Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Physics-based Methods for Group and Crowd Analysis in Computer Vision

Crowd analysis is a popular topic in computer vision, with important applications to video surveillance, social media analysis, and multimedia retrieval, to name just a few areas. In this paper, we review some of the physics-based methods for group and crowd analysis in computer vision. In particular, we examine approaches for physics-based analysis of groups, crowds, and the simulation of crowds. The purpose of this review is to categorize and delineate the various physics-based and physics-inspired approaches that have been applied to the examination of groups and crowds in video.
Research methods for eliciting e-research user requirements

e-Research is a relatively new and expanding means of undertaking multi-disciplinary on-line research. As such, the needs and requirements of the e-Research community are not yet fully understood and it is evident that it needs further investigation and study. This paper aims to provide basic guidance on the research methods that will be useful in order to gather information from the users of multi-disciplinary e-Research-based projects. The paper discusses quantitative and qualitative research methods with examples that have been drawn from social and computer science. We show that there are many ways of gaining information from users, but when used appropriately qualitative methods give more depth of information, whereas quantitative methods are scalable to the whole community.
Effect of Network Topology on the Secrecy Capacity in Multiple-access Relay Channel

A design based on network coding is presented to improve the secrecy capacity of the cooperative communication system where a amplify-and-forward relaying is employed. The system consists of two source nodes, one destination node and one eavesdropper. For a single session between one source and the destination, we derive the approximate formula of secrecy capacity in the high SNR regime. Through detailed computer simulation, we evaluate the effect of node’s location on the secrecy capacity in the multiple-access relay channel.
Translation, postcolonialism and cultural studies : Traduction et post-colonialisme en Inde

Despite the intensity of its current interest in postcoloniality, Anglo-American culture studies remains impenitently monolingual. Few cultural-studies theorists investigate the specifically linguistic question of postcolonial cultural transactions, preferring to treat translation as a metaphor. At the same time, translation studies has been slow to assimilate the complexities of culture in the postcolonial world. This article tries to show how the linguistic sensitivities of translation studies are essential to cultural studies in India, while, inversely, sensitivity to questions of power in cultural translation is needed in translation studies. The work of Gayatri Spivak is mobilized to this end
Selecting an optimal neural network

A relationship between optimal network design and statistical model identification is described. A derivative of Akaike's information criterion (AIC) is given. This modification yields an information statistic which can be used to select a best network for binary classification problems objectively. The technique can be extended to problems with an arbitrary number of classes. <<ETX>>
Improvisational interfaces for visualization construction and scalar function sketching

Presentations are an important aspect of daily communication in most organizations. As sketch, and gesture-capable interfaces such as tablets and smart boards become increasingly common, they open up new possibilities for interacting with presentations. This thesis explores two new interface prototypes to improve upon otherwise tedious presentation needs such as demonstrating models based on scalar functions, and visualization of data. We combine a spreadsheet style interface with sketching of scalar mathematical functions to develop and demonstrate intuitive mathematical models without the need of coding or complex equations. We also explore sketch and gesture based creation of data visualizations.
Research on Utilizing Emulab for Malware Analysis

Virtual environment is widely used for analyzing malware which is increasing very rapidly. However, knowing this trend, hackers are adopting virtual environment detection techniques for malware to kill itself or stop malicious behaviors when detecting virtual environments. Various research is going on in order to thwart any efforts to utilize anti-virtualization techniques, but until now several techniques can evade most of well known virtual environments, making malware analysis very difficult. Emulab developed by Utah University assigns real systems and networks as researchers want in realtime. This research seeks how to use Emulab for malware analysis.
Writing an operating system with a strictly typed assembly language

The recent advances in static program analysis, especially in type theory, have been widely accepted as a fundamental technology for software development. For example, many real-world applications are written in strictlytyped programming languages (e.g., Java, C# and Objective Caml). One strength of the strictly-typed programming languages is that programs written in them never go wrong, that is, the programs cause no unexpected errors, thanks to type safety ensured by their type checkers. However, there is one kind of software that does not fully utilize the type theory: operating systems (OS). Traditional and current OS kernels have been written in the C programming language (weakly-typed unsafe programming language) and untyped assembly languages. Therefore, it is very hard to ensure and verify safety of the OS kernels. One of the reasons why OS kernels have not been written in strictlytyped programming languages is that it has been believed that it is hard or impossible because strictly-typed programming languages do not seem to provide means to implement important mechanisms of OS kernels: memory management and multi-thread management mechanisms. This thesis breaks the mistaken believes about types and operating systems by showing how to write an OS kernel in a strictly-typed programming language. More specifically, this thesis proposes a new strictly-typed assembly language which is flexible and expressive enough to implement OS kernels. The key point of the language is that its type system supports variable-length arrays, explicit alias tracking and integer constraints between variables. Therefore, practical memory management (i.e., malloc/free) and multi-thread management mechanisms can be implemented in the language and fully type-checked. This thesis also introduces a prototype OS kernel implementation written in the language. The safety ensured by the type checker of the language is memory safety and control-flow safety. The memory safety means that a program accesses only memory which the program is permitted to access, while the control-
ProtoGANist: Protocol Reverse Engineering using Generative Adversarial Networks

