Miracle-dz/UniFAR-FTUs huggingface dataset:
National Cyber Safety and Security: Healthcare using IoT

Internet of Things (IoT) integration with different domains provides strategic benefits to organizations and people at large. However, when it comes to security, it is linked with national cyber safety and security in one way or other. The rationale behind this is that IoT combines physical and digital worlds and the whole digital infrastructure may be linked with IoT. Thus, it is important to leverage IoT integrations to have cyber safety and security. In this paper we considered healthcare unit as case study for IoT integration and security verification. We proposed a methodology with underlying mechanisms for searching and data sharing with end to end security. We built a prototype application realized with Things Board IoT platform and Jelastic cloud platform integrated with healthcare infrastructure. Our empirical results revealed that the proposed methodology is able to provide secure data sharing and searching in such distributed environment.
Metaheuristic Approaches for Resource-Constrained Project Scheduling with Flexible Resource Profiles

The resource-constrained project scheduling problem with flexible resource profiles is an optimization problem from the field of project management. It consists of scheduling activities and allocating resources in order to minimize the project duration. In this work, metaheuristic solution approaches are developed and evaluated. First, a hybrid metaheuristic is proposed. It allocates resources with a heuristic schedule generation scheme. Second, a model-based metaheuristic is proposed. It applies mathematical decomposition methods to optimally allocate resources.
UGLEO: A WEB BASED INTELLIGENCE CHATBOT FOR STUDENT ADMISSION PORTAL USING MEGAHAL STYLE

To fulfill the prospective student's information need about student admission, Gunadarma University has already many kinds of services which are time limited, such as website, book, registration place, Media Information Center, and Question Answering’s website (UG-Pedia). It needs a service that can serve them anytime and anywhere. Therefore, this research is developing the UGLeo as a web based QA intelligence chatbot application for Gunadarma University's student admission portal. UGLeo is developed by MegaHal style which implements the Markov Chain method. In this research, there are some modifications in MegaHal style, those modifications are the structure of natural language processing and the structure of database. The accuracy of UGLeo reply is 65%. However, to increase the accuracy there are some improvements to be applied in UGLeo system, both improvement in natural language processing and improvement in MegaHal style.
A Security-Related Reputation Scheme of Android Apps Based on NLP Analysis of Comments

Comments are exploited by product vendors to measure satisfaction of consumers. With the advent of Natural Language Processing (NLP), comments on Google Play can be processed to extract knowledge on applications such as their reputation. Proposals in that direction are either informal or interested merely on functionality. Unlike, this work aims to determine reputation of Android applications in terms of confidentiality, integrity, availability and authentication (CIAA). This work proposes a model of assessing app reputation relying on sentiment analysis and text analysis of comments. While assuming that comments are reliable, we collect Google Play applications subject to comments which include security keywords. An in-depth analysis of keywords based on Naive Bayes classification is made to provide polarity of any comment. Based on comment polarity, reputation is evaluated for the whole application. Experiments made on real applications including dozens to billions of comments, reveal that developers lack to make efforts to guarantee CIAA services. A fine-grained analysis shows that not security reputed applications can be reputed in specific CIAA services. Results also show that applications with negative security polarities display in general positive functional polarities. This result suggests that security checking should include careful comment analysis to improve security of applications.
Recent Approaches And Applications For Designing High Functionality Co-Processed Excipients For Pharmaceuticals.

JETIR2003248 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir. org 1723 Recent Approaches and applications for designing high functionality co-processed excipients for pharmaceuticals. Sumit Marathe, Vipul Patel a Department of Pharmaceutics (PG), Sanjivani College of Pharmaceutical Education and Research, Kopargaon, Maharashtra-423603, India. b Department of Pharmaceutics , Sanjivani College of Pharmaceutical Education and Research, Kopargaon, Maharashtra-423603, India. Abstract:
Learning to Measure the Static Friction Coefficient in Cloth Contact

Measuring friction coefficients between cloth and an external body is a longstanding issue in mechanical engineering, never yet addressed with a pure vision-based system. The latter offers the prospect of simpler, less invasive friction measurement protocols compared to traditional ones, and can vastly benefit from recent deep learning advances. Such a novel measurement strategy however proves challenging, as no large labelled dataset for cloth contact exists, and creating one would require thousands of physics workbench measurements with broad coverage of cloth-material pairs. Using synthetic data instead is only possible assuming the availability of a soft-body mechanical simulator with true-to-life friction physics accuracy, yet to be verified. We propose a first vision-based measurement network for friction between cloth and a substrate, using a simple and repeatable video acquisition protocol. We train our network on purely synthetic data generated by a state-of-the-art frictional contact simulator, which we carefully calibrate and validate against real experiments under controlled conditions. We show promising results on a large set of contact pairs between real cloth samples and various kinds of substrates, with 93.6% of all measurements predicted within 0.1 range of standard physics bench measurements.
Um plugin do tipo report para a identificação do risco de evasão na educação superior a distância que usa técnicas de visualização de dados

