Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring website evaluation criteria using the repertory grid technique: a Web Designers' perspective

This study aims to investigate web designers’ perceptions of an “effective” website. Twenty web designers were interviewed using Kelly’s Repertory Grid Technique in order to elicit factors that they consider important when designing or developing B2C websites. Using grounded theory approach, these elicited data were then classified into 14 meta-categories. The intensive nature of the interviews eventually gave rise to a comprehensive framework that broadens the base of existing web evaluation literature. This framework is based on an adapted Technology Acceptance Model with the 4 dimensions of Perceived Ease of Use, Perceived Usefulness, Perceived Playfulness and Attractiveness.
Survey on expedition of Human Activity detection and recognition algorithm.

: Visual surveillance is an active research topic in image processing. Transit systems are actively seeking new or improved ways to use technology to prevent and respond to suspicious activities accidents, crime, suspicious activities, terrorism, and vandalism. Human behavior-recognition algorithms can be used for prevention of incidents or reactively for investigation after the fact. This paper describes the survey on journey of human activity detection and recognition algorithm, it shows current state-of-the-art image-processing methods for automatic-behavior-recognition techniques. The main goal of this survey is to provide detail information of researches are done till date in this area. Human behavior-recognition methods for transit surveillance. Recognition methods include single person (e.g., loitering), multiple person interactions (e.g., fighting and personal attacks), person–vehicle interactions (e.g., vehicle vandalism), and person– facility/ location interactions (e.g., object left behind and trespassing). This paper is also include the various application of human activity recognition in real market.
Factors affecting the Correlation of Skid-testing Machines and a Proposed Correlation Framework

A manager responsible for pavement skid resistance has two key requirements with regard to the performance of skid-testing machines: (i) the same result should be obtained on a particular section of surfacing, regardless of which skid resistance machine is used (after applying machine to machine conversion factors), and (ii) results should not vary with time (say from one year to the next), due to machine wear or changes in machine components. These apparently simple requirements are, however, very difficult to achieve because of the nature of skid resistance and the number of factors that affect results. An international experiment arranged by the Permanent International Association of Road Congresses was used to derive correlation factors that permitted the results obtained on one machine type to be converted to those of another type. However, there is no guarantee that subsequent copies of one machine type will perform identically to the machine used in the correlation experiment. A second approach is typified by the annual SCRIM correlation trial in which all UK SCRIMs are compared. However, the machine acceptance criteria focus on ensuring that a complying machine does not deviate more than a certain amount from the mean value of all machines. Results could thus vary from year to year, due to a change that affected all machines or to a change in the composition of the fleet. A third approach would be to select a series of test surfaces and to use these surfaces to correlate different machines and to transfer correlations from year to year. The main problem with this scheme is that surfacings are polished by traffic (and testing machines) and undergo appreciable seasonal changes in skid resistance. This paper discusses the factors that must be taken into account in any correlation exercise and proposes a hybrid correlation scheme that may overcome most of the problems associated with alternative approaches. This scheme involves using pavement test sections to correlate different machines, and a recently calibrated British Pendulum Tester, together with the sand patch test, to detect and correct for any differences in the friction properties of the test sections that occur over a period of time.
On the Application of the Open Jackson Queuing Network

In real life, waiting for service is a common phenomenon. As a system gets congested, service delay is inevitable; as the service delay increases, waiting time in the queue gets longer. In a typical hospital, the network is made up of various departments (nodes). In this study we considered the inflow and outflow of an hospital network; this is depicted in the schematic diagram. For an efficient hospital planning, a good patient flow means that patient queuing time is minimized, while poor patient flow means the patient suffer considerable queuing delays. This paper presents the results of a study carried out in a University Hospital Centre; the queuing model adopted used the Open Jackson Queuing Network to minimize the waiting times in the queues. The data collection was done for a period of two weeks, with a week interval in order to observe the system for any anomaly. For each node, the number of arrivals and departures together with the service times were recorded at an interval of five minutes. The study showed that for a good hospital planning, the more the personnel (servers) are made to focus on their assignments, the lesser the time the patients will spend on the queue and this leads to more efficient patient flow
Reasoning Across Language and Vision in Machines and Humans

