Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential effects of white noise in cognitive and perceptual tasks

Beneficial effects of noise on higher cognition have recently attracted attention. Hypothesizing an involvement of the mesolimbic dopamine system and its functional interactions with cortical areas, the current study aimed to demonstrate a facilitation of dopamine-dependent attentional and mnemonic functions by externally applying white noise in five behavioral experiments including a total sample of 167 healthy human subjects. During working memory, acoustic white noise impaired accuracy when presented during the maintenance period (Experiments 1–3). In a reward based long-term memory task, white noise accelerated perceptual judgments for scene images during encoding but left subsequent recognition memory unaffected (Experiment 4). In a modified Posner task (Experiment 5), the benefit due to white noise in attentional orienting correlated weakly with reward dependence, a personality trait that has been associated with the dopaminergic system. These results suggest that white noise has no general effect on cognitive functions. Instead, they indicate differential effects on perception and cognition depending on a variety of factors such as task demands and timing of white noise presentation.
An SDN-based approach to enhance the end-to-end security: SSL/TLS case study

End-to-end encryption is becoming the norm for many applications and services. While this improves privacy of individuals and organizations, the phenomenon also raises new kinds of challenges. For instance, with the increase of devices using encryption, the volumes of outdated, exploitable encryption software also increases. This may create some distrust amongst the users against security unless its quality is enforced in some ways. Unfortunately, deploying new mechanisms at the end-points of the communication is challenging due to the sheer volume of devices, and modifying the existing services may not be feasible either. Hence, we propose a novel method for improving the quality of the secure sessions in a centralized way based on the SDN architecture. Instead of inspecting the encrypted traffic, our approach enhances the quality of secure sessions by analyzing the plaintext handshake messages exchanged between a client and server. We exploit the fact that many of today's security protocols negotiate the security parameters such as the protocol version, encryption algorithms or certificates in plaintext in a protocol handshake before establishing a secure session. By verifying the negotiated information in the handshake, our solution can improve the security level of SSL/TLS sessions. While the approach can be extended to many other protocols, we focus on the SSL/TLS protocol in this paper because of its wide-spread use. We present our implementation for the OpenDaylight controller and evaluate its overhead to SSL/TLS session establishment in terms of latency.
Faculty and teacher candidate computer self-efficacy and the relationship of faculty computer self-efficacy, technology professional development, and technology use (louisiana)

The purpose of this study was to examine the possible relationship among faculty computer self-efficacy, technology professional development, and the extent of technology use in Louisiana's college and university classrooms. Additionally, faculty computer self-efficacy and teacher candidate computer self-efficacy were compared. Participation was voluntary, involving higher education faculty and teacher candidates from the nineteen teacher preparation institutions within Louisiana. Faculty completed online surveys. A 30-item Likert-type scale was used to measure computer self-efficacy. Faculty also reported their involvement in technology training over the last five years and their technology use in the classroom. Teacher candidates completed either an online or print copy of the computer self-efficacy survey. Data analysis involved a factor analysis of the computer self-efficacy scale, the identification of five computer self-efficacy constructs, a 4 x 3 ANOVA, and contingency coefficients to determine the correlation of nonparametric items. Results of this study indicate a significant relationship between faculty computer self-efficacy and the extent of technology use. A significant relationship between technology professional development and technology use was also detected. Analysis of the data failed to confirm an interactive effect between faculty computer self-efficacy and technology professional development related to technology use. Results suggest an inverse relationship between tenure and computer self-efficacy, with observed tenured faculty members' computer self-efficacy scores lower than expected scores. The results of this study also found no difference in faculty members' computer self-efficacy and teacher candidates' computer self-efficacy.
Official Publications of the European Community: A Valuable Resource for U.S. Libraries

publications of the European Communities (EC) for U.S. librarians working in a wide range of subject areas and in a variety of libraries. After offering a synopsis of the Com munity's history, structure, and function, it looks in par ticular at the agencies responsible for Community publica tions and the Communities' depository system. The article then presents an annotated bibliography illustrating the breadth and character of EC official publications. In its fi nal part, the article describes the main acquisitions tools and other bibliographic aids issued by the Community as guides to its publishing output.
Data Mining Prediction of Shovel Cable Service Lifespan

