Automatic Support Research Articles

A New Variant of the Generalized JSM-Method for Automatic Support of Scientific Research

This article reviews a new variant of the generalized JSM-method for automatic support of scientific research (ASRS). This version of the JSM-method applied for ASRS contains ternary predicates of similarity (cause, brake, effect) which contain binary “cause–effect” predicates and explicit brake definitions. The article considers a possibility for the basis of fact partition for the cases of the truth of the generalized method and an inductive method of similarity with a ban on counter-example predicates.

Choreography Realizability Enforcement through the Automatic Synthesis of Distributed Coordination Delegates

In the near future we will be surrounded by a virtually infinite number of software applications that provide services in the digital space. This situation promotes reuse-based software production through composition of existing software services distributed over the Internet. Choreographies are a form of decentralized composition that model the external interaction of the participant services by specifying peer-to-peer message exchanges from a global perspective. When third-party (possibly black-box) services are to be composed, obtaining the distributed coordination logic required to enforce the realizability of the specified choreography is a non-trivial and error prone task. Automatic support is then needed. In this paper, we propose a formal approach to the enforcement of choreography realizability through the automatic synthesis of distributed Coordination Delegates (CDs). CDs are additional software entities with respect to the choreography participants. They are synthesized in order to proxify and control the participant services interaction. When interposed among the services, the synthesized entities enforce the collaboration prescribed by the choreography specification. We state correctness of our synthesis method and illustrate its formalization at work on an explanatory example. • A method for the automatic realizability enforcement of service choreographies. • The method accounts for services communicating synchronously and/or asynchronously. • Coordination Delegates are synthesized to mediate the services' interaction. • The method is rigorously assessed and the proof of its correctness is also given. • The method underlies the R&D work of the EU H2020 CHOReVOLUTION project.

Automatic Support for Improving Management and Treatment of Patients with Obtrusive Sleep Apnea Syndrome

Background : In the context of innovation in telemedicine, the myOSA project is aimed at developing new ICT tools and remote clinical follow-up methods to allow the creation of continuous integrated services for the treatment of Obstructive Sleep Apnea (OSA). Currently in Spain, after a visit with a pulmonologist, patients suffering from severe OSA are treated with a continuous positive airway pressure (CPAP) machine at their home. Pulmonologists and CPAP providers guide patients on how to use the device properly and prescribe them to use the machine at least 4 hours daily in order to benefit the therapy. From that moment on, the medical protocol states the following visits after 1, 3, 6 and 12 months and then once a year. In so doing, it may happen that, during a visit, pulmonologist discovers that the patient is using the CPAP less than 4 hours or s/he is not using it at all. Methods : To improve patients’ compliance and achieve better follow-up, we developed a system that, connecting the CPAP with Internet and providing patients with an app in their smartphone, gives support to both patients and pulmonologists. At her/his home the patient has the CPAP machine connected to Internet and a smartphone with the app. At the hospital, pulmonologists are provided with a web-application that summarizes relevant information and gives also a support in medical decisions. Finally, the MyOSA platform, installed in the cloud, connects all the devices for data exchanging. The core of the MyOSA platform is the Intelligent Monitoring System that is composed of a set of intelligent algorithms aimed at predicting the expected adherence level to the therapy by a given patient. To provide a support to pulmonologists for patients' follow-up, three predictions models have been built: at day 1, at month 1, and at month 3. All predictions regard compliance at month 6. To provide personalized recommendations to patients according to their adherence, clustering has been used to identify similar users Results : The trials just started and a total of 60 patients will participate in the study. Once a patient enters the program, s/he is provided with a CPAP to be used at home and an app to be installed in her/his smartphone. Once a day, the CPAP sends data to the cloud where they are stored and analyzed to identify the level of adherence the patient belongs to. Depending on the predicted adherence, different recommendations are sent. Three kinds of recommendations have been identified: awards, feedback, and alerts. At the same time, a prediction of how patients will follow the therapy is given to pulmonologists through a web-application. Conclusions : The goal of myOSA is to provide patients’ empowerment in using CPAP and improve their follow-up by pulmonologists. Clinical trials just started. In the near future we will improve the system by relying with more data and getting direct feedback from patients. We will also work together with pulmonologists to improve and extend the set of recommendations.

