Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of template matching hardware and its high‐speed processing strategy

The template matching technique is now applied to tracking of outdoor object and position measurement, and there is increasing demand for application of template matching based on the normalized correlation coefficient, which is robust against the variation of illumination. In order to apply the technique based on the normalization technique to practical application problems, however, high-speed calculation of the normalized correlation coefficient is required. This paper intends to improve the speed by implementing by hardware the calculation of the normalized correlation coefficient, which requires a large amount of computation. The proposed method decomposes the normalized correlation coefficient calculation into five operational components, each of which is implementated by hardware. The speed of processing is improved being combined with a parallel processing with a high degree of freedom based on the local property owned by the template matching process. For further improvement of the speed, the application of the pyramid structure is considered. Hardware that can handle the pyramid structure is realized. An evaluation was tried using an actual machine. The performance of the developed hardware is evaluated. The effectiveness of the approach is verified by applying the method to the traffic flow measurement system, as an example of an actual outdoor system. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(11): 1–10, 2001
Analisa Performansi Low Density Parity Check Code Pada Sistem CDMA

The purpose of this final project is to analyze the performance of Low Density Parity Check Code for CDMA system. The analyze is to compare the simulation program of LDPC-CDMA with different parity check matrix LDPC and different amount of active user. The comparison for the system is compare based on Bit Error Ratio for the systems. So it could be declare with using what decoding method it can perform better. For the result of Bit Error Rate, the system LDPC for CDMA with different matrix has different value too. For the small matrix of LDPC has value of BER smaller than bigger matrix of LDPC. Then, for amount of many active user that has BER many too with equal little of active user. Key Words : LDPC, CDMA, BER, Bit Flip, Sum Product.
Tracking and recognition face in videos with incremental local sparse representation model

Abstract. This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. First a robust face tracking algorithm is proposed via employing local sparse appearance and covariance pooling method. In the following face recognition stage, with the employment of a novel template update strategy, which combines incremental subspace learning, our recognition algorithm adapts the template to appearance changes and reduces the influence of occlusion and illumination variation. This leads to a robust video-based face tracking and recognition with desirable performance. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database, which includes 47 celebrities. Our proposed method produces a high face recognition rate at 95% of all videos. The proposed face tracking and recognition algorithms are also tested on a set of noisy videos under heavy occlusion and illumination variation. The tracking results on challenging benchmark videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. In the case of the challenging dataset in which faces undergo occlusion and illumination variation, and tracking and recognition experiments under significant pose variation on the University of California, San Diego (Honda/UCSD) database, our proposed method also consistently demonstrates a high recognition rate.
Motivating Online Performance

This study investigated the effects of goal setting on Internet searches and examined the relationship between Internet self-efficacy and search task performance. Participants searched the Internet and listed the names of the industrial-organizational psychologists they found. Control participants were instructed to “do their best” to find as many names as possible, and experimental participants were assigned the goal of 70 names. As expected, participants in the goal condition worked longer than did those in the control condition. Furthermore, the data generally supported the hypothesis that individuals with goals would produce more results than their non-goal-directed counterparts. Compared to control participants, however, those with goals did not visit a smaller proportion of non-task-related web sites as predicted. Finally, the anticipated positive correlation was found between Internet self-efficacy and the number of correct search results produced. It is concluded that goal setting may be an effective method for increasing online productivity.
Practical methods of tracking of nonstationary time series applied to real-world data

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Two real world data sets, containing electricity load demands and foreign exchange market prices, are used to test several different methods, ranging from linear models with fixed parameters, to non-linear models which adapt both parameters and model order on-line. Training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. The results of our experiments show that there are advantages to be gained in tracking real world non-stationary data through the use of more complex adaptive models.
Multi-Label Classification via Manipulating Labels

Unlike traditional classification problem, multi-label learning task is to predict a label set with unknown size for an example. While the exponential number of possible label sets challenges the task of multi-label learning. Many approaches by manipulating labels have been proposed. In this paper, we propose a new method via manipulating labels for multi-Label Learning: adding a virtual label to the original label set, appending the label subset selected by mutual information for each pairwise labels to the original feature set, and finally learning a binary classifier for each pairwise labels. Extensive experiments show that, compared with advanced multi-label methods, the proposed method induces models with significantly better performance.
Data-Driven Computation of Contact Dynamics During Two-Point Manipulation of Deformable Objects

