Miracle-dz/UniFAR-FTUs huggingface dataset:
An Authentication Scheme for Clustered Wireless Sensor Networks

Wireless sensor networks (WSN) has been widely applied to many fields such as disaster management and military operations. Clustering allows WSN more scalable and makes sensors Energy-saving. As the sensor nodes in WSN are all resource-limited devices and usually deployed unattended in the hazardous environments, security becomes more crucial. Authentication and key negotiation are the fundamental protocols to build a secure WSN. Public key algorithm and digital signature widely used in traditional networks are not very suitable for WSN. In this paper, we propose an authentication scheme for clustered WSN. The proposed scheme is based on LU decomposition which is less computation, storage, communication cost than the schemes based on public key technology. We also present security and performance analysis. Copyright © 2013 IFSA.
Molecular evidence for a kdr‐like pyrethroid resistance mechanism in the malaria vector mosquito Anopheles stephensi

Abstract. The mosquito Anopheles stephensi Liston (Diptera: Culicidae) is the urban vector of malaria in several countries of the Middle East and Indian subcontinent. Extensive use of residual insecticide spraying for malaria vector control has selected An. stephensi resistance to DDT, dieldrin, malathion and other organophosphates throughout much of its range and to pyrethroids in the Middle East. Metabolic resistance mechanisms and insensitivity to pyrethroids, so‐called knockdown resistance (kdr), have previously been reported in An. stephensi. Here we provide molecular data supporting the hypothesis that a kdr‐like pyrethroid‐resistance mechanism is present in An. stephensi. We found that larvae of a pyrethroid‐selected strain from Dubai (DUB‐R) were 182‐fold resistant to permethin, compared with a standard susceptible strain of An. stephensi. Activities of some enzymes likely to confer pyrethroid‐resistance (i.e. esterases, monooxygenases and glutathione S‐transferases) were significantly higher in the permethrin‐resistant than in the susceptible strain, but the use of synergists — piperonyl butoxide (PBO) to inhibit monooxygenases and/or tribufos (DEF) to inhibit esterases — did not fully prevent resistance in larvae (permethrin LC50 reduced by only 51–68%), indicating the involvement of another mechanism. From both strains of An. stephensi, we obtained a 237‐bp fragment of genomic DNA encoding segment 6 of domain II of the para type voltage‐gated sodium channel, i.e. the putative kdr locus. By sequencing this 237 bp fragment, we identified one point mutation difference involving a single A–T base change encoding a leucine to phenylalanine amino acid substitution in the pyrethroid‐resistant strain. This mutation appears to be homologous with those detected in An. gambiae and other insects with kdr‐like resistance. A diagnostic polymerase chain reaction assay using nested primers was therefore designed to detect this mechanism in An. stephensi.
LMNA variants cause cytoplasmic distribution of nuclear pore proteins in Drosophila and human muscle

Mutations in the human LMNA gene, encoding A-type lamins, give rise to laminopathies, which include several types of muscular dystrophy. Here, heterozygous sequence variants in LMNA, which result in single amino-acid substitutions, were identified in patients exhibiting muscle weakness. To assess whether the substitutions altered lamin function, we performed in vivo analyses using a Drosophila model. Stocks were generated that expressed mutant forms of the Drosophila A-type lamin modeled after each variant. Larvae were used for motility assays and histochemical staining of the body-wall muscle. In parallel, immunohistochemical analyses were performed on human muscle biopsy samples from the patients. In control flies, muscle-specific expression of the wild-type A-type lamin had no apparent affect. In contrast, expression of the mutant A-type lamins caused dominant larval muscle defects and semi-lethality at the pupal stage. Histochemical staining of larval body wall muscle revealed that the mutant A-type lamin, B-type lamins, the Sad1p, UNC-84 domain protein Klaroid and nuclear pore complex proteins were mislocalized to the cytoplasm. In addition, cytoplasmic actin filaments were disorganized, suggesting links between the nuclear lamina and the cytoskeleton were disrupted. Muscle biopsies from the patients showed dystrophic histopathology and architectural abnormalities similar to the Drosophila larvae, including cytoplasmic distribution of nuclear envelope proteins. These data provide evidence that the Drosophila model can be used to assess the function of novel LMNA mutations and support the idea that loss of cellular compartmentalization of nuclear proteins contributes to muscle disease pathogenesis.
Meta-analysis: increased mortality associated with hepatitis C in HIV-infected persons is unrelated to HIV disease progression.

