Miracle-dz/UniFAR-FTUs huggingface dataset:
Object detection based on fast and low-memory hybrid background model

We propose a new method to create adaptive background models. Traditionally, each pixel has an adaptive background model which consists of Gaussian mixtures. Each model can approximate small changes and periodic changes of pixel values and it helps us to detect moving objects. However, it cannot adapt to some illumination changes such as gradually varying illumination, precipitously varying illumination and so on. The other model such as using a texture or using prediction of pixel value is effective to handle these changes. Therefore, a hybrid background model which is combined with more than two kind of models. In our approach, we use two different types of the background model. The one is the stochastic background model. The other is the predictive background model based on the exponential smoothing.
Building a Spanish-Portuguese parallel corpus for statistical machine translation

Parallel corpora have long been recognised as valuable resources for building MT applications, but their usefulness have often been limited to the translation between language pairs that include English. In this work we describe our efforts to build a parallel corpus for the Brazilian Portuguese and European Spanish languages. The corpus has been aligned at sentence and word levels and manually inspected for correctness, representing a first step towards the development of translation models for this language pair.
Cost modelling and price forecasting: practice and theory in perspective

Various academics have pointed to the gap between research and practice in the field of building economics and suggest that the way forward may lie in terms of a paradigm shift in that a fundamentally different approach to cost modelling and price forecasting will be necessary. It can be argued that a change in thinking is required rather than an improvement in current practice. Expert systems, a form of artificial intelligence, hold exciting possibilities in this regard.
Face Detection using Principal Component Analysis (PCA)

Face Detection makes it possible to use the facial images of a person to authenticate him into secure system, for criminal identification, for passport verification etc. It is done by Principal Component Analysis (PCA). Face images are projected onto a face space that encodes best variation among known face images. The face space is collection of Eigen face. In the algorithm, initially video segmented using shot boundary detection techniques. Specifically, it can detect both the cut and gradual shot transitions in video. For detecting the shot boundary haar wavelet transform is used. In this method, each frame and its haar wavelet transform image is correlated for detection the shot. By setting the threshold of frame correlation shot boundaries can be detected. Video segmentation can be used in various application like video summarization, video search, and video annotation.
Target identification from a rolling image sequence

An infrared camera mounted on a high-speed airborne projectile provides imagery information necessary for self-guided target tracking capability after launching. However, due to the interaction of air now above and below the wingspread, the rolling phenomenon occurs. An important feature for the identification of target above water mark is the horizontal reference line separating the boundary between waters and sky. However, due to the rolling effect, this reference line may tilt toward certain direction, thus deviates from its horizontal position. The goal here is how to efficiently compensate the rolling effect and de-rotate the reference horizon to its normal position. Given the rolling angle /spl theta/, the rolling effect can be simply compensated by applying one more rotation with reverse angle -/spl theta/. However, this floating point geometric transformation, operated on a pixel-by-pixel basis, is highly computation intensive. In this paper, a simpler approach has been developed by recognizing the inter- and intra-frame properties of the image sequence and reducing the above floating point operation into summation of constants.
Model of the Adaptive Behavior of the Human Operator in Response to a Sudden Change in the Control Situation

An adaptive model is presented to describe the behavior of the human operator in response to sudden changes in plant dynamics and transient disturbances. The plant simulated for tracking experiments is approximately second-order and has rate and attitude feedback augmentation for increased stability. The failure of the rate sensor and/or the attitude sensor results in a sudden transition in the order and gain of the effective plant dynamics. These failures may be accompanied by hard-over transient conditions in either the rate or attitude sensors. The adaptive model suggested has a variable structure, contains mode switching based on pattern recognition as evidence, and incorporates the decision-control logic required for successful adaptation to failures. The model in effect attempts to mimic the control strategy or algorithm used by a trained operator.
Incorporating Online Discussion in Face to Face Classroom Learning: A New Blended Learning Approach.