Barbu, Andrei. Ph.D., Purdue University, December 2013. Reasoning Across Language and Vision in Machines and Humans. Major Professor: Jeffrey M. Siskind. Humans not only outperform AI and computer-vision systems, but use an unknown computational mechanism to perform tasks for which no suitable approaches exist. I present work investigating both novel tasks and how humans approach them in the context of computer vision and linguistics. I demonstrate a system which, like children, acquires high-level linguistic knowledge about the world. Robots learn to play physically-instantiated board games and use that knowledge to engage in physical play. To further integrate language and vision I develop an approach which produces rich sentential descriptions of events depicted in videos. I then show how to simultaneously detect and track objects, recognize events, and produce sentences. This tighter integration of language and vision enables a novel task: sentential video retrieval. A video corpus can be searched for clips which depict a target sentence rather than just a collection of individual query words. This work assumes a compositional representation of events, composing sentence models from word models. Perhaps the reason why humans perform tasks such as the above with ease is because of a tight integration of language and vision exploiting the compositionality inherent in both modalities. I present work indicating that this may be the case. Humans are shown videos while fMRI data is acquired and sentences which describe those videos are recovered compositionally.
LLVM: AN INFRASTRUCTURE FOR MULTI-STAGE OPTIMIZATION

Modern programming languages and software engineering principles are causing increasing problems for compiler systems. Traditional approaches, which use a simple compile-link-execute model, are unable to provide adequate application performance under the demands of the new conditions. Traditional approaches to interprocedural and profile-driven compilation can provide the application performance needed, but require infeasible amounts of compilation time to build the application. This thesis presents LLVM, a design and implementation of a compiler infrastructure which supports a unique multi-stage optimization system. This system is designed to support extensive interprocedural and profile-driven optimizations, while being efficient enough for use in commercial compiler systems. The LLVM virtual instruction set is the glue that holds the system together. It is a lowlevel representation, but with high-level type information. This provides the benefits of a low-level representation (compact representation, wide variety of available transformations, etc.) as well as providing high-level information to support aggressive interprocedural optimizations at linkand post-link time. In particular, this system is designed to support optimization in the field, both at run-time and during otherwise unused idle time on the machine. This thesis also describes an implementation of this compiler design, the LLVM compiler infrastructure, proving that the design is feasible. The LLVM compiler infrastructure is a maturing and efficient system, which we show is a good host for a variety of research. More information about LLVM can be found on its web site at: http://llvm.cs. uiuc.edu/
Carrier synchronization with CEPB-OFDM

The constant envelope paired burst (CEPB) OFDM technique allows the use of a single grossly nonlinear amplifier in low-cost, power-efficient transmitters. It is well known that a good carrier synchronization is mandatory in conventional OFDM schemes; CEPB-OFDM schemes also have strong carrier synchronization requirements. We propose and evaluate a fast, open-loop carrier synchronization scheme that takes advantage of the signal structure of CEPB-OFDM.
Extended genetic programming using reinforcement learning operation

Genetic programming (GP) usually has a wide search space and a high flexibility, so GP may search for a global optimum solution. But GP has two problems. One is slow learning speed and a huge number of generations spending. The other is difficulty in operating continuous numbers. GP searches many tree patterns including useless node trees and meaningless expression trees. In general, GP has three genetic operators (mutation, crossover and reproduction). We propose an extended GP learning method including two new genetic operators, pruning (pruning redundant patterns) and fitting (fitting random continuous nodes). These operators have a reinforcement learning effect, and improve the efficiency of GP's search. To verify the validity of the proposed method, we developed a medical diagnostic system for the occurrence of hypertension. We compared the results of the proposed method with prior ones.
Small ship detection with high frequency radar using an adaptive ocean clutter pre-whitened subspace method

We propose a novel scheme for using high frequency ocean surveillance radar (HFOSR) to detect slow weak target echoes embedded in temporally correlated sea clutter having a continuous spectrum. General Doppler processing CFAR detection of ships in ocean surveillance radar is usually inhibited by the continuous high order sea clutter. Conventional subspace methods can be utilized to enhance the detection, but they deteriorate dramatically in the presence of correlated sea clutter. In our paper an adaptive sea clutter filtering is introduced which improves the threshold and accuracy of the subspace detection method. Both simulated and real ship targets are used to verify the effectiveness of our proposed method.
High-resolution low-power CMOS D/A converter

A very low-power, high-resolution, medium-speed D/A (digital-to-analog) converter is described. The converter was realized using a standard analog CMOS technology. It achieved 15 bits monotonicity and less than 0.7% overall linearity at a clock frequency of 100 kHz, without requiring any trimming or calibration. The measured SNR (signal-to-noise ratio) was 85 dB, and the measured power dissipation was less than 10 mW. <<ETX>>
Simulations of Machines Using MATLAB and Simulink