BACKGROUND It is unclear whether coinfection with hepatitis C virus (HCV) increases mortality in patients with human immunodeficiency virus (HIV) infection during the era of highly active antiretroviral therapy (HAART). With use of a meta-analysis, we estimated the effect of HCV infection on HIV disease progression and overall mortality in the pre-HAART and HAART eras. METHOD The PubMed and EMBASE databases were searched for studies published through 30 April 2008. Additional studies were identified from cited references. Studies reporting disease progression or mortality among HCV-HIV coinfected patients were selected. Cross-sectional studies, studies without HCV-negative control subjects, and studies involving children and/or patients who had undergone liver transplantation were excluded. Two authors reviewed articles and extracted data on the demographic characteristics of study populations and risk estimates. Meta-regression was used to explore heterogeneity. RESULTS Ten studies from the pre-HAART era and 27 studies from the HAART era were selected. In the pre-HAART era, the risk ratio for overall mortality among patients with HCV-HIV coinfection, compared with that among patients with HIV infection alone, was 0.68 (95% confidence interval [CI], 0.53-0.87). In the HAART era, the risk ratio was 1.12 (95% CI, 0.82-1.51) for AIDS-defining events and 1.35 (95% CI, 1.11-1.63) for overall mortality among coinfected patients, compared with that among patients with HIV monoinfection. CONCLUSIONS HCV coinfection did not increase mortality among patients with HIV infection before the introduction of HAART. In contrast, in the HAART era, HCV coinfection, compared with HIV infection alone, increases the risk of mortality, but not the risk of AIDS-defining events. Future studies should determine whether successful treatment of HCV infection could reduce this excess risk of mortality in coinfected patients.
Metrics for the Evaluation of DisCSP: Some Experiments on Multi-robot Exploration

Many algorithms to solve Distributed Constraint Satisfaction Problems (DisCSP) have been introduced in the literature. In this paper, we propose to compare three different algorithms to solve DisCSP. Contrary to algorithms of the literature which are evaluated on graph coloring problems or uniform random binary DisCSPs, we use a multi-robot exploration problem. We show that, for this real world application, the comparison of algorithms may be improved by using additional metrics than those used in the literature. We will define other metrics that can be used for measuring different aspects of the multi-robot exploration problem. The aim of our attempt for defining metrics is to analyze and compare different aspects of complexity of this multi-robot problem. We will observe that using both classical and real world metrics is interesting to obtain a better and more precise comparison.
Interaction of information in word recognition.

Quantitative predictions are made from a model for word recognition. The model has as its central feature a set of "logogens": devices which accept information relevant to a particular word response irrespective of the source of this information. When more than a threshold amount of information has accumulated in any logogen, that particular response becomes available for responding. The model is tested against data available on the effect of word frequency on recognition, the effect of limiting the number of response alternatives, the interaction of stimulus and context, and the interaction of successive presentations of stimuli. The implications of the underlying model are largely upheld. Other possible models for word recognition are discussed as are the implications of the Logogen Model for theories of memory.
My Personal Trainer - An iPhone Application for Exercise Monitoring and Analysis

The obesity epidemic facing the Western world has been a topic of numerous discussions and research projects. One major issue preventing people from becoming more active and following health care recommendations is an increasingly busy life style and the lack of motivation, training, and available supervision. While the use of personal trainers increases in popularity, they are often expensive and must be scheduled in advance. In this research we developed a smartphone application, which assists users with learning and monitoring exercises. A key feature of the application is a novel algorithm for analysing accelerometer data and automatically counting repetitive exercises. This allows users to perform exercises anywhere and anytime, while doing other activities at the same time. The recording of exercise data allows users to track their performance, monitor improvements, and compare it with their goals and the performance of other users, which increases motivation. A usability study and feedback from a public exhibition indicates that users like the concept and find it helpful for supporting their exercise regime. The counting algorithm has an acceptable accuracy for many application scenarios, but has limitations with regard to complex exercises, small number of repetitions, and poorly performed exercises.
Why is dynamic analysis not used as extensively as static analysis: an industrial study

Code Assessments using static and dynamic analyses are important for the maintenance of code quality of software in the industry. These analyses, though understood to be beneficial, have several practical limitations. The intent of our study was to understand the usage of these analyses across various teams in Siemens during code assessments, reasons for the success of static analysis and the challenges in dynamic analysis. The evaluation was conducted through a survey of the practices of developers in different development teams. Additionally interviews were conducted with expert reviewers and their experiences documented. Our study has revealed that institutionalization of code quality initiatives and customized toolkit support for static analysis are some of the key reasons for the successful implementation of static analysis. Some of the problems hindering the widespread adoption of dynamic analysis include the direct dependency with test cases, scalability, and understandability of the results. Various implications of the results are discussed, including the possible improvements that could increase the acceptance of dynamic analysis on a regular basis.
Activation of human dendritic cells through CD40 cross-linking