This paper discusses an innovative blended learning strategy which incorporates online discussion in both in-class face to face, and off-classroom settings. Online discussion in a face to face class is compared with its two counterparts, off-class online discussion as well as in-class, face to face oral discussion, to examine the advantages and disadvantages of the proposed strategy. By integrating online discussion into the flow of the classroom, learners are given dedicated time to foster a habit of critical thinking, reflection and articulating these online, which can subsequently seed further in-class oral discussions, and off-class online discussions. It is found that in-class, online discussion can provide a wider spectrum of discussion perspectives, equalise participation in discussion, and promote cognitive thinking skills and in depth information processing. However, the lack of face to face interactions and the need for sufficient time to do online postings pose challenges in implementing online discussion for face to face classroom learning.
Recent trends in neural networks for multimedia processing

Multimedia has at its very core the field of signal processing technology. The key attributes of neural processing essential to intelligent multimedia processing are presented. The objective is to show why neural networks are a core technology for efficient representation for audio/visual information. Also, it will be demonstrated how the adaptive neural network technology gives a unified solution to a broad spectrum of multimedia applications including image visualization, subject-based retrieval, face detection and recognitions. Region of interest (ROI) coding as well as multidescription coding functionalities are incorporated, too.
Do User Appreciate Novel Interface Features for Literature Search? - A User Study in the Life Sciences Domain

Faced with the challenges to design an easy-to-use, immediately comprehensible and powerful expert-user interface to search very large document collections in the life sciences, we developed several system prototypes. Their main features were faceting of the domain vocabulary for browsing and searching, flexible search-state-dependent drilling of the terminological hierarchy, dynamic query term auto-completions, and highlighting of matched terms (including synonyms and spelling variants). Under lab conditions we then evaluated these features in several task-based scenarios using camera recordings, thinking-aloud protocols and questionnaires. The results reveal that faceting and highlighting were very well received, while autocompletions seemed less important or were misconceptualized as spelling aids.
The Application of Game Theory in RoboCup Soccer Game

In response to characters of multi-robot systems in RoboCup soccer game and dependence between decisions of robots, multi-robot systems task allocation was analyzed by means of game theory in this paper. Formalized description based on game theory for multi-robot system task allocation was offered, and a game theory based task allocation algorithm for multi-robot systems (GT-MRTA) was proposed. Experiments show that GT-MRTA has low complexity, and less time-consumption, can obtain comparative schemes with centralized method, and shows good robustness to communication failure and robot failure.
Practical implementation of a hybrid fuzzy neural network for one-day-ahead load forecasting

The paper presents the development and practical implementation of a hybrid short-term electrical load forecasting model for a power system control centre. This hybrid architecture incorporates a Kohonen self-organising feature map with unsupervised learning for classification of daily load patterns, a supervised backpropagation neural network for mapping the temperature/load relationship, and a fuzzy expert system for postprocessing of neural network outputs. This load forecaster requires minimum operator intervention and can be trained adaptively on-line. The developed model has been tested extensively in the actual operating environment and has been shown to outperform the existing regression-based model.
Enhancing the Flexibility of Kademlia under Churn

Churn is a great challenge for the development and deployment of Distributed Hash Table networks, but current churn treatments ignore the effect of adapting the size of routing table flexibly to handle churn. In this paper, we research the routing table self-adaptive mechanism for Kademlia. Based on quantifying the influence of K, the parameter representing the size of a k-bucket, on routing performance, a self-adaptive algorithm of K is proposed. This algorithm adapts K to the variation of churn rate which is estimated by the validity of data in routing table. Simulation results show that it can reduce the lookup latency under churn. Even if the churn rate varies remarkably, the network is flexible enough to provide fine performance.
Methodology for Validating Building Energy Analysis Simulations

The objective of this report was to develop a validation methodology for building energy analysis simulations, collect high-quality, unambiguous empirical data for validation, and apply the validation methodology to the DOE-2.1, BLAST-2MRT, BLAST-3.0, DEROB-3, DEROB-4, and SUNCAT 2.4 computer programs. This report covers background information, literature survey, validation methodology, comparative studies, analytical verification, empirical validation, comparative evaluation of codes, and conclusions.
A direct conversion receiver analysis for multistandard wireless applications

This paper presents a direct conversion receiver modified for tri-band operation (GSM900/GSM1800/PCS1900). The IF stage in zero frequency, splits into I and Q branches while offset cancellation takes place in the digital domain and is not included in the objectives of this paper. The overall receiver performance is evaluated in terms of the noise figure, intermodulation, blocking requirements, SNR, and C/I. Multimode operation is ensured by the fulfillment of the most demanding system specifications among the three participants. The complete system analysis that was carried out using HP-ADS, subjects the receiver into four different sets of tests and the main objective is the extraction of feasible building block specifications in order to guide a silicon design process.
Explicit Dynamic Formulation for n-R Planar Manipulators with Frictional Interaction between End-Effecter and Environment

