Miracle-dz/UniFAR-FTUs huggingface dataset:
Test-case prioritization with model-checkers

The order in which test-cases are executed has an influence on the rate at which faults can be detected. In this paper we demonstrate how test-case prioritization can be performed with the use of model-checkers. For this, different well known prioritization techniques are adapted for model-based use. New property based prioritization techniques are introduced. In addition it is shown that prioritization can be done at test-case generation time, thus removing the need for test-suite post-processing. Several experiments are used to show the validity of these ideas.
Social Engineering in Social Networking sites: How Good becomes evil

Social Engineering (ES) is now considered the great security threat to people and organizations. Ever since the existence of human beings, fraudulent and deceptive people have used social engineering tricks and tactics to trick victims into obeying them. There are a number of social engineering techniques that are used in information technology to compromise security defences and attack people or organizations such as phishing, identity theft, spamming, impersonation, and spaying. Recently, researchers have suggested that social networking sites (SNSs) are the most common source and best breeding grounds for exploiting the vulnerabilities of people and launching a variety of social engineering based attacks. However, the literature shows a lack of information about what types of social engineering threats exist on SNSs. This study is part of a project that attempts to predict a persons’ vulnerability to SE based on demographic factors. In this paper, we demonstrate the different types of social engineering based attacks that exist on SNSs, the purposes of these attacks, reasons why people fell (or did not fall) for these attacks, based on users’ opinions. A qualitative questionnaire-based survey was conducted to collect and analyse people’s experiences with social engineering tricks, deceptions, or attacks on SNSs.
The First Steps of Transposable Elements Invasion

Transposable elements are often considered as selfish DNA sequences able to invade the genome of their host species. Their evolutive dynamics are complex, due to the interaction between their intrinsic amplification capacity, selection at the host level, transposition regulation, and genetic drift. Here, we propose modeling the first steps of TE invasion, i.e., just after a horizontal transfer, when a single copy is present in the genome of one individual. If the element has a constant transposition rate, it will disappear in most cases: the elements with low-transposition rate are frequently lost through genetic drift, while those with high-transposition rate may amplify, leading to the sterility of their host. Elements whose transposition rate is regulated are able to successfully invade the populations, thanks to an initial transposition burst followed by a strong limitation of their activity. Self-regulation or hybrid dysgenesis may thus represent some genome-invasion parasitic strategies.
An improved PSO based on Initial selection of particles

In this paper, an attempt has been made to develop an Improved Particle Swarm Optimization based on Initial selection of particles (IPSO IS). IPSO IS has been implemented on four mathematical benchmark functions and its performance has been compared to Basic Particle Swarm Optimization (BPSO). It was observed that IPSO IS resulted in faster convergence and more accurate results.
Various temperature effects on the growth of carbon nanotubes (CNTs) by thermal chemical vapor deposition (TCVD) method

The growth behavior of carbon nanotubes synthesized from C2H2 at the temperature range of 700 to 900°C and using thermal chemical vapor deposition (TCVD) method has been investigated. Thin films of nickel (Ni) catalyst, were coated on SiO2 substrates by ion beam sputtering technique. The formation of catalytic nickel nanoparticles was much increased when using NH3 as the environment gas which implies both the formation and the separation of graphitic layer. Scanning electron microscopy (SEM) and Raman results show that the temperature of growth is the effective parameter on the formation of carbon nanotubes (CNTs).
An Optimal Energy Efficient Video Distribution Over Cooperative Wireless Networks