This study represents a preliminary step towards data-driven computation of contact dynamics during manipulation of deformable objects at two points of contact. A modeling approach is proposed that characterizes the individual interaction at both points and the mutual effects of the two interactions on each other via a set of parameters. Both global as well as local coordinate systems are tested for encoding the contact mechanics. Artificial neural networks are trained on simulated data to capture the object behavior. A comparison of test data with the output of the trained system reveals a mean squared error percentage between 1% and 3% for simple interactions.
A relevance feedback method in image retrieval by analyzing feedback log file

Content-based image retrieval is one of the most important research areas in multimedia databases. In recent years, it has been more and more popular to use the mechanism of relevance feedback to improve retrieval performance. We propose a novel approach of accelerating the feedback process by analyzing the feedback log file. Experimental results show that our method brings dramatic improvement to retrieval efficiency compared with the traditional relevance feedback method.
Improving Survey Response Rates in Online Panels

Identifying ways to efficiently maximize the response rate to surveys is important in survey-based research. However, evidence on the response rate effect of donation incentives and especially altruistic and egotistic text appeal interventions is sparse and ambiguous. Via a randomized survey experiment among 6,162 members of an online survey panel, this article shows how low-cost incentives and cost-free text appeal interventions may affect the survey response rate in online panels. The experimental treatments comprise (a) a cash prize lottery incentive, (b) two donation incentives that promise a monetary donation to a good cause in return for survey response, (c) an egotistic text appeal, and (d) an altruistic text appeal. Relative to a control group, we find higher response rates among recipients of the egotistic text appeal and the lottery incentive. Donation incentives yield lower response rates.
SECURITY THREATS ON CLOUD COMPUTING VULNERABILITIES

Clouds provide a powerful computing platform that enables individuals and organizations to perform variety levels of tasks such as: use of online storage space, adoption of business applications, development of customized computer software, and creation of a “realistic” network environment. In previous years, the number of people using cloud services has dramatically increased and lots of data has been stored in cloud computing environments. In the meantime, data breaches to cloud services are also increasing every year due to hackers who are always trying to exploit the security vulnerabilities of the architecture of cloud. In this paper, three cloud service models were compared; cloud security risks and threats were investigated based on the nature of the cloud service models. Real world cloud attacks were included to demonstrate the techniques that hackers used against cloud computing systems. In addition, countermeasures to cloud security breaches are presented.
Fast space vector modulation algorithm for multilevel inverters and its extension for operation of the cascaded h-bridge inverter with non-constant DC sources

A major advancement in the research area of space vector modulation (SVM) in recent years is the introduction of the nearest three vector algorithm. Nonetheless, computational intensity is still a drawback of SVM methods in real time applications, especially in the case of multilevel inverter operation. A very fast and scalable SVM algorithm is introduced in this study, referred to as the fixed coordinate sub-triangle SVM (FCST-SVM) approach. The proposed method permits the determination of switch states and efficient calculation of switch on-time durations for a given N-level inverter topology. Calculations are performed based on a proposed sub-triangle approach with fixed coordinates in the space vector diagram. Avoiding calculating the coordinates of the nearest three voltage vectors is a significant advantage of the proposed algorithm compared with previous SVM approaches applied to multilevel inverters. An example of the application of this method is presented for the multilevel cascaded H-bridge inverter when non-constant isolated DC sources are subjected to random voltage deviations. MATLAB simulation results verify the feasibility of the proposed approach. Experimental results, using a TMS320F2812 digital signal processor, are also presented to verify the effectiveness of the proposed approach in decreasing the computational overhead in comparison with the conventional SVM method. For example, it is found that computational requirements for SVM decrease by a factor of 9.5 compared with the conventional method in the case of a three-phase nine-level cascaded H-bridge inverter.
An Energy Efficient Query Processing Mechanism using Cache Filtering in Cluster-based Wireless Sensor Networks

As following the development of the USN technology, sensor node used in sensor network has capability of quick data process and storage to support efficient network configuration is enabled. In addition, tree-based structure was transformed to cluster in the construction of sensor network. However, query processing based on existing tree structure could be inefficient under the cluster-based network. In this paper, we suggest energy efficie nt query processing mechanism using filtering through data attribute classification in cluster-based sensor network. The suggestion mechanism use advantage of cluster-based network so reduce energy of query processing and designed more intelligent query dissemination. And, we prove excellence of energy efficient side with MATLab.
Experiments using an evolutionary programmed neural network with adaptive boosting for computer aided diagnosis of breast cancer

