Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Registration Model Under Arbitrarily-Shaped Locally Variant Illumination and Occlusions

In this proposal, we focus on the geometric registration of images with arbitrarilyshaped locally varying intensity changes due to shadows or occlusions that tend to degrade the performance of geometric registration, thereby degrading subsequent processing; such as image mosaicking, and super-resolution. The traditional algorithms assume only global intensity variation correction, thus can not handle such local ones as well as occluded areas. Our approach proposes a general image registration model with illumination correction that can handle Arbitrarily-Shaped regions of Locally Variant Illumination and Occlusions; ASLV IO model. Our model is set in an iterative coarse-to-fine framework with steps to compensate the occluded data, steps to estimate the geometric registration with illumination correction, and steps to refine the arbitrarily-shaped local intensity regions. The proposed model is presented as a first step to a Bounded Total Variation (BTV)-based super-resolution (SR) application. The results show that our proposed registration model outperforms linear scalar model (LSM) in sup-pixel registration accuracy. Also, the ASLV IO-based super-resolved images outperform those LSM-based qualitatively and quantitatively. Evaluation of the proposed model is performed on real and simulated satellite images demonstrating its efficiency.
Comparative Study of State Estimation of Fuel Cell Hybrid System Using UKF and EKF

The principle of state estimation of a dynamic system using Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is briefly described. Two nonlinear examples are considered to compare the state estimation using UKF and EKF. A Solid Oxide Fuel Cell (SOFC) combined with Gas Turbine (GT) hybrid system is described and system level modeling of the hybrid system is discussed. The state estimation using UKF and EKF is applied to the fuel cell hybrid system.
Methods for Sentiment Analysis of Twitter Messages

This paper deals with the study of existing sentiment analysis methods, as well as development and implementation of methods for sentiment analysis of Twitter messages. The method designed includes algorithms for determining the polarity of a message and its aspects. The purpose of this paper is to investigate and develop methods for analysis of the tonality of messages in the social media. To achieve this purpose, the following tasks were set: 1. Review the existing automatic sentiment analysis methods. 2. Study the text features of social media messages in the context of developing methods for their sentiment analysis. 3. Develop a method for automatic sentiment analysis of Twitter messages.
The Need for Standardization in Crowdsourcing

Crowdsourcing has shown itself to be well-suited for the ac-complishment of certain kinds of small tasks, yet many crowd-sourceable tasks still require extensive structuring and managerial effort before using a crowd is feasible. We argue that this overhead could be substantially reduced via standardization. In the same way that task standardization enabled the mass production of physical goods, standardization of basic “building block” tasks would make crowdsourcing more scalable. Standardization would make it easier to set prices, spread best practices, build meaningful reputation systems and track quality. All of this would increase the demand for paid crowdsourcing—a development we argue is positive on both efﬁciency and welfare grounds. Standardization would also allow more complex processes to be built out of simpler tasks while still being able to predict quality, cost and time to completion. Realizing this vision will require interdisciplinary research effort as well as buy-in from online labor platforms.
Blind Multichannel Image Deconvolution and Denoising

A multichannel blind deconvolution and image denoising method based on the Mutually Referenced Equalizers (MRE) approach is proposed. The main idea is to exploit the information diversity conveyed by the channels in order to estimate the degradations and the original signal. In the noiseless case a perfect restoration is obtained. To deal with the noisy case, a regularization term is introduced in the procedure leading to regularized-MRE. To further enhance the image quality an image noise filtering method called Non Local Mean (NLM) could be used. The method is tested on actual images artificially degraded. The obtained results confirm the efficiency of the proposed approach in restoring noisy blurred images.
On the Indifferentiability of the Integrated-Key Hash Functions

Most of today’s popular hash functions are keyless such that they accept variable-length messages and return fixed-length fingerprints. However, recent separation results reported on several serious inherent weaknesses in these functions, motivating the design of hash functions in the keyed setting. The challenge in this case, however, is that on one hand, it is economically undesirable to abundant the already adopted (keyless) functions in favour of new (keyed) ones, and on the other hand, the process of converting a keyless function to a keyed one is, evidently, non-trivial. A solution to this dilemma is to adopt the ”integratedkey” approach that creates keyed hash functions out of ”unmodified” keyless primitives. In this paper, we adopt several integrated-key constructions and prove that they are indifferentiable from random oracle, showing in details how to develop indifferentiability proofs at the integrated-key setting. The presented indifferentiability proof is generic and can be applied on other hash functions constructed in this setting with sufficiently similar structures to the constructions in this paper.
Low Power Folded Cascode OTA

