Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware and software fault-tolerance of softcore processors implemented in sram-based fpgas

Softcore processors are an attractive alternative to using expensive radiation-hardened processors for space-based applications. Since they can be implemented in the latest SRAM-based FPGA technologies, they are fast, flexible and significantly less expensive. However, unlike ASIC-based processors, the logic and routing of a softcore processor are vulnerable to the effects of single-event upsets (SEUs). To protect softcore processors from SEUs, this dissertation explores the processor design-space for the LEON3 softcore processor implemented in a commercial SRAM-based FPGA. The traditional mitigation techniques of triple modular redundancy (TMR) and duplication with compare (DWC) and checkpointing provide reliability to a softcore processor at great spatial cost. To reduce the spatial cost, terrestrial ASIC-based processor protection techniques are applied to the LEON3 processor. These techniques come at the cost of time instead of area. The software fault-tolerance techniques used to protect the logic and routing of the LEON3 softcore processor include a modified version of software implemented fault tolerance (SWIFT), consistency checks, software indications, and checkpointing. To measure the reliability of a mitigated LEON3 softcore processor, an updated hardware fault-injection model is created, and novel reliability metrics are employed. The improvement in reliabilty over an unmitigated LEON3 is measured using four metrics: architectural vulnerability factor (AVF), mean time to failure (MTTF), mean useful instructions to failure (MuITF), and reliability-area-performance (RAP). Traditional reliability techniques provide the best reliability: DWC with checkpointing improves the MTTF and MuITF by almost 35x and TMR with triplicated input and outputs improves the MTTF and MuITF by almost 6000x. Software fault-tolerance provides significant reliability for a much lower area cost. Each of these techniques provides greater processor protection than a popular state-of-the-art rad-hard processor.
Organisational Culture: Institutionalisation of GIS for Forest Monitoring in Costa Rica

GIS has been welcomed worldwide for its potential production of accessible, timely, and accurate information for decision making. In recent discussions it has been presented as a condition for governance. The implementation of GIS in large and complex institutions like ministries or planning institutes, however, is still problematic. Therefore, attention has shifted from the mere ‘technical factors' to organisational and institutional problems of GIS implementation, looking for the right ‘fit’ of GIS for the organisation in question. This paper will sketch the importance of organisational culture for understanding GIS implementation, and draw some conclusions on the implications for the use of GIS in promoting good governance. The paper discusses a Costa Rican case study on the implementation of GIS for forest monitoring. This case shows the strengths and weaknesses of different organisational cultures in adopting GIS, and illustrates that the (inter)organisational complexity of monitoring forest resources requires more attention to the potential variety in data demands from different organisations.
COMPARISON OF USING SIMPLE GENETIC ALGORITHM AND PARALLEL GENETIC ALGORITM IN HEAT TRANSFER MODEL OPTIMIZATION

In this paper the comparison of using simple genetic algorithm and parallel genetic algorithm is presented. As the optimization problems the parameter setting of the heat transfer model of a building and the building's model calibration were chosen. The model simulation requires huge computing capacity and it is time consuming. Therefore the pressure of simulation evaluations number is concerned and the use of parallelism is desirable. Genetic algorithms and parallelization were implemented in Matlab and the simulation of heat transfer model, which is the part of the fitness function, is performed in Comsol Multiphysics.
A comprehensive search for expert classification methods in disease diagnosis and prediction

Healthcare data analysis is currently a challenging and crucial research issue for the development of a robust disease diagnosis and prediction system. Many specific and a few common methods have been discussed in the literature for healthcare data classification. The present study implements 32 classification methods of six categories (Bayes, function‐based, lazy, meta, rule‐based, and tree‐based) with the objective of searching the best and common categories and methods in healthcare data mining. The performance of each classification method has been evaluated based on analysis time, classification accuracy, precision, recall, F‐measure, area under the receiver operating characteristic curve, root mean square error, kappa coefficient, Kulczynski's measure, and Fowlkes–Mallows index and compared with more than 90 classification methods used in past studies. Seventeen healthcare datasets related to thyroid, cancer, skin disease, heart disease, hepatitis, lymphography, audiology, diabetes, surgery, arrhythmia, postsurvival, liver, and tumour have been used in the performance assessment of the classification methods. The tree‐based classification methods have a better performance (with an average classification accuracy of 79.92% and maximum accuracy of 99.50%; an analysis time of 3.91 s for the logistic model tree classifier) than the other methods. Furthermore, the association of datasets and classification methods has been discussed.
A taxonomy of various attacks on smart card–based applications and countermeasures