The natural algorithmic approach of mixed trigonometric-polynomial problems

The aim of this paper is to present a new algorithm for proving mixed trigonometric-polynomial inequalities of the form \t\t\t∑i=1nαixpicosqixsinrix>0\\documentclass[12pt]{minimal}\t\t\t\t\\usepackage{amsmath}\t\t\t\t\\usepackage{wasysym}\t\t\t\t\\usepackage{amsfonts}\t\t\t\t\\usepackage{amssymb}\t\t\t\t\\usepackage{amsbsy}\t\t\t\t\\usepackage{mathrsfs}\t\t\t\t\\usepackage{upgreek}\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\t\t\t\t\\begin{document}$$\\sum_{i=1}^{n}\\alpha _{i}x^{p_{i}} \\cos ^{q_{i}} x\\sin ^{r_{i}} x>0 $$\\end{document} by reducing them to polynomial inequalities. Finally, we show the great applicability of this algorithm and, as an example, we use it to analyze some new rational (Padé) approximations of the function cos2x and to improve a class of inequalities by Yang. The results of our analysis could be implemented by means of an automated proof assistant, so our work is a contribution to the library of automatic support tools for proving various analytic inequalities.

Automated Medical Articles Analysis for Human Genetic Diagnostic

DNA can provide vast amount of information about an organism. Geneticists with the use of Next-Generation Sequencing technology are able to analyze genes variations in search of pathogenic mutations in humans. The amount of data obtained from NGS can be huge, what makes it impossible to analyze without automatic support. A number of applications was created to assist NGS data study, but the results of their computations is a list of potential causal variants, which can be treated as a suggestion. For final diagnosis doctors’ experience and broad scientific knowledge is required.To support the process of identifying pathogenic variants we are developing a solution to extract valuable information for selected genetic variants from medical articles. In our article, we would like to present our work on extending existing variant prioritization methods of Exomiser and OmimExplorer applications with the knowledge extracted from articles available in PubMed database using semantic text analysis. The results were compared with Google Scholar search results. We evaluated our work with the data of nine anonymous patients. Such approach enables medical personnel to profit from the newest scientific knowledge during the diagnosis process.

An Efficient Image Segmentation and Classification of Tumors in Mammography Image using K-Means Clustering with ROI and ANN Classifier

Breast cancer is commonly occurred in women that causes of death. An early detection of breast cancer can be used to long survival of patients. The tumor is detected in breast using mammography X-ray image. This mammography is one of the special cases of CT scan that is utilized to discover the breast cancer. It is screening method to find the malignant tumor cells in breast among women at early stage to avoid the deaths of patient. However, the radiologists not forever provide accurate results and detection of malignant tumor in breast is a one of the challenging issue due to the tumor cells structure. Therefore, this paper presents K-means clustering with Probabilistic neural networks for improvement of mammogram image quality to provide accurate results. K- Means clustering is applied to segment the mammogram image efficiently and presents an automatic support system for image classification using trained probabilistic neural networks and Artificial Neural networks (ANN) classification will be employed to classify the stage of breast cancer tumor that is caring, malignant or standard. Describing the ROI (Region of Interest), utilizing Region growing segmentation technique, evaluating the area of assumed cancer and establish the image classification of the area on the mammogram image using Sample K-Means Clustering and PNN techniques. Gray Level Co-occurrence Matrix (GLCM) is to extract the features from the texture that features may be in the form of structure, location and outside etc. In this process, the preprocessing method is applied to reduce the noise from the mammogram image for the image enhancement. For the preprocessing, the filter methods are used to give image quality. The performance of k-means clustering and PNN techniques provides fast and accurate image segmentation and classification than the other techniques to detect tumor cells in breast at early stage efficiently.

TaxoPublish: Towards a solution to automatically personalize taxonomies in e-catalogs

Taxonomies are utilized in e-catalogs to facilitate customers navigating through a marketplace with the help of hierarchically structured concepts. However, when entering the e-catalog, each customer is shown the identical taxonomy regardless their individual requirements. Customers are distracted when navigating to preferred concepts as those are siblings of not required concepts. Provided progress in dynamic taxonomies, catalog segmentation, and personalized directories lacks in a fully automatic support for modifying the taxonomy according to the user’s requirements. The existing works need an explicit user-query, are missing information about the domain, or require the modification through the provider. In this paper, TaxoPublish expert system based on logic programming is presented. The proposed system predicts the customers requirements for automatically modifying the taxonomy in B2B context. With TaxoPublish, retailers can now provide personalization in the form of personalized e-catalogs without any human effort, and without missing any information about the domain. TaxoPublish is using knowledge provided through a Customer Relationship Management system for predicting customers preferences, and knowledge of a Product Information Management system for performing taxonomic operations based on two novel types of taxonomic concepts. Through the usage of logic programming and the cross-platform database model, TaxoPublish can be applied as expert system over distributed and heterogeneous data warehouse architectures across various domains. The comprehensive experiments on two public and one private database show that TaxoPublish expert system is capable of fully-automatic taxonomy modification with an accuracy similar to the expert manual modifications.