Using data mining technology (fuzzy mining technology), a reasonable and effective method to predict the lifespan of shovel cables is proposed. Shovel cables are expected to last approximately 2000 hours of operation. However, current lifespan range from 400 to over 1800 hours over an entire shovel fleet. The proposed approach can discover the correlation (i.e., the degree of fuzzy association) between a cable 's lifespan and operating variables. The degree of fuzzy association is defined based on the distribution of the variables for the lifespan and the concept of semantic proximity (SP) between two lifespan. In addition we adopt the inverse document frequency (IDF) weight function to measure the weights of the variables in order to superpose the association degrees. Given the proximity degree (PD) between two time-series, the time-series can be successfully classified using the fuzzy equivalence partition method. To implement the method, we introduce "growing window", "scaling", and approximate computation pruning techniques in order to reduce both I/O and CPU costs. Extensive experiments on real datasets are conducted, and the experimental results are analyzed thoroughly.
Inference in Binary Pair-wise Markov Random Fields through Self-Avoiding Walks

In a recent result, Weitz [31] established equivalence between the marginal distribution of a node, say v, in any binary pair-wise Markov Random Field (MRF), say G, with the marginal distribution of the root node in the selfavoid walk tree of the G starting at v. In this paper, we exploit this remarkable connection to obtain insights in the performance of the widely popular Belief Propagation heuristic for computing marginal distribution (sum-product) and max-marginal distribution (max-product). We obtain a tight characterization of the size of self-avoiding walk tree for any connected graph as a function of number of edges. This may be of interest in its own right.
Anchor supervised distance estimation in anisotropic wireless sensor networks

Distance estimation is a key issue in range-free localization algorithms for wireless sensor networks. Approaches that assume isotropy of networks, such as Dv-hop and Gradient, cannot obtain accurate distance estimations in anisotropic sensor networks thus are not applicable to such networks. The anisotropy of sensor networks comes from two aspects: uneven nodal distribution and irregularity of deployment region. Existing localization algorithms for anisotropic wireless sensor networks usually only deal with one of the two aspects. In this paper, we propose an anchor supervised distance estimation approach which can simultaneously cope with both of the two aspects. In this approach, an anchor node selects a “friendly” subset from all other anchor nodes to which its distance estimates are accurate and broadcasts the selection result to neighboring common nodes. The common nodes then use these friendly anchors to perform distance estimation. We analyze distance estimation accuracy of this approach through extensive simulations. The results show that, compared with Dv-hop, our proposed approach dramatically reduces distance estimation error in anisotropic wireless sensor networks with an average factor of 67%. Consequently, the localization error of Dv-hop is reduced by an average factor of 71% if enhanced with our distance estimation approach.
A learning based approach for reduction of the pendulum behavior of ducted-fan helicopter

Pendulum-like behavior of the helicopter is an effect occurs in the case where the movement of the body of the helicopter lags with respect to its rotors, leading to undesired oscillations when the helicopter is required to hover at desired positions. In this paper, an online learning based controller is designed to dampen the oscillation by coupling a two layers forward neural network with a classical PD control method. The neural network is able to approximate the nonlinear dynamics of the pendulum in an online manner, thus improving the overall performance of the linear PD controller. Simulations results have shown that the proposed technique is capable of suppressing the undesired oscillation in an automated manner and alleviating the needs for adjusting the tuning parameters of the controller.
Contingent Valuation and Endangered Species: Methodological Issues and Applications

Part 1 Environmental values - the institutional and biological setting for species conservation: the institutional setting - the Victorian flora and fauna guarantee act endangered species in Victoria. Part 2 Welfare economic principles of species conservation: welfare economic principles and issues the economics of species conservation. Part 3 The contingent valuation method: the contingent valuation method estimating changes in welfare from discrete choice surveys. Part 4 The survey application to species conservation: a contingent valuation survey of endangered species in Victoria estimation of willingness to pay. Part 5 Conclusions: conservation values - estimation and methodological inference from contingent valuation to species conservation policy.
Sentiment Analysis for Cantonese Opinion Mining

With the dramatic growth of E-commerce's popularity, the amount of products and services reviews are increasing rapidly. Many researchers put effects on mining user opinions from reviews. There are many research works in opinion mining. However, all current methods are focusing on how to handling the opinion mining for English language, Chinese, Japanese and so on. There lacks works on how to conduct Cantonese Opinion Mining. To solve the problem, in the paper, we propose a method to handle sentiment analysis for Cantonese opinion mining.
Aggregate two-way co-clustering of ads and user analysis for online advertisements