Recently supporting real-time video transmission services with diverse QoS constraints has become one of the essential requirements for wireless communications networks. Consequently, how to efficiently guarantee QoS for video broadcast attracts more and more research attention. However, the unstable wireless environments and the popular layer structured video signals impose a great deal of challenges in delay QoS provisionings. Due to the highly varying wireless channels, the deterministic delay QoS requirements are usually hard to guarantee. As a result, statistical delay QoS guarantees, in terms of effective bandwidth or capacity and queue-length-bound or delay-bound violation probabilities, have been proposed and demonstrated as the powerful way to characterize delay QoS provisioning for wireless traffics. Besides QOS, many energy-efficiency techniques for wireless networks can be found in the literature, which include lower- layers adaptation, optimization and end-users applications specifically designed to save energy. Thus energy-efficiency is becoming a key factor for the successful deployment of wireless networks. The development and validation of mechanisms for energy- efficient video broadcasting require the measurement of the energy consumption needed for video transmission. This paper presents an optimal energy efficient video transmission for cooperative wireless networks.
Combining the named-entity recognition task and NP chunking strategy for robust pre-processing1

In this paper we aim at outlining the joint exploitation of two nominal grammars – named-entity grammar and chunk grammar in the process of building a treebank. Their contribution towards unified and effective NP shallow parser is stressed upon. Taking into account their specific underlying principles, the points of interrelation are discussed and related problems are pointed out.
Tourism activity recognition and discovery based on improved LDA model

LDA (Latent Dirichlet Allocation) model is a kind of unsupervised learning model which can extract the hidden topic from text in recent years. In this paper, we proposed a novel LDA model based on the traditional LDA model, which is integrated into the information of text category (Activity-topic LDA). In this paper, the Activity-topic LDA is proposed to improve the original latent Dirichlet allocation (LDA) model. On the basis of the LDA, the proposed method adds the tourism activity information, and obtains the probability distribution model of the tourism activities. Based on this model, we can identify and discover the theme of tourism activities.
Long-Term Depression of NMDA Receptor-Mediated Synaptic Transmission Is Dependent on Activation of Metabotropic Glutamate Receptors and Is Altered to Long-Term Potentiation by Low Intracellular Calcium Buffering

Synaptic plasticity of NMDA receptor (NMDAR)-mediated transmission was investigated in the rat dentate gyrus in vitro. Isolated NMDAR EPSCs were recorded from granule cells of the dentate gyrus in response to stimulation of the medial perforant path. Long-term potentiation (LTP) or long-term depression (LTD) of NMDAR EPSCs was observed in response to brief high-frequency stimulation (HFS), with the direction and extent of plasticity dependent on the concentration and type (EGTA vs BAPTA) of the intracellular Ca2+ buffer. LTD was induced in higher concentrations of EGTA and BAPTA than LTP, and BAPTA was ∼100-fold more potent than EGTA. Although LTD was induced in a high concentration of EGTA (10 mm), a high concentration of BAPTA (10 mm) blocked both LTP and LTD. LTP of AMPA receptor (AMPAR)-EPSCs exhibited a lower dependency on Ca2+ buffering than LTP of NMDAR EPSCs, because LTP of AMPAR EPSCs was induced by HFS in high EGTA (10 mm). We also identified a role for metabotropic glutamate receptor 5 (mGluR5) in NMDAR plasticity. HFS LTD was blocked by the group I/II mGluR antagonist LY341495 ((2S)-2-amino-2-[(1S, 2S)-2-carboxycycloprop-1-yl]-3(xanth-9-yl)propanoic acid) and by the mGluR5-selective antagonist 2-methyl-6-(phenylethynyl)pyridine hydrochloride (MPEP). Similarly, low-frequency stimulation-induced LTD of NMDAR EPSCs was also blocked by MPEP. These findings suggest that the direction of plasticity of NMDARs is determined by the intracellular free Ca2+ concentration and is dependent on activation of mGluR5.
Research on the Embedded Security Architecture Based on the Control Flow Security

As an important parameter reflecting the system’s behavior evidence, control flow has been the focus for attackers to carry out attacks on the security of a system. However, most of the security guards for control flow have been resting on the software level without considering the system architecture. Moreover, the current security guarding methods for control flow have much higher demands for the system’s resources, and they are not suitable for the embedded application environment which has limited resources. To solve these problems, this paper introduces a security guarding architecture which combines the Cache with the random permutation. This security guarding architecture takes full advantage of the Cache’s modulation function on the system’s performance and the restraining function on the control flow information leakage. Combining with the random permutation algorithm, it provides with a high level security guard for the system control flow, and brings a better balance between the system security and the system efficiency.
Ranking model selection and fusion for effective microblog search

