Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Collaborative Filtering Recommendation on Sparse Data Sets

Ontologies are widely used to represent user profiles in personalized web information gathering. Nowadays, how to gather useful and meaningful information from the Web has become challenging to all users because of the explosion in the amount of Web information. However, the mainstream of Web information gathering techniques has many drawbacks, as they are mostly keyword-based. It is argued that the performance of Web information gathering systems can be significantly improved if user background knowledge is discovered and a knowledge-based methodology is used. In this paper, a knowledge-based model is proposed for Web information gathering. The model uses a world knowledge base and user local instance repositories for user profile acquisition and the capture of user information needs. The knowledge-based model was successfully evaluated by comparing a manually implemented user concept model. The proposed knowledge-based model contributes to better designs of knowledge-based and personalized Web information gathering systems. Keywords: Knowledge-based Information Gathering; Ontology; World Knowledge Base; Local Instance Repository; User Information.
SMILE: An Informality Classification Tool for Helping to Assess Quality and Credibility in Web 2.0 Texts

The data made available by Web 2.0 applications such as social networks, on-line chats or blogs have give access to multiples sources of information. Due to this dramatic increase in available information, the perception of quality and credibility plays an important role in social media, thus making necessary to discard low quality and uninteresting content. Moreover, the informal features of Web 2.0 texts such as emoticons, typos, slang or loss of formatting impact negatively on user perception regarding content quality and credibility. For this reason, this paper proposes the SMILE system, a novel unsupervised real-time tool for assessing user-generated content quality and credibility using informality levels. As a test case, we focus on Yahoo! Answers, a relevant Web 2.0 application by its amount of users, content and textual diversity. The results of our study show that informality analysis can be used as criteria to help assess the credibility and quality of Web 2.0 information sources.
Zinc Tetrafluoroborate: A Versatile and Robust Catalyst for Various Organic Reactions and Transformations

Abstract This account discusses the use of aqueous zinc(II) tetrafluoroborate as a mild and selective catalyst for various important organic transformations. In general, the advantages of the methodologies discussed include high yields; short reaction times; excellent regio-, chemo-, and stereoselectivities; mild reaction conditions; and high efficiency. The use of the catalyst in a synthesis of the antihypertensive drug metoprolol is also discussed. 1 Introduction 2 Protection and Deprotection 2.1 Selective Deprotection of tert-Butyl(dimethyl)silyl Ethers 2.1 Tetrahydropyranylation and Depyranylation of Alcohols 2.2 Conversion of Aldehydes into Geminal Diacetates or Cyanoacetates 2.3 Selective Thioacetalization of Aldehydes 2.4 Mild and Efficient Cleavage of Oximes, Hydrazones, and Semicarbazones 3 Rearrangements of Ten-Membered-Ring Allenes 4 Organic Methodologies 4.1 Mannich Reaction of Aldimines with Silyl Enol Ethers in Aqueous Media 4.2 Synthesis of Highly Substituted Pyrroles under Solvent-Free Conditions 4.3 One-Pot Biginelli Reaction at Room Temperature 4.4 Epoxide Ring Opening with Amines and Synthesis of Metoprolol 5 Conclusions
Gate-diffusion input (GDI)-a novel power efficient method for digital circuits: a design methodology

GDI (gate diffusion input)-a new technique of low power digital combinatorial circuit design-is described. This technique allows reducing power consumption, delay and area of digital circuits, while maintaining low complexity of logic design. A detailed design methodology is described. Performance comparison with traditional CMOS and PTL design techniques is presented. The different methods are compared with respect to the layout area, number of devices, delay and power dissipation. Several logic circuits have been implemented in various design styles. Their properties are discussed, simulation results are reported.
Final cut pro

Final cut pro is the darling of many video editors, especially those who have had to pay for their own editing system. When it first hit the market it was a fraction of the cost of its cheapest competitor. As FCP has gone through its revisions to version 2, which ironed out a few bugs and added plenty of new features, and is n ow onto version 3 which fixes more limitations, adds even more features and runs on Macintosh OSX, it has really become an incredibly powerful tool. Coming from a television background, I was not overly impressed with version 1, when version 2 came along I started to warm to the programme, and now, with version 3, I'm something of a convert.
Facilitating the Mental Integration of Multiple Sources of Information in Multimedia Learning Environments