Deployment of smart card–based applications is becoming prevalent these days. Security of these applications depends upon the nature of underlying hardware and software platforms, cryptographic algorithms, along with a number of other security mechanisms. To develop a secure smart card–based application, preliminary evaluation of the security of embedded software as well as how that software will work in real time is required. Over the past few years, significant improvements have been made in smart card technology for its large‐scale acceptance. However, attackers are in continuous search for exploiting the unknown issues related to this technology. This survey discusses smart card technology in detail and provides a comprehensive taxonomy of security attacks on smart card–based applications and their countermeasures. In addition, it covers some of the open source tools available for its development. Finally, it provides a brief outline of some of the major open challenges and future research directions that are yet to be explored.
Anti-Inflammatory Effects of L-Type Calcium Channel Blockers

Objective: Calcium ion has an important role in the synthesis and release of chemical mediators during inflammation. The aim of this study was to examine the anti-inflammatory eff ects of L-type calcium chanel blockers on acute and chronic inflammation models in rats and also to examine the effects of n icardipine on capillary vascular permeability in rabbits. Material and Methods: Effects on acute phase of inflammation of verapamil, diltiazem and nicardipine (10 mg/kg dose s) were compared with diclofenac sodium (25 mg/kg) in histamin e-induced paw edema model. After measuring the right hind paw volumes of rats, drugs were injected intraperitoneally 30 minutes la ter. Paw edema was induced by 0.1 mL subplantar histamine (0.1%) i njection to the same paw. Subsequent paw volumes were measured at 3 0 minutes intervals. Effects of the drugs on the chronic phase wer e tested with cotton pellet granuloma method in rats, and effect of nicardipine on capillary vascular permeability was examined with h yaluronidase test in rabbits. Results were compared with control g ups. Results: In acute inflammation model, after the his tamine injections, maximal paw edema was observed at 30 min. While ver apamil and diltiazem did not have an anti-inflammatory effect, nicardipine and diclofenac sodium showed significant anti-inflammator y activities of 63.77% (p= 0.000) and 42.93% (p= 0.002), respective ly. While verapamil and diltiazem had similar effects to those of the control group and to each other (p> 0.05), nicardipine had the mo st p tent activ ity (p< 0.05). Also, nicardipine (3 mg/kg) significantl y reduced hyaluronidase-induced capillary permeability (p= 0.000). Calcium channel blockers did not show anti-proliferative effects oi the chronic phase of inflammation, Conclusion: Since nicardipine, a calcium channel blocker signif cantly inhibited histamineinduced acute inflammation and the dissemination area of hyaluronidase, its antiinflammatory effect may be due to its higher vaso-selectivity on peripheral vascular smooth muscles and on preventing the increase of va scul r permeability.
Estimation and Tracking: Principles, Techniques, and Software

Brief review of linear algebra and linear systems brief review of probability theory brief review of statistics some basic concepts in estimation linear estimation in static systems linear dynamic systems with random inputs state estimation in discrete-time linear dynamic systems estimation for Kinematic models computational aspects of estimation extensions of discrete-time estimation continuous-time linear state estimation state estimation for nonlinear dynamic systems adaptive estimation and manoeuvering targets problem solutions.
Multispectral IKONOS imagery automatic road extraction from high-resolution

An automatic road extraction strategy from pansharpened multispectral (PS-MS) IKONOS imagery is presented in this paper. Roads are modeled as a network of continuous and homogeneous elongated regions with constant width. Road seeds are selected through two steps. First, initial road seeds are generated based on shape and structural analysis through segmentation and grouping. A line detection method based on differential geometry is then used to extract additional road seeds and these are integrated with the initial seeds using graph theory. Missing pieces of the road network are added using a road tracker based on profile matching. Evaluations of the extracted road network using test sites on residential areas are reported. The automated extraction completeness ranges between 70-90% and the correctness ranges between 65-85%. Keywords-automatic road extraction; IKONOS
Distributed Operating Systems & Algorithms

From the Publisher: Distributed Operating Systems and Algorithms integrates into one text both the theory and implementation aspects of distributed operating systems for the first time. This innovative book provides the reader with knowledge of the important algorithms necessary for an in-depth understanding of distributed systems; at the same time it motivates the study of these algorithms by presenting a systems framework for their practical application. The first part of the book is intended for use in an advanced course on operating systems and concentrates on parallel systems, distributed systems, real-time systems, and computer networks. The second part of the text is written for a course on distributed algorithms with a focus on algorithms for asynchronous distributed systems. While each of the two parts is self-contained, extensive cross-referencing allows the reader to emphasize either theory or implementation or to cover both elements of selected topics. Features: Integrates and balances coverage of the advanced aspects of operating systems with the distributed algorithms used by these systems. Includes extensive references to commercial and experimental systems to illustrate the concepts and implementation issues. Provides precise algorithm description and explanation of why these algorithms were developed. Structures the coverage of algorithms around the creation of a framework for implementing a replicated server-a prototype for implementing a fault-tolerant and highly available distributed system. Contains programming projects on such topics as sockets, RPC, threads, and implementation of distributed algorithmsusing these tools. Includes an extensive annotated bibliography for each chapter, pointing the reader to recent developments. Solutions to selected exercises, templates to programming problems, a simulator for algorithms for distributed synchronization, and teaching tips for selected topics are available to qualified instructors from Addison Wesley.
Complicated Effect of He-Ne Laser Therapy on Pro-/Anti-Inflammatory Cytokines from Serum in Rats