Automatic support vector data description

Event handlers have wide range of applications such as medical assistant systems and fire suppression systems. These systems try to provide accurate responses based on the least information. Support vector data description (SVDD) is one of the appropriate tools for such detections, which should handle lack of information. Therefore, many efforts have been done to improve SVDD. Unfortunately, the existing descriptors suffer from weak data characteristic in sparse data sets and their tuning parameters are organized improperly. These issues cause reduction of accuracy in event handlers when they are faced with data shortage. Therefore, we propose automatic support vector data description (ASVDD) based on both validation degree, which is originated from fuzzy rough set to discover data characteristic, and assigning effective values for tuning parameters by chaotic bat algorithm. To evaluate the performance of ASVDD, several experiments have been conducted on various data sets of UCI repository. The experimental results demonstrate superiority of the proposed method over state-of-the-art ones in terms of classification accuracy and AUC. In order to prove meaningful distinction between the accuracy results of the proposed method and the leading-edge ones, the Wilcoxon statistical test has been conducted.

Hotspot: Making computer vision more effective for human video surveillance

Studies have shown that the human capability of monitoring multiple surveillance videos is limited. Computer vision techniques have been developed to detect abnormal events to support human video surveillance; however, their results are often unreliable, thus distracting surveillance operators and making them miss important events. This article presents Hotspot as a surveillance video visualization system that can effectively leverage noisy computer vision techniques to support human video surveillance. Hotspot consists of two views: a designated focus view to summarize videos with detected events and a video-bank view surrounding the focus view to display source surveillance videos. The focus view allows an operator to quickly dismiss false alarms and focus on true alarms. The video-bank view allows for extended human video analysis after an important event is detected. Hotspot further provides visual links to assist quick attention switch from the focus view to the video-bank view. Our experiments show that Hotspot can effectively integrate noisy, automatic computer vision detection results and better support human video surveillance tasks than the baseline video surveillance with no or only basic computer vision support.

Micro Static Var Compensator for Over-Voltage Control in Distribution Networks with High Penetration of Rooftop Photovoltaic Systems

A micro static var compensator (µSVC) is introduced in this work to prevent the over-voltage problems in radial distribution networks with high number of rooftop photovoltaic (PV) connections. The µSVC is aimed to use in the PV system that has the fixed-power factor inverter, which cannot provide the active voltage controllability. The µSVC is a small shunt compensator installed parallel with the PV system and providing the automatic reactive power support to deal with the dynamic voltage variations at the point of common coupling. Two reactive power control methods, Q(P) and Q(V), can be employed into each µSVC depending on the location of PV systems. Moreover, the coordinated reactive power control among µSVCs, without communication system requirement, is presented for enhancing the Volt-Var controllability to the group of PV systems located in the same feeder. The dynamic voltage control performances are examined on simulation in DIgSILENT PowerFactory software. The results showed that the proposed control method can mitigate the rise of voltage level sufficiently.

THE AUTOMATIC SYSTEM’S MODEL OF DECISION-MAKING SUPPORT FOR DISPATCHING CONTROL OF THE CITY PASSENGER TRAFFIC

Purpose. This scientific work considers the further development of mathematical models and algorithms for automatic decision support for dispatching management of the city passenger traffic. Methodology. Systems of dispatching management for the city passenger transport are to provide the carrying out of the routes according schedules with minimal deviations from the planned ones through the using of appropriate control actions. The systems’ algorithm focuses on the selection of control actions that compensate the disturbances. It is proposed to use the index of the waiting time minimum for passengers of buses and taxis at stops as a criterion for evaluating of dispatching control systems work. Findings. Based on the conducted analysis of the research within the existing theory of traffic flow of vehicles, it was proposed the model for the system of dispatching management for urban passenger moving units considering the effect of the most important stochastic factors on the schedule of buses and taxis movement in large cities. The obtained system of equations that models the parameter of movement on the bus routes allows you to assess quickly the influence of disturbing effects on the service quality indicators of passengers and, if necessary, to draw up the optimal schedule. Originality. The authors propose a new model for decision support of dispatching management for the city passenger transport. They take into account the effect of the most important stochastic factors, such as the overflowing buses and taxis, their descent from the lines, delays, deviations from the speed limit on the route, etc., on indicators of service quality, as well as optimizing the schedule. Practical value. The results allow to improve approaches to building models using in the systems of dispatching management of urban bus routes, as well as to improve the selection of control actions for similar systems in large cities of Ukraine.

