Miracle-dz/UniFAR-FTUs huggingface dataset:
Azimuth Resampling Processing for Highly Squinted Synthetic Aperture Radar Imaging With Several Modes

The linear range walk yields a significant range-azimuth coupling effect in a highly squinted synthetic aperture radar (SAR). Although the linear range walk correction (LRWC) technique can effectively mitigate such coupling effect, it causes azimuth variation in the resulting signal and, as such, the so-called “azimuth-shift invariance” property becomes invalid. In order to eliminate the azimuth variation, a new spectrum processing approach based on azimuth resampling is proposed in this paper. After performing the LRWC, the azimuth resampling is carried out in the 2-D frequency domain and transforms the signal spectrum to be equivalent to that of a broadside SAR. For squinted beamsteering SAR (BS-SAR), e.g., spotlight SAR, sliding spotlight SAR, and Terrain Observation by Progressive Scans SAR, the azimuth resampling is combined with the azimuth signal reconstruction algorithm. As a result, both the azimuth variation, which is induced by the LRWC, and the aliasing, which is caused by antenna beam steering, can be avoided. Therefore, after the azimuth resampling, the squinted SAR data can be focused by exploiting a conventional broadside SAR imaging algorithm. An analysis of the motion error for airborne SAR data processing is also provided. Simulation and real data results show the effectiveness of the proposed algorithm.
Echinacea—A Source of Potent Antivirals for Respiratory Virus Infections

Extracts of Echinacea species have been used traditionally in North America for the control of symptoms of colds, influenza, and other diseases, and some of them have become very popular as “herbal medicines”. Recent studies have revealed that preparations derived from certain species and plant parts, but not all of them, possess potent antiviral activities, at non-cytotoxic concentrations, particularly against membrane-containing viruses. Thus all strains of human and avian influenza viruses tested (including a Tamiflu-resistant strain), as well as herpes simplex virus, respiratory syncytial virus, and rhinoviruses, were very sensitive to a standardized Echinacea purpurea preparation. In mechanistic studies the influenza virus-specific hemagglutinin and neuraminidase were inhibited. In addition some extracts displayed anti-inflammatory activity in virus-infected cells, and numerous other effects on the expression of cellular genes. Multiple components, either discrete compounds or mixtures, appeared to be responsible for the various antiviral activities.
The Approximability of Some NP-hard Problems Thesis proposal

An α-approximation algorithm is an algorithm guaranteed to output a solution that is within an α ratio of the optimal solution. We are interested in the following question: Given an NP-hard optimization problem, what is the best approximation guarantee that any polynomial time algorithm could achieve? We mostly focus on studying the approximability of two classes of NP-hard problems: Constraint Satisfaction Problems (CSPs) and Computational Learning Problems. Our research in the field of CSPs is to show that certain Semidefinite Programming (SDP) algorithms are the optimal polynomial time approximation algorithm; our work in the learning area is to prove that tasks are inherently hard; i.e., there is no betterthan-trivial algorithm for the problems. We have shown some preliminary results on the approximability of several problems from these two classes including Max-Cut, Satisfiable 3-CSPs and agnostic learning of monomials.
Shading Curves: Vector‐Based Drawing With Explicit Gradient Control

A challenge in vector graphics is to define primitives that offer flexible manipulation of colour gradients. We propose a new primitive, called a shading curve, that supports explicit and local gradient control. This is achieved by associating shading profiles to each side of the curve. These shading profiles, which can be manually manipulated, represent the colour gradient out from their associated curves. Such explicit and local gradient control is challenging to achieve via the diffusion curve process, introduced in 2008, because it offers only implicit control of the colour gradient. We resolve this problem by using subdivision surfaces that are constructed from shading curves and their shading profiles.
The learning of a class of n-dimensional Boolean functions with a two input perceptron using the boosting algorithm

In this paper we present a particular implementation of the Boosting algorithm: its application to the learning of a class of boolean functions. The Boosting algorithm is that proposed by Schapire and Freund, denominated Boosting by sub-sampling. The goal of concept learning, according to the PAC weak model, is to generate a description of another function, called the hypothesis, which is close to the concept, by using a set of examples. The hypotheses which derives from weak learning is Boosted to provide a better hypothesis. Its application concerns the learning of a particular class, the member of the class of boolean functions. First, a weak learning algorithm, trying to learn a class concept with two input Perceptron, was established, and Boosting then applied to improve its learning capacity.
AraTRM: Attack Resistible Ant-based Trust and Reputation Model

