Search results for: Attributes selection

Authors: Ying Zhao

Abstract:

Keystroke authentication is a new access control system to identify legitimate users via their typing behavior. In this paper, machine learning techniques are adapted for keystroke authentication. Seven learning methods are used to build models to differentiate user keystroke patterns. The selected classification methods are Decision Tree, Naive Bayesian, Instance Based Learning, Decision Table, One Rule, Random Tree and K-star. Among these methods, three of them are studied in more details. The results show that machine learning is a feasible alternative for keystroke authentication. Compared to the conventional Nearest Neighbour method in the recent research, learning methods especially Decision Tree can be more accurate. In addition, the experiment results reveal that 3-Grams is more accurate than 2-Grams and 4-Grams for feature extraction. Also, combination of attributes tend to result higher accuracy.

Keywords: Keystroke Authentication, Pattern recognition, MachineLearning, Instance-based Learning, Bayesian, Decision Tree.

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Authors: Long Jye Sheu, Wei Ching Chen, Yen Chu Chen, Wei Tai Weng

Abstract:

In this paper, a two-channel secure communication using fractional chaotic systems is presented. Conditions for chaos synchronization have been investigated theoretically by using Laplace transform. To illustrate the effectiveness of the proposed scheme, a numerical example is presented. The keys, key space, key selection rules and sensitivity to keys are discussed in detail. Results show that the original plaintexts have been well masked in the ciphertexts yet recovered faithfully and efficiently by the present schemes.

Keywords: fractional chaotic systems, synchronization, securecommunication.

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Authors: Tainchi Lu, Chochih Lin

Abstract:

With a rapid growth in 3D graphics technology over the last few years, people are desired to see more flexible reacting motions of a biped in animations. In particular, it is impossible to anticipate all reacting motions of a biped while facing a perturbation. In this paper, we propose a three-level tracking method for animating a 3D humanoid character. First, we take the laws of physics into account to attach physical attributes, such as mass, gravity, friction, collision, contact, and torque, to bones and joints of a character. The next step is to employ PD controller to follow a reference motion as closely as possible. Once the character cannot tolerate a strong perturbation to prevent itself from falling down, we are capable of tracking a desirable falling-down action to avoid any falling condition inaccuracy. From the experimental results, we demonstrate the effectiveness and flexibility of the proposed method in comparison with conventional data-driven approaches.

Keywords: Character Animation, Forward Dynamics, Motion Tracking, PD Control.

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Authors: Taimur Qureshi, Djamel A Zighed

Abstract:

Many supervised induction algorithms require discrete data, even while real data often comes in a discrete and continuous formats. Quality discretization of continuous attributes is an important problem that has effects on speed, accuracy and understandability of the induction models. Usually, discretization and other types of statistical processes are applied to subsets of the population as the entire population is practically inaccessible. For this reason we argue that the discretization performed on a sample of the population is only an estimate of the entire population. Most of the existing discretization methods, partition the attribute range into two or several intervals using a single or a set of cut points. In this paper, we introduce a technique by using resampling (such as bootstrap) to generate a set of candidate discretization points and thus, improving the discretization quality by providing a better estimation towards the entire population. Thus, the goal of this paper is to observe whether the resampling technique can lead to better discretization points, which opens up a new paradigm to construction of soft decision trees.

Keywords: Bootstrap, discretization, resampling, soft decision trees.

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Authors: Cyril Brom, Zuzana Vlckova

Abstract:

IVE toolkit has been created for facilitating research,education and development in the field of virtual storytelling and computer games. Primarily, the toolkit is intended for modelling action selection mechanisms of virtual humans, investigating level-of-detail AI techniques for large virtual environments, and for exploring joint behaviour and role-passing technique (Sec. V). Additionally, the toolkit can be used as an AI middleware without any changes. The main facility of IVE is that it serves for prototyping both the AI and virtual worlds themselves. The purpose of this paper is to describe IVE's features in general and to present our current work - including an educational game - on this platform.

Keywords: AI middleware, simulation, virtual world.