NINJA-OPS: Fast Accurate Marker Gene Alignment Using Concatenated Ribosomes.

The explosion of bioinformatics technologies in the form of next generation sequencing (NGS) has facilitated a massive influx of genomics data in the form of short reads. Short read mapping is therefore a fundamental component of next generation sequencing pipelines which routinely match these short reads against reference genomes for contig assembly. However, such techniques have seldom been applied to microbial marker gene sequencing studies, which have mostly relied on novel heuristic approaches. We propose NINJA Is Not Just Another OTU-Picking Solution (NINJA-OPS, or NINJA for short), a fast and highly accurate novel method enabling reference-based marker gene matching (picking Operational Taxonomic Units, or OTUs). NINJA takes advantage of the Burrows-Wheeler (BW) alignment using an artificial reference chromosome composed of concatenated reference sequences, the “concatesome,” as the BW input. Other features include automatic support for paired-end reads with arbitrary insert sizes. NINJA is also free and open source and implements several pre-filtering methods that elicit substantial speedup when coupled with existing tools. We applied NINJA to several published microbiome studies, obtaining accuracy similar to or better than previous reference-based OTU-picking methods while achieving an order of magnitude or more speedup and using a fraction of the memory footprint. NINJA is a complete pipeline that takes a FASTA-formatted input file and outputs a QIIME-formatted taxonomy-annotated BIOM file for an entire MiSeq run of human gut microbiome 16S genes in under 10 minutes on a dual-core laptop.

Detection and Classification of Brain Tumor using Radial Basis Function

Aim: This paper proposes the automatic support system for detecting the tumor cells by analyzing the scalp EEG by means of RBF technique. Objectives: To acquire the EEG signal from the various electrodes. The artificial neural network will be focused to split up the EEG signal whether cyst i.e. tumor or regular. Methods: The EEG signal is been acquired from the subject using EEG scalp electrodes. The various features such as mean, variance, co-variance, Eigen values and Eigen vectors are extracted from those signals using the Principal Component Analysis. Radial Basis Function (RBF) networks are feed-forward networks which uses a supervised training algorithm are used for function approximation, time series prediction and system control. The RBF is used to train and classify the signal whether the subject is normal or suffering from abnormalities. Results: The features are been extracted using the Principal Component Analysis and the features are skilled. Thus the acquired signals are been classified as cyst or normal. Conclusion: Thus in this paper an automatic system is been developed for diagnosing the tumor cells by means of analyzing EEG signal which is non-invasive method. It can also extend for analyzing other diseases seizures of epilepsy, Alzheimer's disease.

Decision Support System for Diabetes Mellitus through Machine Learning Techniques

recently, the diseases of diabetes mellitus have grown into extremely feared problems that can have damaging effects on the health condition of their sufferers globally. In this regard, several machine learning models have been used to predict and classify diabetes types. Nevertheless, most of these models attempted to solve two problems; categorizing patients in terms of diabetic types and forecasting blood surge rate of patients. This paper presents an automatic decision support system for diabetes mellitus through machine learning techniques by taking into account the above problems, plus, reflecting the skills of medical specialists who believe that there is a great relationship between patient’s symptoms with some chronic diseases and the blood sugar rate. Data sets are collected from Layla Qasim Clinical Center in Kurdistan Region, then, the data is cleaned and proposed using feature selection techniques such as Sequential Forward Selection and the Correlation Coefficient, finally, the refined data is fed into machine learning models for prediction, classification, and description purposes. This system enables physicians and doctors to provide diabetes mellitus (DM) patients good health treatments and recommendations.

Semantic SLAs for Services with Q-SLA

This paper reports the re-engineering efforts for OWL-Q, a prominent semantic quality-based service description language. These efforts have focused on making OWL-Q more compact without reducing its level of expressiveness as well as enriching it with semantic rules towards semantic validation of quality specifications and new knowledge derivation. It also presents a new OWL-Q extension called Q-SLA advancing the state-of-the-art by covering all possible information aspects needed which along with the semantic rules enable proper and automatic support to all service management activities. A particular use-case is also provided to highlight the main benefits of Q-SLA.