This paper extend ongoing CAD breast cancer research based on an evolutionary programmed neural network with adaptive boosting using a reduced set of discriminators for the Duke University and University of South Florida (USF) data sets. Early detection of non-palpable lesions by mammography can lead to reduced mortality. The high false positive rate of mammography has motivated CAD research efforts to improve its positive predictive value (PPV). Trials in which the discriminating features were reduced from 16 clinical patient history features to 7 are presented. In order to explore where discrimination improvements can be achieved, we define three approaches. Examining the reduced data sets revealed new patterns of conflicting data, where different outcomes for the same pattern of input features make the data sets inconsistent, contributing to performance degradation. This was confirmed by using decision trees to recursively partition the input space based on data-driven splitting criteria. We then describe modeling challenges posed by the mammography features, which are linguistic categorical variables, well suited to modeling by neuro-fuzzy paradigms. A series of comparative trials using the adaptive neuro-fuzzy inference system (ANFIS) is presented. Guided by these rules, two additional simplification strategies were implemented -reduction of the patient age state set and lesion variable reduction, imparting improved classification performance. At 100% sensitivity, where the clinical imperative of missing no cancer is met, specificity improved by 230% for the EP/AB Hybrid and by 76% for the ANFIS model. These results suggest that the number of benign outcome biopsies for suspicious mass lesions detected by mammography can be effectively reduced, while additionally reducing patient morbidity and overall healthcare cost.
Analysis and Design of a Nonlinear Robust Controller

A nonlinear controller and its design method are proposed. The development of the controller is motivated by fuzzy control. The controller has the advantages of simple structure, rapid computation, zero steady error, few parameters to set, and robustness to structure uncertainties and to parameter variations. Comparing the new controller with the PID controller, we find the performance of the new controller to be better. Its sensitivity to variations of the system is smaller than that of the PID and it requires no prefilter to avoid large overshoot. Digital simulations are performed to verify the capability of the proposed new nonlinear controller.
Research on compound learning algorithm of Bayesian networks structures

Today, there are more mature and relative perfect means of how to learn structures or parameters from completed data and learn parameters of fixed structure from uncompleted data. But it is a more difficult thing that learning structures of Bayesian Networks from uncompleted data. A compound learning algorithm is proposed; it combines the EM algorithm, Monte Carlo sampling algorithm and evolution algorithm together, uses EM algorithm to learn parameters of networks in uncompleted data, then samples the best network, converts the uncompleted data to completed data, and then evolves the structure using evolution algorithm. This algorithm could get over the defect of EM algorithm that frequently gains local maximum. Because data processing is based on posterior networks structures, structures of Bayesian Networks is optimizing and optimizing with evolution computing, the reliability of complementary data is higher. Learning rate is high and performance of this algorithm is good.
On finite-time break-up in three-dimensional unsteady interacting boundary layers

The occurrence of finite-time break-up singularities in nonlinear interactive boundary layers is investigated for three-dimensional unsteady motions, extending previous two-dimensional theory. This is with a view to linking with experimental observations of hairpin vortices and the like in transitional and turbulent boundary layers. Particular cases of three-dimensional interaction are tackled first, in which locally the normalized pressure and displacement remain comparable, two main contexts being for liquid-layer flow at subcritical Froude numbers and sublayer flow over relatively short steep humps. More general interactions are then considered afterwards. Computational and analytical properties are studied, with special interest in the mixed elliptic-hyperbolic type of the partial-differential equation for the local pressure at break-up. The main theoretical and practical consequences are also addressed.
Two-machine lot streaming with attached setup times

Lot streaming is a fundamental production scheduling technique to squeeze manufacturing lead times by splitting a large lot of n identical items into sublots. This article presents a full characterization of optimal solutions for two-stage lot streaming with attached machine setup times to minimize the makespan. An O(n 3) time dynamic programming algorithm is presented for the discrete variant of the problem, in which all sublot sizes need to be integral. Since this running time can be prohibitively long for larger n, the continuous variant is also analyzed and an O(n) time algorithm for its solution is presented. Also, rounding procedures for the optimal continuous solution to obtain an approximate solution for the discrete problem are designed and analyzed. It is shown that a particular class of rounding procedures, using dynamic programming, has a compelling absolute worst-case and empirical performance.
Inheritance based Intelligent Technique Employing Nested-XOR with Recursion for Recognition and Classification of Odors using Multi-Sensor Nose System

