Miracle-dz/UniFAR-FTUs huggingface dataset:
ATPG-based grading of strong fault-secureness

Robust circuit design has become a major concern for nanoscale technologies. As a consequence, for design validation, not only the functionality of a circuit has to be considered, but also its robustness properties have to be analyzed. In this work we propose a method to verify the strong fault-secureness by use of constrained SAT-based ATPG. Strongly fault-secure circuits can be seen as the widest class of circuits achieving the totally self-checking (TSC) goal, which requires that every fault be detected the first time it manifests itself as an error at the outputs. As the strongly fault-secure property guarantees to achieve the TSC goal even in the case of fault accumulation, the effects of all possible fault sequences have to be taken into consideration to verify this property. To speed up the complex analysis of multiple faults we develop rules to derive detectability or redundancy information for multiple faults from the respective information for single faults. For the case of not strongly fault-secure circuits our method provides measures to grade the “extent” of strong fault-secureness given by the implementation.
Automated blocking of malicious code with NDIS intermediate driver

With the evolution of malware technology, modern malware often hide its malicious behaviour in various methods. One of the popular manners is to conceal the network communication. This concealment technique poses obstacles to security mechanisms, which detecting the malicious behaviours. In this paper, we give an overview of the automated blocking malicious code project, a new approach to computer security via malicious software analysis and automatic blocking software. In particular, this project focuses on building a unified executable program analysis platform and using it to provide novel solutions to a broad spectrum of different security problems. We propose a technique for the Network Driver Interface Specification (NDIS) integrate together with a unified malicious software analysis platform. The NDIS model supports hybrid network transport NDIS drivers, called NDIS intermediate drivers. This driver lies between transport driver and NDIS driver. The advantage of using NDIS intermediate drivers is, it can see the entire network traffic taking place on a system as the drivers lie between protocol drivers and network drivers. By intercepting security-related properties from network traffic directly, our project enables a principled, root cause based approach to computer security, offering novel and effective solutions.
Cooperative Exploration for USAR Robots with Indirect Communication

To coordinate a team of robots for exploration is a challenging problem, particularly in unstructured areas, as for example post-disaster scenarios where direct communication is severely constrained. Furthermore, conventional methods of SLAM, e.g. those performing data association based on visual features, are doomed to fail due to bad visibility caused by smoke and fire. We use indirect communication (based on RFIDs), to share knowledge and use a gradient-like local search to direct robots towards interesting areas. To share a common frame of reference among robots we use a feature based SLAM approach (where features are RFIDs). The approach has been evaluated on a 3D simulation based on USARSim.
On qualitative differences in learning

This paper describes an attempt to identify different levels of processing of information among groups of Swedish university students who were asked to read substantial passages of prose. Students were asked questions about the meaning of the passages and also about how they set about reading the passages. This approach allows processes and strategies of learning to be examined, as well as the outcomes in terms of what is understood and remembered. The starting point of this research was that learning has to be described in terms of its content. From this point differences in what is learned, rather than differences in how much is learned, are described. It was found that in each study a number of categories (levels of outcome) containing basically different conceptions of the content of the learning task could be identified. The corresponding differences in level of processing are described in terms of whether the learner is engaged in surfuce-level or deeplevel processing.
On the Measurement of Indignation

Recently, a lot of attention is given to income variations occurring at the top of the income distribution. “What happens to the top 1%? ” is a question of crucial importance on the political level (Occupy Wall Street Movement) as well as on income inequality measurement level. Despite this increased interest, there is no rigorous measurement framework available in the literature for the measurement of “indignation”. To fill this gap, this paper proposes a simple framework for the measurement of indignation. It exposes the ethical principles underlying an indignation index and develops restricted positional dominance conditions that produce robust orderings of indignation between income distributions. It also proposes a parametric class of indignation indices that may be used to produce complete orderings when the restricted positional dominance tests do not lead to satisfactory orderings. Finally, the paper offers a brief empirical illustration using the World Top Incomes Database.
Thrombin‐induced prostacyclin biosynthesis in human endothelium: Role of guanine nucleotide regulatory proteins in stimulus/coupling responses

