Miracle-dz/UniFAR-FTUs huggingface dataset:
Reasoning Three-dimensional Relationships From `two-dimensional Drawings By Planning

On interpretation a three-dimensiona l object by labelling edges of the object from its two-dimensional image is an important step in computer vision. A new method using planning to solve the line-drawing labelling problem is presented. The labelling problem can be formulated as the Constraint Satisfaction Problem (CSP) and has been proved to be NP­ complete. In this paper, a new labelling scheme, based on Huffman-Clowes's junction categories, is introduced to solve the problem using constrained resource planning methodologies called Four-Corner CRP model proposed by Keng& Yun. The experiments show that our approach can speed up labelling by reducing the number of backtracks in searching a consistent label between a pair of junctions because of planning in advance.
Flight simulation in synthetic environments

The Virtual Reality Applications Center at Iowa State University, VRAC, has the mission of applying virtual reality technology to the challenges of engineering and science. We typically create virtual worlds that allow the user to interact with 3D real time graphics. We refer to the virtual worlds enabling this interaction as synthetic environments. A synthetic environment produces sufficient sensory cues to make the user believe they are actually in a different geographic location with a different velocity and orientation than they have in the real world. These cues may include a large field of view real time stereoscopic visual display, surround sound digital audio, and haptic interactions. The user's head is tracked to enable the appropriate viewpoint for the stereoscopic display, and other parts of the user's body may be tracked to enable interaction with the environment and haptic feedback. Classical flight simulators are a subset of synthetic environments. Although they do not usually use stereoscopic displays, they do use large field of view visual systems, digital audio, force feedback via the flight controls, and force feedback on the simulator as a whole via a motion base. We believe the addition of stereoscopic 3D graphics will be particularly valuable in hovering, formation flying, and landing.
Neural automata based object recognition

In this paper, we introduce a novel concept called neural automata which has been formulated using the fundamental notions of neural network, cellular automata and genetic algorithms. This concept has been formally theorised based on five axioms which are expressed in the form of five closed sentences of Markov's constructive logical calculus. We show how neural automata could be used to identify objects of interest in a specific domain of interpretation.
Empowering Women in the Workforce: Leadership Roles

Global general perception of leadership roles. Worldwide leadership roles are traditionally held by men. While the share of women in the workforce is increasing, it remains difficult for them to reach senior positions, which are mostly held by men. The Qatar National Development Strategy 2011-2016 seeks to increase the number of women in leadership roles by 30 per cent. Perceptions in the GCC that have traditionally limited career advancements for women. A number of cultural, organisational and personal challenges hamper women from reaching senior positions. Women are perceived to work responsibilities. In addition, traditional views on what constitutes appropriate spheres for women’s employment influence education choices for education and promote occupational discrimination. How to promote women to leadership positions. Research and evidence indicate that the appointment of women as top managers can positively improve the performance of a company. Global corporations, conscious of the value of gender balance at all levels, are more eager to hire and promote women. Developing professional women’s skills in decision-making and leadership help them realise their full potential. Examples of development opportunities include providing training, instituting career development initiatives and mentoring programs, and ensuring networking opportunities. Sustaining continuous growth for women in leadership roles. Today, some professional women in Qatar are already in challenging leadership roles. We need to ensure that we sustain these current positions while encouraging continuous growth. [Actual paper will review statistics and demographics for GCC, and in particular State of Qatar, and how they align with the Qatar National Vision 2030 for Qatarization].
9 Spatio-temporal Databases in the Years Ahead

CHOROCHRONOS has been a fruitful and enjoyable project. It contributed many innovative ideas in the areas of ontology and data modeling, query evaluation and prototype systems for spatio-temporal databases. Our ideas have already found uses in various application domains such as moving object databases (see Chapter 4), environmental information systems (see the Dedale application in Chapter 5), interactive multimedia applications and virtual worlds (see Chapter 8). CHOROCHRONOS has opened many avenues for research in spatio-temporal databases, but it also left us with lots of challenging research problems awaiting solution. Many of these problems have already been emphasized in the concluding sections of each chapter, and there is no reason to repeat them here. As an epilogue to this book, we would like to challenge the reader by discussing three important application areas and the role spatio-temporal databases can play in these.
Variance Analysis of Parallel Hammerstein Models

