Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient static scheduling of loops on synchronous multiprocessors

This dissertation investigates efficient compile-time scheduling techniques for exploiting parallelism on synchronous multiprocessors. Synchronous multiprocessors, e.g. Very Long Instruction Word (VLIW) machines, are very effective in utilizing unstructured fine-grained parallelism in programs. The effectiveness of such machines is crucially dependent on the static compile-time analysis and detection of potential parallelism. The first part of the dissertation focuses on scheduling sequential loops on multiprocessors with multiple identical processor units. The problem of determining the maximal initiation rate for the execution of a sequential loop with uniform dependence distances on a synchronous multiprocessor is addressed and cast as an eigenvalue problem in a path algebra. A low-order polynomial algorithm for the determination of the optimal loop initiation rate is developed, and a schedule that exploits fine-grained parallelism and achieves the optimal initiation rate is developed under an idealized unbounded processor model. Next, the concepts developed above are extended to deal with perfectly-nested loops with uniform dependences. A strategy is developed to identify both the loop level and fine-grained expression level parallelism in nested loops, and to efficiently schedule such loops on synchronous multiprocessors. Loop scheduling techniques such as Do-Across, Wavefront scheduling, and fine-grained scheduling techniques such as loop unfolding are shown to be derivable within the presented framework.
Performance evaluation of programming models for SMP‐based clusters

Abstract Recently, computing clusters based on shared‐memory multiprocessors (SMP's) is becoming popular for high performance computing (HPC) applications. With the recent prevalence of CPU's, which are small‐scale SMP's themselves, multi‐core CPU's SMP clusters will become increasingly popular in the near future. SMP clusters have characteristics of both SMP's and MPP's. Therefore, developing parallel programs which can efficiently exploits characteristics of both SMP and MPP in SMP clusters is a challenging task. Standard parallel programming models such as MPI, OpenMP, or Hybrid (a combination of the two former models) are commonly used for SMP clusters. Depending on the characteristics of applications, however, some programming models are better than others. To identify and select a suitable programming model for an application on SMP clusters needs a quantity of analysis of the application behavior and its performance. In this paper, we conduct experimental studies to evaluate the benefits and limits of MPI and OpenMP on three SMP‐based systems using standard HPC applications parallelized using MPI, OpenMP, and Hybrid model. The performance results and final analysis may lead to an optimal programming model for the applications.
Pose-robust face recognition using shape-adapted texture features

Unconstrained environments with variable ambient illumination and changes of head pose are still challenging for many face recognition systems. To recognize a person independent of pose, we first fit an active appearance model to a given facial image. Shape information is used to transform the face into a pose-normalized representation. We decompose the transformed face into local regions and extract texture features from these not necessarily rectangular regions using a shape-adapted discrete cosine transform. We show that these features contain sufficient discriminative information to recognize persons across changes in pose. Furthermore, our experimental results show a significant improvement in face recognition performance on faces with pose variations when compared with a block-DCT based feature extraction technique in an access control scenario.
Analysis of gradient descent learning algorithms for multilayer feedforward neural networks

The authors investigate certain dynamical properties of gradient-type learning algorithms as they apply to multilayer feedforward neural networks. These properties are more related to the multilayer structure of the net than to the particular output units at the nodes. The analysis is carried out on a simplified deterministic gradient algorithm in two steps. First, a global analysis of an associated ordinary differential equation (ODE) is performed using LaSalle's theory. Then, a local analysis of the gradient algorithm is performed by linearizing along a nominal ODE trajectory. A simple numerical example is given to illustrate the analysis. <<ETX>>
Browsing through rapid-fire imaging: requirements and industry initiatives

It is well established that humans possess cognitive abilities to process images extremely rapidly. At GTE Laboratories we have been experimenting with Web-based browsing interfaces that take advantage of this human facility. We have prototyped a number of browsing applications in different domains that offer the advantages of high interactivity and visual engagement. Our hypothesis, confirmed by user evaluations and a pilot experiment, is that many users will be drawn to interfaces that provide rapid presentation of images for browsing tasks in many contexts, among them online shopping, multimedia title selection, and people directories. In this paper we present our application prototypes using a system called PolyNav and discuss the imaging requirements for applications like these. We also raise the suggestion that if the Web industry at large standardized on an XML for meta-content that included images, then the possibility exist that rapid-fire image browsing could become a standard part of the Web experience for content selection in a variety of domains.
Research on the BOVW Model of Infrared Video Based on the Motion History