This work belongs to the field of educational research known as Learning Analytics and aims to provide a system that presents students in dropout risk of courses in the Virtual Learning Environment (VLE) Moodle. The system uses social, cognitive, and behavioral indicators, created on the basis of VLE data and displays them through a data visualization tool. VLEs generate reports and logs on student activities, however they are often difficult to understand for tutors, teachers and educational managers. Thus, they do not allow the identification of dropout problems in a more objective way. Therefore, it is believed that the use of a solution that collects data of indicators related to the accesses, interactions and grades of the students in a VLE and presents them through infographics, can help teachers, tutors and managers to identify students who may be in the process of leaving a distance course. A report plugin for Moodle VLE was designed and implemented, containing filtering features, sending notifications and interactive graphics generated by the Google Charts tool. The focus group method was used to evaluate the plugin with teachers, tutors and managers of one higher education online course, followed by a qualitative analysis of the data collected. It was concluded that the plugin enhances the teachers’ awareness of the students dropout risk, making it easier and more objective while allowing the graphical visualization of students’ strategic cognitive, social and behavioral data. It was considered that the development of Learning Analytics tools needs to prioritize strategic data of students, such as access data, interactions and their grades in disciplines, since these can help professionals in the identification of the risk of students dropout.
Risk factors for the development of systemic sclerosis: a systematic review of the literature

Abstract Objectives Although numerous studies have investigated the roles of various genetic, epigenetic and environmental factors that may impact its aetiology, SSc is still regarded as an idiopathic disease. Given that there is significant heterogeneity in what has been proposed to influence the development of SSc, this systematic review was conducted to assess the impacts of different factors on the aetiology of scleroderma. Methods The search was performed in the PubMed, CINAHL and SCOPUS databases on 17 May 2017. Any study that made explicit reference to scleroderma or SSc that had information about the risk factors or epidemiology of the disease was included. The extracted outcome variables were prevalence, gender preponderance, geographical distribution, family history and various proposed environmental risk factors. Results One thousand five hundred and seventy-four articles were screened for eligibility. Thirty-four articles were eligible for the systematic literature review. Conclusion Age between 45 and 64 years, female sex, positive family history and exposure to silica were found to be risk factors. There were conflicting findings regarding the impact of exposure to organic solvents and microchimerism. No relationship between infectious agents, alcohol consumption or cigarette smoking and the development of SSc was identified.
Semantic Indoor Image Segmentation using Spatial Class Simplification

In this paper, we propose a method to learn the redesigned class with background and object for semantic segmentation of indoor scene image. Semantic image segmentation is a technique that divides meaningful parts of an image, such as walls and beds, into pixels. Previous work of semantic image segmentation has proposed methods of learning various object classes of images through neural networks, and it has been pointed out that there is insufficient accuracy compared to long learning time. However, in the problem of separating objects and backgrounds, there is no need to learn various object classes. So we concentrate on separating objects and backgrounds, and propose method to learn after class simplification. The accuracy of the proposed learning method is about 5 ~ 12% higher than the existing methods. In addition, the learning time is reduced by about 14 ~ 60 minutes when the class is configured differently In the same environment, and it shows that it is possible to efficiently learn about the problem of separating the object and the background.
Primary Care Optometry

PART ONE: ANOMALIES OF REFRACTION AND BINOCULAR VISION 1. Anomalies of Refraction 2. Epidemiology of Ametropia 3. Myopia 4. Hyperopia NEW! 5. Anomalies of Binocular Vision PART TWO: THE OPTOMETRIC EXAMINATION 6. The Patient History 7. The Preliminary Examination 8. The Ocular Health Examination 9. Objective Refraction 10. Subjective Refraction 11. The Binocular Vision Examination PART THREE: OPTOMETRIC DIAGNOSIS AND MANAGEMENT 12. Management of Anomalies of Refraction and Binocular Vision 13. Ophthalmic Lenses 14. Contact Lenses 15. Nonsurgical Methods of Myopia Control or Reduction 16. Refractive Surgery 17. Age-Related Vision Problems NEW! 18. Age-Related Vision Loss NEW! 19. Care of the Vision-Impaired Patient NEW! 20. The Role and Scope of the Primary Care Optometrist
Exploiting structure in multiobjective optimization and optimal control

Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. Since – in contrast to the solution of a single objective optimization problem – the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging. This is even more the case when many problems have to be solved, when the number of objectives is high, or when the objectives are costly to evaluate. Consequently, this thesis is devoted to the identification and exploitation of structure both in the Pareto set and the dynamics of the underlying model as well as to the development of efficient algorithms for solving problems with additional parameters, with a high number of objectives or with PDE-constraints. These three challenges are addressed in three respective parts. In the first part, predictor-corrector methods are extended to entire Pareto sets. When certain smoothness assumptions are satisfied, then the set of parameter dependent Pareto sets possesses additional structure, i.e. it is a manifold. The tangent space can be approximated numerically which yields a direction for the predictor step. In the corrector step, the predicted set converges to the Pareto set at a new parameter value. The resulting algorithm is applied to an example from autonomous driving. In the second part, the hierarchical structure of Pareto sets is investigated. When considering a subset of the objectives, the resulting solution is a subset of the Pareto set of the original problem. Under additional smoothness assumptions, the respective subsets are located on the boundary of the Pareto set of the full problem. This way, the “skeleton” of a Pareto set can be computed and due to the exponential increase in computing time with the number of objectives, the computations of these subsets are significantly faster which is demonstrated using an example from industrial laundries. In the third part, PDE-constrained multiobjective optimal control problems are addressed by reduced order modeling methods. Reduced order models exploit the structure in the system dynamics, for example by describing the dynamics of only the most energetic modes. The model reduction introduces an error in both the function values and their gradients, which has to be taken into account in the development of algorithms. Both scalarization and set-oriented approaches are coupled with reduced order modeling. Convergence results are presented and the numerical benefit is investigated. The algorithms are applied to semi-linear heat flow problems as well as to the Navier-Stokes equations.
Energy-aware fixed-priority scheduling for periodic tasks with shared resources and IO devices

Many researches have focused on energy management for the processor. However, DPM via I/O device scheduling for real-time periodic tasks with shared resources has drawn little attention. We address the problem of the I/O device energy consumption minimisation for shared resources. Furthermore, we present an energy-aware IO devices fixed-priority scheduling algorithm based on the RM scheduling. It contains two parts: job scheduling and device scheduling. Job scheduling ensures that each task can complete its execution within its deadline. Device scheduling decides to turn off devices to save energy. The experimental results show that it can achieve significant energy savings.
Information Retrieval System to Find Articles and Clauses in UUD 1945 Using Vector Space Model Method

This study aims to find articles and clauses from the 1945 Constitution (UUD 1945) using the Vector Space Model method that calculates the similarity of many documents. One document is represented by one clause from each article of the 1945 Constitution. The next step is pre-processing by deleting unnecessary words (stopwords) and changing it into basic words (stemmer) in the Indonesian language. Each document will be indexed to speed up query and simplify the weighting. Words weighting in documents is performed using the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm by calculating the frequency of words in documents and all documents. The document search results will be presented in the ranking with the largest number of scoring appears at the top (descend sorting). The word search in this system more or less takes 90-100 milliseconds in 73 documents.
Designing Intelligent System for Arabic Instructor Performance Evaluation

Instructor evaluation is an important field in the educational process because it develops the level of instructor which can improve the educational level of students consequently. In this work, intelligent techniques are used for instructor performance evaluation in Arabic. This study used group of Arab lecturers through YouTube websitefor testing this system. The proposed system converts instructor's speech (system inputs) to text, and then analyzes the text to extract related knowledge for instructor evaluation that depends on a set of criteria, finally provides advice to the instructor (system outputs). Experimental results demonstrate the effectiveness of the proposed system in instructor performance evaluation. The proposed system can improve reliability and efficiency of instructors’ performance; provide the basis for performance improvement that will affect students’ academic outcomes.
Study on Gender Specific Variability of Gastrointestinal, Genitourinary and Depression Related Non Motor Symptoms of Parkinson Disease

Introduction: Parkinson’s disease is a second most common neurodegenerative disorder after Alzheimer’s disease. It affects 1% of population over age of 50 years. Three main features of PD is asymmetric onset of bradykinesia, rigidity and resting tremors. Non motor symptoms are common in patients of Parkinson’s Disease. These remain undiagnosed most of the time. Aim and Objectives: To analyse gender specific, age related and demographic variability of non motor symptoms (Gastrointestinal, genitourinary and depression related symptoms) of Parkinson’s disease. Material and Methods: Present study Included 100 cases (65 Males and 35 Females) of Parkinson’s disease presenting at department of neurology, Dayanand Medical College and hospital, Ludhiana. 30 questionnaire of non motor symptoms in hindi and Punjabi were given to patients. Gender, age, duration of non motor symptoms, Parkinson’s phenotype and complete demographic profile of each patient taken into account for study. Thereafter results were statistically analysed for all parameters. Conclusion: It is concluded that mean age and duration of non-motor symptoms were higher in males as compared to females. GI symptoms amongst non-motor symptoms were commonest and amongst GI symptoms constipation was most common and there was positive correlation of H&Y with age and non motor symptoms especially gastrointestinal, genitourinary and depression related symptoms.
Information dissemination processes in directed social networks