In this thesis we generalize some recent results on variance analysis of Hammerstein models. A variance formula for an arbitrary number of parallel blocks is derived. This expression shows that the variance increases in one block due to the estimation of parameters in other blocks but levels off when the number of parameters in other blocks reach the number of parameters in that block. As a second contribution, the problem of how to design the input so that the identification process leads to a more accurate model is considered. In other words, how to choose the input signal so that the model error described previously is minimized, is studied. The investigations show that the optimal input probability density function has a surprisingly simple format. In summary, some of the derived results can be used directly in practice, while some might be used for further research.
Local Features and Kernels for Classication of Texture and Object Categories: A Comprehensive Study

Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the 2 distance. We r st evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classier s. We then conduct a comparative evaluation with several state-of-the-art recognition methods on 4 texture and 5 object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the inuence of background correlations on recognition performance.
A new self-stabilizing bridge protocol for extended-LANs

Various solutions to the problem of combating bridge/link failures exist, most noticeably the spanning tree algorithm (STA) by R. Perlman (1985). Solutions based on a loop detect packet were proposed by R. Perlman and G. Varghese (1988) but never presented a viable alternative to work based on the STA. A more powerful loop detect packet based algorithm is described. The problems which plagued the previous loop detect approach are dealt with, and further extensions proposed. This algorithm is for multiple port local/remote bridges. <<ETX>>
An Efficient Image Indexing Method Based on Class Specific Hyper Graph

An efficient image indexing method based on class specific hyper graph is proposed. The presented indexing method works efficiently and the relevance of the original image data is enhanced. Because of that an ordered image database benefits the efficient searching. The relevance of images depends on the similarity between different images. According to clustering theory, we can take any sample image in the database as one clustering center, and then the siblings of the center and their siblings are consistently searched, which is known as similarity spread. After that, the disordered image database is sorted out and the searching result is not tedious any more. The proposed method has been tested by an open arts press image database, which shows that our method can obviously improve the indexing speed. Moreover, the indexing results make the whole image searching system capable of association.
A novel approach to improve the scalability of the relational model

Nowadays, the relation model faces the challenge of being applied to massively distributed databases and cloud databases. It can not be easily scaled out in such computing environments. The main reason is lack of a proper data distribution unit and a uniform data distribution model. In this paper, a new data distribution model is proposed. As semantic clusters of data, data multitrees are taken as the distribution units. Schema multitree and data multitree are defined, and then a method of designing the schema graph is proposed to ensure that the data graph is a data multitree. Three theorems proved the correctness of the proposed method. Since relational databases can be viewed as data multitrees, the sematic related data can be split or unified together easily with multiree operations, the scalability of relational model can be improved. In addition, this data distribution model is transparent to programmers.
Sub-surface imaging by combining airborne SAR and GPR: application to water detection in arid zones

We investigate the penetration capabilities of microwaves, particularly L-band, for the mapping of sub-surface moisture. The experiment site is the Pyla dune, a bare sandy area allowing high signal penetration and presenting large subsurface wet structures (paleosoils) at varying depths. The SAR penetration depth is estimated by inverting a scattering model for which the sub-surface structure geometric and dielectric properties are determined by the GPR data analysis. We observed a specific phase difference between HH and VV channels, due to a wet layer covered by dry sand. Our results suggest that airborne radar systems in a lower frequency range (P-band) should be able to detect sub-surface moisture down to at least ten meters.
An Intertemporal Comparison of Income and Welfare for Two Mexican Regions

This paper compares the evolution of income (GDP per capita) with utility derived welfare indices for two Mexican regions from 1992-2000. A methodology is proposed based on implicit true standard of living indices. Results show that welfare dynamics differed between regions and varied considerably compared to GDP per capita measures for the same period, thereby posing three questions: how well aggregate income measures reflect welfare, the role of CPI as a cost of living index, and the existence of different thresholds for wealth and welfare conditioned on development levels. This research reaffirms the importance of household preferences in evaluating welfare.
Accuracy Enhancements for Positioning of Mobile Devices in Wireless Communication Networks

