Search results for: information processing model

Authors: Georgia Garani

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A generalised relational data model is formalised for the representation of data with nested structure of arbitrary depth. A recursive algebra for the proposed model is presented. All the operations are formally defined. The proposed model is proved to be a superset of the conventional relational model (CRM). The functionality and validity of the model is shown by a prototype implementation that has been undertaken in the functional programming language Miranda.

Keywords: nested relations, recursive algebra, recursive nested operations, relational data model.

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Authors: G. Mesfin, M. Shuhaimi

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This paper proposes a new optimization techniques for the optimization a gas processing plant uncertain feed and product flows. The problem is first formulated using a continuous linear deterministic approach. Subsequently, the single and joint chance constraint models for steady state process with timedependent uncertainties have been developed. The solution approach is based on converting the probabilistic problems into their equivalent deterministic form and solved at different confidence levels Case study for a real plant operation has been used to effectively implement the proposed model. The optimization results indicate that prior decision has to be made for in-operating plant under uncertain feed and product flows by satisfying all the constraints at 95% confidence level for single chance constrained and 85% confidence level for joint chance constrained optimizations cases.

Keywords: Butane, Feed composition, LPG, Productspecification, Propane.

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Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez

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In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: Information theory, prediction model, prosthetic alignment, transtibial prosthesis.

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Authors: S. Areerachakul, N. Ployong, S. Na Songkla

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This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by Electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: Artificial neural network, classification, students.

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Authors: Ke He, Wumaier Parezhati, Haruka Yamashita

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Recently, online marketplaces in the e-commerce industry, such as Rakuten and Alibaba, have become some of the most popular online marketplaces in Asia. In these shopping websites, consumers can select purchase products from a large number of stores. Additionally, consumers of the e-commerce site have to register their name, age, gender, and other information in advance, to access their registered account. Therefore, establishing a method for analyzing consumer preferences from both the store and the product side is required. This study uses the Doc2Vec method, which has been studied in the field of natural language processing. Doc2Vec has been used in many cases to analyze the extraction of semantic relationships between documents (represented as consumers) and words (represented as products) in the field of document classification. This concept is applicable to represent the relationship between users and items; however, the problem is that one more factor (i.e., shops) needs to be considered in Doc2Vec. More precisely, a method for analyzing the relationship between consumers, stores, and products is required. The purpose of our study is to combine the analysis of the Doc2vec model for users and shops, and for users and items in the same feature space. This method enables the calculation of similar shops and items for each user. In this study, we derive the real data analysis accumulated in the online marketplace and demonstrate the efficiency of the proposal.

Keywords: Doc2Vec, marketing, online marketplace, recommendation system.

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Authors: Michal Javornik, Otto Dostal, Karel Slavicek

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The purpose of this article is to introduce an advanced system for the support of processing of medical image information, and the terminology related to this system, which can be an important element to a faster transition to a fully digitalized hospital. The core of the system is a set of DICOM compliant applications running over a dedicated computer network. The whole integrated system creates a collaborative platform supporting daily routines in the radiology community, developing communication channels, supporting the exchange of information and special consultations among various medical institutions as well as supporting medical training for practicing radiologists and medical students. It gives the users outside of hospitals the tools to work in almost the same conditions as in the radiology departments.

Keywords: DICOM, Integration, Medical Education, MedicalImaging

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Authors: Kanda Runapongsa Saikaew, Chaluemwut Noyunsan

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Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system.

Keywords: Facebook, social media, credibility measurement.

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Authors: N. Guru Prasath, Sangjin Ma, Chang-Wan Kim

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A butterfly valve is a quarter turn valve which is used to control the flow of a fluid through a section of pipe. Generally, butterfly valve is used in wide range of applications such as water distribution, sewage, oil and gas plants. In particular, butterfly valve with larger diameter finds its immense applications in hydro power plants to control the fluid flow. In-lieu with the constraints in cost and size to run laboratory setup, analysis of large diameter values will be mostly studied by computational method which is the best and inexpensive solution. For fluid and structural analysis, CFD and FEM software is used to perform large scale valve analyses, respectively. In order to perform above analysis in butterfly valve, the CAD model has to recreate and perform mesh in conventional software’s for various dimensions of valve. Therefore, its limitation is time consuming process. In-order to overcome that issue, python code was created to outcome complete pre-processing setup automatically in Salome software. Applying dimensions of the model clearly in the python code makes the running time comparatively lower and easier way to perform analysis of the valve. Hence, in this paper, an attempt was made to study the fluid-structure interaction (FSI) of butterfly valves by varying the valve angles and dimensions using python code in pre-processing software, and results are produced.