This paper includes modeling and dynamic analysis of the planar manipulators which consist of n revolute joints and its end-effecter may possess a frictional contact with an arbitrary surface. The dynamic analysis has been done by using the Lagrangian method. The main contribution of this work is presenting the equations of motion of the system in an explicit and straightforward manner. To this end, the absolute angles of the links are chosen as the generalized coordinates to constitute the Lagrangian, and by this way the Coriolis acceleration terms included the product of the joint velocities, will be hidden in structure of the equations of motion. It is very interested in the simplified results that show a clear conformity with the interpretations of the Euler's equation of motion of the rigid body. In following, the results are applied to a case study model to explain the deriving the equations of motion by our formulation. An inchworm robot included an articulated mechanism is considered to this end.
Operational data processing to improve SPOT image resolution

Mainly digital images are under sampled. It is the same for SPOT digital image satellite. The very meaning is that the instrument is too much powerful for this sampling. The worth side effect is that artifacts (aliasing) are introduced in the image, the good side is that images can be improved if the sampling density is increased. In this paper we use images from the two HRVIR instruments onboard SPOT1-4 satellite to multiply by a factor two the density and the resolution of the image.
Inside SSL: the secure sockets layer protocol

As enterprises conduct more and more of their business activities online, the need for security becomes more crucial. Organizations must implement protocols to address a variety of security-related tasks. An e-business would want to allow an easy access to its site but still provide data security and authentication for e-commerce transactions. For this level of need, one security protocol SSL, the secure sockets layer, has been widely implemented and is now the de facto standard for providing secure e-commerce.
Bayesian hierarchical models for spatially misaligned data in R

Spatial misalignment occurs when at least one of multiple outcome variables is missing at an observed location. For spatial data, prediction of these missing observations should be informed by within location association among outcomes and by proximate locations where measurements were recorded. This study details and illustrates a Bayesian regression framework for modelling spatially misaligned multivariate data. Particular attention is paid to developing valid probability models capable of estimating parameter posterior distributions and propagating uncertainty through to outcomes' predictive distributions at locations where some or all of the outcomes are not observed. Models and associated software are presented for both Gaussian and non‐Gaussian outcomes. Model parameter and predictive inference within the proposed framework is illustrated using a synthetic and forest inventory data set. The proposed Markov chain Monte carlo samplers were written in c++ and leverage R's Foreign Language Interface to call fortran blas (Basic Linear Algebra Subprograms) and lapack (Linear Algebra Package) libraries for efficient matrix computations. The models are implemented in the spMisalignLM and spMisalignGLM functions within the spBayes r package available via the Comprehensive R Archive Network (cran) (http://cran.r-project.org).
Obliterable of graphics and correction of skew using Hough transform for mobile captured documents

CBDA is an emerging field in Computer Vision and Pattern Recognition. In recent technology camera are incorporated to several electronic equipments and are very interesting and thus playing a vital role by replacing scanner with hand held imaging devices like Digital Cameras, Mobile phones and gaming devices attached with these camera. The goal of the work is to remove graphics from the document which plays a vital role in recognition of characters from the mobile captured documents. In this paper we have proposed a novel method for separating or removal of graphics like logos, animations other than the text from the document and method to reduce noise and finally textual content skew is estimated and corrected using Hough Transform. The experimental results show the efficacy compared to the result of well known existing methods.
A moving objects extraction method based on true motion information

In this paper, a moving objects extraction method based on true motion information is proposed. First of all, three dimensional recursive search algorithm (3DRS) combined with median filter is used instead of full search in searching for motion vectors (MVs) which reflect the true motion of objects in video sequences. After that a new parameter segmentation factor (SF) is constructed to describe motion feature of each block in a picture. Thus, moving objects extraction with the fuzzy C-means clustering algorithm (FCM) over SFs is implemented, during which gathering degree is used to distinguish the foreground which refers to the moving objects from the background. Experimental results show that this method is effective for still scenes or scenes with consistent global motion.
A novel multi-view object recognition in complex background

Recognizing objects from arbitrary aspects is always a highly challenging problem in computer vision, and most existing algorithms mainly focus on a specific viewpoint research. Hence, in this paper we present a novel recognizing framework based on hierarchical representation, part-based method and learning in order to recognize objects from different viewpoints. The learning evaluates the model’s mistakes and feeds it back the detector to avid the same mistakes in the future. The principal idea is to extract intrinsic viewpoint invariant features from the unseen poses of object, and then to take advantage of these shared appearance features to support recognition combining with the improved multiple view model. Compared with other recognition models, the proposed approach can efficiently tackle multi-view problem and promote the recognition versatility of our system. For an quantitative valuation The novel algorithm has been tested on several benchmark datasets such as Caltech 101 and PASCAL VOC 2010. The experimental results validate that our approach can recognize objects more precisely and the performance outperforms others single view recognition methods.
A Survey on Current Content based Image Retrieval Methods