Aiming at the feature representation problem of infrared video, this paper presents an approach for bag of visual words model by considering the spatial and the temporal relations among the visual features. In this approach, motion mask image was generated based on motion history image, optical flow vector is mapping to visual code book. By using motion history value and motion energy value calculating the weighting coefficient, the BOVW was constructed based on angle voting. Experiments have demonstrated improved performances on motion feature representation in the infrared video.
Visual Data in Organizational Research

This paper urges organizational researchers to collect data from subjects in the form of pictures, diagrams, computer graphics, and other visual representations. Drawing on theoretical and empirical work in cognitive psychology, neurophysiology, linguistics, and artificial intelligence, it presents a rationale for collecting visual data, provides examples, and suggests research questions and settings where visual data may be preferable to verbal data.
Lower bound of the filter ratio of area-maximizing points

In a spatial database and geographic information system (GIS), it is important to use a filter-refinement method to improve the performance of spatial query processing. The key factor to the filter-refinement method is employment of a highly efficient approximation. There are many kinds of established approximations but area-maximizing points (AMP) is suggested as an efficient and optimal point-based approximation with regards to the window query, which is one of the most important operations in a spatial database and GIS. This paper presents a lower bound of the filter ratio of AMP. The lower bound of the filter ratio of an approximation is very significant because it means the worst-case performance of the approximation. We show that the AMP shows the filter ratio greater than or equal to 0.5. This is the property that other approximations do not have.
Crowd self-organization, streaming and short path smoothing

Pedestrians implicitly cooperate by forming lanes inside dense crowd in order to facilitate flow and prevent complete passage blocking. Our aim is to re-enforce this self-organization phenomenon in dense crowd for the purpose of virtual crowd animation and navigation simplification. The mechanism of a flow grid is introduced to measure flow over an area. The flow grid is a perception mechanism of the surrounding area and favors dynamic lane formation (streams). It provides feedback to the navigation algorithm of the avatars, to enable them to choose a route that both meets their goal (wanted direction) and a trajectory that assists in self-organization of the crowd. A very simplified yet fairly effective navigation method suitable for dense crowds is also presented. It demonstrates that self-organization of the avatars can help in simplifying local navigation. The method produces short distance, intermediate positions ahead in time and, as a post-processing step, smoothes them out before the avatar needs to use them.
Compilation of definitions of simulation

Pritsker & Associates, Inc. Over the years he has, he says, been asked many times for a precise definition of simulation, and in response he normally gave those included in his own books. Recently, however, he decided to compare different definitions and compiZed the Zist that follows for the use of his students and associates. He reports that it has indeed turned out to be useful to them and hopes that it will also be useful to our readers. We think it will and invite you to send in your favorite definitions. We’ZZ collect them and at some future time publish an addendum to the present set.
The development of a decision support system for mobile learning: a case study in Taiwan

While mobile learning (m-learning) has considerable potential, most of previous strategies for developing this new approach to education were analysed using the knowledge, experience and judgement of individuals, with the support of statistical software. Although these methods provide systematic steps for the implementation of m-learning strategies, they do not consider the correlations among the key success factors. This study aims to develop a decision support system for m-learning, and design m-learning activities that combine lessons in Chinese rhetoric with an outdoor learning activity at the National University of Tainan, in Taiwan. To verify the feasibility and value of the proposed decision support system for m-learning, the fuzzy analytic hierarchy process was used to analyse the three types of participants in this case study – college students, system developers and instructors from related fields. The evaluation indexes of the success factors of m-learning were used to obtain the opinions of each participant. The relative weight of each evaluation index was calculated, and the entire weight system of the indexes was established, which can then be used as a strategic tool for m-learning development.
Compressed image transmission based on fountain codes

In this paper, we propose a joint source-channel coding (JSCC) scheme for image transmission over wireless channel. In the scheme, fountain codes are integrated into bit-plane coding for channel coding. Compared to traditional erasure codes for error correcting, such as Reed-Solomon codes, fountain codes are rateless and can generate sufficient symbols on the fly. Two schemes, the EEP (Equal Error Protection) scheme and the UEP (Unequal Error Protection) scheme are described in the paper. Furthermore, the UEP scheme performs better than the EEP scheme. The proposed scheme not only can adaptively adjust the length of fountain codes according to channel loss rate but also reconstruct image even on bad channel.
Forecast for Performance Improvement of Graduate Students utilizing Decision Trees Algorithms