The regulation of prostacyclin (PGl2) synthesis by cultured human umbilical vein endothelium (HUVEC) was investigated. HUVEC monolayer generation of PGl2 was monitored by RIA of 6‐keto PGF1α and dose‐dependent increases observed with human α‐and γ‐thrombins, histamine, or arachidonate. Alpha thrombin (10 nM) produced levels of 6‐keto PGF1α approximating responses with 1 μM γ‐thrombin, 5μM arachidonate, or 10 μM histamine. Diisopropyl phosphorofluo‐ridate‐inactivated α‐thrombin did not stimulate PGl2 release, demonstrating that catalytic activity was required for thrombin‐stimulated PGl2 release. Sodium fluoride (NaF), at concentrations known to activate guanine nucleotide regulatory proteins (G proteins), directly stimulated HUVEC PGl2 synthesis in a dose‐dependent and time‐dependent manner (20 mM NaF, 4.4 ± 0.5‐fold increase at 10 min, 11.9 ± 1.5‐fold increase at 30 min). Neither α‐thrombin nor NaF‐stimulated PGl2 release was dependent upon the availability of extracellular Ca+ +. The hypothesis that G proteins are involved in agonist‐stimulated PGl2 synthesis was further supported by studies using digitonin‐permeabilized HUVEC monolayers challenged with another G protein activator, guanosine 5′‐0‐3‐thiotrisphosphate (GTP γ S), which effected significant dose‐dependent increases in PGl2 synthesis compared with control levels of 6‐keto PGFα. In contrast, the G‐protein inhibitor GDP β S, (guanosine 5′‐0‐2‐thiodiphosphate), attenuated α‐thrombin‐mediated prostaglandin generation. Treatment of HUVEC monolayers with pertussis toxin (1 μg/ml) did not inhibit the PGl2 synthesis stimulated by either α‐thrombin, NaF, or histamine but catalyzed the ADP ribosylation of a 40 kDa membrane protein which cross‐reacted with antisera against a synthetic peptide corresponding to an amino acid sequence common to the α‐subunit of other G‐proteins. Preincubation of HUVEC microsomal membranes with α‐thrombin diminished pertussis toxin‐catalyzed ADP ribosylation in a time‐dependent manner. These data suggest that thrombin stimulation of PGl2 synthesis by HUVEC monolayers requires the catalytically functional enzyme and further suggests that the thrombin‐occupied receptor is coupled to phospholipase activities by a pertussis toxin‐insensitive guanine nucleotide regulatory protein in human endothelial cell membranes.
Correlative Association between Resident Plasmids and the Host Chromosome in a Diverse Agrobacterium Soil Population

Soil samples collected from a fallow field which had not been cultivated for 5 years harbored a population of Agrobacterium spp. estimated at 3 × 107 CFU/g. Characterization of 72 strains selected from four different isolation media showed the presence of biovar 1 (56%) and bv. 2 (44%) strains. Pathogenicity assays on five different test plants revealed a high proportion (33%) of tumorigenic strains in the resident population. All tumorigenic strains belonged to bv. 1. Differentiation of the strains by restriction fragment length polymorphism analysis, sodium dodecyl sulfate-polyacrylamide gel electrophoresis of cellular proteins, and utilization patterns of 95 carbon substrates (Biolog GN microplate) revealed a diversified bv. 1 population, composed of five distinct chromosomal backgrounds (chr A, C, D, E, and F), and a homogeneous bv. 2 population (chr B). chr A, B, C, and D were detected at similar levels throughout the study site. According to opine metabolism, pathogenicity, and agrocin sensitivity, chr A strains carried a nopaline Ti plasmid (pTi), whereas chr C strains had an octopine pTi. In addition, four of six nontumorigenic bv. 1 strains (two chr D, one chr E, and one chr F) had distinct and unusual opine catabolism patterns. chr B (bv. 2) strains were nonpathogenic and catabolized nopaline. Although agrocin sensitivity is a pTi-borne trait, 14 chr B strains were sensitive to agrocin 84, apparently harboring a defective nopaline pTi similar to pAtK84b. The other two chr B strains were agrocin resistant. The present analysis of chromosomal and plasmid phenotypes suggests that in this Agrobacterium soil population, there is a preferential association between the resident plasmids and their bacterial host.
SybilControl: practical sybil defense with computational puzzles

