Miracle-dz/UniFAR-FTUs huggingface dataset:
Causality-weighted active learning for abnormal event identification based on the topic model

Abnormal event identification in crowded scenes is a fundamental task for video surveillance. However, it is still challenging for most current approaches because of the general insufficiency of labeled data for training, particularly for abnormal data. We propose a novel active-supervised joint topic model for learning activity and training sample collection. First, a multi-class topic model is constructed based on the initial training data. Then the remaining unlabeled data stream is surveyed. The system actively decides whether it can label a new sample by itself or if it has to ask a human annotator. After each query, the current model is incrementally updated. To alleviate class imbalance, causality-weighted method is applied to both likelihood and uncertainty sampling for active learning. Furthermore, a combination of a new measure termed query entropy and the overall classification accuracy is used for assessing the model performance. Experimental results on two real-world traffic videos for abnormal event identification tasks demonstrate the effectiveness of the proposed method.
Video Games, Training, and Investigating Complex Skills

We are utilizing Space Fortress in a basic research program that is designed to integrate cognitive and social learning theory in the development of group protocols for training complex skills. We present evidence that groups of 2, 3 and 4 can learn Space Fortress as well as 1 using 1/2, 1/3 and 1/4 the trainer time and resources respectively. We also present preliminary empirical steps towards individualizing training within groups according to individual differences in selective attention. We discuss implications for developing automated instruction that is designed for small groups rather than for individuals.
Modeling synergistic relationships between words and images

Texts and images provide alternative, yet orthogonal views of the same underlying cognitive concept. By uncovering synergistic, semantic relationships that exist between words and images, I am working to develop novel techniques that can help improve tasks in natural language processing, as well as effective models for text-to-image synthesis, image retrieval, and automatic image annotation. Specifically, in my dissertation, I will explore the interoperability of features between language and vision tasks. In the first part, I will show how it is possible to apply features generated using evidence gathered from text corpora to solve the image annotation problem in computer vision, without the use of any visual information. In the second part, I will address research in the reverse direction, and show how visual cues can be used to improve tasks in natural language processing. Importantly, I propose a novel metric to estimate the similarity of words by comparing the visual similarity of concepts invoked by these words, and show that it can be used further to advance the state-of-the-art methods that employ corpus-based and knowledge-based semantic similarity measures. Finally, I attempt to construct a joint semantic space connecting words with images, and synthesize an evaluation framework to quantify cross-modal semantic relationships that exist between arbitrary pairs of words and images. I study the effectiveness of unsupervised, corpus-based approaches to automatically derive the semantic relatedness between words and images, and perform empirical evaluations by measuring its correlation with human annotators.
Decision Support and Executive Information Systems

Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd decision support and executive information systems as the choice of reading, you can find here.
An evolving methodology for managing multimedia courseware production

It is often claimed that techniques such as ‘multimedia’ and the use of blended learning environments can be used to achieve powerful interactive pedagogies. Indeed, the advent of easy‐to‐use multimedia technologies has meant that a plethora of digital learning products is now becoming available. Despite the relative ease‐of‐use of these new technologies, managing large‐scale multimedia courseware development projects is still very problematic. This paper discusses some of the issues involved and outlines an emerging methodology for managing such projects.
Implementation of a Semi-Implicit Pressure-Based Multigrid Fluid Flow Algorithm on a Graphics Processing Unit

A semi-implicit pressure based multigrid algorithm for solving the incompressible Navier-Stokes equations was implemented on a Graphics Processing Unit (GPU) using CUDA (Compute Unified Device Architecture). The multigrid method employed was the Full Approximation Scheme (FAS), which is used for solving nonlinear equations. This algorithm is applied to the 2D driven cavity problem and compared to the CPU version of the code (written in Fortran) to assess computational speed-up. © 2009 ASME
Advanced Functional Programming: First International Spring School on Advanced Functional Programming Techniques, Bastad, Sweden, May 24 - 30, 1995. Tutorial Text