Retrieving information from the Web is becoming a common practice for internet users. However, the size and heterogeneity of the Web challenge the effectiveness of classical information retrieval techniques. Content-based retrieval of images and video has become a hot research area. The reason for this is the fact that we need effective and efficient techniques that meet user requirements, to access large volumes of digital images and video data. This paper gives a brief survey of current CBIR (Content Based Image Retrieval) methods and technical achievement in this area. The survey includes a large number of papers covering the research aspects of system design and applications of CBIR, image feature representation and extraction, Multidimensional indexing. Furthermore future research directions are suggested.
Essential Skills for System Dynamics Practitioners: A Delft University of Technology Perspective

There is little consensus on the skills set required of a system dynamics practitioner. In this paper we use the teaching approach and learning goals of the system dynamics courses at the Delft University of Technology as a starting point to explore the development of system dynamics modeling skills. System dynamics is embedded in the curriculum of students at the Faculty of Technology, Policy and Management (TPM) of the Delft University of Technology. The staged approach by which real world complexity is introduced in the system dynamics curriculum is first explained and the learning goals of the system dynamics education at TPM are described. The role of the quadruple jump approach to system dynamics education in achieving the learning goals of the curriculum is then illustrated using the case of urban dynamics. Finally, we compare the results of the teaching approach, as exemplified in the learning goals, with the skills needed by system dynamics modelers as derived from literature. We conclude that the question of which skills can be taught and which need to be learnt in (professional) practice remains open.
A Modified Method for User Authority Ranking in Chinese Microblog

Nowadays, microblog has emerged as the most promising social networking service in the Web2.0 age with diverse features for information dissemination, interpersonal communication and many other aspects. Chinese microblog shows great potential owing to the rich marketing source and powerful media influence brought by the huge user population. As the microblog community is largely influenced by some authoritative users, identifying these users and understanding the information dissemination pattern are theoretically and practically significant for us to promote user services, develop further applications and seek commerce opportunities. In this paper, we proposed a modified method for user authority ranking. A user-message graph modified according to Chinese microblog was applied to our study, and an algorithm based on ObjectRank was used to calculate ueser authority scores. We analyzed Sina microblog, as it was the most widely adopted Chinese microblog and more resources for research was accessible.
NARC: The News Article Revision Comparator

Currency of information in news consumption is an important facet of information quality which involves both the journalist providing updated information and the consumer being aware of updates and changes to the news stream. We are addressing information quality and currency in online news articles from the viewpoint of news consumption with the intent of reducing the consumption effort involved in getting the most up-to-date information on a breaking news story. The goal of this research is thus to develop a web-based user interface which (1) allows users to easily and quickly see updates to news articles online and (2) blends into existing consumption patterns by integrating into news websites. We have built NARC to address these issues by providing an integrated interface which allows users to quickly perceive changes to news articles using an inline text visualization.
A multistage amplifier technique with embedded frequency compensation

A new multistage operational amplifier topology requires only N-2 embedded compensation networks for N gain stages. The compensation circuits do not load the output stage, and noninverting gain stages are not required as in previous multistage approaches. Consequently, high gain, wide bandwidth, fast slewing, and excellent power efficiency are achieved. A low-power resistance-capacitance compensation technique assures stability and fast settling over process, voltage, and temperature variations. Implemented in a 0.6-/spl mu/m n-well CMOS process, a single ended three-stage prototype dissipates 6.9 mW at 3.0 V with 102 dB gain, 47 MHz bandwidth, and 69 V//spl mu/s average slew rate with 40 pF load.
Virtual spacing for flexible traffic control

Weighted fair queuing emerges as an elegant and effective solution to the problem of integrating real time services with strict delay constraints and high-speed data services for which significant delays cannot always be avoided. Virtual spacing is a weighted fair queuing scheduling algorithm intended to be applied in the context of the ATM based BISDN. After discussing how virtual spacing allows flexible traffic control for a variety of service types, we discuss the issue of its implementation in switching nodes. The aim of the paper is to highlight the advantages of weighted fair queuing in enabling a truly integrated services network and to propose a technologically feasible implementation.