— Understudy execution in college courses is of incredible concern to the advanced education where a few components may influence the execution. This paper is an endeavor to apply the data mining methods, especially arrangement, to help in upgrading the nature of the higher instructive framework by assessing understudy information to study the principle characteristics that may influence the understudy execution in courses. The arrangement standard era procedure is taking into account the Decision tree as an arrangement system where the created tenets are contemplated and assessed. A framework that encourages the utilization of the created guidelines is manufactured which permits understudies to foresee the last review in a course under study.
Study on Mobility Management Cost of Mobility Support Protocols

The mobility management cost of mobility support protocols is studied. Firstly a suitable network model is proposed and the signaling overhead brought to network in order to support MHs' (mobile host) mobility using various mobility support protocols is analyzed theoretically. On this base, a numerical simulation method is applied to compare the signaling overhead when only using mobile IP with introducing hierarchical mobility and when applying different micro-mobility protocols such as MIP-RR, CIP and HAWAII. The results show that introducing hierarchical mobility decreases the network signaling overhead observably comparing with only using mobile IP. In different micro-mobility protocols, the route maintenance strategy of deleting old routes explicitly brings less signaling overhead than frequently sending refresh messages periodically, and the route update method of sending route update messages to crossover MRA (mobile routing Agent) brings less signaling overhead than sending messages to GW (gateway).
Specification and runtime workflow support in the ASKALON Grid environment

We describe techniques to support the runtime execution of scientific workflows in the ASKALON Grid environment. We present a formal model and three middleware services that support in combination the effective execution in heterogeneous and dynamic Grid environments: performance prediction, scheduling, and enactment engine. We validate our techniques with concrete experimental results for two real-world applications executed in the Austrian Grid environment.
Mapping of Science by Combined Co-Citation and Word Analysis. I. Structural Aspects

The claim that co-citation analysis is a useful tool to map subject-matter specialties of scientific research in a given period, is examined. A method has been developed using quantitative analysis of content-words related to publications in order to: (1) study coherence of research topics within sets of publications citing clusters, i.e., (part of) the “current work” of a specialty; (2) to study differences in research topics between sets of publications citing different clusters; and (3) to evaluate recall of “current work” publications concerning the specialties identified by co-citation analysis. Empirical support is found for the claim that co-citation analysis identifies indeed subject-matter specialties. However, different clusters may identify the same specialty, and results are far from complete concerning the identified “current work. ” These results are in accordance with the opinion of some experts in the fields. Low recall of co-citation analysis concerning the “current work” of specialties is shown to be related to the way in which researchers build their work on earlier publications: the “missed” publications equally build on very recent earlier work, but are less “consensual” and/or less “attentive” in their referencing practice. Evaluation of national research performance using co-citation analysis appears to be biased by this “incompleteness. ”
Corner detection using Gabor-type filtering

In this paper a new corner detection technique using Gabor filters is proposed. It is suitable for both binary and gray level images with varying backgrounds. This technique is based on a scale interaction model, where the difference of two lowpass filters (with different bandwidths) is utilized to extract the corners, line intersections and line endings in the input image. The filtering is done iteratively until the change in the output is below a certain threshold. This technique does not require tracking the boundary and computing the curvature. The important features of the scheme include simplicity, robustness, speed of processing and availability of simple controls to tune the technique for various computer vision applications.
Evaluation of a readout circuit with ASIC oscillator for acoustic-wave sensor application

This article is concerned with the study of readout electronics for new-generation acoustic-wave sensors with high sensitivity. An integrated circuit programmable- frequency square wave oscillator is used in open-loop configuration and resonant frequency shift is detected using an undersampling analogue-to-digital converter. Simulated and measured results are reported which show that a simple setup with a low resolution frequency sweep provides sufficient accuracy to detect a frequency difference of less than 1 kHz (0.06% deviation from the nominal frequency), suitable for binary screening applications.
Investigation of ANN-GA and Modified Perturb and Observe MPPT Techniques for Photovoltaic System in the Grid Connected Mode