Multimedia learning environments commonly comprise different sources of information such as various linguistic as well as static and dynamic pictorial representations. In this context, it is frequently assumed that the (simultaneous) presentation of different and possibly complementary sources of information improves learning. During the last 10 years, however, psychological and educational research have collected extensive evidence that the presentation of different sources of information in multimedia learning environments might not improve and even impede learning. If multimedia learning is to be successful, different sources of information need to be integrated into coherent mental representations. In many cases, however, the presentation and use of different sources of information does not result in the construction of appropriate mental representations. Starting from various potentials and problems of multimedia learning, the theory of structure mapping is proposed as a framework that can be used to design multimedia learning environments that systematically encourage and support the mental integration of multiple sources of information. A multimedia learning environment for statistics is described which has been implemented according to the design principles proposed. (Contains 10 references.) (AEF) Reproductions supplied by EDRS are the best that can be made from the original document.
FINGERPRINT CLASSIFICATION BASED ON RECURSIVE NEURAL NETWORK WITH SUPPORT VECTOR MACHINE

Fingerprint classification based on statistical and structural (RNN and SVM) approach. RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in this support vector machine. SVMs are combined with a new error correcting codes scheme. This approach has two main advantages. (a) It can tolerate the presence of ambiguous fingerprint images in the training set and (b) It can effectively identify the most difficult fingerprint images in the test set. In this experiment on the fingerprint database NIST-4 (National Institute of Science and Technology), our best classification accuracy of 94.7% is obtained by training SVM on both fingerCode and RNN –extracted futures of segmentation algorithm which has used very sophisticated “region growing process”.
An Ontology-based Framework for Itembank Integration and Knowledge Sharing

The Internet is abounding with information, but there is simply no easy way to find all the information we need in a single place, well organized and easily accessible, let alone share. For this reason, we present a novel framework for resolving these issues. The proposed framework incorporates and unifies techniques from a variety of disciplines such as information extraction, information retrieval, fuzzy clustering, and ontology. To evaluate the performance of the proposed framework, we use it to solve the itembank integration and knowledge sharing problem faced by many e-Learning systems. Our experimental results show that the proposed framework can not only effectively integrate information available in the Internet, but it can also properly organize the information into a unified itembank of an e-Learning system, thus making it possible for the users of such a system to not just find but share all the information.
Geometry map algorithms for interactive deformation and real-time visualization

Laparoscopic surgery is a popular technique that requires intensive training and practice before actual surgery. Computer simulation provides a virtual training environment for surgeons to practise. Surgery simulators are becoming practical with new frameworks that include core components such as enhanced medical imaging, rapid medical data modeling, realistic visualization procedures, and high fidelity animation techniques. The emphasis on cinematic quality rendering has gained importance with the widespread availability of affordable high end graphics capabilities on desktop computers. However, the challenge is the computation of man-machine interaction with the deformable tissues/organs. Modeling organs for surgery simulation is a formidable challenge due to three reasons. First, the organs and body parts have not only very complex shapes, but are deformable too. The second challenge is to provide a mechanism for surgeons to interact with these complex anatomical objects. The third challenge is the expensive 3D deformation computation required for haptic feedback. We propose a new technique that is extensively superior to the conventional rigid Geometry Images. In our approach, a parameterized representation of virtual organs with multi-layered 2D flat maps is used for the purpose of surgery simulation. An unstructured 3D input mesh is parameterized and resampled into a regular 2D parameterized model called Geometry Map. To accomplish real-time interaction with i ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library
Analysis of client/server transaction delay through a local area network switch

Local area network (LAN) switches are increasingly being used to subdivide large LAN segments into multiple connected LAN "microsegments". A LAN switch is similar to a LAN bridge, but is typically higher in performance and has more ports. Communication between stations on different segments is through the LAN switch. A performance analysis is presented for a token ring LAN switch for the class of protocols known as request-response protocols. Request-response protocols are typical of many client/server applications including transaction processing. A performance model based on a detailed accounting of network delay components is presented. The performance model quantifies the importance of minimizing latency within a LAN switch. Two LAN switch frame forwarding architectures are described and evaluated. The architectures are store-and-forward and cut-through frame forwarding. The performance model demonstrates that the benefits of low delay in cut-through switching extend even to situations where attached token ring segments have a very high utilization.
Microphone array source localization using realizable delay vectors

This paper presents an alternative approach to acoustic source localization which modifies the traditional two-step localization procedure to not require explicit time-delay estimates. Instead, the cross-correlation functions derived from various microphone pairs are simultaneously maximized over a set of potential delay combinations consistent with candidate source locations. The result is a procedure that combines the advantages offered by the phase transform (PHAT) weighting (or any reasonable cross-correlation-type function) and a more robust localization procedure without dramatically increasing computational load. Simulations are performed across a range of reverberation conditions to illustrate the utility of the proposed method relative to conventional generalized cross-correlation (GCC) filtering approaches and a more modern eigenvalue-based technique.
An adaptive weight value-based multi-scale retinex algorithm for color image enhancement