Dendritic cells, the professional antigen-presenting cells (APC) involved in T cell priming, express CD40, a molecule which triggering plays a key role in B cell growth and differentiation as well as monocyte activation. Herein we demonstrate that dendritic Langerhans cells (D-Lc) generated by culturing cord blood CD34+ progenitor cells with granulocyte/macrophage colony-stimulating and tumor necrosis factor alpha (TNF-alpha) express functional CD40 at a density higher than that found on B cells. Culturing D-Lc on CD40-ligand (CD40L) transfected L cells allowed D-Lc survival as 50 +/- 15% of seeded cells were recovered after 4 d while only 5% survived over control L cells. CD40 activation induced important morphological changes with a reduction of cytoplasmic content and a remarkable increase of dendrite development as well as an altered phenotype. In particular, CD40 triggering induced maintenance of high levels of major histocompatibility complex class II antigens and upregulation of accessory molecules such as CD58, CD80 (B7-1) and CD86 (B7-2). CD40 engagement also seems to turn on D-Lc maturation as illustrated by upregulation of CD25, a molecule usually expressed on interdigitating dendritic cells of secondary lymphoid organs. Finally, CD40 activated D- Lc secreted a limited set of cytokines (TNF-alpha, IL-8, and macrophage inflammatory protein 1 alpha [MIP-1 alpha]) whereas a similar activation induced elutriated monocytes to secrete IL-1 alpha, IL-1 beta, IL-6, IL-8, IL-10, TNF-alpha, and MIP-1 alpha. As D-Lc activated T cells upregulated CD40L, it is likely that CD40 activation of D-Lc observed herein with a fibroblast cell line stably expressing CD40L, mimics physiological interactions between dendritic cells and T cells.
Decision Support Systems for Geo-Environmental Engineering with Specific Reference to Contaminated Land Investigation

ABSTRACT: Site investigation as the first stage of contaminated land remediation is an interdisciplinary, resource demanding operation that involves processes and activities from different fields, encompassing a wide range of professional expertise. Site investigation is a critically important stage as the findings provide the basis of any decision making, and as such any problems not dealt with at this stage affects the effectiveness of the whole remedial process. In this stage, Decision Support Systems as an emerging concept in geo-environmental projects provides a framework that helps deal with the uncertainties, complexities, non-linearities and potential risks, especially with their multiple and sometimes conflicting criteria. In this paper, a review of Decision Support Systems and the roles they have played in geo-environmental engineering will be explored.
An efficient algorithm for nonpreemptive periodic task scheduling under energy constraints

In this paper, we address the energy-aware DVS scheduling problems for nonpreemptive periodic task sets in real-time systems. Based on greedy method, we propose a novel heuristic, average power directed (APD), to maximize the system value under energy constraints, as well as keep the system functional during the whole mission period. Under the energy constrained situation given in this paper, experiments on large number of task sets demonstrate that compared with greedy method, APD method increased the system value by average 35.1%. Even compared with genetic algorithm (GA), the APD method achieves improvement by 7.3%. When the system becomes tighter energy constrained, and the energy efficiency of APD increases rapidly within large scope, which proves the energy awareness of this method. APD method has high performance and linear complexity, which facilitate it an efficient energy-aware task scheduling algorithm.
Wait-Free Gathering of Mobile Robots

The problem of gathering multiple mobile robots to a single location, is one of the fundamental problems in distributed coordination between autonomous robots. The problem has been studied and solved even for robots that are anonymous, disoriented, memoryless and operate in the semi-synchronous (ATOM) model. However all known solutions require the robots to be faulty-free except for the results of [Agmon and Peleg 2006] who solve the gathering problem in presence of one crash fault. This leaves open the question of whether gathering of correct robots can be achieved in the presence of multiple crash failures. We resolve the question in this paper and show how to solve gathering, when any number of robots may crash at any time during the algorithm, assuming strong multiplicity detection and chirality. In contrast it is known that for the more stronger byzantine faults, it is impossible to gather even in a 3-robot system if one robot is faulty. Our algorithm solves the gathering of correct robots in the semi-synchronous model where an adversary may stop any robot before reaching its desired destination. Further the algorithm is self-stabilizing as it achieves gathering starting from any configuration (except the bivalent configuration where deterministic gathering is impossible).
AdaMKL: A Novel Biconvex Multiple Kernel Learning Approach

In this paper, we propose a novel large-margin based approach for multiple kernel learning (MKL) using biconvex optimization, called Adaptive Multiple Kernel Learning (AdaMKL). To learn the weights for support vectors and the kernel coefficients, AdaMKL minimizes the objective function alternately by learning one component while fixing the other at a time, and in this way only one convex formulation needs to be solved. We also propose a family of biconvex objective functions with an arbitrary Lp-norm (p>=1) of kernel coefficients. As our experiments show, AdaMKL performs comparably with state-of-the-art convex optimization based MKL approaches, but its learning is much simpler and faster.
A fuzzy active relational database for employee performance evaluation