Functional parsers. - Monads for functional programming. - The design of a pretty-printing library. - Functional programming with overloading and higher-order polymorphism. - Programming with Fudgets. - Constructing medium sized efficient functional programs in Clean. - Merging monads and folds for functional programming. - Programming with algebras. - Graph algorithms with a functional flavour.
Improved Depth Map Generation for Automatic 2D-to-3D Conversion

Recently, three dimensional (3D) technologies have grown incredibly. 3D adds the third dimension of depth. Image depth of the 3D media can offer more realistic and life like scene to the audience for feeling. However, lacking of 3D contents is a major problem; 2D-to-3D conversion is proposed as an alternative to meet the demand of 3D contents in 3D systems. Different techniques are proposed to generate 3D content from existing 2D. Most of the 2D-to-3D conversion techniques are semi-automatic methods [1], [6], [10], [12] but also timeconsuming, costly and a lot of human intervention required. There are some automatic methods [7], [8], [16], [17], [20], but still need to be improved because they not yet achieved the quality offered as semi-automatic methods. Therefore, here propose three types of methods depth map generation methods that can be used in automatic 2D to 3D conversion; local, global and improved depth map generation by using edge information. Local and global methods are based on a different approach of learning conversion from examples. In local method, a point transformation is learned that relates the local image attributes, such as color, spatial position, and, motion in the case of video frame at each pixel, to corresponding scene-depth at that pixel. The second method globally estimates the entire depth map directly from a repository of 3D images. Third method generates the depth map directly from the query image by using the edge information without using a large repository. Keywords—3Dimages; stereoscopic images; image conversion; nearest neighbor classification; bilateral filtering.
Enhancing Customer Relationships with Customer Knowledge Management and P2P (Peer-To-Peer) Technology

Abstract For the rapid advances of Internet technologies in recent years, electronic commerce (EC) has gained many attentions as a major theme for enterprises to keep their competitiveness. Amongst all possible endeavours for the EC, research has shown that effective management of customer relationships is a critical source for enterprises to succeed their EC business. Therefore, it is commonly recognized as an important objective for an enterprise to promote its customer relationships through an effective information system to achieve the EC. In this paper, we present a generic architecture for such a customer relationship management information system (CRMIS) that supports an effective management of customer relationships by emphasizing on the use of knowledge from customers (or the so-called customer knowledge management, CKM). In addition, as peer-to-peer (P2P) networks have been spreading and their practical applications on business are promising, our CKM architecture also employs the P2P model to enhance further its effectiveness on customer relationships. To illustrate, the architecture is applied to an exemplified CRMIS for book publishing.
Online pre-laboratory exercises enhance student preparedness for first year biology practical classes

Effective preparation prior to a practical class is essential if meaningful learning is to occur. If effective preparation does not occur, then students are at risk of “information overload” as they attempt to simultaneously come to terms with novel technical or manipulative tasks as well as learning new concepts. We designed on-line multi-media pre-laboratory exercises (Pre-labs) to support dissection-based practicals in a first year biology unit. The aim of this study was to gauge the effectiveness of the Pre-labs in improving students’ perceptions of their preparedness for practical classes. We surveyed the students before and after introduction of the Pre-labs, and monitored use of the Pre-labs on the class on-line learning site. The surveys showed that 68% of students reported they like to “see or be shown what to do”. In the initial survey, only 15% of students reported doing a substantial amount of preparation for practical classes. However, the majority of students used the “visual” Pre-labs regularly, and reported finding them ‘very useful’ in preparing them for the practical class, and 47 % (compared with an initial 22.4%) reported being well-prepared for class. Better preparation should lead to enhanced learning outcomes for students as well as better meeting ethical guidelines for instructors designing practicals based on animal specimens.
A point process model for the dynamics of limit order books