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Authors: Razagh Hafezi, Ahmad Keshavarz, Vida Moshfegh

Abstract:

This paper presents a novel algorithm of stereo correspondence with rank transform. In this algorithm we used the genetic algorithm to achieve the accurate disparity map. Genetic algorithms are efficient search methods based on principles of population genetic, i.e. mating, chromosome crossover, gene mutation, and natural selection. Finally morphology is employed to remove the errors and discontinuities.

Keywords: genetic algorithm, morphology, rank transform, stereo correspondence

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Authors: Sonia Manhas, Parvinder S. Sandhu, Vinay Chopra, Nirvair Neeru

Abstract:

The cost of developing the software from scratch can be saved by identifying and extracting the reusable components from already developed and existing software systems or legacy systems [6]. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. We have used metric based approach for characterizing a software module. In this present work, the metrics McCabe-s Cyclometric Complexity Measure for Complexity measurement, Regularity Metric, Halstead Software Science Indicator for Volume indication, Reuse Frequency metric and Coupling Metric values of the software component are used as input attributes to the different types of Neural Network system and reusability of the software component is calculated. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Keywords: Software reusability, Neural Networks, MAE, RMSE, Accuracy.

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Authors: P. V. Pramila, V. Mahesh

Abstract:

Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and management of pulmonary diseases. However, the test requires considerable patient effort and cooperation, markedly related to the age of patients resulting in incomplete data sets. This paper presents, a nonlinear model built using Multivariate adaptive regression splines and Random forest regression model to predict the missing spirometric features. Random forest based feature selection is used to enhance both the generalization capability and the model interpretability. In the present study, flow-volume data are recorded for N= 198 subjects. The ranked order of feature importance index calculated by the random forests model shows that the spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the demographic parameter height are the important descriptors. A comparison of performance assessment of both models prove that, the prediction ability of MARS with the `top two ranked features namely the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and R2= 0.99 for normal and abnormal subjects. The Root Mean Square Error analysis of the RF model and the MARS model also shows that the latter is capable of predicting the missing values of FEV1 with a notably lower error value of 0.0191 (normal subjects) and 0.0106 (abnormal subjects) with the aforementioned input features. It is concluded that combining feature selection with a prediction model provides a minimum subset of predominant features to train the model, as well as yielding better prediction performance. This analysis can assist clinicians with a intelligence support system in the medical diagnosis and improvement of clinical care.

Keywords: FEV1, Multivariate Adaptive Regression Splines Pulmonary Function Test, Random Forest.

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Authors: Susana Matos, António Pinto, Conceição Castilho, Paula Reis Correia, António Cardoso Monteiro

Abstract:

Yogurts are prepared by fermenting milk with bacterial cultures consisting of a mixture of Streptococcus ssp. thermophilus and Lactobacillus delbrueckii ssp. bulgaricus. The main aim of this investigation was to develop a majority goat yogurt, with the addition of sheep milk in order to have a final product with good physicochemical quality properties and sensorial attributes. Four types of yogurts were prepared presenting the following proportion of goat and sheep milk respectively: C100 – 100%; C80 – 80%/20%; C60 – 60%/40%; C50 – 50%/50%. The goat milk was from the Serrana Jarmelista breed and the sheep milk from the Serra da Estrela breed. The inclusion of sheep milk improved attractiveness to consumers, and it also improved the nutritional value of the product, mainly the fatty acid and mineral contents. The C50 yogurt was preferred by 28% of the panellists, followed by the C100 with 16%  and the commercial cow yogurt was 40% of preferences.

Keywords: Goat, sheep, yogurt.

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Authors: Zeynep Orhan, Zeynep Altan

Abstract:

This paper summarizes the results of some experiments for finding the effective features for disambiguation of Turkish verbs. Word sense disambiguation is a current area of investigation in which verbs have the dominant role. Generally verbs have more senses than the other types of words in the average and detecting these features for verbs may lead to some improvements for other word types. In this paper we have considered only the syntactical features that can be obtained from the corpus and tested by using some famous machine learning algorithms.

Keywords: Word sense disambiguation, feature selection.