Keywords: Butterfly valve, fluid-structure interaction, automatic CFD analysis, flow coefficient.

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Authors: Florian Mach, Eric Hund, Malte Stonis

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The dismantling of disused industrial facilities such as nuclear power plants or refineries is an enormous challenge for the planning and control of the logistic processes. Existing control models do not meet the requirements for a proper dismantling of industrial plants. Therefore, the paper presents an approach for the control of dismantling and post-processing processes (e.g. decontamination) in plant decommissioning. In contrast to existing approaches, the dismantling sequence and depth are selected depending on the capacity utilization of required post-processing processes by also considering individual characteristics of respective dismantling tasks (e.g. decontamination success rate, uncertainties regarding the process times). The results can be used in the dismantling of industrial plants (e.g. nuclear power plants) to reduce dismantling time and costs by avoiding bottlenecks such as capacity constraints.

Keywords: Dismantling management, logistics planning and control models, nuclear power plant dismantling, reverse logistics.

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Authors: P. Joyce Beryl Princess, Y. Harold Robinson

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Image Processing is a structure of Signal Processing for which the input is the image and the output is also an image or parameter of the image. Image Resolution has been frequently referred as an important aspect of an image. In Image Resolution Enhancement, images are being processed in order to obtain more enhanced resolution. To generate highly resoluted image for a low resoluted input image with high PSNR value. Stationary Wavelet Transform is used for Edge Detection and minimize the loss occurs during Downsampling. Inverse Discrete Wavelet Transform is to get highly resoluted image. Highly resoluted output is generated from the Low resolution input with high quality. Noisy input will generate output with low PSNR value. So Noisy resolution enhancement technique has been used for adaptive sub-band thresholding is used. Downsampling in each of the DWT subbands causes information loss in the respective subbands. SWT is employed to minimize this loss. Inverse Discrete wavelet transform (IDWT) is to convert the object which is downsampled using DWT into a highly resoluted object. Used Image denoising and resolution enhancement techniques will generate image with high PSNR value. Our Proposed method will improve Image Resolution and reached the optimized threshold.

Keywords: Image Processing, Inverse Discrete wavelet transform, PSNR.

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Authors: Mario Mastriani, Marcelo Naiouf

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The Gram-Schmidt Process (GSP) is used to convert a non-orthogonal basis (a set of linearly independent vectors) into an orthonormal basis (a set of orthogonal, unit-length vectors). The process consists of taking each vector and then subtracting the elements in common with the previous vectors. This paper introduces an Enhanced version of the Gram-Schmidt Process (EGSP) with inverse, which is useful for signal and image processing applications.

Keywords: Digital filters, digital signal and image processing, Gram-Schmidt Process, orthonormalization.

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Authors: Nripjit, Anand K Tyagi, Nirmal Singh

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The present work consecutively on synthesis and characterization of composites, Al/Al alloy A 384.1 as matrix in which the main ingredient as Al/Al-5% MgO alloy based metal matrix composite. As practical implications the low cost processing route for the fabrication of Al alloy A 384.1 and operational difficulties of presently available manufacturing processes based in liquid manipulation methods. As all new developments, complete understanding of the influence of processing variables upon the final quality of the product. And the composite is applied comprehensively to the acquaintance for achieving superiority of information concerning the specific heat measurement of a material through the aid of thermographs. Products are evaluated concerning relative particle size and mechanical behavior under tensile strength. Furthermore, Taguchi technique was employed to examine the experimental optimum results are achieved, owing to effectiveness of this approach.