Helium-neon (He-Ne) laser is one of the most common low energy lasers used in physiotherapy for promoting wound healing. This study was about to investigate the effects of He-Ne laser therapy on the pro-/anti-inflammatory cytokines in rats up to 10 days of treatment. 16 non-pregnant female rats were divided into 4 groups randomly, 4 rats in each group. They are negative control (NC) group, positive control (PC) group, laser treatment (LA) group and erythromycin ointment (ER) treatment group. The LA group was treated with 25 mW of He-Ne laser for 30 min every day for 10 days, with 632.8 nm of wavelength immediately after three incisions were made on the mid-dorsal line of each rat. The results showed that He-Ne laser therapy could decrease the level of IL-1β compared with the PC and ER groups; while increase the level of IL-6 compared with the NC and ER groups, and the level of TNF-α compared with the PC and ER group. He-Ne laser therapy also increased the concentration of IL-10 compared with the other three groups, and the concentration of TGF-β compared with the NC and ER groups. These results indicated that laser acupuncture increased the production of IL-6, IL-10, TGF-β and TNF-α after 10 days of treatment, which was the proliferation stage of wound healing process, and the effect of He-Ne laser therapy on pro-inflammatory cytokines was not always consistent, which might be time-dependent of He-Ne laser treatment.
Enhanced Web Objects Classification using Social Tags

—The automatic classification of Web objects into semantic categories is very important to facilitate indexing, browsing, searching, and mining these objects. But this is a very challenging task, because web objects often suffer from a lack of easy-extractable features with semantic information, interconnections between each other, and training examples with category labels. Social tags reflect the web objects semantics from users’ points of view, which makes them an ideal web objects feature that overcomes the difficulties of web object classification. In this paper we study the impact of using social tagging on the performance of text classification techniques in web objects classification. An automated system for web objects classification has been developed that is based on social tags exploration. The system has three phases: data preprocessing, classification and evaluation phases. It accepts a training dataset that represents a set of web pages with its URLs, tags, titles and categories. Using this dataset, the system constructs a predictive model that is later used to assign labels to web objects based on their tags. In the classification step, the system employs three known text classification techniques namely, Support Vector Machine, Naïve Bayes, and Decision Tree, through the WEKA software. Experiments have been conducted to evaluate the effectiveness of using social tags with each one of the three text classification techniques in web objects classification. The experimental results indicate that using tags significantly improve the classification performance.
Janus POSS Based on Mixed [2:6] Octakis-Adduct Regioisomers.

A series of regioisomeric Janus-type polyhedral oligomeric silsesquioxanes (POSS) with multiple, mixed surface functional groups has been successfully synthesized based on the cubic T8 -POSS framework in two consecutive thiol-ene reactions. The first thiol-ene addition of β-mercaptoethanol leads to a statistical mixture of multi-adducts where the regioisomers of bis-adducts (ortho, meta, and para isomers) can be isolated at synthetically useful quantities by flash column chromatography. Then, the second thiol-ene reaction readily installs a variety of functional groups onto the remaining vinyl groups of each regioisomer, providing an easy access to precisely defined, hetero-bifunctional Janus POSS nano-building blocks. The configurations and structures of the products have been unambiguously proven by using (1) H, (13) C, and (29) Si NMR spectroscopy as well as MALDI-TOF mass spectrometry.
A Distributed Method for Bottleneck Node Detection in Wireless Sensor Network

Wireless sensor networks (WSNs) have been considered as a promising method for reliably monitoring both civil and military environments under hazardous or dangerous conditions. Due to the special property and difference from the traditional wireless network, the lifetime of the whole network is the most important aspect. The bottleneck nodes widely exist in WSNs and lead to decrease the lifetime of the whole network. In order to find out the bottleneck nodes, the traditional centralized bottleneck detection method MINCUT has been proposed as a solution for WSNs. However they are impractical for the networks that have a huge number of nodes. This paper first proposes a distributed algorithm called DBND (Distributed Bottleneck Node detection) that can reduce the time for location information collection, lower the algorithm complexity and find out the bottleneck nodes quickly. We also give two simple suggestions of how to solve the bottleneck problem. The simulation results and analysis show that our algorithm achieves much better performance and our solutions can relax the bottleneck problem, resulting in the prolonging of the network lifetime.
Contribution to the understanding of explanatory factors for the decision-maker problem within the framework of economic intelligence