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Authors: Mohammed T. Simsim

Abstract:


This paper discusses telecominication market developments in Saudi Arabia. Empirical research was carried in the holy city of Makkah to study the customer's preference for mobile cellular service and  
the factor influencing their subscription of the mobile 
phone service. Results indicate that the financial factor sicnificantly influence the customer's selection of the service provider.  


   

  
  



 


  


  




 
 
  



 
 


  
 


 
   

Keywords: Customer, Makkah, Mobile phone, Operator, Saudi Arabia

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Authors: Prompt Udomdech, Eleni Papadonikolaki, Andrew Davies

Abstract:

Building Information Modelling (BIM) is the major technological trend among built environment organisations. Digitalising businesses and operations, BIM brings forth a digital transformation in any built environment industry. The adoption of BIM presents challenges for organisations, especially Small- and Medium-sized Enterprises (SMEs). The main problem for built environment SMEs is the lack of project actors with adequate BIM competences. The research highlights learning in projects as the key and explores into the learning of BIM in projects of designers and engineers within Thai design and engineering SMEs. The study uncovers three impeding attributes which are: a) lack of English proficiency; b) unfamiliarity with digital technologies; and c) absence of public standards. This research expands on the literature of BIM competences and adoption.

Keywords: BIM competences and adoption, digital transformation, learning in projects, SMEs, and developing built environment industry.

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Authors: Ming Lun Wang, Yan Wang, Ning Ruo Sun

Abstract:

With the development of cloud computing, more and more users start to utilize the cloud storage service. However, there exist some issues: 1) cloud server steals the shared data, 2) sharers collude with the cloud server to steal the shared data, 3) cloud server tampers the shared data, 4) sharers and key generation center (KGC) conspire to steal the shared data. In this paper, we use advanced encryption standard (AES), hash algorithms, and accountable key-policy attribute-based encryption without key escrow (WOKE-AKP-ABE) to build a security cloud storage scheme. Moreover, the data are encrypted to protect the privacy. We use hash algorithms to prevent the cloud server from tampering the data uploaded to the cloud. Analysis results show that this scheme can resist conspired attacks.

Keywords: Cloud storage security, sharing storage, attributes, Hash algorithm.

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Authors: S. Priyanto, E.P Friandi

Abstract:

Yogyakarta, as the capital city of Yogyakarta Province, has important roles in various sectors that require good provision of public transportation system. Ideally, a good transportation system should be able to accommodate the amount of travel demand. This research attempts to develop a trip generation model to predict the number of public transport passenger in Yogyakarta city. The model is built by using multiple linear regression analysis, which establishes relationship between trip number and socioeconomic attributes. The data consist of primary and secondary data. Primary data was collected by conducting household surveys which randomly selected. The resulted model is further applied to evaluate the existing TransJogja, a new Bus Rapid Transit system serves Yogyakarta and surrounding cities, shelters.

Keywords: Multiple linear regression, shelter evaluation, travel demand, trip generation.

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Authors: Daniel L. Clinciu, Ikrom Abdulaev, Brian D. Oscar

Abstract:

Various personality profile tests are used to identify personality strengths and limits in individuals, helping both individuals and managers to optimize work and team effort in organizations. One such test, the Hartman’s personality profile, emphasizes four driving "core motives" influenced or affected by both strengths and limitations classified into four colors: Red - motivated by power; Blue - discipline and loyalty; White - peace; and Yellow – fun loving. Two shortcomings of Hartman’s personality test are noted; 1) only one selection for every item / situation allowed and 2) selection of an item / option even if not applicable. A test taker may be as much nurturing as he is opinionated but since “opinionated” seems less attractive the individual would likely select nurturing, causing a misidentification in personality strengths and limits. Since few individuals have a “strong” personality, it is difficult to assess their true personality strengths and limits allowing only one choice or requiring unwanted choices, undermining the potential of the test. We modified Hartman’s personality profile allowing test takers to make either multiple choices for any item / situation or leave them blank if applicable. Sixty-eight participants (38 males and 30 females), 17 - 49 years old, from countries in Asia, Europe, N. America, CIS, Africa, Latin America, and Oceania were included. 58 participants (85.3%) reported the modified test, allowing multiple / no choices better identified their personality strengths and limits, while 10 participants (14.7%) expressed the original (one choice version) was sufficient. The overall results show that our modified test enhanced the identification and balance of core personalities’ strengths and limits, aiding test takers, managers and organizations to better assess individual characteristics, particularly useful in making task-related, teamwork, and management decisions.

