy in image segmentation and found that deep learning only generates a prediction map and relies on other segmentation methods to complete the segmentation task. In addition, the performance of deep learning is determined by the number of outputs. Consequently, deep learning could not achieve high segmentation accuracy unless the resolution of the image is extremely small.
Rheumatosphere AR: Public Engagement and Education with Interactive Print Posters

Rheumatoid arthritis (RA) affects around 1% of the UK population, places a heavy burden on society and has severe consequences for the individuals affected. Early diagnosis and treatment significantly increase the chance of long-term sustained remission. Raising awareness of RA amongst the general public is important to help decrease the time of diagnosis of the disease.In this poster and demo we explore the use of Augmented Reality (AR) in the creation of interactive print posters for public engagement. Previous studies have shown that AR can be effective in a teaching and learning context, where the coexistence of real and virtual objects aids learners in understanding abstract ideas and complicated spatial relationships. It has also been suggested that it raises motivation in users through interactivity and novelty.We explore the use of AR in public engagement, and outline the design, devel-opment and evaluation of an engagement experience utilising AR. For this, a set of informative printed posters was produced and these enhanced by an accompa-nying interactive AR application. Evaluation involved participants at a science outreach event at the Glasgow Science Centre. The poster includes a demo of the public engagement application: Rheumatosphere AR.
Method for Equipment Support Facility Location According to the Prescription Mission Conditions

For the facility location problem of operations equipment support, a locating decision-making model of operations equipment support is proposed.The model decision-making target model is the minimum support-delayed time of the equipment system, and influence factors are considered including the operational task facility effect on repair capabilities of facility location, equipment technical faults, and battle damage.A two-phase heuristics algorithm using location and decision-service-relation, neighborhood search, and genetic algorithm are used.The decision-making target is acquired through Monte Carlo simulation.An example is used to verify the models and the effectuality of the algorithm, and it is demonstrated that the mission conditions have a significant influence on the operations equipment support facility location.