Low power is one of the key research area in today’s electronic industry. Need of low power has created a major pattern shift in the field of electronics where power dissipation is equally important as area, performance etc. Several low power portable electronic equipments, low voltage design techniques have been developed and have driven analog designers to create techniques eg. Self cascode mosfet and stacking technique. For this aim in mind we designed a Folded Cascode using low power techniques and analyzed its various properties through the Spice simulations for 0.13 micron CMOS technology from TSMC and the supply voltage 1.8V.
A Context-Awareness for Mechanical Maintenance

Ongoing development of information technology provides a fundamental environment of maintaining vast manual information in digital form. Demand of digital information increases because of easy handling and presentation of documents on various structure manner. As a result, technical documents like mechanical device maintenance manuals have numerous advantages to move from page version into electronic edition. However, it is still difficult for context-aware systems to provide information relevant to specific context because the information scatters in multiple parts of the document. The object detection techniques of context-aware systems could not recognize all objects completely. In this paper, we propose a context-aware system which can recommends relevant information through analyzing context delivered by a vedio device located in workplace around. To compare the current context with documents, three metrics are used for computing similarities between contextual information and keywords extracted from documents. For our experiments, we preprocessed hundreads of TASKs in the aircraft's maintenance manual and made several cases for context. Our experiments showed that our proposing system could provide information related with context.
One Leader at One Time: OLOT Routing in Delay Tolerant Networks

Routing is a challenge problem in Delay-tolerant networks (DTNs) due to the intermittent connectivity environment. To cope with it, both single-copy and multi-copy routing protocols have been proposed. The difference between them is how to handle with the messages that have been forwarded to the next hop. These messages will be removed from buffer immediately in the single-copy protocols, while be kept and forwarded to other relays in the multi-copy cases. However, there is a gap between the two protocols. In our previous work, we have indicated it and proposed a Snail Crawling(SC) method to fill the gap. In this paper, we indicate that the SC method is just an enhanced method but not a routing protocol. By adopting heuristic strategies in messages distribution of SC, we get a new routing protocol One Leader at One Time(OLOT), in which only one node can forward message to another relay at the same time. Simulation results show that both the delivery rate and the overhead of OLOT are better than the SC method.
Phenotype Information Retrieval for Existing GWAS Studies

The database of Genotypes and Phenotypes (dbGaP) is archiving the results of different Genome Wide Association Studies (GWAS). dbGaP has a multitude of phenotype variables, but they are not harmonized across studies. We proposed a method to standardize phenotype variables by classifying similar variables based on semantic distances. We first extracted variables description, enriched them using domain knowledge, and computed the distances among them. We used clustering techniques to classify the most similar variables. We used domain experts to audit clusters, annotated the clusters with appropriate labels, and used re-clustering to build a semantically-driven Genotypes and Phenotypes (sdGaP) ontology using the UMLS semantic network and metathesaurus. The sdGaP ontology allowed us to expand user queries and retrieve information using a semantic metric called density measure (DM). We illustrated the potential improvement of information retrieval using the sdGaP ontology in one search scenario using the variables from the Cleveland Family Study.
Land, Trees, and Women: Evolution of Land Tenure Institutions in Western Ghana and Sumatra

This research report examines three questions that are central to IFPRI research: How do property-rights institutions affect efficiency and equity? How are resources allocated within households? Why does this matter from a policy perspective? As part of a larger multicountry study on property rights to land and trees, this study focuses on the evolution from customary land tenure with communal ownership toward individualized rights, and how this shift affects women and men differently.This study's key contribution is its multilevel econometric analysis of efficiency and equity issues. Using a combination of community, village, and household surveys in Western Ghana and Sumatra, two areas with traditional matrilineal inheritance systems, the authors and their collaborators analyze the effectiveness of village-, household-, and parcel-level property-rights institutions and arrangements.
Improving the performance of RED deployment on a class based queue with shared buffers