Keywords: Organizational behavior, personality tests, personality limitations, personality strengths, task management, team work.

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Authors: Siyuan Jing, Kun She

Abstract:

Neighborhood Rough Sets (NRS) has been proven to be an efficient tool for heterogeneous attribute reduction. However, most of researches are focused on dealing with complete and noiseless data. Factually, most of the information systems are noisy, namely, filled with incomplete data and inconsistent data. In this paper, we introduce a generalized neighborhood rough sets model, called VPTNRS, to deal with the problem of heterogeneous attribute reduction in noisy system. We generalize classical NRS model with tolerance neighborhood relation and the probabilistic theory. Furthermore, we use the neighborhood dependency to evaluate the significance of a subset of heterogeneous attributes and construct a forward greedy algorithm for attribute reduction based on it. Experimental results show that the model is efficient to deal with noisy data.

Keywords: attribute reduction, incomplete data, inconsistent data, tolerance neighborhood relation, rough sets

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Authors: Naruemon Prapasuwannakul, Supitcha Boonchai, Nawapat Pengpengpit

Abstract:

The aim of this study was to determine physicochemical and sensory properties of germinated brown rice ice cream as affected by replacement of cream, milk, stabilizer, and emulsifier with green coconut pulp. Five different formulations of ice cream were performed. Regular formulation of ice cream consisted of GBR juice, milk cream, milk powder, stabilizer, emulsifier, sucrose and salt. Replacing of cream, milk, stabilizer, and emulsifier with coconut pulp resulted in an increase in viscosity and overrun, but a decrease in hardness, melting rate, lightness (l*) and redness (a*). However, there was no significant difference among all formulations on any sensory attributes. The results also showed that the ice cream with replacement of coconut pulp contained less fat and protein than those of the regular ice cream. The findings suggested that green coconut pulp can be used as alternative ingredient to replace fat, milk stabilizer and emulsifier even in a high carbohydrate ice cream formulation.

Keywords: Ice cream, germinated brown rice, coconut pulp, milk, cream.

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Authors: A. Appe, B. Poluparthi, L. Kasivajjula, U. Mv, S. Bagadi, P. Modi, A. Singh, H. Gunupudi, S. Troiano, J. Paul, J. Stovall, J. Yamamoto

Abstract:

The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data are considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP (SHapley Additive exPlanations), to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since it is data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for e.g., quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP, a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: Competition, DAGs, hospital, healthcare, machine learning, market share, random forest, SHAP.

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Authors: Chih-Cheng Chen, Yung-Tan Lee, Shih-Yang Lee, Shih-Kuei Huang, Tien-Tze Chen, Qiu-Jun Chen

Abstract:

The evaluation of the contribution of professional baseball starting pitchers is a complex decision-making problem that includes several quantitative attributes. It is considered a type of multi-attribute or multi-criteria decision making (MADM/MCDM) problem. This study proposes a model using the Grey Relational Analysis (GRA) to evaluate the starting pitcher contribution for teams of the Chinese Professional Baseball League. The GRA calculates the individual grey relational degree of each alternative to the positive ideal alternative. An empirical analysis was conducted to show the use of the model for the starting pitcher contribution problem. The results demonstrate the effectiveness and feasibility of the proposed model.

Keywords: Starting pitchers, Grey Relational Analysis, Chinese Professional Baseball

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Authors: Analiza Acuña-Villacorte

Abstract:

The underlying principle behind the harmonization in international education does not solely aim for the comparability but also the compatibility of outputs produced. The international standard in the different professions particularly in engineering defines the required graduate attributes to attain suitable qualifications and recognitions. This study described the language practices of the Electronics Engineering students of Bulacan State University, Philippines who will be deployed for their internship program. The purpose of the study was achieved by determining the language proficiency of the students in terms of speaking, listening, reading, and writing, and checking the adherence of the University to the commitment of intensifying community building for the Association of Southeast Asian Nation Vision 2020. The analysis of variance of the variables defined the significance between the causal variables and dependent variables. Thus, this study identified the mechanism that would regulate language practices in the Electronics Engineering program.