Keywords: MMC, Thermographs, Tensile strength, Taguchi technique, Optimal parameters

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Authors: Shattyk Aliyev, Zhakypbek Altayev, Zuchra Ismagambetova, Yerkin Massanov

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Information society is an absolutely new public formation at which the infrastructure and the social relations correspond to the socialized essence of «information genotype» mankind. Information society is a natural social environment which allows the person to open completely the information nature, to use intelligence for joint creation with other people of new information on the basis of knowledge earlier saved up by previous generations.

Keywords: Information society, Philosophy, Education, Globalization and innovation.

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Authors: Gia Surguladze, Lily Petriashvili, Nino Topuria, Giorgi Surguladze

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Modelling of building processes of a multimodal freight transportation support information system is discussed based on modern CASE technologies. Functional efficiencies of ports in the eastern part of the Black Sea are analyzed taking into account their ecological, seasonal, resource usage parameters. By resources, we mean capacities of berths, cranes, automotive transport, as well as work crews and neighbouring airports. For the purpose of designing database of computer support system for Managerial (Logistics) function, using Object-Role Modeling (ORM) tool (NORMA–Natural ORM Architecture) is proposed, after which Entity Relationship Model (ERM) is generated in automated process. Software is developed based on Process-Oriented and Service-Oriented architecture, in Visual Studio.NET environment.

Keywords: Seaport resources, business-processes, multimodal transportation, CASE technology, object-role model, entity relationship model, SOA.

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Authors: Bita Tadayon, Nasser Salmasi

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This research deals with a flexible flowshop scheduling problem with arrival and delivery of jobs in groups and processing them individually. Due to the special characteristics of each job, only a subset of machines in each stage is eligible to process that job. The objective function deals with minimization of sum of the completion time of groups on one hand and minimization of sum of the differences between completion time of jobs and delivery time of the group containing that job (waiting period) on the other hand. The problem can be stated as FFc / rj , Mj / irreg which has many applications in production and service industries. A mathematical model is proposed, the problem is proved to be NPcomplete, and an effective heuristic method is presented to schedule the jobs efficiently. This algorithm can then be used within the body of any metaheuristic algorithm for solving the problem.

Keywords: flexible flowshop scheduling, group processing, machine eligibility constraint, mathematical modeling.

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Authors: Francisca Grimón, Marylin Giugni, Josep Monguet F., Joaquín Fernández, Luis León G.

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In blended learning environments, the Internet can be combined with other technologies. The aim of this research was to design, introduce and validate a model to support synchronous and asynchronous activities by managing content domains in an Adaptive Hypermedia System (AHS). The application is based on information recovery techniques, clustering algorithms and adaptation rules to adjust the user's model to contents and objects of study. This system was applied to blended learning in higher education. The research strategy used was the case study method. Empirical studies were carried out on courses at two universities to validate the model. The results of this research show that the model had a positive effect on the learning process. The students indicated that the synchronous and asynchronous scenario is a good option, as it involves a combination of work with the lecturer and the AHS. In addition, they gave positive ratings to the system and stated that the contents were adapted to each user profile.

Keywords: Blended Learning, System Adaptive, Model, Clustering Algorithms.

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Authors: Chanchai Supaartagorn

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Structured Query Language (SQL) is the standard de facto language to access and manipulate data in a relational database. Although SQL is a language that is simple and powerful, most novice users will have trouble with SQL syntax. Thus, we are presenting SQL generator tool which is capable of translating actions and displaying SQL commands and data sets simultaneously. The tool was developed based on Model-View-Controller (MVC) pattern. The MVC pattern is a widely used software design pattern that enforces the separation between the input, processing, and output of an application. Developers take full advantage of it to reduce the complexity in architectural design and to increase flexibility and reuse of code. In addition, we use White-Box testing for the code verification in the Model module.

Keywords: MVC, relational database, SQL, White-Box testing.