A point process model for the dynamics of limit order books Ekaterina Vinkovskaya This thesis focuses on the statistical modeling of the dynamics of limit order books in electronic equity markets. The statistical properties of events affecting a limit order book -market orders, limit orders and cancellationsreveal strong evidence of clustering in time, cross-correlation across event types and dependence of the order flow on the bid-ask spread. Further investigation reveals the presence of a self-exciting property that a large number of events in a given time period tends to imply a higher probability of observing a large number of events in the following time period. We show that these properties may be adequately represented by a multi-dimensional self-exciting point process with multiple regimes that reflect changes in the bid-ask spread. We propose a tractable parametrization of the model and perform a Maximum Likelihood Estimation of the model using high-frequency data from the Trades and Quotes database for US stocks. We show that the model may be used to obtain predictions of order flow and that its predictive performance beats the Poisson model as well as Moving Average and Auto Regressive time series models.
Fuzzy neuro-computational technique and its application to modelling and control

The authors present a model building technique which combines the strength of the fuzzy set theory and the neural network based structures. This technique can simultaneously deal with two types of knowledge, a nonfuzzy one and a fuzzy one, which usually describe the behavior of complex processes. The proposed method can also be directly applied to the construction of a new type of intelligent fuzzy controller. Some aspects of the adequacy of this fuzzy neuro-computational model are also discussed. A numerical example is provided. <<ETX>>
A Systematic Approach to Fuzzy Parsing

This paper presents a systematic approach to implementing fuzzy parsers. A fuzzy parser is a form of syntax analyzer that performs analysis on selected portions of its input rather than performing a detailed analysis of a complete source text. Fuzzy parsers are often components of software tools and also of program development environments that extract information from source texts. This paper clarifies the term ‘fuzzy parser’ and introduces an object‐oriented framework for implementing reusable and efficient fuzzy parsers. Applications of this framework are described via examples of two software tools. These tools exploit the facilities provided by fuzzy parsers for different purposes, and also for different languages. © 1997 John Wiley & Sons, Ltd.
Estimation of Jitter Effects in Oscillators and Frequency Synthesizers Due to Prototypical Perturbation Sources

The Phase Locked Loops (PLLs) are widely used in contemporary electronic systems for frequency synthesis, clock recovery, frequency multiplication and other purposes. Because of continuous increase in operating frequency of clocking systems the requirements on the clock spectral purity and low jitter became very demanding and are one of major designers’ concerns. Frequency synthesizers used in microprocessors are integrated on the same substrate as the rest of the circuit and thus suffer from a substantial switching noise injected into global supply and ground busses. Usually when the reference signal comes from a crystal oscillator, VCO becomes a main source of phase noise. A designer of VCO needs to determine the best circuit structure by considering different prototypical perturbations scenarios and predicting the worst case and jitter response when the perturbation signals are switched on and off. Therefore the time efficient estimation of the jitter effects resulting from many shapes, frequencies and phases of perturbation is critical. The presented dissertation demonstrates simulation methodology for rapid estimation of jitter in oscillators, particularly in VCOs, caused by perturbation sources such as power supply and substrate couplings. The methodology is also extended to these types of PLLs in which the VCO instability is a main contributor to the output timing jitter. Simulation of oscillatory circuits is strongly effected by the round-off errors. Special technique was developed to eliminate these effects.
A Hybrid Layered Approach for Ontology Matching

Ontologies act as a key technology for the visualization of the semantic web and their existence in different domains produces a heterogeneity problem for information integration. The process of ontology matching solves the heterogeneity problem by identifying the semantically related identical entities available from different ontologies. Finding similarity between the class names across the input ontologies is the major step performed in every ontology matching process. Most of the existing methods compare the class names only based on their similarity irrespective of their real meaning. This paper suggests a hybrid layered approach for matching the ontology classes. This approach calculates syntactic, semantic and structural similarity between the classes from input ontologies in successive layers. Finally, the alignment layer generates the final matching results by combining the results obtained from the previous layers and generates semantic mappings between them. This approach can also be applied to match other entities of the ontology. The results obtained prove that the proposed approach overtakes the other existing methods, thereby improving the accuracy of the results achieved.
Development of the autonomic mobile platform for RoboCup robot