Re-ranking was shown to have positive impact on the effectiveness for microblog search. Yet existing approaches mostly focused on using a single ranker to learn some better ranking function with respect to various relevance features. Given various available rank learners (such as learning to rank algorithms), in this work, we mainly study an orthogonal problem where multiple learned ranking models form an ensemble for re-ranking the retrieved tweets than just using a single ranking model in order to achieve higher search effectiveness. We explore the use of query-sensitive model selection and rank fusion methods based on the result lists produced from multiple rank learners. Base on the TREC microblog datasets, we found that our selection-based ensemble approach can significantly outperform using the single best ranker, and it also has clear advantage over the rank fusion that combines the results of all the available models.
Stability and bifurcation analysis in a delayed predator-prey model with harvesting

In this paper, we investigate a delayed stage-structured predator-prey model with continuous harvesting on the prey and Ivlev's functional response. Positivity and boundness of solutions are obtained. Furthermore, we investigate the stability and Hopf bifurcations by analyzing the distribution of the roots of associated characteristic equation. A explicit algorithm for determining the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions are derived by using the normal form and the center manifold theory.
Exploratory Graphics for Functional Actigraphy Data

Actigraphy is an emerging technology for measuring a patient’s overall activity level nearly continuously over time. An actigraph is a watch–like device attached to the wrist or ankle that uses an accelerometer to measure movement every minute, or even more often. Actigraphy data can best be described as functional data. In this paper, we will present exploratory graphical displays for various components of functional actigraphy data.
Effects of new cultivars of ninebark on feeding and ovipositional behavior of the specialist ninebark beetle, Calligrapha spiraeae (Coleoptera: Chrysomelidae)

Additional index words. Physocarpus opulifolius, resistance, anthocyanins, pest management Abstract. Two newly developed cultivars of ninebark (Physocarpus opulifolius (L.) Maxim. ), a yellow-leaved cultivar called 'Dart's Gold' and a purple-leaved cultivar called 'Monlo', were compared with the native for resistance to feeding and oviposition by the specialist ninebark beetle (Calligrapha spiraeae (Say)). When offered the choice between two types of ninebark, beetles statistically preferred native (P < 0.0001) and 'Dart's Gold' (P < 0.0001) over 'Monlo', but showed no preference between native and 'Dart's Gold' (P = 0.0743). Long-term performance bioassays showed significantly more eggs on 'Dart's Gold' than 'Monlo' (P = 0.0468). 'Monlo' contained the most anthocyanins (P < 0.0001) and chlorophyll B (P < 0.0001) and the least total nitrogen (P = 0.0283) and Kjeldahl nitrogen (P = 0.0014). Anthocyanins are known to act as feeding deterrents and bind with nitrogen, which may explain why beetles avoided feeding on 'Monlo' in preference tests.
Adaboost face detection algorithm based on correlation of classifiers

In order to enhance the ensemble of the traditional Adaboost algorithm and reduce its complexity, two improved Adaboost algorithms are proposed, which are based on the correlation of classifiers. In the algorithm, Q-statistic is added in the training weak classifiers, every weak classifier is related not only to the current classifier, but also to previous classifiers as well, which can effectively reduce the weak classifier similarity. Simulations result in CMU (Carnegies Mellon University) show that the algorithms are of better detection rate and lower false alarm rate, compared with traditional Adaboost algorithm and FloatBoost.
Improving Robustness of Stopping Multi-objective Evolutionary Algorithms by Simultaneously Monitoring Objective and Decision Space