There are limited dynamic range problems when real world scenes are captured from a digital camera and displayed on monitors.Retinex is one of the most popular theories for dynamic range compression, color constancy and color rendition.However, there still exist some problems, such as hue shift and excessive noise, especially for those images including large bright areas and dark regions.In order to solve these problems,an adaptive weight value-based multi-scale retinex algorithm is proposed in this paper. Compared to other methods, visual experiments and data evaluation show that the proposed algorithms can achieve better performance for color image enhancement and noise suppression simultaneously. What is more, there is no need for color correction phase in the proposed algorithm, which makes it more efficient and convenient to apply.
Automatic Visual Data Interpretation for Pipeline Infrastructure Assessment

Due to the rapid aging of pipeline systems, municipalities have become proactive in checking their conditions regularly to ensure continuity of services and to protect the investment made in these networks. It is critical to have effective methods for assessing the condition of pipelines, evaluating the level of deterioration, and determining the type and probability of defects to allow selection of the maintenance/repair/rehabilitation strategies that will be least disruptive, most cost-effective and safe. This leads to collecting up-to-date and accurate pipeline condition data and automating pipeline construction and rehabilitation processes. Among various existing pipeline electronic inspection techniques, RedZone Robotics Inc. provides one of the most flexible and industry-leading robotic equipment with superior data acquisition capabilities. They are also developing a new generation of robotic crawler for acquiring data independent of human operators. This innovative development creates the need to automate pipeline condition assessment, especially to automatically highlight and classify pipe defects. Therefore, the primary objective of this project is to advance image-content based automatic pipeline inspection and condition assessment with the participation of RedZone Robotics Inc. located in the Commonwealth of Pennsylvania. The research will take an innovative approach to bring automated pipeline infrastructure visual data interpretation and assessment to a new level of unprecedented quality and performance, and to close the semantic gap between image features and
Modelling parallel metaheuristics and hyperheuristics for auto-tuning

This paper studies the auto-tuning of parallel metaheuristics and hyperheuristics. The modelling of the shared-memory scheme is considered for both types of algorithms, and a first study of message-passing metaheuristic schemes is introduced. A theoretical model of the execution time of a parametrized metaheuristic scheme is empirically adapted for a particular metaheuristic through experimentation. The parallelization of the shared-memory scheme is achieved through the independent parallelization of the basic functions in the metaheuristic scheme. The model is used to decide at running time the number of threads to obtain a reduced execution time. The number of threads is different for the different basic functions in the scheme, and depends on the problem to be solved, the metaheuristic or hyperheuristic scheme, the implementation of the basic functions and the computational system where the problem is solved. The auto-tuning methodology for shared-memory parametrized metaheuristic schemes can in turn be applied to shared-memory hyperheuristics developed on top of them. In the case of the message-passing scheme, an island model implemented with the master-slave scheme is used, and new metaheuristic-parallelism parameters representing the migration frequency, the size of the migration and the number of processes are introduced. The applicability of the proposal is shown with a minimization of electricity consumption in exploitation of wells problem and with the problem of obtaining satisfactory metaheuristics for that problem. Experimental results with these two problems show that satisfactory execution times can be achieved in metaheuristics with auto-tuning techniques based on theoretical-empirical models of the execution time.
Offset-compensated switched-capacitor integrators

The performance of several switched-capacitor (SC) integrators is compared, and a description representation is introduced. Offset-compensated circuits which reduce the capacitance spread in very large time-constant SC integrators are presented. The theoretical equations are derived for all SC circuits and are then confirmed by simulations. <<ETX>>
Large-Scale Display Interaction Techniques to Support Face-to-Face Collaboration

This research details the development of a large–scale, computer vision–based touch screen capable of supporting a large number of simultaneous hand interactions. The system features a novel lightweight multi–point tracking algorithm to improve real–time responsiveness. This system was trialled for six months in an exhibition installation at World Expo 2005 in Aichi, Japan, providing a robust, fault–tolerant interface. A pilot study was then conducted to directly compare the system against other, more established input methods (a single–touch case, a two–mouse case and a physical prototype) to determine the effectiveness and affordances of the multi–touch technology for arranging information on a large–scale wall space in a paired collaborative task. To assist in this study, a separate visualisation and interaction classification tool was developed, allowing the replay of XML log data in real time to assist in the video analysis required for observation and hypothesis testing.
Fuzzy Min-Max Neural Networks-Part 1 : Classification