A new platform of the autonomic mobile robot designed for the middle size RoboCup robot is introduced in this paper. Great innovations are made not only on the platform servo system but also on the driving controller design. Simple encoders and flat motors driven directly by the controller are used. High performance control of multiple motors is implemented by a DSP special for digital control. The platform has a reliable concise structure and characteristics of high performance-price ratio and, at the same time, is open and universal.
Are environmental health officers and transport planners in English local authorities working together to achieve air quality objectives

Since 1997, Local Air Quality Management (LAQM) has been used as a process through which local authorities in England identify and manage specific air quality problems within their jurisdictions in order to achieve the air quality objectives (AQO). However, the limitation of this process is that of policy disconnect between diagnosis and solutions proffered within it. Over 90% of air quality ‘hot-spots’ identified through the LAQM are due to traffic-related sources. Hence, the air quality action plans prepared by the environmental health officers (EHO) are improperly calibrated as a policy instrument for tackling most of the problems discovered through the LAQM. The inclusion of air quality as one of the four shared priorities in the second round of the Local Transport Plan (LTP2) therefore implies that the EHO need to engage with the transport planners (TP) at the local level in order to address most of these problems i.e. traffic-related air pollution. Since LAQM and LTP operate as two parallel frameworks with a separate agenda and timetable, adequate connectivity between both policy packages is thereby dependent on the type and level of inter-professional engagement between the departments and officials responsible for both policies at every level of government involved. This paper presents emerging issues from the questionnaire survey of EHO and TP in over 200 local authorities in 2007 as part of a three-year investigation into the effectiveness of achieving the AQO through the LTP in English local authorities. While there is wide support for the achievement of AQO through the LTP, the two groups identified differences in time-scale for delivering both policies, prioritisation of air quality within LTP, and unequal expectations as major factors affecting the integration. These factors indicate the existence of institutional complexities between parallel policy communities in ensuring integration.
Mobile Telephony in Nigeria

Purpose – This paper aims to describe the development of mobile phone services in Nigeria.Design/methodology/approach – A description of the evolution of telecommunications in the country.Findings – Indicates that an increasing number of inhabitants are taking advantage of the global system of mobile communication. The advent of mobile telephony, also suggests that apart from increasing the number of those with access to and use of telephone service it has created more jobs for Nigerians and limited the need to travel. Operators are faced with challenges such as interconnectivity, limited mobile coverage, a shortage of trained and qualified personnel, poor financial capital, among other issues.Originality/value – This is of interest to information management professionals, particularly those interested in Africa. Paper type General review
Vision-Based Path Following Without Calibration

Route-based knowledge, in which the spatial layout of an environment is recorded from the perspective of a ground-level observer, is an important component of human and animal navigation systems (Shelton & Gabrieli, 2002). In this representation, navigating from one location to another involves comparing current visual inputs with a sequence of views captured along the path in a previous instance. Applications that would benefit from such a path-following capability include courier and delivery robots (Burgard et al., 1999), robotic tour guides (Shen & Hu, 2006), or reconnaissance robots following a scout (Crawford et al., 2004). Furthermore, a solution to this problem would be useful for the general problem of navigating between two arbitrary locations in an environment by following a sequence of such paths.
Segmentation of brain MR images

Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process (noodle.med.yale.edu 1993). Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM), Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This chapter describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik (1990) anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results.
Computing on encrypted data and its applicability to a coalition operations environment

Coalition operations often invoke the sharing of information and IT infrastructure amongst partners. Whilst there may be a coalition ‘need to share’ data this is often tempered by a ‘need to know’ principle that often prevents valuable information from being exchanged, particularly with classified data. Ideally, coalition partners would wish to share data that can be used to compute specific results that are only relevant to a given operation, without revealing all of the shared information. In this paper we will present the concept of a secure coalition cloud architecture that is capable of storing encrypted data and of performing arbitrary computations on the encrypted data on behalf of users, without at any stage having to decrypt it. To do this we make use of a fully homomorphic encryption scheme using a novel approach for managing encryption and decryption keys in a public key infrastructure (PKI) setting.