Appropriate stopping criteria for multi-objective evolutionary algorithms (MOEA) are an important research topic due to the computational cost of function evaluations, particularly on real-world problems. Most common stopping criteria are based on a fixed budget of function evaluations or the monitoring of the objective space. In this work, we propose a stopping criterion based on monitoring both the objective and decision space of a problem. Average Hausdorff distance (AHD) and genetic diversity are used, respectively. Two-sided t-tests on the slope coefficients after regression analyses are used to detect the stagnation of the AHD and the genetic diversity. The approach is implemented for two widely used MOEAs: NSGA-II and SPEA2. It is compared to a fixed budget, the online convergence detection approach, and the individual monitoring of each space on four bi-objective and two three-objective benchmark problems. Our experimental results reveal that the combined approach achieved significantly better results than the approaches considering only one of the spaces. In particular, we find that the combined consideration runs longer and hence more robustly ensures a well-approximated Pareto front. Nevertheless, on average 29% and 17% function evaluations are saved for NSGA-II and SPEA2, respectively, compared to standard budget recommendations.
Making it practical and effective: Fast and precise May-Happen-in-Parallel analysis

May-Happen-in-Parallel (MHP) analysis is a very important and fundamental mechanism to facilitate concurrent program analysis. But the limitation of its efficiency keep it away from being practical and effective in analyzing large scale real world concurrent programs. We proposed a novel MHP algorithm by performing a reachability analysis on a so-called parallel reachability graph of a program. The MHP algorithm mainly comprises two phases: pre-computation of initial MHP information and top-down propagation of this information along the parallel reachability graph. Our algorithm is fast as it has a low complexity O(|N|+|E|), in which N is the number of nodes in the parallel reachability graph and E is the number of edges in this graph. Our preliminary experiment on 13 concurrent programs indicates that our approach is extremely faster than two state-of-art approaches, respectively achieving a relative geometry average speed up of 395.53× and 136.37×, while yielding the same precision with these two approaches.
Fuzzy Stabilization Loop of One Axis Gimbal System

The application of inertial stabilization system is to stabilize the sensor’s line of sight toward a target by isolating the sensor from the disturbances induced by the operating environment. The purpose of this paper is to present a model of control servo system for one axis gimbal mechanism using fuzzy PID type controller. The gimbals torque relationships are derived using Newton’s law considering the base angular motion and dynamic unbalance. Then, the stabilization loop is constructed and the proposed fuzzy controller is designed. The overall control system is simulated using MATLAB/Simulink, then the system performance is investigated in different cases for both conventional PI and fuzzy PID controller. A comparison study is made based on some performance criteria. The results obtained in different conditions confirms that a further improved system performance can be achieved using the proposed fuzzy controller as compared to the conventional PI controller. The simulation results proves the efficiency of the proposed fuzzy controller which offers a better response than PI one, and improves further the transient and the steady-state performance. General Terms Inertial stabilization systems, fuzzy control, servo systems
Evidence That the Origin of Naked Kernels During Maize Domestication Was Caused by a Single Amino Acid Substitution in tga1

teosinte glume architecture1 (tga1), a member of the SBP-box gene family of transcriptional regulators, has been identified as the gene conferring naked kernels in maize vs. encased kernels in its wild progenitor, teosinte. However, the identity of the causative polymorphism within tga1 that produces these different phenotypes has remained unknown. Using nucleotide diversity data, we show that there is a single fixed nucleotide difference between maize and teosinte in tga1, and this difference confers a Lys (teosinte allele) to Asn (maize allele) substitution. This substitution transforms TGA1 into a transcriptional repressor. While both alleles of TGA1 can bind a GTAC motif, maize-TGA1 forms more stable dimers than teosinte-TGA1. Since it is the only fixed difference between maize and teosinte, this alteration in protein function likely underlies the differences in maize and teosinte glume architecture. We previously reported a difference in TGA1 protein abundance between maize and teosinte based on relative signal intensity of a Western blot. Here, we show that this signal difference is not due to tga1 but to a second gene, neighbor of tga1 (not1). Not1 encodes a protein that has 92% amino acid similarity to TGA1 and that is recognized by the TGA1 antibody. Genetic mapping and phenotypic data show that tga1, without a contribution from not1, controls the difference in covered vs. naked kernels. No trait differences could be associated with the maize vs. teosinte alleles of not1. Our results document how morphological evolution can be driven by a simple nucleotide change that alters protein function.
Algorithm for correlation of static and dynamic obstacle avoidance for non-holonomic mobile robot