Many reported vulnerabilities are related to the way that a system accepts, processes, and interprets protocol packets and the information contained therein. Adversariescan trigger these vulnerabilities by sending specially crafted packets to the system. Typical solutions to this problem include generating packets in accordance with the protocol format, sending them to the system, and observing the resulting behavior on the system. However, these solutions fall apart when dealing with a black box system and black box protocols, because it is unclear how to generaterealistic protocol packets. We present ProtoGANist, a system to model unknown protocol message formats and produce messages similar to the underlying formatusing generative machine learning models. Given sample messages from a black-box protocol and a black-box system that uses the protocol, our goal is to learn to produce randomized protocol-compliant messages. The difficulty of this task lies in the complexity of the protocol message format. Message ? fields' values, lengths, and overallstructure may be defi? ned by complex functions that depend on other ? fields. These dependencies are ? difficult for existing tools to capture, primarily because they may be a result of several operations performed on the value or length of many ? fields, such as in checksums. Generative Adversarial Networks (GANs) have been shown to have the ability to learn to generate samples that are similar to the data given to them. GANs traditionally have been used in image processing to create generative models of images. We leverage this capability in a novel way for the purposes of learning the message format of an unknown protocol. Ground-truth sample messages of the unknown protocol are provided to the GAN system. We show that ProtoGANist isable to identify and learn about complex message format features. We demonstrate that this feature of ProtoGANist is able to outperform other state-of the-art tools in this manner with a separate testing system. This testing system is able to produce protocols with different characteristics to test the complexities that may exist in protocol message formats.
Experimental issues of finite time compensation of dynamic friction for robots

Compensation of dynamic friction draws the attention in the high precision research community since the seminal paper of Panteley, which employs the model of Canudas. Later, the Canudas model was further studied to uncover some undesirable dynamical properties of the nature of dynamic friction, which this model captures very well, and those properties have known some time ago by the tribology research community. However, it is precisely the ability to reproduce hard nonlinearities and presliding regime at very small displacements that makes this very simple model a viable choice for engineers dealing with friction. Altogether, this control scheme allows to compensate asymptotically hard nonlinearities due to friction at several velocity regimes. However, the asymptotic behavior of this scheme may be unsuitable for fast precision tasks, wherein bidirectional motion is involved. Thus, ideally, finite time convergence instead of asymptotic convergence is a better control choice. In this paper, the experimental verification of an adaptive second order sliding mode controller for robust compensation of dynamic friction in finite time is presented. All system parameters are considered unknown. The closed-loop system exhibits a well-posed terminal attractors with chattering-free controller, and experiments on a planar robot actuated by direct-drive motors are presented. Further implication of this technique are analyzed and discussed.
Analytical Study of Various Information Retrieval Models Based on Mathematical Approaches

Information retrieval involves finding most relevant information within a large amount of documents and provides various forms of contents for people who search information. The user information need should be presented as a query. Finding relevant texts through the user's query is difficult by growing texts in internet. A model which is used to find the similarities between user's query and available information is the first step of designing information retrieval systems. Several models were presented during decades. In this paper, we carefully study information retrieval models and discuss about advantages and disadvantages of each model. We also classify these models into three main classes based on mathematical rules: set theoretic model, algebraic model and probabilistic model. Each class has some sub classes which are carefully studied . Finally we propose some metrics to evaluate these models and compare them with each other.
On Reducing Broadcast Transmission Cost and Redundancy in Ad Hoc Wireless Networks Using Directional Antennas

Using directional antennas to conserve bandwidth and energy consumption in ad hoc wireless networks has attracted much attention of the research community in recent years. However, limited research has focused on applying directional antennas to broadcasting. We devise a link reduction (LR) based broadcasting protocol for ad hoc wireless networks using directional antennas. LR outperforms most existing omnidirectional and directional broadcasting schemes in the sense that its normalized transmission cost and redundancy are significantly reduced, and consequently it is more bandwidth and energy- efficient. Based on 2-hop neighborhood information, LR relies on no location or angle-of-arrival (AOA) information. LR is a localized protocol and it achieves full delivery in ideal networks. Simulation is conducted in ideal networks where packet collision, channel contention, or node mobility is avoided.
Design and Implementation of μ-Webpage based on QR Code