Many distributed systems are subject to the Sybil attack, where an adversary subverts system operation by emulating the behavior of multiple distinct nodes. Most recent works addressing this problem leverage social networks to establish trust relationships between users. However, social networks are not appropriate in all systems. They can be subverted by social engineering techniques, require nodes to maintain and be aware of social network information, and may require overly optimistic assumptions about the fast-mixing nature of social links. This paper explores an alternate approach. We present SybilControl, a novel decentralized scheme for controlling the extent of Sybil attacks. It is an admission and retainment control scheme for nodes in a distributed system that requires them to periodically solve computational puzzles. SybilControl consists of a distributed protocol to allow nodes to collectively verify the computational work of other nodes, and mechanisms to prevent the malicious influence of misbehaving nodes that do not perform the computational work. We investigate the practical issues involved with deploying SybilControl into existing DHTs, particularly with handling churn. SybilControl is shown to provide strict bounds on the size of Sybil attacks, given adversaries with finite resources. We also show through simulations that the performance overhead of enabling SybilControl is manageable using commonplace DHT churn-handling techniques. This provides strong evidence that SybilControl can be practically deployed.
The Dynamics and Semantics of Collaborative Tagging

The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including the dynamics of such systems and whether coherent classification schemes can emerge from undirected tagging by users. Currently millions of users are using collaborative tagging without centrally organizing principles, and many suspect this exhibits features considered to be indicative of a complex system. If this is the case, it remains to be seem whether collaborative tagging by users over time leads to emergent classification schemes that could be formalized into an ontology usable by the Semantic Web. This paper uses data from “popular” tagged sites on the social bookmarking site del.icio. us to examine the dynamics of such collaborative tagging systems. In particular, we are trying to determine whether the distribution of tag frequencies stabilizes, which indicates a degree of cohesion or consensus among users about the optimal tags to describe particular sites. We use tag co-occurrence networks for a sample domain of tags to analyze the meaning of particular tags given their relationship to other tags and automatically create an ontology. We also produce a generative model of collaborative tagging in order to model and understand some of the basic dynamics behind the process.
Simultaneous EGFR and VEGF Alterations in Non-Small Cell Lung Carcinoma Based on Tissue Microarrays

BACKGROUND: Epidermal growth factor receptor (EGFR) overexpression is observed in significant proportions of non-small cell lung carcinomas (NSCLC). Furthermore, overactivation of vascular endothelial growth factor (VEGF) leads to increased angiogenesis implicated as an important factor in vascularization of those tumors. PATIENTS AND METHODS: Using tissue microarray technology, forty-paraffin (n = 40) embedded, histologically confirmed primary NSCLCs were cored and re-embedded into a recipient block. Immunohistochemistry was performed for the determination of EGFR and VEGF protein levels which were evaluated by the performance of computerized image analysis. EGFR gene amplification was studied by chromogenic in situ hybridization based on the use of EGFR gene and chromosome 7 centromeric probes. RESULTS: EGFR overexpression was observed in 23/40 (57.5%) cases and was correlated to the stage of the tumors (p = 0.001), whereas VEGF was overexpressed in 35/40 (87.5%) cases and was correlated to the stage of the tumors (p = 0.005) and to the smoking history of the patients (p = 0.016). Statistical significance was assessed comparing the protein levels of EGFR and VEGF (p = 0.043, k = 0.846). EGFR gene amplification was identified in 2/40 (5%) cases demonstrating no association to its overall protein levels (p = 0.241). CONCLUSIONS: A significant subset of NSCLC is characterized by EGFR and VEGF simultaneous overexpression and maybe this is the eligible target group for the application of combined anti-EGFR/VEGF targeted therapies at the basis of genetic deregulation .
Paradoxical low-flow, low-gradient aortic stenosis despite preserved left ventricular ejection fraction: new insights from weights of operatively excised aortic valves.