Trust and reputation models play an important part in ensuring the security of routing or data forwarding in networks, where the service consumers have no or little knowledge of the service providers. Trust and reputation models generate an accurate assessment for each identity in networks to provide enough information about the service provider and about how good the services offered. However the performance of a trust and reputation model may decline in potentially adversarial environments. In this paper, firstly, we listed a number of security threats applicable in the field of trust and reputation management. Secondly, we proposed statistical anomalies detection algorithms to cope with these threats. Finally, based on ant colony theory we designed a trust and reputation model, named AraTRM, and integrated these algorithms into AraTRM to evaluate the effectiveness of our proposed anomalies detection algorithms. Experimental results have shown that the proposed model is robust in adversarial environments and achieves high accuracy as well.
Emotional Agents Outreach: An Undergraduate Research project

— Emotions are psychological reactions that develop as a consequence of certain cognitions and interpretations. In this research we will introduce the OCC (Ortony, Clore, and Collins) theory as our model for understanding emotions. The use of advanced emotional agents has been used on younger kids in a form of video games and animated movies. But the use of this techonology in the teaching learning environment has not yet been popular. So, in our research we will introduce the use of agents in teaching learning environments in a way they would attract and inspire younger kids to be more interested in studying. We will be using Scratch, a programming language used to create agents in a simpler way, as our tool. After this research, with further modiﬁcation and studies, we will be able to get an updated educational system which involves emotional agents that will help students attain their intended success.
Clustering Web Search Results Based on Interactive Suffix Tree Algorithm

Clustering is an effective way to organize Web search results, which allows users to navigate into relevant documents quickly. Traditional clustering techniques are inadequate to Chinese search results and do not generated clusters with highly readable names. In this paper, we propose a new method to clustering Web search results which is based on interactive suffix tree algorithm (ISTC). This method uses phrase extracted from the snippets as characteristics of clustering. In the course of interaction with users, it only returns cluster label to users in the first tier. When users want to make further interaction, users can select a document which they are interested in for the second clustering instead of the traditional recursive clustering. ISTC can also be applied to Chinese and English information processing which avoids the recursive algorithm for achieving linear time complexity and improving the efficiency of search engine. Experimental results verify our methodpsilas feasibility and effectiveness.
Day-Ahead Self-Scheduling of Thermal Generator in Competitive Electricity Market Using Hybrid PSO

This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead self- scheduling for thermal power producer in competitive electricity market. The objective functions considered to model the self- scheduling problem are: 1) to maximize the profit from selling energy in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting bi- objective optimization problem which has both binary and continuous optimization variables considered as constrained mixed integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a day- ahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the day- ahead LMPs. The effect of risk is explicitly modeled by taking into account the estimated variance of the day-ahead LMPs.
A multiple kernel convolution score method for bin picking of plastic packed object

In this paper, an object detection and localization method for bin picking of plastic wrapped objects is described. Since such objects are deformable and have non-Lambertian surfaces, it is difficult to apply conventional feature point approaches or edge based template matching. To solve this problem, we propose a new method which is called “KCS (kernel convolution score)”. It measures the total score of convolution between local image and multiple kernels which are generated according to the characteristics of the target object. The local image having maximum score is considered as the most promising object for picking. In the last part of this paper, the limitation of existing edge and template matching in the given problem is discussed with the experimental results. Performance evaluation shows that the proposed method could localize the object which is appropriate for picking up with 91.67% success rate.
Attribute-Based Signatures with Efficient Revocation

Based signatures (ABS for short) allow an entity to sign messages with a fine-grained control over identity information. The signature attests not to the identity of the individual who endorsed a message, but instead to a claim regarding the attributes he/she holds. ABS has been well investigated since its introduction but little has been done on the revocation in ABS. In this paper, we divide ABS revocation as fine-grained attribute-revocation and coarse-grained user-revocation. The latter is the focus of this paper, and we present a concrete design-to address the issue of coarse-grained user-revocation in ABS without the need of any other third parties.
Lumbar Degenerative Disc Disease: Current and Future Concepts of Diagnosis and Management