Many corporation and business rely on relational databases for their daily activities and decision making. Employee performance evaluation considered a very time consuming task faced by many large business mangers especially if it involved huge amount of transaction sales. This paper present a methodology based on fuzzy logic techniques to enhance this decision making process. The proposed methodology, involves of building a fuzzy set theory on top of a relational database to automatically generate series of fuzzy tables. These fuzzy tables can be queried for employee performance using SQL (Structured Query Language).
Online t-interval scheduling

This paper deals with non-preemptive online t-interval scheduling. A tinterval is a union of t half-open intervals (segments). In online scheduling the t-intervals are presented incrementally and each presented interval must be accepted or lost forever. A presented t-interval which overlaps a previously accepted t-interval cannot be accepted. The decision of whether or not to accept an interval is made without knowledge of the future. Scheduling t-intervals has an application in bandwidth allocation, transmission of continuous-media data, linear resource allocation and genomic sequence similarity. Online scheduling is of increasing importance in a highspeed world with an uncertain future. The most famous version of the problem is the interval scheduling problem (t = 1). This version has been analyzed both when it is online and offline. It has also been analyzed with weighted intervals. Scheduling t-intervals for t > 1 is on the other hand area covered to lesser extent. The performance of the algorithm is the ratio between the number of tintervals in its output vs. the optimal offline schedule. If the intervals are weighted, the performance is the ratio between the total weight of these sets. The maximum ratio, taken over all input instances, is the competitive ratio. The competitive ratio is measured with respect to different factors. One factor is the input size n. Another one is ∆, the ratio between the length of the longest and the shortest intervals.
Strategies for Guiding Interactive Search: An Empirical Investigation Into the Consequences of Label Relevance for Assessment and Selection

ABSTRACT When searching a novel Web page, people often estimate the likelihood that labeled links on the page will lead to their goal. A rational analysis of this activity suggests that people should adjust their estimate of the likelihood that any one item will lead to the goal in a manner that is sensitive to the context provided by the likelihoods that other items on the page will lead to the goal. Two experiments were designed to provide evidence to discriminate between this account and others found in the literature (e.g., satisficing and assess-all accounts). The experiments systematically manipulated the relevance of the distractor items and the location of the target item on the page. The results showed that (a) a high-value item was more likely to be selected when it was first encountered if the relevance of competing distractors was relatively low and (b) more items were assessed prior to selection when the distractors were of greater semantic relevance to the goal. The location manipulation showed that if more distractors were assessed prior to the target item, then the relevance of the distractors had a greater influence on the decision as to whether to select the target immediately. These results suggest that decisions as to when to select an item from the page are sensitive to the context provided by the likelihoods of all of the items so far assessed and not just to the most recent item. The findings are therefore inconsistent with both satisficing and assess-all accounts of interactive search.
Assessment of Social Services’ Disaster Risk Management: Case Finland

Local actors are crucial in disaster risk management; thus, their capacities should be assessed periodically. In this study, local social services&#8217; preparedness plans were assessed using Emergency Management Program Measurement and quantitative document analysis. The specific aim was to study how social service areas of various sizes were prepared for disasters in Finland. The results showed that disaster risk management capacity varied significantly between large and small service areas. Larger areas were better prepared for supporting management, and had better procedures to activate their response, modern public information and deeper cooperation with the private sector and the civil society. The differences might derive from the fact that large areas have more resources allocated to disaster risk management.
Facial Expression Analysis by Support Vector Regression

Real-time Facial Expression analysis is one the important topics in the development of the next generation affect-sensitive user interfaces. However current algorithms and techniques are computationally expensive, and therefore are not suitable for real-time applications. In this paper we present a real-time system for analyzing the six basic facial expressions of the human user, using regression analysis. In this research we applied linear lighting correction algorithm and contrast-limited adaptive histogram equalization to enhance facial features. Polygonized vector image are applied instead of pixel represented image.
Multilayer Change-Point Detection on Stock Order Flows by Wavelet Transformation

In empirical finance, the increase or decrease in the number of stock buy/sell orders is aroused by the information asymmetry, which eventually affects the change of the stock price. To monitor the change in the stock order flow, we propose a multilayer change-point detection algorithm which makes use of the multi-resolution property of wavelet transformation. We first detect the change-points in the lower level resolutions of wavelet transforms and then map them back to the points in the original time series. Different weights are assigned to the different levels for computing the confidence of the mapped points to be the change-points in the original time series. The change-points obtained by our method are more reliable than the change-points detected only from the original time series. The experiments on both artificial Poisson sequences and real-world stock order flows from Shanghai Stock Exchange (SSE) show the effectiveness of our detection method.