In an economic intelligence context, the decision-maker is more and more intersted in changes of him complex environment.To help him face these situations, we propose a decision-maker model based on a set of parameters that describe the decision-maker? s behavior as regards a problem. These parameters are related to the decision-maker as an individual, to the organization and the environment to which it belongs. The model of the decision-maker presented in this paper, thanks to its support for the decision-maker, allows the watcher to better understand a concrete problem posed by a given decision-maker and thus allows the watcher to seek for and to produce analyses of the most relevant information.
Live Media Streaming in VANETs using Traffic Flow

Multimedia streaming is a trend in VANETs infotainment service with establishment of infrastructure and communication standards. This paper considers the scenario of live multimedia streaming multicast to vehicles. We propose the relay selection scheme to deliver media streaming with stable link to improve QoS (quality of service). Through evaluation, our proposed scheme is able to have video delivered with a satisfying delay and acceptable loss ratio.
Getting started on metrics-Jet Propulsion Laboratory productivity and quality

A description is given of the SPA (Software Product Assurance) Metrics Study, part of an effort to improve software quality and the productivity and predictability of software development. The first objective was to collect whatever data could be found from as many projects for which they were still available. Data were assembled on the basis of four basic parameters: source lines of code, dollars, work years, and defects. By using these four basic parameters, it was possible to construct quality and productivity baselines. Quality was defined as the number of defects per thousand lines of source code. Productivity was defined in two ways: dollars per source line of code and source lines of code per work month. Preliminary results of this study are presented. <<ETX>>
Training convolutional neural networks in virtual reality for grasp detection from 3D images

The focus of this project has been on training convolutional neural networks for grasp detection with synthetic data. Convolutional neural networks have had great success on a wide variety of computer vision tasks, but they require large amounts of labelled training data, which currently is non existent for grasp detection tasks. In this thesis, a novel approach for generating large amounts of synthetic data for grasp detection is proposed. By working solely with depth images, realistic looking data can be generated with 3D models in a virtual environment. It is proposed to use simulated physics to ensure that the generated depth images captures objects in natural poses. Additionally, the use of heuristics for choosing the best grip vectors for the objects in relation to their environment is proposed, to serve as the labels for the generated depth images. A virtual environment for synthetic depth image generation was created and a convolutional neural network was trained on the generated data. The results show that neural networks can find good grasps from the synthetic depth images for three different types of objects in cluttered scenes. A novel way of creating real world data sets for grasping using a head mounted display and tracked hand controllers is also proposed. The results show that this may enable easy and fast labelling of real data which can be performed without training by non-technical people.
Falls in Swedish hurdle and steeplechase racing and the condition of the track surface

Falls in National Hunt races is a tragic part of the sport. The present study focuses on the relation between racing track conditions and the number of falls in Swedish jump racing. The assumption was that more horses fell on heavy or soft going than on good or firm going. Results from all jump races at Taby Racecourse (1992-2001) were recorded. Parameters registered were: type and number of races, racing surface and condition, total time to finish the race, number of starting horses and number of falls. In this period 212 races, corresponding to 1,556 horse starts, were registered. Falls were registered in 42 races and in total 61 horses fell. The fall frequency on horse level was significantly higher in steeplechases than in hurdle races (odds ratio =3.69; 95% confidence interval (CI) = 1.99-6.85). For the steeplechases recorded in this study, significantly more falls were seen in long distance races (>4,000 m) on soft to heavy going (odds ratio = 5.20; 95% CI = 1.48-18.26), whereas no significant trend.
Forest Cover Type Prediction using Cartographic Variables

Information regarding forest land is highly required for developing ecosystem management. This paper provides an analysis related to classification and prediction estimation using machine learning techniques. The approach is to predict the forest cover type using the cartographic variables like aspect, slope, soil type, wilderness area etc. Various Data mining techniques such as decision tress, random forest, regression trees, and gradient boosting machines are used for prediction of the forest cover type. Using these machine learning methods models have been developed and tested for accuracy ranging from 19.4% to 74.8%. Kaggle dataset which is the standard benchmarking dataset, is taken for comparison studies. The comparisons of these models are done to identify a better model for predicting the forest cover type with better accuracy. For performance comparison, metrics like accuracy and error rate are used. An important aspect of the study is the use of different performance measures to evaluate the learning methods.