AIMS We reported that patients with small aortic valve area (AVA) and low flow despite preserved left ventricular ejection fraction (LVEF), i.e. 'paradoxical' low flow (PLF), have worse outcomes compared with patients with normal flow (NF), although they generally have a lower mean gradient (MG). The aortic valve weight (AVW) excised at the time of valve replacement is a flow-independent marker of stenosis severity. The objective of this study was to compare the AVW of patients with PLF and MG<40 mmHg with the AVW of patients with NF and MG≥40 mmHg. METHODS AND RESULTS We recruited 250 consecutive patients undergoing valve replacement (Cohort A) for severe stenosis. Among them, 33 (13%) were in PLF [LVEF > 50% but stroke volume index (SVi) ≤ 35 mL/m(2)] with MG < 40 mmHg (PLF-LG group) and 105 (42%) were in NF (LVEF > 50% and SVi > 35 mL/m(2)) with MG ≥ 40 mmHg (NF-HG group). Despite a much lower MG (29 ± 7 vs. 53 ± 10 mmHg; P < 0.0001), patients in the PLF-LG group had a similar AVA (0.73 ± 0.12 vs. 0.69 ± 0.13; P = 0.19) compared with those in the NF-HG group. The AVW [median (interquartile): 1.90 (1.63-2.50) vs. 2.60 (1.66-3.32)] and prevalence of bicuspid phenotype (15 vs. 42%) were lower in the PLF-LG group than in the NF-HG group. However, AVWs analysed separately in the tricuspid and bicuspid valves were similar in both groups [tricuspid valves: 1.80 (1.63-2.50) vs. 2.30 (1.58-3.00) g; P = 0.26 and bicuspid valves: 2.72 (1.73-3.61) vs. 2.60 (2.10-3.55) g; P = 0.93]. When using cut-point values of AVW established in another series of non-consecutive patients (n = 150, Cohort B) with NF and concordant Doppler-echocardiographic findings, we found that the percentage of patients with evidence of severe stenosis in Cohort A was 70% in patients with PLF-LG and 86% in patients with NF-HG. CONCLUSION The aortic valve weight data reported in this study provide evidence that a large proportion of patients with PLF and low-gradient have a severe stenosis and that the gradient may substantially underestimate stenosis severity in these patients. A multi-parametric approach including all Doppler-echocardiographic parameters of valve function as well as other complementary diagnostic tests may help correctly identify these patients.
Thirty Years of Research on Crown-of-Thorns Starfish (1986–2016): Scientific Advances and Emerging Opportunities

Research on the coral-eating crown-of-thorns starfish (CoTS) has waxed and waned over the last few decades, mostly in response to population outbreaks at specific locations. This review considers advances in our understanding of the biology and ecology of CoTS based on the resurgence of research interest, which culminated in this current special issue on the Biology, Ecology and Management of Crown-of-Thorns Starfish. More specifically, this review considers progress in addressing 41 specific research questions posed in a seminal review by P. Moran 30 years ago, as well as exploring new directions for CoTS research. Despite the plethora of research on CoTS (>1200 research articles), there are persistent knowledge gaps that constrain effective management of outbreaks. Although directly addressing some of these questions will be extremely difficult, there have been considerable advances in understanding the biology of CoTS, if not the proximate and ultimate cause(s) of outbreaks. Moving forward, researchers need to embrace new technologies and opportunities to advance our understanding of CoTS biology and behavior, focusing on key questions that will improve effectiveness of management in reducing the frequency and likelihood of outbreaks, if not preventing them altogether.
The evolution of memory and mental models using genetic programming

This paper applies genetic programming to the evolution of intelligent agents that gradually build internal representations of their surroundings for later use in planning. The method used allows for the creation of dynamically determined representations that are not predesigned by the human creator of the system. In an illustrative path-planning problem, evolved programs learn a model of their world and use this internal representation to plan their successive actions. The results show that the proposed method is successful in evolving programs that solve the planning problem and is thus a worthy basis for further investigation.
Signaling emotion in tagclouds