Social networks can have asymmetric relationships. In the online social network Twitter, a follower receives tweets from a followed person but the followed person is not obliged to subscribe to the channel of the follower. Thus, it is natural to consider the dissemination of information in directed networks. In this work we use the mean-field approach to derive differential equations that describe the dissemination of information in a social network with asymmetric relationships. In particular, our model reflects the impact of the degree distribution on the information propagation process. We further show that for an important subclass of our model, the differential equations can be solved analytically.
Therapeutic Potential of α-Her-2 Medications in Colorectal Adenocarcinomas: Do We Have Enough Evidence?

Colorectal cancer is a substantial health concern. The potential role of Human epidermal growth factor (Her-2) in breast and gastric cancer is extensively exploited. α-Her-2 therapy (Trastuzumab/Herceptin) in breast cancer has shown a dramatic response whereas α-Her-2 therapy for gastric cancer has also been approved. Role of this potential therapeutic marker in colorectal cancer is still highly debatable. In this mini review, we have attempted to represent the available data regarding the over expression of Her-2 and its therapeutic potential in colorectal cancer.
Aplicación de Machine Learning para el problema de Robotic Grasping

El área de robótica, debido a su creciente utilización en un gran número de áreas, desde los hogares hasta grandes industrias, presenta muchos desafíos. Entre estos desafíos, la capacidad de manipular objetos específicamente el agarre robótico o robotic grasping siempre ha sido una tarea compleja para los robots por la gran cantidad de factores que influyen en un ambiente no estructurado y, además, por su falta de capacidad para comprender los datos de percepción recibidos. Técnicas para poder realizar tareas de agarre robótico incluyen métodos de visión por computadora y de inteligencia artificial. En esta memoria de título se diseñó un sistema que permite al robot Baxter identificar objetos dentro de un área de trabajo, recibir la orden de agarre de un objeto en específico o de manera aleatoria y ejecutar la acción eligiendo un punto de agarre eficiente. Los resultados mostraron un buen desempeño de Baxter al realizar los distintos agarres a pesar de la falta de precisión en algunas acciones.
A Periodic Multinode Charging and Data Collection Scheme With Optimal Traveling Path in WRSNs

To deal with the problem of energy shortage in wireless sensor networks, wireless power transfer is a promising technology. Using a mobile device (MD) to charge multiple sensor nodes simultaneously is a more efficient way to meet the requirements from a large-scale network. In this article, a periodic multinode charging and data collection scheme based on MD is proposed to maintain a perpetual network operation, in which the network is divided into multiple cells, and the MD periodically traverses each cell to charge and collect data for the sensor nodes with the objective of maximizing the amount of data collected by the unit energy of the MD. To realize a multinode charging, an anchor optimization strategy to optimize the position of anchor point in the cell is proposed. Then, in order to solve the path planning problem of MD, a discrete fireworks algorithm (DFWA) based on population entropy (PE-FWA) is proposed. Compared with MD scheduling algorithm and DFWA, experimental results show that PE-FWA has better performance.
Compilation of extended recursion in call-by-value functional languages

This paper formalizes and proves correct a compilation scheme for mutually-recursive definitions in call-by-value functional languages. This scheme supports a wider range of recursive definitions than previous methods. We formalize our technique as a translation scheme to a lambda-calculus featuring in-place update of memory blocks, and prove the translation to be correct.
Cellular Basis for the ECG Features of the LQT1 Form of the Long-QT Syndrome

Background—This study examines the cellular basis for the phenotypic appearance of broad-based T waves, increased transmural dispersion of repolarization (TDR), and torsade de pointes (TdP) induced by β-adrenergic agonists under conditions mimicking the LQT1 form of the congenital long-QT syndrome. Methods and Results—A transmural ECG and transmembrane action potentials from epicardial, M, and endocardial cells were recorded simultaneously from an arterially perfused wedge of canine left ventricle. Chromanol 293B, a specific IKs blocker, dose-dependently (1 to 100 μmol/L) prolonged the QT interval and action potential duration (APD90) of the 3 cell types but did not widen the T wave, increase TDR, or induce TdP. Isoproterenol 10 to 100 nmol/L in the continued presence of chromanol 293B 30 μmol/L abbreviated the APD90 of epicardial and endocardial cells but not that of the M cell, resulting in widening of the T wave and a dramatic accentuation of TDR. Spontaneous as well as programmed electrical stimulatio.