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Authors: Richard Pircher, Lukas Zenk, Hanna Risku

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Using activity theory, organisational theory and didactics as theoretical foundations, a comprehensive model of the organisational dimensions relevant for learning and knowledge transfer will be developed. In a second step, a Learning Assessment Guideline will be elaborated. This guideline will be designed to permit a targeted analysis of organisations to identify the status quo in those areas crucial to the implementation of learning and knowledge transfer. In addition, this self-analysis tool will enable learning managers to select adequate didactic models for e- and blended learning. As part of the European Integrated Project "Process-oriented Learning and Information Exchange" (PROLIX), this model of organisational prerequisites for learning and knowledge transfer will be empirically tested in four profit and non-profit organisations in Great Britain, Germany and France (to be finalized in autumn 2006). The findings concern not only the capability of the model of organisational dimensions, but also the predominant perceptions of and obstacles to learning in organisations.

Keywords: Activity theory, knowledge management organisational theory, "Process-oriented Learning and Information Exchange" (PROLIX).

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Authors: Armin Ghayoori, Faramarz Hendessi, Asrar Sheikh

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In developing a text-to-speech system, it is well known that the accuracy of information extracted from a text is crucial to produce high quality synthesized speech. In this paper, a new scheme for converting text into its equivalent phonetic spelling is introduced and developed. This method is applicable to many applications in text to speech converting systems and has many advantages over other methods. The proposed method can also complement the other methods with a purpose of improving their performance. The proposed method is a probabilistic model and is based on Smooth Ergodic Hidden Markov Model. This model can be considered as an extension to HMM. The proposed method is applied to Persian language and its accuracy in converting text to speech phonetics is evaluated using simulations.

Keywords: Hidden Markov Models, text, synthesis.

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Authors: Fuyang Peng, Bo Deng, Chao Qi, Mou Zhan

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This paper presents the design and implementation of the WebGD, a CORBA-based document classification and retrieval system on Internet. The WebGD makes use of such techniques as Web, CORBA, Java, NLP, fuzzy technique, knowledge-based processing and database technology. Unified classification and retrieval model, classifying and retrieving with one reasoning engine and flexible working mode configuration are some of its main features. The architecture of WebGD, the unified classification and retrieval model, the components of the WebGD server and the fuzzy inference engine are discussed in this paper in detail.

Keywords: Text Mining, document classification, knowledgeprocessing, fuzzy logic, Web, CORBA

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Authors: Ralf God, Lothar Kerschgens, Leonardo Goratti, Steven Lemaire

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Ubiquitous connectivity is a reality and a basic need for users on ground. Air travel connectivity in the cabin is also becoming increasingly important for passengers during cabin use. Wireless sensor networks that provide information to cabin management systems are being used by airlines to optimize cabin crew workload. In networked cabin systems, communications and digitally transmitted data must be managed by airlines in every direction. Security and privacy, information processing and knowledge management are the current and future requirements for a smart and connected cabin.

Keywords: Smart and connected cabin management, Internet of Things, power management, airline business.

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Authors: Sui Xin, Li Chunsheng, Tian Di

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Short message integrated distributed monitoring systems (SM-DMS) are growing rapidly in wireless communication applications in various areas, such as electromagnetic field (EMF) management, wastewater monitoring, and air pollution supervision, etc. However, delay in short messages often makes the data embedded in SM-DMS transmit unreliably. Moreover, there are few regulations dealing with this problem in SMS transmission protocols. In this study, based on the analysis of the command and data requirements in the SM-DMS, we developed a processing model for the control center to solve the delay problem in data transmission. Three components of the model: the data transmission protocol, the receiving buffer pool method, and the timer mechanism were described in detail. Discussions on adjusting the threshold parameter in the timer mechanism were presented for the adaptive performance during the runtime of the SM-DMS. This model optimized the data transmission reliability in SM-DMS, and provided a supplement to the data transmission reliability protocols at the application level.

Keywords: Delay, SMS, reliability, distributed monitoringsystem (DMS), wireless communication.