Clustering plays an important role in data mining, as it is used by many applications as a preprocessing step for data analysis. Traditional clustering focuses on grouping similar objects, while two-way co-clustering can group dyadic data (objects as well as their attributes) simultaneously. In this research, we apply two-way co-clustering to the analysis of online advertising where both ads and users need to be clustered. The key data that connect ads and users are contained in the user-ad link matrix, which denotes the ads that a user has linked. We proposed a three-staged clustering that makes use of the three data matrices to enhance clustering performance. In addition, an iterative cross co-clustering algorithm is also proposed for two-way co-clustering. The experiment is performed using the advertisement and user data from Morgenstern, a financial social website that focuses on the agent of advertisements. The result shows that three staged clustering provides better performance than traditional clustering, while iterative co-clustering completes the task more efficiently.
Manganese Superoxide Dismutase Mediates the Early Release of Mitochondrial Cytochrome C and Subsequent DNA Fragmentation after Permanent Focal Cerebral Ischemia in Mice

Recent studies have shown that release of mitochondrial cytochrome c is a critical step in the apoptosis process. We have reported that cytosolic redistribution of cytochrome c in vivooccurred after transient focal cerebral ischemia (FCI) in rats and preceded the peak of DNA fragmentation. Although the involvement of reactive oxygen species in the cytosolic redistribution of cytochrome cin vitro has been suggested, the detailed mechanism by which cytochrome c release is mediated in vivo has not yet been established. Also, the role of mitochondrial oxidative stress in cytochrome c release is unknown. These issues can be addressed using knock-out mutants that are deficient in the level of the mitochondrial antioxidant manganese superoxide dismutase (Mn-SOD). In this study we examined the subcellular distribution of the cytochrome c protein in both wild-type mice and heterozygous knock-outs of the Mn-SOD gene (Sod2 −/+) after permanent FCI, in which apoptosis is assumed to participate. Cytosolic cytochrome c was detected as early as 1 hr after ischemia, and correspondingly, mitochondrial cytochrome c showed a significant reduction 2 hr after ischemia (p< 0.01). Cytosolic accumulation of cytochrome c was significantly higher in Sod2 −/+ mice compared with wild-type animals (p < 0.05). N-benzyloxycarbonyl-val-ala-asp-fluoromethyl ketone (z-VAD.FMK), a nonselective caspase inhibitor, did not affect cytochrome c release after ischemia. A significant amount of DNA laddering was detected 24 hr after ischemia and increased in Sod2 −/+ mice. These data suggest that Mn-SOD blocks cytosolic release of cytochrome c and could thereby reduce apoptosis after permanent FCI.
Grit, a GTPase-Activating Protein for the Rho Family, Regulates Neurite Extension through Association with the TrkA Receptor and N-Shc and CrkL/Crk Adapter Molecules

ABSTRACT Neurotrophins are key regulators of the fate and shape of neuronal cells and act as guidance cues for growth cones by remodeling the actin cytoskeleton. Actin dynamics is controlled by Rho GTPases. We identified a novel Rho GTPase-activating protein (Grit) for Rho/Rac/Cdc42 small GTPases. Grit was abundant in neuronal cells and directly interacted with TrkA, a high-affinity receptor for nerve growth factor (NGF). Another pool of Grit was recruited to the activated receptor tyrosine kinase through its binding to N-Shc and CrkL/Crk, adapter molecules downstream of activated receptor tyrosine kinases. Overexpression of the TrkA-binding region of Grit inhibited NGF-induced neurite elongation. Further, we found some tendency for neurite promotion in full-length Grit-overexpressing PC12 cells upon NGF stimulation. These results suggest that Grit, a novel TrkA-interacting protein, regulates neurite outgrowth by modulating the Rho family of small GTPases.
Ambient Noise Tomography in the Western US using Data from the EarthScope / USArray Transportable Array

The teleseismic transmission of seismic surface waves from earthquakes results in a loss of the high frequencies needed to infer information about Earth’s crust and uppermost mantle. A recent innovation in seismic imaging based on using long time sequences of ambient seismic noise moves beyond some of the limitations imposed on earthquakebased methods to reveal high resolution information about the crust and uppermost mantle. This method is called Ambient Noise Tomography (ANT) and has been applied predominantly to seismic surface waves. The application of ANT to data from ambitious new deployments of seismic arrays, such as the EarthScope USArray in the United States, has led to the development of large-scale seismic models of the earth’s crust and uppermost mantle at unprecedented resolution. In addition, new methods of data analysis and interpretation of ambient noise data that exploit the array nature of the TA are currently under development and hold out the promise for more reliable information about crustal and uppermost mantle anisotropy
Comparative Study on Buck and Buck-Boost DC-DC Converters for MPP Tracking for Photovoltaic Power Systems