A supervised learning neural network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregate (union) of fuzzy set hyperboxes. A fuzzy set hyperbox is an n-dimensional box defined by a min point and a max point with a corresponding membership function. The min-max points are determined using the fuzzy min-max learning algorithm, an expansionxontraction process that can learn nonlinear class boundaries in a single pass through the data and provides the ability to incorporate new and refine existing classes without retraining. The use of a fuzzy set approach to pattern classification inherently provides degree of membership information that is extremely useful in higher level decision mak- ing. This paper will describe the relationship between fuzzy sets and pattern classification. It explains the fuzzy min-max classifier neural network implementation, it outlines the learning and recall algorithms, and it provides several examples of operation that demonstrate the strong qualities of this new neural network classifier. AmRN classification is a key element to many engi- P neering solutions. Sonar, radar, seismic, and diagnostic applications all require the ability to accurately classify a situation. Control, tracking, and prediction systems will often use classifiers to determine input-output relationships. Because of this wide range of applicability, pattern classification has been studied a great deal (13), (15), (19). This paper describes a neural network classifier that creates classes by aggregating several smaller fuzzy sets into a single fuzzy set class. This technique, introduced in (42) as an extension of earlier work (41), can learn pattern classes in a single pass through the data, it can add new pattern classes on the fly, it can refine existing pattern classes as new information is received, and it uses simple operations that allow for quick execution. Fuzzy min-max classification neural networks are built using hyperbox fuzzy sets. A hyperbox defines a region of the n-dimensional pattern space that has patterns with full class membership. A hyperbox is completely defined by its min point and its max point, and a membership function is defined with respect to these hyperbox min-max points. The min-max (hyperbox) membership function combination defines a fuzzy set, hyperbox fuzzy sets are aggregated to form a single fuzzy set class, and the resulting structure fits naturally into a neural network framework; hence this classification system is called a fuzzy min-max classification neural network. Learning in the fuzzy min-max classification neural network is performed by properly placing and adjusting hyperboxes in the pattern space.
Image Quality Assessment Techniques: An Overview

The image quality assessment (IQA) provides computational models to measure the perceptual quality of an image. The paper gives the comparative evaluation of different objective full reference IQA (FR-IQA) schemes such as mean squared error (MSE), peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and gradient based similarity index (GSI) can be used to evaluate quality of an image. MSE and PSNR are conventional and widely used methods. The SSIM is based on human visual system which can extract the structural changes in the image. The GSI relies on the principle that image gradients can be used to extract important visual information such as contrast and structural changes in the image with reference to a reference image. The experiments are conducted with publicly available subject-rated database i.e. Laboratory for Image and Video Engineering (LIVE) database images.
Proceedings of the 23rd ACM international conference on Multimedia

It is our great pleasure to welcome you to the 2015 ACM MULTIMEDIA Conference, the premier conference for multimedia experts and practitioners from across academia and industry. This year we are in beautiful Brisbane, Australia, our first time in this wonderful country and we're delighted to be here and looking forward to experiencing some of the delights and treasures this great country has to offer. This year's conference is the 23rd in the annual series continues our tradition to bring together a wide and diverse set of activities including panel sessions, tutorials, a series of workshops, technical demos, an open source software competition, a doctoral symposium, an interactive art exhibition, a video program and of course scientific papers and posters including special sessions on brave new ideas and grand challenges, as well as "regular" topics. What a busy week we are going to have! One of the great parts of the program that we have lined up for you is the two keynote talks. On Tuesday October 27th, Judy Kay from the University of Sydney will talk about "Harnessing Big Personal Data, with scrutable user modeling for privacy and control", bringing her extensive background in Human-Computer Interaction to the really topical issue of personal data, and how to manage and use it for good and practical purposes. On Thursday October 29th, Zhengyou Zhang from Microsoft Research will talk about "Vision-enhanced Immersive Interaction and Remote Collaboration with Large Touch Displays" where he will address issues like telepresence and computer supported cooperative work and how those technologies have, and continue to develop.
Performance analysis of OFDM carrier synchronization with diversity

Orthogonal frequency division multiplexing (OFDM) carrier frequency synchronization performance under low SNR can be improved by using diversity. We investigate the improvement in synchronization performance using real data obtained on the Stevens wireless testbed. The experimental setup is comprised of a wideband 65 Msamples/second digital to analog converter, a radio frequency (RF) transmitter which transmits at 2.4 GHz frequency with 12 MHz bandwidth and two RF receiver chains whose outputs are sampled at 65 Msamples/sec. Therefore, the receiver can oversample both in time and in space. The sensitivity of the diversity method to sampling clock errors and calibration errors is discussed.