Low back pain as a result of degenerative disc disease imparts a large socioeconomic impact on the health care system. Traditional concepts for treatment of lumbar disc degeneration have aimed at symptomatic relief by limiting motion in the lumbar spine, but novel treatment strategies involving stem cells, growth factors, and gene therapy have the theoretical potential to prevent, slow, or even reverse disc degeneration. Understanding the pathophysiological basis of disc degeneration is essential for the development of treatment strategies that target the underlying mechanisms of disc degeneration rather than the downstream symptom of pain. Such strategies ideally aim to induce disc regeneration or to replace the degenerated disc. However, at present, treatment options for degenerative disc disease remain suboptimal, and development and outcomes of novel treatment options currently have to be considered unpredictable.
J an 2 00 6 WAVE ENERGY LOCALIZATION BY SELF-FOCUSING IN LARGE MOLECULAR STRUCTURES : A DAMPED STOCHASTIC DISCRETE NONLINEAR SCHRÖDINGER EQUATION MODEL

Wave self-focusing in molecular systems subject to thermal effects, such as thin molecular films and long biomolecules, can be modeled by stochastic versions of the Discrete Self-Trapping equation of Eilbeck, Lomdahl and Scott, and this can be approximated by continuum limits in the form of stochastic nonlinear Schrödinger equations. Previous studies directed at the SNLS approximations have indicated that the self-focusing of wave energy to highly localized states can be inhibited by phase noise (modeling thermal effects) and can be restored by phase damping (modeling heat radiation). Here it is shown that the continuum limit is probably ill-posed in the presence of spatially uncorrelated noise, at least with little or no damping, so that discrete models need to be addressed directly. Also, as has been noted by other authors, omission of damping produces highly unphysical results. Numerical results are presented here for the first time for the discrete models including the highly nonlinear damping term, and new numerical methods are introduced for this purpose. Previous conjectures are in general confirmed, and the damping is shown to strongly stabilize the highly localized states of the discrete models. It appears that the previously noted inhibition of nonlinear wave phenomena by noise is an artifact of modeling that includes the effects of heat, but not of heat loss.
Modified energy based method for word endpoints detection of continuous speech signal in real world environment

Accurately identifying the word endpoints is an important step of speech recognition process. This paper proposes a robust word endpoints detection algorithm of continuous speech signal collected from real world environment. In this process energy feature is used along with zero crossing rate feature to locate the endpoints of word in speech signal. A set of 100 different sentences have been recorded from 10 speakers which are used as referred dataset. Proposed method is applied on that dataset and accuracy has been measured by computing the average difference between ground truth endpoints (manually estimated) and system generated endpoints. This algorithm attains 85.5% accuracy whereas the entropy based method gives the accuracy of 78.6 % which shows the superiority of the proposed method. Euclidean distance and Manhattan distance for the proposed algorithm is 2.4 and 2.8 respectively which is also quite acceptable.
Exploring Audio-Visual Metaphors to Communicate Web Search Results: An Experimental Platform

This paper describes an experimental platform that was designed to investigate multimedia metaphors in communicating web search results. An experimental version of a web browser (to handle and represent search results) is the first step towards the exploration of multimedia metaphors in this research programme. The overall aim of our research programme is to investigated how search results extracted from unstructured sources can be browsed, manipulated and displayed using multimedia metaphors. Visual metaphors in graphical representations include two-dimensional bar charts, two-dimensional pie charts, three-dimensional bar charts, three-dimensional line charts and three-dimensional step charts. Auditory metaphors included synthesized, recorded speech and earcons. Demonstrations, observations and usability tests were carried out in order to establish an overall viewpoint of the validity of the approach of using different type of communications metaphors and media to communicate the search results. Therefore the paper concludes with general prima facie conclusions and the outline of a formal and controlled set of experiments.
Empirical and theoretical active memory : The proper contextReply to commentsby

The context of the target article is delimited again, underlining the intended location of the argument in the bottom-up hierarchy of brain study. The central message suggested is that collective delay activity distributions (reverberations) in cortical modules extend the role of a spike (a potential information carrier across long distances) to an active memory of structured, learned information that can be carried across long time intervals. Moreover, the population code of the reverberations makes them readable down the cortical processing stream. Most of the critical comments are then interpreted and addressed in relation to misreading of the proper context. The price for the limitation of the context (in cognitive, behavioral and computational terms) is compared with the advantages of a clear, direct contact with experiment on the one hand and with a well controlled body of modeling and analysis on the other. The wide spectrum of reactions to the target article indicates to me that perhaps the dimensions and the orientation of the context addressed has not been made suuciently clear. I would therefore try and clarify it by a metaphor: the of Hodgkin-Huxley model for the mechanism of spike emission of a neuron. Clearly, neither the phenomenon of neural spike emission nor the model accounting for it have resolved any cognitive problem, behaviorist or otherwise. Yet the idea that spikes may be a basic element in the construction of an account for brain function was immediately appealing. There was no direct connection of spikes to behavior, except perhaps for the fundamental observation that whatever the brain concludes must travel over long distances and spikes can provide a natural carrier. Nor was the model compelling in its details. In fact, by now many elements have been added to the model: additional ionic channels and time constants etc. And yet the mere beginning of an analytic control over a neuro-physiological phenomenon of computational appeal was itself very appealing. Spikes have led to an amazing range of speculation. Most impressive is perhaps the beautiful, if extremely naive, program of McCulloch and Pitts, who proposed 1
Do best practice frameworks fit open source software customization?