This paper presents the problem of path planning technique for a mobile robot. The mobile robot has nonholonomic constraints. This path planning is an important issue in mobile robots. Specially, when the environment has obstacles, path planning has to find a collision free path for mobile robot to move from the initial position to the final position. This path planning is optimized for the minimum distance. Kinematics model of the non-holonomic mobile robot has been considered. By the derived kinematics model of the system a family of feasible trajectories and their steering control has been derived to avoid collision with the obstacles. The developed algorithm is quite suitable for the dynamic environment.
Factor graph approach to distributed facility location in large-scale networks

In this paper, we present a new approach to solving the distributed facility location problem using the recent modeling and computational methodology of factor graph and message-passing. We first formulate the problem as finding a valid network configuration that minimizes the overall cost. We then represent the problem using a factor graph, and derive simplified, localized, broadcast-based message-passing rules which can elect a near-optimal set of facility nodes in a few iterations. Simulation results for small-world network topologies show that the algorithm is able to achieve good convergence rate and approximation ratio, and scalable to the network size.
Neuroscience and Biobehavioral Reviews Review Concerns about Cultural Neurosciences: a Critical Analysis

Ten years ago, neuroscientists began to study cultural phenomena by using functional MRI. Since then the number of publications in this field, termed cultural neuroscience (CN), has tremendously increased. In these studies, particular concepts of culture are implied, but rarely explicitly discussed. We argue that it is necessary to make these concepts a topic of debate in order to unravel the foundations of CN. From 40 fMRI studies we extracted two strands of reasoning: models investigating universal mechanisms for the formation of cultural groups and habits and, models assessing differences in characteristics among cultural groups. Both strands simplify culture as an inflexible set of traits and specificities. We question this rigid understanding of culture and highlight its hidden evaluative nature. Open access under CC BY-NC-ND license. Open access under CC BY-NC-ND license.
A Study of Improving the Performance of Mining Multi-Valued and Multi-Labeled Data

Nowadays data mining algorithms are successfully applying to analyze the real data in our life to provide useful suggestion. Since some available real data is multi-valued and multi-labeled, researchers have focused their attention on developing approaches to mine multi-valued and multi- labeled data in recent years. Unfortunately, there are no algorithms can discretize multi-valued and multi-labeled data to improve the performance of data mining. In this paper, we proposed a novel approach to solve this problem. Our approach is based on a statistical-based discretization metric and the simulated annealing search algorithm. Experimental results show that our approach can effectively improve the performance of the-state-of-art multi-valued and multi-labeled classification algorithm.
Predicting Protein-Protein Interactions with Collaborative Filtering Algorithms

Collaborative Filtering (CF) has proven to be a valuable tool for predicting relevant items in many different domains from e-commerce to information retrieval. In this paper we explore the applicability of CF to predicting protein-protein interactions by using the interaction patterns between first and second order neighbors in the interaction network. We investigate three different prediction strategies, describe the algorithms that support the prediction process and evaluate the performance of the methods on the Saccharomyces cerevisiae interaction network containing 3360 proteins and 13669 interactions. We find that for a large number of proteins the methods perform surprisingly well, on average being able to correctly predict missing interactions in 20% of cases! The most significant factor affecting the quality of predictions appears to be the number of interactions already known about a given protein. It is equally intriguing that this prediction performance is achieved without using any kind of prior biological knowledge during the prediction process. We believe that with a judicious use of such knowledge the quality of the predictions could be greatly improved.