We address the deployment of random early detection (RED) on class based queues (CBQs) with shared buffer by deriving novel RED algorithms for the complete sharing and sharing with minimum allocation schemes and comparing their performance with RED under complete partitioning that is equivalent to using a dedicated buffer for each CBQ. In our novel RED algorithms, packet drop decision is based on the estimated average lengths of both the effective subqueue and the whole queue. Simulation results ascertain and demonstrate the superiority of the proposed schemes in terms of lower packet drop ratio under congestion situations, with different RED parameters under diverse RED drop probabilities and weight factors.
WRF Simulations of the 20-22 January 2007 Snow Events over Eastern Canada: Comparison with In-Situ and Satellite Observations

Abstract One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve cold-season precipitation measurements in mid- and high latitudes through the use of high-frequency passive microwave radiometry. For this purpose, the Weather Research and Forecasting model (WRF) with the Goddard microphysics scheme is coupled with a Satellite Data Simulation Unit (WRF–SDSU) to facilitate snowfall retrieval algorithms over land by providing a virtual cloud library and corresponding microwave brightness temperature measurements consistent with the GPM Microwave Imager (GMI). When this study was initiated, there were no prior published results using WRF at cloud-resolving resolution (1 km or finer) for high-latitude snow events. This study tested the Goddard cloud microphysics scheme in WRF for two different snowstorm events (a lake-effect event and a synoptic event between 20 and 22 January 2007) that took place over the Canadian CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Sat.
defensive publications in an r&d race

This paper considers a multistage dynamic R&D race in which the competitors strategically publish research findings. Publications change the prior art, thus affecting patentability. Firms publish when they are behind in the race and their rival is close to winning it. Publication sets back both competitors and gives the follower a chance to catch up. Publications prolong the race. Firms are more likely to publish the more patient they are, and the higher their probability of success. Asymmetry between the firms generates additional incentives to publish such as protecting profits from a previous patent and increasing a strong firm's probability of winning. When firms face a joint decision on publications and the intensity of research, publications substitute investment.
A Novel Algorithm for Dynamic Mining of Association Rules

Nowadays, communication network turns to be more complex, once there occurs a failure, there will result in multi-alarm-events which require relevant transactions. This paper describes the alarm correlation in communication networks based on data mining. The association rules from self-adaptation for dynamic network resource and service that build new rule by fully utilizing and maintaining rules formed before while transactions grow/delete or minimum support changes which enables the framework is easily updated and new discovery methods be readily incorporated within it. Both theoretical analysis and computer simulations illustrate outstanding performance of the proposed models, which can be further optimized by experiments for specific environment.
Anisotropic meshing of implicit surfaces

This paper proposes a new grid-based method for adaptive anisotropic meshing of implicit surfaces. Grid-based methods are considered the major technique for implicit surface meshing, mainly due to their efficiency and simplicity. However, these methods suffer from a number of inherent drawbacks, resulting from the fact that the imposed Cartesian grid is generally not well adapted to the iso-surface, either in size or in orientation. To overcome the above obstacles, we propose a new implicit surface meshing method. The main idea of this method is first to construct a geometric field which is induced by the shape of the surface. This geometric field represents the natural directions and grid cell size for each point in R/sup 3/. Then, the imposed volumetric grid is deformed toward the object's shape according to the produced geometric field. The iso-surface meshes can be extracted from the resulting adaptive grid by any conventional grid-based contouring technique, since the topology of the grid is not modified during the adaptation process. These meshes provide better approximation of the unknown surface and exhibit anisotropy, which is present in this surface.
Trust-based improved recommendation of IT-related Web resources

The explosive growth of the Web makes increasingly harder to identify relevant resources among large result sets yield by search engines. Researcher looking for specialized documents have little guidance beyond the documents' own metadata. This article describes the use of trust-based computing to improve ranking and relevance of results by combining topic-specific trust among community members and individual's resources evaluation. The approach has been prototyped with Antu, built over the IT-specific faceted keyword-based search tool Yarquen. An experimental study was conducted with informatics graduate students, and found improved relevance of the suggested documents. This result suggests that trust-based approaches have huge potential to improve recommendations in specialized communities.
Robotic arm detection in space with image recognition made in Linux with the Hough circles method

This paper presents a method to recognize a robotic arm in space using the Hough circles method. The robotic arm has colored bottle stoppers glued at the joints, which are recognized with color filtering. After this step, the biggest colored spot is detected and marked using the Hough circle method. The joints are numbered, this way each joint's position is known. The joints can be united with lines and this way a skeleton of the robotic arm can be drawn which can be loaded in a PC to create a control application.
An analysis of the applicability of federal law regarding hash-based searches of digital media