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Authors: Mohammed Abdalla Osman Mukhtar, Azween Abdullah, Alan Giffin Downe

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Model mapping and transformation are important processes in high level system abstractions, and form the cornerstone of model-driven architecture (MDA) techniques. Considerable research in this field has devoted attention to static system abstraction, despite the fact that most systems are dynamic with high frequency changes in behavior. In this paper we provide an overview of work that has been done with regard to behavior model mapping and transformation, based on: (1) the completeness of the platform independent model (PIM); (2) semantics of behavioral models; (3) languages supporting behavior model transformation processes; and (4) an evaluation of model composition to effect the best approach to describing large systems with high complexity.

Keywords: MDA; PIM, PSM, QVT, Model Transformation

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Authors: Yong-Hui Li, Jing-Wen Huang

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As the advancement of technology, online shopping channel develops rapidly in recent years. According to the report of Taiwan Network Information Center, there are almost eighty percents of internet population shopping in online channel. Synthesizing insights from the previous research, this study develops the conceptual model to integrate Theory of Perceived Risk (TPR) and Technology Acceptance Model (TAM) to apply in online shopping. Using data collected from 637 respondents from online survey website, we use structural equation modeling to test measurement and structural models. The results suggest the need for consideration of perceived risk as an antecedent in the Technology Acceptance Model. The limitations and implications are discussed.

Keywords: perceived risk, perceived usefulness, perceived ease of use, behavioral intention, actual purchase behavior

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Authors: R. Nootjarat, W. Chantatub, P. Chongstitvatana

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This research attempts to explore gaps in Information Systems (IS) and innovation literatures by developing a model of Information Technology (IT) capability in enabling innovation. The research was conducted by using semi-structured interview with six innovators in business consulting, financial, healthcare and academic organizations. The interview results suggest four elements of ITenabled innovation capability which are information (ability to capture ideas and knowledge), connectivity (ability to bridge geographical boundary and mobilize human resources), communication (ability to attain and engage relationships between human resources) and transformation (ability to change the functions and process integrations) in defining IT-enabled innovation platform. The results also suggests innovators- roles and IT capability.

Keywords: Innovation Platform, IT Capability, Innovators, Innovation Delivery

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: Deep neural models, natural language inference, recognizing textual entailment, sentence-to-sentence relation.

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Authors: Yong-Gyu Lee, Gilwon Yoon

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Bleeding in the digestive duct is an important diagnostic parameter for patients. Blood in the endoscopic image can be determined by investigating the color tone of blood due to the degree of oxygenation, under- or over- illumination, food debris and secretions, etc. However, we found that how to pre-process raw images obtained from the capsule detectors was very important. We applied various image process methods suitable for the capsule endoscopic image in order to remove noises and unbalanced sensitivities for the image pixels. The results showed that much improvement was achieved by additional pre-processing techniques on the algorithm of determining bleeding areas.

Keywords: blood detection, capsule endoscopy, image processing.

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Authors: Ákos Pintér, István Dénes

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The so-called all-pass filter circuits are commonly used in the field of signal processing, control and measurement. Being connected to capacitive loads, these circuits tend to loose their stability; therefore the elaborate analysis of their dynamic behavior is necessary. The compensation methods intending to increase the stability of such circuits are discussed in this paper, including the socalled lead-lag compensation technique being treated in detail. For the dynamic modeling, a two-port network model of the all-pass filter is being derived. The results of the model analysis show, that effective lead-lag compensation can be achieved, alone by the optimization of the circuit parameters; therefore the application of additional electric components are not needed to fulfill the stability requirement.

Keywords: all-pass filter, frequency compensation, stability, linear modeling

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Authors: Nasr Al-Hinai, Tarek Y. ElMekkawy

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The performance of schedules released to a shop floor may greatly be affected by unexpected disruptions. Thus, this paper considers the flexible job shop scheduling problem when processing times of some operations are represented by a uniform distribution with given lower and upper bounds. The objective is to find a predictive schedule that can deal with this uncertainty. The paper compares two genetic approaches to obtain predictive schedule. To determine the performance of the predictive schedules obtained by both approaches, an experimental study is conducted on a number of benchmark problems.

Keywords: Genetic algorithm, met-heuristic, robust scheduling, uncertainty of processing times

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. Our work applies modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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