Problem statement: Odor originality and fraud is an important issue in today’s world. In addition knowing the original material that an odor belongs to even after being mixed with others is also of vital importance. In addition measuring quality of mixed odors in terms of their affinity and belonging to a specific category or is critical. Approach: Design, testing and analysis of multi-sensor odor system for the objectives of odor recognition, classification and correlation are carried out. Results: Various mixtures are tested and checked for originality through inheritance using the built multi-sensor electronic nose. The testing devices and interpretation using Nested-XOR based algorithm with recursion showed clearly some odors to be related to others or derived from the same source. Conclusion/Recommendations: Clear evidence of ability to trace components of an odor mixture and to determine its originality and quality.
Counting on Serials: Management and Serials Metrics

35 It is hard to disassociate serials metrics from the wider realm of performance indicators and metrics that are employed in modern information services management. So it is worth beginning with a very brief overview. The business of managing modern information services becomes increasingly challenging within a framework of escalating demands, resource constraints and a climate of greater accountability embodied in some cases in ‘league tables’ and prescriptive standards. Within this context the emphasis on an evidence-based approach to managing becomes stronger. There is a growth in the use of an array of quantitative and qualitative measures to support policy determination and strategic development as well as to assess operational effectiveness. Reliable performance and descriptive metrics are important for those who are seeking to understand the way in which organisations are functioning, to take account of what has been achieved, or not achieved, in the past and to plan where they may go in the future. The trend towards greater accountability brings in its wake a feature of evidence-based management that involves going beyond counting what happens – inputs and outputs – to seeking an indication of what is being achieved – outcomes and impact. There is a growth in the application of ‘soft indicators’ to identify the subtle yet important changes that take place at a personal and community level in the wake of cultural, social and learning initiatives, including those centred on information and library services. The digital environment adds new dimensions to the assessment of performance. On the one hand, it holds the promise (not always fulfilled) of making activity more quantifiable and therefore more assessable; on the other hand, it makes the process of deciding far more complex and uncertain, because there are so Dr. J. Eric Davies, Director LISU [Library and Information Statistics Unit], Loughborough University, Loughborough. Leics. LE11 3TU, England. Tel: 01509 223070 Fax: 01509 223072 E-mail: j.e. davies@lboro.ac.uk URL: http://www.lboro.ac.uk/ departments/dis/lisu/jed.html KEY ISSUES
Synergetic Multi-Semantic Multi-Instance Learning for Scene Recognition

In this paper, the problem of scene representation is modeled by simultaneously considering the stimulus-driven and instance-related factors in a probabilistic framework. In this framework, a stimulus-driven component simulates the low-level processes in human vision system using semantic constrain; while a instance-related component simulate the high-level processes to bias the competition of the input features. We interpret the synergetic multi-semantic multi-instance learning on five scene database of LabelMe benchmark, and validate scene classification on the fifteen scene database via the SVM inference with comparison to the state-of-arts methods.
Intelligent grasp planning strategy for robotic hands

Providing robot hands with the intelligence required for effective multifinger grasping is a difficult problem. In particular, a good choice of grasp points on an object to be manipulated is critical in order to achieve dexterous manipulation. A methodology and algorithmic implementation is proposed for the choice of the feasible grasp points on irregular objects. The main thrust of this analysis is the embedding of grasp mechanics - namely intuitive human-like force distribution and computational efficiency - into a practical methodology for intelligent grasp planning. The strategy of grasp reasoning is to plan the grasp so that the fingers will be guaranteed to firmly grasp the object during handling and generate fine motion to perform tasks using the same planned grasp points. We also require the ability to change some of the grasp positions while holding the object firmly. The key to our approach is that the reasoning is based on a solid mathematical model of grasp mechanics - information from the mechanics of differing grasp candidates is automatically included at the grasp strategy stage. Therefore, our grasp has the advantage of being intelligence-based without sacrificing the physics of the grasp. Additionally, our underlying method for grasp mechanics is designed to be simple, computationally efficient, and intuitively natural, and can be easily employed in real time. Thus the grasp chosen is one that feels right, and is also based on solid physical principles, combining the intelligence of the expert with the mechanics needed for precise control.