1. INTRODUCTION Why Simulate Mechanisms? / Kinematic Simulations / Dynamic Simulation of Mechanisms / Cross Reference with Traditional Kinematics Texts / Summary 2. VECTOR LOOP AND VECTOR CHAIN EQUATIONS Introduction / The Planar Vector / Single Loop Equations / Derivatives of Vectors / Other Common Mechanisms / Vector Chains / Summary 3. SOLUTIONS OF THE POSITION PROBLEM Overview / Numerical Solutions of Nonlinear Algebraic Equations / The Position Problem of a Four-Bar Linkage / MATLAB Solution of the Position Problem of a Four-Bar Linkage / Position Solutions and Initial Guesses / Summary 4. KINEMATIC SIMULATIONS USING SIMULINK(R) What Is a Kinematic Simulation? / Velocity Solution via Kinematic Simulation / Acceleration Solution via Kinematic Simulation / The Consistency Check / Kinematic Simulation of a Four-Bar Mechanism / Summary 5. INTRODUCING DYNAMICS Introduction / Step 1: Simulation of Slider on Inclined Plane / Step 2: Adding the Pendulum / Step 3: Assembling the Matrix Equation / Step 4: Create a Dynamic Simulation / Step 5: Set Initial Conditions and Run Simulation / Summary 6. THE SIMULTANEOUS CONSTRAINT METHOD Introduction / Description of the Approach / Application of Simultaneous Constraint Method for the Slider Crank / Dynamic Simulation of the Slider Crank / Simulation Studies of the Slider Crank / Summary 7. SIMULATING MECHANISMS THAT CHANGE The Geneva Mechanism / Summary 8. TWO-LINK PLANAR ROBOT Overview / Vector Equations / Dynamic Equations / The Simultaneous Constraint Matrix / Dynamic Simulation / Robot Coordinate Control / Conclusions 9. THE TREBUCHET Introduction / The Vector Loop / The Equations of Motion / The Matrix Equation / The Dynamic Simulation / Simulation Results / Summary
Performance evaluation of latent factor models for rating prediction

Since the Netflix Prize competition, latent factor models (LFMs) have become the comparison “staples” for many of the recent recommender methods. Meanwhile, it is still unclear to understand the impact of data preprocessing and updating algorithms on LFMs. The performance improvement of LFMs over baseline approaches, however, hovers at only low percentage numbers. Therefore, it is time for a better understanding of their real power beyond the overall root mean square error (RMSE), which as it happens, lies at a very compressed range, without providing too much chance for deeper insight. We introduce an experiment based handbook of LFMs and reveal data preprocessing and updating algorithms’ power. We perform a detailed experimental study regarding the performance of classical staple LFMs on a classical dataset, Movielens 1M , that sheds light on a much more pronounced excellence of LFMs for particular categories of users and items, for RMSE and other measures. In particular, LFMs exhibit surprising and excellent advantages when handling several difficult user and item categories. By comparing the distributions of test ratings and predicted ratings, we show that the performance of LFMs is influenced by rating distribution. We then propose a method to estimate the performance of LFMs for a given rating dataset. Also, we provide a very simple, open-source library that implements staple LFMs achieving a similar performance as some very recent (2013) developments in LFMs, and at the same time being more transparent than some other libraries in wide use.
Key management for secure group communication in a wireless environment

In recent years, group based applications and protocols have gradually gained popularity in term of data traffic distribution across a group of members. Examples of such applications are software updates, multimedia conferencing and information dissemination service. Since these applications typically involve communication over open network such as Internet, security has become an important requirement for them. An efficient key management is able to provide confidentiality and integrity of data exchange between members of a group. This dissertation is focused to study group key management schemes which are appropriate for wireless environment. However, most previous works on group key management have been prominently proposed for deployment in wired environment. Thus, it is the aim of this research to propose an appropriate group key management scheme for wireless environment. This work contains three main parts. First of all, a number of group key management schemes which are further classified into three design approaches including centralized, decentralized and distributed are studied. As a result, the advantages and drawbacks of these approaches are compared in order to identify the appropriate group key management design for wireless environment. Secondly, based on extracted result from the comparisons, a decentralized approach is selected as a desired architecture for group key management in this work. The components and their roles as well as protocols which involve in this architecture are then described in detail. Thirdly, an implementation of the proposed approach is conducted by using Java application programming interface (API) platform. The implementation provides a realistic evaluation of proposed scheme and tested for different aspects in regarding to join operation, leave operation and updating key materials. The work is finally concluded by identifying future research directions.
A VLSI design of a pipeline Reed-Solomon decoder

A pipeline structure of a transform decoder similar to a systolic array is developed to decode Reed-Solomon (RS) codes. The error locator polynomial is computed by a modified Euclid's algorithm which avoids computing inverse field elements. The new decoder is regular and simple, and naturally suitable for VLSI implementation.
PERBANDINGAN KINERJA ALGORITMA ID3 DAN C4.5 DALAMPENGKLASIFIKASIAN SPAM-MAIL