Keywords: Communicative competence, descriptive design, language practices, mechanisms, outcomes-based education.

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Authors: Daniela Elena Marinescu, Ioana Manafi, Dumitru Marin

Abstract:

We consider a Principal-Agent model with the Principal being a seller who does not know perfectly how much the buyer (the Agent) is willing to pay for the good. The buyer-s preferences are hence his private information. The model corresponds to the nonlinear pricing problem of Maskin and Riley. We assume there are three types of Agents. The model is solved using “informational rents" as variables. In the last section we present the main characteristics of the optimal contracts in asymmetric information and some possible extensions of the model.

Keywords: Adverse selection, asymmetric information, informational rent, nonlinear pricing, optimal contract

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Authors: Rıza Gürhan Korkut, Metin Çelik, Süleyman Özkaynak

Abstract:

The early-stage design and development projects for the freight village initiatives require a comprehensive analysis of both qualitative and quantitative data. Considering the literature review on structural and operational management requirements, this study proposed an original taxonomy on criterion structure to assess freight village conceptualization. The potential challenges and uncertainties of the developed taxonomy are extended. Besides requirement analysis, this study is also expected to contribute to forthcoming research on benchmarking of freight villages in different regions. The methodology used in this research is a systematic review on several articles as per their modelling approaches, sustainability, entities and decisions made together with the uncertainties and features of their models taken into consideration. The major findings of the study that are the categories for assessing the projects attributes on their environmental, socio-economical, accessibility and location aspects.

Keywords: Freight village, logistics centers, operational management, taxonomy.

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Authors: T. Arsan

Abstract:

Video streaming over lossy IP networks is very important issues, due to the heterogeneous structure of networks. Infrastructure of the Internet exhibits variable bandwidths, delays, congestions and time-varying packet losses. Because of variable attributes of the Internet, video streaming applications should not only have a good end-to-end transport performance but also have a robust rate control, furthermore multipath rate allocation mechanism. So for providing the video streaming service quality, some other components such as Bandwidth Estimation and Adaptive Rate Controller should be taken into consideration. This paper gives an overview of video streaming concept and bandwidth estimation tools and then introduces special architectures for bandwidth adaptive video streaming. A bandwidth estimation algorithm – pathChirp, Optimized Rate Controllers and Multipath Rate Allocation Algorithm are considered as all-in-one solution for video streaming problem. This solution is directed and optimized by a decision center which is designed for obtaining the maximum quality at the receiving side.

Keywords: Adaptive Video Streaming, Bandwidth Estimation, QoS, Software Architecture.

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Authors: Ł. Kiedrowski, H. Gierszal, W. Hołubowicz

Abstract:

This paper presents the H-ARQ techniques comparison for OFDM systems with a new family of non-binary LDPC codes which has been developed within the EU FP7 DAVINCI project. The punctured NB-LDPC codes have been used in a simulated model of the transmission system. The link level performance has been evaluated in terms of spectral efficiency, codeword error rate and average number of retransmissions. The NB-LDPC codes can be easily and effective implemented with different methods of the retransmission needed if correct decoding of a codeword failed. Here the Optimal Symbol Selection method is proposed as a Chase Combining technique.

Keywords: H-ARQ, LDPC, Non-Binary, Punctured Codes.

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Authors: Jorge Cardoso

Abstract:

It has been recognized that due to the autonomy and heterogeneity, of Web services and the Web itself, new approaches should be developed to describe and advertise Web services. The most notable approaches rely on the description of Web services using semantics. This new breed of Web services, termed semantic Web services, will enable the automatic annotation, advertisement, discovery, selection, composition, and execution of interorganization business logic, making the Internet become a common global platform where organizations and individuals communicate with each other to carry out various commercial activities and to provide value-added services. This paper deals with two of the hottest R&D and technology areas currently associated with the Web – Web services and the semantic Web. It describes how semantic Web services extend Web services as the semantic Web improves the current Web, and presents three different conceptual approaches to deploying semantic Web services, namely, WSDL-S, OWL-S, and WSMO.