Quality Wars: The Groupe des Trente and the Renewal of the Short Subject in France, 1953–1963

The Groupe des Trente (Group of Thirty, hereafter G30) was a collective of French film directors, producers, critics, and technicians. It formed in 1953 with a mission to protest proposed changes in state funding policies that the group described as a threat to the unique form of the short subject (le court metrage). The origins of the policies in question draw on reforms instituted some 20 years earlier and revised during the 1940-44 German occupation by the Comite de l’Organisation dse l’Industrie Cinematographique (Organizing Committee of the Cinema Industry, aka COIC) and its postwar successor, the Centre National de la CInematographie (National Center for Cinema, aka CNC). In 1953, the G30 lobbied against a proposed replacement of automatic support for the short subject to a quality subsidy (prime a la qualite) to be awarded competitively. The second part of the G30’s mission was to promote dialogue and exchange of screenings with filmmakers working in U.S.S.R. capitals including Moscow, Leningrad, and Prague.

On the Automated Synthesis of Enterprise Integration Patterns to Adapt Choreography-based Distributed Systems

The Future Internet is becoming a reality, providing a large-scale computing environments where a virtually infinite number of available services can be composed so to fit users' needs. Modern service-oriented applications will be more and more often built by reusing and assembling distributed services. A key enabler for this vision is then the ability to automatically compose and dynamically coordinate software services. Service choreographies are an emergent Service Engineering (SE) approach to compose together and coordinate services in a distributed way. When mismatching third-party services are to be composed, obtaining the distributed coordination and adaptation logic required to suitably realize a choreography is a non-trivial and error prone task. Automatic support is then needed. In this direction, this paper leverages previous work on the automatic synthesis of choreography-based systems, and describes our preliminary steps towards exploiting Enterprise Integration Patterns to deal with a form of choreography adaptation.

Automatic Tube Compensation Versus Pressure Support Ventilation as a Weaning Mode, Does It Make a Difference?

Automatic Tube Compensation Versus Pressure Support Ventilation as a Weaning Mode, Does It Make a Difference?

Principle for the Validation of a Driving Support using a Computer Vision-Based Driver Modelization on a Simulator

This paper presents a new structure for a driving support designed to compensate for the problems caused by the behaviour of the driver without causing a feeling of unease. This assistance is based on a shared control between the human and an automatic support that computes and applies an assisting torque on the steering wheel. This torque is computed from a representation of the hazards encountered on the road by virtual potentials. However, the equilibrium between the relative influences of the human and the support on the steering wheel are difficult to find and depend upon the situation. This is why this driving support includes a modelization of the driver based on an analysis of several face features using a computer vision algorithm. The goal is to determine whether the driver is drowsy or whether he is paying attention to some specific points in order to adapt the strength of the support. The accuracy of the measurements made on the face features is estimated, and the interest of the proposal as well as the concepts raised by such assistance are studied through simulations.

A web-based self-help intervention for partners of cancer patients based on Acceptance and Commitment Therapy: a protocol of a randomized controlled trial.

BackgroundThere is a growing recognition that cancer not only affects the lives of the patients, but also the lives of their partners. Partners of cancer patients are highly involved in the illness trajectory by providing informal care and they often experience distress. However, supporting interventions for this group are scarce and existing interventions bear several limitations. On the basis of the need for theory- and evidence-based supportive interventions for partners of cancer patients, the web-based self-help intervention Hold on, for each other has been developed. This intervention is based on Acceptance and Commitment Therapy. The primary objective of the RCT is to investigate the (cost-) effectiveness of the intervention. Additional goals are (1) to examine if psychological flexibility, self-compassion, mastery, supportive behavior, posttraumatic growth and resilience are mediators of the intervention’s effects on the partners’ mental health; (2) to examine the moderating effects of the socio demographics (age, gender, education, working situation, family situation) and disease-related characteristics of the patients (sort of cancer, stage of disease, duration and treatment of cancer); and (3) to investigate to what extend participants are satisfied with the intervention, which parts of the intervention are mostly used, and how adherent the users are.Methods/DesignA three-armed randomized controlled trial (RCT) will be conducted to compare two versions of the intervention Hold on, for each other with a waiting list control condition. Both intervention conditions contain the same content and differ only with regard to the form of professional support (personal support versus automatic support). Adult partners of cancer patients with mild to moderate depressive and anxiety symptoms, will be recruited through a multi-component strategy. Online measurements by self-assessment will be made on four measurement points (prior to randomization (baseline-measurement) and 3, 6 and 12 months after baseline).DiscussionWhen proven effective, Hold on, for each other can be an invaluable contribution to the healthcare system and it could be offered to all partners of cancer patients who are in need for additional support.Trial registrationDutch Trial Register, trial registration number NTR4035, date of registration: 17 March 2013.