Positioning of mobile devices in wireless communication networks is nowadays being intensively investigated due to the combined benefit of location information and communication. Typical solutions for such scenario rely on robust algorithms that estimate position from indirect measurements of the physical length of the communication links. Since these solutions do not require integration of additional hardware into the mobile nodes, they are cheap and simple to implement. As a price to pay, accuracy is typically lower in comparison to dedicated positioning systems. Thus, an important challenge to solve, that this dissertation aims at, is to enhance the accuracy of position estimation in wireless communication networks without the use of additional hardware. To reach this goal, the adopted approach is divided in three phases: (i) give a deeper insight on the problems by analyzing current solutions; (ii) propose solutions for enhancing accuracy of position information; and (iii) exploit the potential of such position strategies in cooperative schemes within wireless communications. The dissertation starts by analyzing common localization and tracking solutions generally used in iv
Placement of multimedia blocks on zoned disks

We examine the impact on optimal data placement schemes for random retrieval due to a new trend in disk technology, namely zoned magnetic disks, which record data with a constant linear density and have a constant angular velocity. This modifies the optimal data placement that minimizes response time, since now the data transfer time varies with block placement whereas the transfer time and cylinder capacity was constant for traditional magnetic disks. We focus on the optimal placement of video objects stored in multimedia servers that consist of fixed-size blocks of constant-bit-rate encoded data or varying-size blocks of variable-bit-rate encoded data. The differences in popularity and access rates among these objects allow minimization of seek times by varying the data placement. We describe an optimal placement of fixed-size blocks on zoned disks that minimizes response time. Video objects are accessed by streams that playout the blocks at a given rate, requiring disk access by a deadline in order to avoid glitches in playout. For the placement of varying-size blocks on zoned disks, we study a scheme that minimizes the probability that a block access misses its deadline. This is done by adjusting the value representing the popularity of blocks to take into account both block size and the relative importance of seek time and transfer time using a weighting factor. We quantify the improvements in disk throughput for a given block-deadline miss probability.
A cross-disciplinary technology transfer for search-based evolutionary computing: from engineering design to software engineering design

Although object-oriented conceptual software design is difficult to learn and perform, computational tool support for the conceptual software designer is limited. In conceptual engineering design, however, computational tools exploiting interactive evolutionary computation (EC) have shown significant utility. This article investigates the cross-disciplinary technology transfer of search-based EC from engineering design to software engineering design in an attempt to provide support for the conceptual software designer. Firstly, genetic operators inspired by genetic algorithms (GAs) and evolutionary programming are evaluated for their effectiveness against a conceptual software design representation using structural cohesion as an objective fitness function. Building on this evaluation, a multi-objective GA inspired by a non-dominated Pareto sorting approach is investigated for an industrial-scale conceptual design problem. Results obtained reveal a mass of interesting and useful conceptual software design solution variants of equivalent optimality—a typical characteristic of successful multi-objective evolutionary search techniques employed in conceptual engineering design. The mass of software design solution variants produced suggests that transferring search-based technology across disciplines has significant potential to provide computationally intelligent tool support for the conceptual software designer.
Internet Addiction and Its Relationship to Chronotype and Personality in a Turkish University Student Sample

This study sought to investigate whether Internet addiction (IA) is associated with age, gender, BIG-5 personality, and chronotype in a Turkish university student sample. Six hundred and sixteen students filled out a set of questionnaires. We here report—to our knowledge for the first time—an association between IA and chronotype. Evening types and males had higher IA scores than others. Furthermore, agreeable and conscientious students reported lower IA. No consistent relationship was observed between students’ IA scores and openness to experience, extraversion, and neuroticism. We conclude that evening types may be more prone to IA than morning-oriented persons because eveningness is related to personality styles that foster Internet addiction.
Information measure of knowledge extracted from neurons as a tool for analyzing Boolean learning in artificial neural networks

Neural network research depends on convergence and learning characteristics traditionally derived from error measures. Recent studies have attempted more direct extraction of knowledge from a network, but they require control of the training process. We show how Boolean information may be extracted and measured efficiently from a neuron's internal representation. The information measure is compared with training error by observing twelve-input three-layer networks during multiple training runs. The experiment indicates a natural termination point for training by backpropagation.
Non-Standard Reasoning Services for the Debugging of Description Logic Terminologies

Current Description Logic reasoning systems provide only limited support for debugging logically erroneous knowledge bases. In this paper we propose new non-standard reasoning services which we designed and implemented to pinpoint logical contradictions when developing the medical terminology DICE. We provide complete algorithms for unfoldable ACC-TBoxes based on minimisation of axioms using Boolean methods for minimal unsatisfiability-presening sub-TBoxes, and an incomplete bottom-up method for generalised incoherence-preserving terminologies.
An Image Retrieval Method for Binary Images Based on DBN and Softmax Classifier