QR(Quick Response) Code has been developed to provide greater storage capacity and more functionality compared to 1D bar codes. With the emergence of increasingly mobile devices equipped with cameras such as smart-phones and tablets, QR codes have become very popular and more important in mobile businesses. Typically, most QR codes are used as a URL link for redirecting users to webpages. However, the URL based QR codes are required to be connected over the internet and to be run a server. This can incur unnecessary traffics in the Internet. Furthermore, it is not suitable for the country lagging behind others in its network infrastructure. In this paper, we propose a server-less -webpage to provide mobile web services and be optimized for the capabilities and limitations of QR Code. We have implemented the -webpage in Android, and the results showed that the proposed mechanism can provide web-services without requiring extra servers or incurring mobile traffic data compared to the URL-based QR Codes.
POINTNET FOR THE AUTOMATIC CLASSIFICATION OF AERIAL POINT CLOUDS

Abstract. During the last couple of years, there has been an increased interest to develop new deep learning networks specifically for processing 3D point cloud data. In that context, this work intends to expand the applicability of one of these networks, PointNet, from the semantic segmentation of indoor scenes, to outdoor point clouds acquired with Airborne Laser Scanning (ALS) systems. Our goal is to of assist the classification of future iterations of a national wide dataset such as the Actueel Hoogtebestand Nederland (AHN), using a classification model trained with a previous iteration. First, a simple application such as ground classification is proposed in order to prove the capabilities of the proposed deep learning architecture to perform an efficient point-wise classification with aerial point clouds. Then, two different models based on PointNet are defined to classify the most relevant elements in the case study data: Ground, vegetation and buildings. While the model for ground classification performs with a F-score metric above 96%, motivating the second part of the work, the overall accuracy of the remaining models is around 87%, showing consistency across different versions of AHN but with improvable false positive and false negative rates. Therefore, this work concludes that the proposed classification of future AHN iterations is feasible but needs more experimentation.
High performance and scalable mpi intra-node communication middleware for multi-core clusters

Cluster of workstations is one of the most popular architectures in high performance computing, thanks to its cost-to-performance effectiveness. As multi-core technologies are becoming mainstream, more and more clusters are deploying multicore processors as the build unit. In the latest Top500 supercomputer list published in November 2008, about 85% of the sites use multi-core processors from Intel and AMD. Message Passing Interface (MPI) is one of the most popular programming models for cluster computing. With increased deployment of multi-core systems in clusters, it is expected that considerable communication will take place within a node. This suggests that MPI intra-node communication is going to play a key role in the overall application performance. This dissertation presents novel MPI intra-node communication designs, including user level shared memory based approach, kernel assisted direct copy approach, and efficient multi-core aware hybrid approach. The user level shared memory based approach is portable across operating systems and platforms. The processes copy messages into and from a shared memory area for communication. The shared buffers are organized in a way such that it is efficient in cache utilization and memory usage. The kernel assisted direct copy approach takes help from the operating system kernel and directly copies message from one process to another so that it only needs one copy and improves performance from the shared memory based approach. In this approach, the memory copy can be either CPU based or DMA based. This dissertation explores both directions and for DMA based memory copy, we take advantage of novel mechanism such as I/OAT to achieve better performance and computation and communication overlap. To optimize performance on multicore systems, we efficiently combine the shared memory approach and the kernel assisted direct copy approach and propose a topology-aware and skew-aware hybrid approach. The dissertation also presents comprehensive performance evaluation and analysis of the approaches on contemporary multi-core systems such as Intel Clovertown cluster and AMD Barcelona cluster, both of which are quad-core processors based systems. Software developed as a part of this dissertation is available in MVAPICH and MVAPICH2, which are popular open-source implementations of MPI-1 and MPI-2 libraries over InfiniBand and other RDMA-enabled networks and are used by several hundred top computing sites all around the world.
State estimation of the Switched Reluctance Motor MFR 132.5 with MHE and UKF Estimators