The output characteristics of Photovoltaic (PV) arrays are nonlinear and change with the cell's temperature and solar radiation. Maximum Power Point Tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). This paper presents an integrated offline Genetic Algorithm (GA) and artificial neural network (ANN) to track the solar power optimally based on various operation conditions due to the uncertain climate change. Data are optimized by GA and then these optimum values are used in neural network training. The obtained results show minimal error of MPP, optimal voltage (Vmpp) and superior capability of the suggested method in the MPPT. The simulation results are presented by using Matlab/Simulink and show that ANN-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point; also, this method has well regulated PV output power and it produces extra power rather than Modified Perturb&Observe (MP&O) method for different conditions. Moreover, to control both line voltage and current, a grid side P-Q controller has been applied.
Detection and Protection Against Intrusions on Smart Grid Systems

The wide area monitoring of power systems is implemented at a central control center to coordinate the actions of local controllers. Phasor measurement units (PMUs) are used for the collection of data in real time for the smart grid energy systems. Intrusion detection and cyber security of network are important requirements for maintaining the integrity of wide area monitoring systems. The intrusion detection methods analyze the measurement data to detect any possible cyber attacks on the operation of smart grid systems. In this paper, the model-based and signal-based intrusion detection methods are investigated to detect the presence of malicious data. The chi-square test and discrete wavelet transform (DWT) have been used for anomaly-based detection. The false data injection attack (FDIA) can be detected using measurement residual. If the measurement residual is larger than expected detection threshold, then an alarm is triggered and bad data can be identified. Avoiding such alarms in the residual test is referred to as stealth attack. There are two protection strategies for stealth attack: (1) Select a subset of meters to be protected from the attacker (2) Place secure phasor measurement units in the power grid. An IEEE 14-bus system is simulated using real time digital simulator (RTDS) hardware platform for implementing attack and detection schemes.
Toward a theory of semantic representation

Abstract We present an account of semantic representation that focuses on distinct types of information from which word meanings can be learned. In particular, we argue that there are at least two major types of information from which we learn word meanings. The first is what we call experiential information. This is data derived both from our sensory-motor interactions with the outside world, as well as from our experience of own inner states, particularly our emotions. The second type of information is language-based. In particular, it is derived from the general linguistic context in which words appear. The paper spells out this proposal, summarizes research supporting this view and presents new predictions emerging from this framework.
Algorithms and Protocols for Wireless, Mobile Ad Hoc Networks

Advances in wireless networking and mobile communication technologies, coupled with the proliferation of portable computers, have led to development efforts for wireless and mobile ad hoc networks. This book focuses on several aspects of wireless ad hoc networks, particularly algorithmic methods and distributed computing with mobility and computation capabilities. It covers everything you need to build a foundation for the design of future mobile ad hoc networks and is complemented with a set of exercises that challenge you to test your understanding of the material.
Parallel K-Means Algorithm for Shared Memory Multiprocessors

Clustering is the task of assigning a set of instances into groups in such a way that is dissimilarity of instances within each group is minimized. Clustering is widely used in several areas such as data mining, pattern recognition, machine learning, image processing, computer vision and etc. K-means is a popular clustering algorithm which partitions instances into a fixed number clusters in an iterative fashion. Although k-means is considered to be a poor clustering algorithm in terms of result quality, due to its simplicity, speed on practical applications, and iterative nature it is selected as one of the top 10 algorithms in data mining [1]. Parallelization of k-means is also studied during the last 2 decades. Most of these work concentrate on shared-nothing architectures. With the advent of current technological advances on GPU technology, implementation of the k-means algorithm on shared memory architectures recently start to attract some attention. However, to the best of our knowledge, no in-depth analysis on the performance of k-means on shared memory multiprocessors is done in the literature. In this work, our aim is to fill this gap by providing theoretical analysis on the performance of k-means algorithm and presenting extensive tests on a shared memory architecture.
OpenCL Parallel Blocked Approach for Solving All Pairs Shortest Path Problem on GPU

Pairs Shortest Path Problem (APSP) finds a large number of practical applications in real world. This paper presents a blocked parallel approach for APSP using an open standard framework OpenCL, which provides development environment for utilizing heterogeneous computing elements of computer system and to take advantage of massive parallel capabilities of multi-core processors such as graphics processing unit (GPU) and CPU. This blocked parallel approach exploits the local shared memory of GPU, thereby enhancing the overall performance. The proposed solution is for directed and dense graphs with no negative cycles and is based on blocked Floyd Warshall (FW) and Kleene"s algorithm. Like Floyd Warshall this approach is also in-place and therefore requires no extra memory.