Abstract : The Fourth Amendment of the United States (U.S.) Constitution limits the ability of the government to search U.S. persons without cause or justification. The application of the Fourth Amendment to digital forensics search techniques is still evolving. This thesis summarizes current federal law and recent judicial rulings that can apply Fourth Amendment doctrine to current digital forensics techniques. It uses three hypothetical scenarios to show how current law could be applied to new techniques now under development: the use of sector hashes to find traces of digital contraband; the use of random sampling to rapidly triage large digital media; and the use of similarity functions to find documents that are similar but not identical to target documents.
Automatic mashup generation from multiple-camera concert recordings

A large number of videos are captured and shared by the audience from musical concerts. However, such recordings are typically perceived as boring mainly because of their limited view, poor visual quality and incomplete coverage. It is our objective to enrich the viewing experience of these recordings by exploiting the abundance of content from multiple sources. In this paper, we propose a novel \Virtual Director system that automatically combines the most desirable segments from different recordings resulting in a single video stream, called mashup. We start by eliciting requirements from focus groups, interviewing professional video editors and consulting film grammar literature. We design a formal model for automatic mashup generation based on maximizing the degree of fulfillment of the requirements. Various audio-visual content analysis techniques are used to determine how well the requirements are satisfied by a recording. To validate the system, we compare our mashups with two other mashups: manually created by a professional video editor and machine generated by random segment selection. The mashups are evaluated in terms of visual quality, content diversity and pleasantness by 40 subjects. The results show that our mashups and the manual mashups are perceived as comparable, while both of them are significantly higher than the random mashups in all three terms.
A framework for dynamically generating predictive models of workflow execution

The ability to accurately predict the performance of software components executing within a Cloud environment is an area of intense interest to many researchers. The availability of an accurate prediction of the time taken for a piece of code to execute would be beneficial for both planning and cost optimisation purposes. To that end, this paper proposes a performance data capture and modelling architecture that can be used to generate models of code execution time that are dynamically updated as additional performance data is collected. To demonstrate the utility of this approach, the workflow engine within the e-Science Central Cloud platform has been instrumented to capture execution data with a view to generating predictive models of workflow performance. Models have been generated for both simple and more complex workflow components operating on local hardware and within a virtualised Cloud environment and the ability to generate accurate performance predictions given a number of caveats is demonstrated.
Introducing ASK, A Simple Knowledgeable System

ASK, A S imple K nowledgeable System, is a total system for the structuring, manipulation and communication of information. It is a simple system in the sense that its development concentrated on clean engineering solutions to what could be done now with good response times. The user interface is a limited dialect of English. In contrast to expert systems, in which experts build the knowledge base and users make use of this expert knowledge, ASK is aimed at the user who wishes to create, test, modify, extend and make use of his own knowledge base. It is a system for a research team, a management or military staff, or a business office.This paper is designed to give you a feel for the general performance of the ASK System and overview of its operational capabilities. To this end, the movie you see will continue throughout the talk. Indeed, the talk itself is a commentary on this background movie. The movie is bona fide and in real time, it is of the ASK System in action. (Many of the illustrations from the movie are reproduced in the written paper.)
Weed Science Research and Funding: A Call to Action

Abstract Weed science has contributed much to agriculture, forestry and natural resource management during its history. However, if it is to remain relevant as a scientific discipline, it is long past time for weed scientists to move beyond a dominating focus on herbicide efficacy testing and address the basic science underlying complex issues in vegetation management at many levels of biological organization currently being solved by others, such as invasion ecologists and molecular biologists. Weed science must not be circumscribed by a narrowly-defined set of tools but rather be seen as an integrating discipline. As a means of assessing current and future research interests and funding trends among weed scientists, the Weed Science Society of America conducted an online survey of its members in summer of 2007. There were 304 respondents out of a membership of 1330 at the time of the survey, a response rate of 23%. The largest group of respondents (41%) reported working on research problems primarily focused on herbicide efficacy and maintenance, funded mainly by private industry sources. Another smaller group of respondents (22%) reported focusing on research topics with a complex systems focus (such as invasion biology, ecosystem restoration, ecological weed management, and the genetics, molecular biology, and physiology of weedy traits), funded primarily by public sources. Increased cooperation between these complementary groups of scientists will be an essential step in making weed science increasingly relevant to the complex vegetation management issues of the 21st century.