In order to create more attractive tagclouds that get people interested in tagged content, we propose a simple but novel tagcloud where font size is determined by tag's entropy value, not the popularity to its content. Our method raises users' emotional interest in the content by emphasizing more emotional tags. Our initial experiments show that emotional tagclouds attract more attention than normal tagclouds at first look; thus they will enhance the role of tagcloud as a social signaller.
A dissociative electron attachment cross-section estimator

Dissociative electron attachment (DEA) is the major process where molecules are destroyed in low-energy plasmas. DEA cross sections are therefore important for a whole variety of applications but are both hard to measure or compute accurately. A method for estimating DEA cross sections based a simple resonance plus survival model is presented. Test results are presented for DEA of molecular oxygen and molecular chlorine, for which experimental measurements are available for comparison, and SiBr and SiBr2, for which no previous data is available. The estimator has been implemented as part of Quantemol-N expert system which uses the R-matrix method to predict resonance positions and widths.
Modeling Software Execution Environment

Software execution environment, interfaced with software through library functions and system calls, constitutes an important aspect of the software's semantics. Software analysis ought to take the execution environment into consideration. However, due to lack of source code and the inherent implementation complexity of these functions, it is quite difficult to co-analyze software and its environment. In this paper, we propose to extend program synthesis techniques to construct models for system and library functions. The technique samples the behavior of the original implementation of a function. The samples are used as the specification to synthesize the model, which is a C program. The generated model is iteratively refined. We have developed a prototype that can successfully construct models for a pool of system and library calls from their real world complex binary implementations. Moreover, our experiments have shown that the constructed models can improve dynamic test generation and failure tolerance.
Neural Network Trainer with Second Order Learning Algorithms

Although neural networks have been around for over 20 years, we still have difficulties training them. Training is often difficult and time consuming. The paper describes a software (NNT) developed for neural network training. In addition to the traditional Error Back Propagation (EBP) algorithm, several second order algorithms were implemented. These algorithms are modifications of the Levenberg Marquet algorithm and they are able to train arbitrarily connected feedforward neural networks. In most cases the training process is more than 100 times faster than EBP training. These algorithms can also find solutions for very difficult networks where the EBP algorithm fails.
DQR: a probabilistic approach to diversified query recommendation

Web search queries issued by casual users are often short and with limited expressiveness. Query recommendation is a popular technique employed by search engines to help users refine their queries. Traditional similarity-based methods, however, often result in redundant and monotonic recommendations. We identify five basic requirements of a query recommendation system. In particular, we focus on the requirements of redundancy-free and diversified recommendations. We propose the DQR framework, which mines a search log to achieve two goals: (1) It clusters search log queries to extract query concepts, based on which recommended queries are selected. (2) It employs a probabilistic model and a greedy heuristic algorithm to achieve recommendation diversification. Through a comprehensive user study we compare DQR against five other recommendation methods. Our experiment shows that DQR outperforms the other methods in terms of relevancy, diversity, and ranking performance of the recommendations.
Big Data Processing Using Hadoop

The rapid growth of Internet and WWW has led to vast amounts of information available online. This information needs to be processed, analyzed, and linked to achieve correct Information. In order to store, manage, access, and process vast amount of data available online and the data that is created in structured and unstructured form, Data intensive computing is needed which satisfies the need to search, analyze, mine, and visualize the large amount of data and information. Nowadays various data intensive technologies (parallel, map reduce) are used which uses computing applications which require large volumes of data and devote most of their processing time to I/O and manipulation of data. We will analysis already available Data intensive technologies with Hadoop data intensive to provide high performance that should be fault resilient over hardware failures, reduce communications errors, and software bugs and execute a variety of data intensive analysis benchmarks. Keywords—Big Data Problem, Hadoop cluster, Hadoop Distributed File System, Parallel Processing, Map Reduce.