ABSTRACT Currently, the common methods for image retrieval are content-based, while the abilities of image feature representation of these methods are very limited. In this paper, a new image retrieval method for binary images based on Deep Belief Networks (DBN) and Softmax classifier is proposed, which classifies the image data-set into some categories with the DBN and Softmax classifier first, and then classifies the query image in the same way, and those images in the same category will be returned as the similar images of the query image. Unlike the existing image retrieval models, the new method aims to provide a more effective representation and extraction measure by simulating the architecture of human visual system, and it is not necessary to set the threshold manually for this method like most of the existing methods based on the hamming distance computation. Experimental results show that the proposed method can get better recall and precision than some existing methods, such as perceptual hash algorithm and shape-based algorithm.
The study on Web product reviews mining based on an improved text categorization algorithm

With the rapid development of the Internet and e-business, more and more netizens tend to publish comments of the various products on the net. However, to obtain information from these data will face some problems: huge, emir-structured and non-structured data. In this regard, performing data mining is particularly important. This paper puts forward to a new method of classifying these reviews, and then introduces the mining process of the product comments based on the text classification algorithm; finally, verifying this new method’s feasibility and accuracy with two products of IT168 website's comments. The example analysis results show that the method is practical, and also get good classification results.
Robust route and channel selection in cognitive radio networks

In this paper, the joint route and channel selection problem with reliability guarantee in cognitive radio network (CRN) is addressed. Since the interference to licensed spectrum owners needs to be strictly controlled, cognitive radio enabled users (CUs) should vacate the using channel immediately on detecting licensed users and the end to end performance may be seriously degraded. Furthermore, the route constructed for a session may impose great negative influence on other current or latish sessions in the network. To account for these challenges, the route reliability is formulated based on the link valid probability and a metric named interference impact is proposed to value the influence of a route to the adjacent links. With the objective of minimizing the interference impact while providing throughput and reliability guarantee, the joint route and channel selection problem is formulated and shown to be in the form of integer nonlinear programming (INLP). Based on the analysis of the problem-specific feature, an on-demand route discovery algorithm is proposed to find reliable candidate routes, and a heuristic cross-layer optimization algorithm is proposed to choose the near-optimal route. Simulation results demonstrate that the proposed algorithm can guarantee the current route requirement while improving whole network throughput.
LMI approach to gain scheduled H/sub /spl infin// control beyond PID control for gyroscopic rotor-magnetic bearing system

Ultra high speed rotation in active magnetic bearing (AMB) systems with their complex structure requires powerful control system design approaches for robust stability and robust performance. Previous studies in robust control theory have offered comprehensive gain-scheduled control design approaches such as linear matrix inequality (LMI) based gain-scheduled control for linear parameter varying systems (LPVS) which are a certain class of linear time-varying systems such that state-space matrices of their plants are a fixed function of time-varying physical parameters. Rotor systems modelled with inclusion of gyroscopic effect can be considered as LPV systems because of the affine dependence of system natural frequencies on rotational speed which is taken as a time-varying parameter. These varying parameter values can be measured online during control operation. In this study, a commercially available and vertically designed rotor bearing system is modelled and controlled using LMI based gain-scheduled controller and compared with a PID controller. The results are reasonable and encouraging for future studies of AMB systems.
A Multiclass-based Classification Strategy for Rethorical Sentence Categorization from Scientific Papers

Rapid identification of content structures in a scientific paper is of great importance particularly for those who actively engage in frontier research. This paper presents a multi-classifier approach to identify such structures in terms of classification of rhetorical sentences in scientific papers. The idea behind this approach is based on an observation that no single classifier is the best performer for classifying all rhetorical categories of sentences. Therefore, our approach learns which classifiers are good at what categories, assign the classifiers for those categories and apply only the right classifier for classifying a given category. This paper employsk-fold cross validation over training data to obtain the category-classifier mapping and then re-learn the classification model of the corresponding classifier using full training data on that particular category. This approach has been evaluated for identifying sixteen different rhetorical categories on sentences collected from ACL-ARC paper collection. The experimental results show that the multi-classifier approach can significantly improve the classification performance over multi-label classifiers.