Klasifikasi spam mail digunakan untuk memisahkan antara spam-mail dengan non spam mail (atau yang biasa disebut ham atau legitimate mail). Klasifikasi spam mail ini berguna untuk menghemat waktu dan penghematan biaya yang digunakan untuk menghapus spam mail dari inbox. Untuk itu diperlukan metode yang paling baik untuk mengklasifikasikan spam mail. Algoritma decision tree merupakan salah satu metode yang bisa digunakan untuk klasifikasi spam mail. Algoritma decision tree telah banyak mengalami pengembangan. Algoritma ID3 dan C4.5 adalah salah satu pengembangan dari algoritma decision tree. Penelitian ini membandingkan kinerja dari dua algoritma tersebut. Tujuan dari penelitian ini adalah membandingkan kinerja kedua algoritma yang dimiliki oleh algoritma ID3 dan algoritma C4.5, dalam mengklasifikasikan spam mail. Dari perbandingan ini akan diketahui persentase precision, recall dan accuracy dari data yang digunakan. Data yang digunakan adalah data yang diambil dari UCI Machine Learning Repository berjumlah 4601 data email yang terdiri dari 1813 email spam dan 2788 email non-spam. Data yang diambil akan diubah menjadi data katergori dengan teknik distribusi frekuensi. Dengan menggunakan alat bantu WEKA (Waikato Environment for Knowledge Analysis) 3-4 yang digunakan untuk menguji algoritma ID3 dan c4.5. Dari pengukuran kinerja algoritma ID3 dan C4.5 bahwa algoritma ID3 memiliki kinerja yang lebih baik dibandingkan algoritma C4.5.
Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model

Considering the complexity of vegetables price forecast, the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm by using the characteristics of genetic algorithm and neural work. Taking mushrooms as an example, the parameters of the model are analyzed through experiment. In the end, the results of genetic algorithm and BP neural network are compared. The results show that the absolute error of prediction data is in the scale of 10%; in the scope that the absolute error in the prediction data is in the scope of 20% and 15%. The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model, especially the absolute error of prediction data is within the scope of 20%. The accuracy of genetic algorithm based on neural network is obviously good than BP neural network model, which represents the favorable generalization capability of the model.
System development with integration architectures

The concepts presented in this paper show a possible alternative of the existing post-facto, bottom-up approach used in developing an integrated system. Based on the needs of distributed and complex application systems, the authors propose a pre-planned development process. This integrated approach introduces the notion of a meta-level of control and management which coordinates otherwise independent projects. Domain analysis and integration architectures are identified as the two main components of the proposed generic integration framework. Finally, they describe project management as an embedded sub-task in a generic, domain oriented development effort. <<ETX>>
Neural network face recognition using statistical feature extraction

Recognition method of human face using statistical analysis feature extraction and a neural network algorithm is proposed. In the preprocessing step we detect the edges of the face image by using the Sobel algorithm. Then we propose a new method to transform the two-dimension black and white image to a one-dimension vector. Finally, based on the statistical analysis, we extract seven features. In the recognition step we use the fast backpropagation (FBP) algorithm. Computer simulation results with 100 test images of 10 persons (the images of each person in a various pauses, facial expression, and facial details) show that the proposed method yields a high recognition rate.
OPTIQUE DE L'APPARENCE VISUELLE

Ce memoire d’Habilitation a Diriger des Recherches definit un domaine d’etude intitule « l’optique de l’apparence visuelle ». Il s’agit, pour un objet donne, de mesurer la lumiere reflechie ou transmise par cet objet, d’associer ces mesures radiometriques a l’apparence visuelle de l’objet, et enfin de developper des modeles de prediction de l’apparence visuelle en connaissant (ou en estimant) les proprietes optiques et les structures a l’echelle nano ou micrometrique des constituants de l’objet. Ce champ de recherche est amene a se developper tant le besoin de quantifier l’aspect visuel devient essentiel pour de nombreuses applications (etudes sur les œuvres d’art, impression, cosmetique, synthese d’images, etc.) et pour la vente de n’importe quel produit manufacture : voiture, ordinateur, vetement, nourriture, packaging, etc. Pour cela, plusieurs challenges sont a relever : developper des collaborations entre chercheurs de disciplines differentes, definir une nouvelle metrologie de l’apparence visuelle, creer des outils pour exploiter les donnees mesurees, et adapter les systemes de mesure et les modeles de prediction de l’apparence aux nouveaux materiaux (materiaux a effet, materiaux X-chromes, impression 3D, etc.).
A New Moving Human Detection Method in Color Video Image

Moving object detection is the basic of video applications such as computer vision, object recognition and tracking, surveillance security etc. Background subtraction and symmetrical differencing are the popular methods of motion detection. The main idea of them is to compare the current video frame with a specified background image or a background model or the next video frame. For background subtraction, the obtaining of initialization is crucial and many methods have been employed, so it is necessary to model background to adapt the changes of background. In this paper, the single gaussian modeling as the initialization background model combined with an improved linear alternate background updating method is proposed. And then, a novel moving human detection method which employs background subtraction and symmetrical differencing based on rgb color difference model is presented. The experimental results show that the detection method can detect moving human effectively and real-time.