Most management models that support software development, information technology (IT) service management, or other IT related tasks have been developed based on extensive experience with these tasks. Their recommendations are, therefore, often named base, good, or best practice. We examine in this paper whether these models are also suitable to support use and adaptation of open source software (OSS) within adopting organizations. OSS is widely used as it exhibits some very appealing features. However, if companies take full advantage of the flexibility OSS affords, especially the possibility to customize it, relating processes get quite complex. Careful control of these processes becomes crucial and use of proper management processes almost mandatory. In the paper, we first determine the special needs of OSS customization and then examine three popular models as to how well they can support this activity. The examined models are ITIL Version 3, SPICE (ISO/IEC 15504), and V-Modell XT. Our research shows that none of the models sufficiently covers the needs of OSS customization. While some aspects of OSS customization can be dealt with in the models or require only minimal modifications of the models, none of the models includes best practices for the management of intertwined and concurrent internal and external development.
Patterns of human activity behavior: from data to information and clinical knowledge

Advances in wearable/mobile device technologies make possible long-term recording of data in our everyday life contexts. Of particular interest is availability of inertial sensors allowing to monitor daily physical activity behavior, which is thought to include useful information on physiology, age/disease related functional capacity, and quality of life. The challenging task in this interdisciplinary research context is to translate the raw data into interpretable information and knowledge that can be further exploited to provide valid hypothesis, objective evaluation and diagnosis. The aim of this paper is to present a methodological framework that brings together monitoring technology, mathematical tools and modern clinical concepts of physiological complexity, with the aim to reveal and quantify aspects of age-/health-related physical behavior embedded in patterns of everyday life activity.
OGSA first impressions: a case study re-engineering a scientific applicationwith the open grid services architecture

We present a case study of our experience re-engineeringa scientific application using the Open Grid Services Architecture(OGSA), a new specification for developing Gridapplications using web service technologies such as WSDLand SOAP. During the last decade, UCL? s Chemistry departmenthas developed a computational approach for predictingthe crystal structures of small molecules. However,each search involves running large iterations of computationallyexpensive calculations and currently takes a fewmonths to perform. Making use of early implementationsof the OGSA specification we have wrapped the Fortranbinaries into OGSI-compliant service interfaces to exposethe existing scientific application as a set of loosely coupledweb services. We show how the OGSA implementationfacilitates the distribution of such applications across alarge network, radically improving performance of the systemthrough parallel CPU capacity, coordinated resourcemanagement and automation of the computational process.We discuss the difficulties that we encountered turning Fortranexecutables into OGSA services and delivering a robust,scalable system. One unusual aspect of our approachis the way we transfer input and output data for the Fortrancodes. Instead of employing a file transfer service wetransform the XML encoded data in the SOAP message tonative file format, where possible using XSLT stylesheets.We also discuss a computational workflow service that enablesusers to distribute and manage parts of the computationalprocess across different clusters and administrativedomains. We examine how our experience re-engineeringthe polymorph prediction application led to this approachand to what extent our efforts have succeeded.
The Religious and Moral Beliefs of Adolescents in St. Petersburg

Students aged 15 to 17 years in state secular and non-state Christian schools in St. Petersburg were surveyed as to their opinions of religion. The study showed an extremely small proportion of consistent atheists. On average, less than 10% of the surveyed students in state schools answered with certainty that there is no God while just 4% decisively reject religion as such. 80% proclaimed themselves believers but only 2% visit churches at least weekly and very few know the Christian Gospel. The majority of students thought that religion is a personal matter. There were significant differences between the state secular and non-state Christian schools so the survey supports the view that religious education strongly influences opinions and systems of ethical values.