The measure of the instant angular position of the rotor is integral part of the control of Switching Reluctance Motor drivers. The direct measurement of this variable adds complexity and cost to the system and it doesn't give good results sometimes. This has motivated the development of estimators for the rotor angular position of the SRM. In this paper are presented a Moving Horizon Estimator and an Unscented Kalman Filter for estimating the speed and rotor angular position of the SRM MFR 132.5. Both estimators are based on a grey box model of the motor, also presented in this paper. It was carried out the simulation on PC of the designed estimators for estimating the speed and the angular position of the rotor under different motor operation conditions and considering an error in the initial estimation of the angular position and noisy measurements.
A Unified Analysis of English Passive Voice and Aspects

English passive voice and aspects are generated in the same process and have the same D-structure. In a passive sentence, progressive sentence, and perfect sentence, T (tense) takes a VP headed by be or have, and be or have takes a Participle Phrase (PartP), and –en or –ingtakes a VP as its complement. Be or have in the upper VP merges with T, and the verb in the lower VP merges with Part. The specifier or complement of the lower VP moves cyclicly through the specifier of Part and the specifier of the upper verb to occupy the subject position. The single theory on the production of passive, progressive, and perfect sentences observes the principle of economy of derivation and, therefore, contributes to the explanation of the relative easiness of a child in acquiring his or her mother tongue.
Auto Encoder Feature Learning with Utilization of Local Spatial Information and Data Distribution for Classification of PolSAR Image

The distribution of data plays a key role in the designing of a machine learning model. Therefore, this paper proposes a novel auto encoder network based on the distribution of polarimetric synthetic aperture radar (PolSAR) data matrix. Designed specifically for PolSAR data matrix, the proposed mixture auto encoder (MAE) feature learning method defines data error term in the loss function according to the data distribution. Instead of the pixel itself, all pixels in the neighborhood are used as input to train the proposed MAE. Then, a corresponding classification network is also given by discarding the decoder process of the proposed MAE and connecting with a Softmax classifier. The MAE is trained using the unlabeled data, while the training process of the classification network is completed with the help of a small number of labeled pixels. In view of the phenomenon of misclassification in the predicted result image, two post-processing steps acting on local spatial are also given, which accomplished by the proposed two filters. Extensive experiments by four methods were made over three real PolSAR images including the proposed classification network. The experimental results show that introducing data distribution into the auto encoder network leads to an average 4% improvement in overall accuracy for three PolSAR images. Moreover, the post-processing steps with the proposed filters bring a new level of discrimination on the classification performance of PolSAR images.
Analysis of the impact of intra-die variance on clock skew

In this work, we analyze the impact of local process variations on the clock skew of VLSI circuits designed in deep sub-micron technologies. As the size of active repeaters decreases, the utilization of dense buffering schemes, up to complete replacement of metal wiring with active devices, has been proposed in order to realize efficient and noise-immune clock distribution networks. However, local variance of MOSFET electrical parameters, such as V/sub T/ and I/sub DSS/, increases with scaling of device dimensions, thus causing large intra-die variability of the timing properties of clock buffers. As a consequence, we expect local mismatch to be a significant source of clock skew in deep sub-micron technologies. In order to accurately verify this assumption, we applied advanced statistical simulation techniques and accurate mismatch characterization data to the statistical simulation of relatively small clock distribution networks. The comparison with Monte Carlo simulations performed by neglecting the mismatch effect confirmed that it is necessary to account for local device variations in the design and sizing of the clock distribution network.
Measurement of Web Usability: An Approach

This paper is about usability of technology available in our lives. How easy it is to use the instruments available to us, how easily are we able to do tasks by using the gadgets available to us. It discusses the benefits of usability and discusses how to improve usability. This paper also has comparisons of the websites of two technology giants, Apple and Samsung. It discusses about each and every aspect of their sites and provides reviews about the ease of usability and the user experience. This paper also discusses the ways to make web more usable and the tools to make it more user friendly and easy to use.
Implementing density functional theory (DFT) methods on many-core GPGPU accelerators

Density Functional Theory (DFT) is one of the most widely used quantum mechanical methods for calculations of the electronic structure of molecules and surfaces, which achieves an excellent balance of accuracy and computational cost. However, for large molecular systems with few hundred atoms, the computational costs are become very high. Therefore, there is a fast growing demand for much more efficient implementations to utilize DFT for macro molecules. General Purpose Graphics Processors (GPUs) are highly parallel, multi-threaded, many-core processors with tremendous computational capability, which out-paces CPUs in terms of floating-point performance. They are particularly focused for computation intensive and highly data-parallel computations. This thesis will introduce the scope of fine grained parallelism with highly data-parallel GPU implementations of several algorithmic parts of DFT. Furthermore, experimental results and benchmarks will be presented in comparison with a current state of art DFT implementation (Molpro).