Keywords: Semantic Web, Web service, Web process, WWW

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Authors: Justus S, K Iyakutti

Abstract:

Object Relational Databases (ORDB) are complex in nature than traditional relational databases because they combine the characteristics of both object oriented concepts and relational features of conventional databases. Design of an ORDB demands efficient and quality schema considering the structural, functional and componential traits. This internal quality of the schema is assured by metrics that measure the relevant attributes. This is extended to substantiate the understandability, usability and reliability of the schema, thus assuring external quality of the schema. This work institutes a formalization of ORDB metrics; metric definition, evaluation methodology and the calibration of the metric. Three ORDB schemas were used to conduct the evaluation and the formalization of the metrics. The metrics are calibrated using content and criteria related validity based on the measurability, consistency and reliability of the metrics. Nominal and summative scales are derived based on the evaluated metric values and are standardized. Future works pertaining to ORDB metrics forms the concluding note.

Keywords: Measurements, Product metrics, Metrics calibration, Object-relational database.

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Authors: Cyril Brom, Zuzana Vlckova

Abstract:

IVE toolkit has been created for facilitating research,education and development in the ?eld of virtual storytelling andcomputer games. Primarily, the toolkit is intended for modellingaction selection mechanisms of virtual humans, investigating level-of-detail AI techniques for large virtual environments, and for exploringjoint behaviour and role-passing technique (Sec. V). Additionally, thetoolkit can be used as an AI middleware without any changes. Themain facility of IVE is that it serves for prototyping both the AI andvirtual worlds themselves. The purpose of this paper is to describeIVE?s features in general and to present our current work - includingan educational game - on this platform.Keywords? AI middleware, simulation, virtual world.

Keywords: AI middleware, simulation, virtual world

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Authors: I. Gokasar, G. Bakioglu

Abstract:

With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications.

Keywords: Traffic App, real–time information, traffic congestion, regression analysis, dummy variables.

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Authors: Govind Shay Sharma, Randhir Singh Baghel

Abstract:

The stock management of raw materials and finished goods is a significant issue for industries in fulfilling customer demand. Optimization of inventory strategies is crucial to enhancing customer service, reducing lead times and costs, and meeting market demand. This paper suggests finding an approach to predict the optimum stock level by utilizing past stocks and forecasting the required quantities. In this paper, we utilized Artificial Neural Network (ANN) to determine the optimal value. The objective of this paper is to discuss the optimized ANN that can find the best solution for the inventory model. In the context of the paper, we mentioned that the k-means algorithm is employed to create homogeneous groups of items. These groups likely exhibit similar characteristics or attributes that make them suitable for being managed using uniform inventory control policies. The paper proposes a method that uses the neural fit algorithm to control the cost of inventory.

Keywords: Artificial Neural Network, inventory management, optimization, distributor center.

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Authors: B. Tondu, N. Bardou

Abstract:

The recent development of humanoid robots has led robot designers to imagine a great variety of anthropomorphic forms for human-like machine. Which form is the best ? We try to answer this question from a double meaning of the anthropomorphism : a positive anthropomorphism corresponing to the realization of an effective anthropomorphic form object and a negative one corresponding to our natural tendency in certain circumstances to give human attributes to non-human beings. We postulate that any humanoid robot is concerned by both these two anthropomorphism kinds. We propose to use gestalt theory and Heider-s balance theory in order to analyze how negative anthropomorphism can influence our perception of human-like robots. From our theoretical approach we conclude that an “even shape" as defined by gestalt theory is not a sufficient condition for a good integration of future humanoid robots into a human community. Aesthetic perception of the robot cannot be splitted from a social perception : a humanoid robot, any how the efforts made for improving its appearance, could be rejected if it is devoted to a task with too high affective implications.

Keywords: Robot appearance, humanoid robot, uncanny valley, human-robot-interaction.

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