This research work addresses a comparative examination of the two basic non-isolated DC-DC converters that could be interfaced effectively for maximum power point tracking (MPPT) in photovoltaic (PV) systems via tracking algorithm of controlling the duty ratio of these converters. Examination of two famous DC-DC convertor topologies i.e. buck, and buck-boost converters has been performed here to scrutinize the behavior of converter behavior relating to changing atmospheric attributes, sequentially the deviation in the duty ratio (due to MPPT), and tracking efficiency. With the variant in the atmospheric conditions, the working value of resistance at the maximum power point (Rmpp) varies. In order to efficiently operate the system at the maximum power point, the MPPT algorithm must make the system work near to the value of Rmpp for the intermittent atmospheric pattern of varying insolation and temperature. The effectiveness of the MPPT algorithm can be scaled by this very obligation. The simulation study verifies that, although buck, and buck-boost converters are implemented as power converters for MPPT control, they are don't equally efficient. The consequence of diverse loads having values different to Rmpp on converter-side output is analyzed for the two important topologies, and it is inferred that the buck-boost converter topology most efficiently tracks the maximum power point (MPP) in case of varying temperature, insolation, and loading effect.
Language Models and Topic Models for Personalizing Tag Recommendation

More and more content on the Web is generated by users. To organize this information and make it accessible via current search technology, tagging systems have gained tremendous popularity. Especially for multimedia content they allow to annotate resources with keywords (tags) which opens the door for classic text-based information retrieval. To support the user in choosing the right keywords, tag recommendation algorithms have emerged. In this setting, not only the content is decisive for recommending relevant tags but also the user's preferences. In this paper we introduce an approach to personalized tag recommendation that combines a probabilistic model of tags from the resource with tags from the user. As models we investigate simple language models as well as Latent Dirichlet Allocation. Extensive experiments on a real world dataset crawled from a big tagging system show that personalization improves tag recommendation, and our approach significantly outperforms state-of-the-art approaches.
On-the-fly Point Clouds through Histogram Pyramids

Image Pyramids, as created during a reduction process of 2D image maps, are frequently used in porting non-local algorithms to graphics hardware. A Histogram pyramid (short: HistoPyramid), a special version of image pyramid, collects the number of active entries in a 2D image. We show how a HistoPyramid can be utilized as an implicit indexing data structure, allowing us to convert a sparse 3D volume into a point cloud entirely on the graphics hardware. In the generalized form, the algorithm reduces a highly sparse matrix with N elements to a list of its M active entries in O(N) + M (log N) steps, despite the restricted graphics hardware architecture. Our method can be used to deliver new and unusual visual effects, such as particle explosions of arbitrary geometry models. Beyond this, the algorithm is able to accelerate feature detection, pixel classification and binning, and enable high-speed sparse matrix compression.
How to Get a Perfectly Random Sample from a Generic Markov Chain and Generate a Random Spanning Tree of a Directed Graph

A general problem in computational probability theory is that of generating a random sample from the state space of a Markov chain in accordance with the steady-state probability law of the chain. Another problem is that of generating a random spanning tree of a graph or spanning arborescence of a directed graph in accordance with the uniform distribution, or more generally in accordance with a distribution given by weights on the edges of the graph or digraph. This article gives algorithms for both of these problems, improving on earlier results and exploiting the duality between the two problems. Each of the new algorithms hinges on the recently introduced technique of coupling from the past or on the linked notions of loop-erased random walk and “cycle popping. ”
No trade-off between the size and timing of emergence in the damselfly, Calopteryx virgo L.

Many species of insects have been reported to show seasonally declining size at emergence. This has been explained as an adaptive response to time constraint between size and age at maturity (emergence). We studied seasonal variation in the size of damselfly Calopteryx virgo L. in six different creeks in central Finland. The length of hind wings was measured for 942 males and 285 females covering the flying period from mid June to mid August. The length of the hind wings of damselflies did not decrease towards the end of flying period in any river or either sex. In light of this study, seasonal reduction of body size is not a general phenomenon among odonates contrary to common understanding.
MODEL BASED VISUAL RELATIVE MOTION ESTIMATION AND CONTROL OF A SPACECRAFT UTILIZING COMPUTER

An algorithm is developed for estimating the motion (relative attitude and relative position) of large pieces of space debris, such as failed satellite. The algorithm is designed to be used by a debris removal space robot which would perform six degreeof-freedom motion control (control its position and attitude simultaneously). The information required as feedback signals for such a controller is relative–position, velocity, attitude and angular velocity–and these are expected to be measured or estimated from image data. The algorithm uses a combination of stereo vision, 3D model matching, applying the ICP (Iterative Closest Point) algorithm, and extended Kalman Filter to increase the reliability of estimates. To evaluate the algorithm, a simulator is prepared to simulate the on-orbit optical environment in terrestrial experiments, and the motion of a miniature satellite model is estimated using images obtained from the simulator. In addition to that, six DOF(Degrees Of Freedom) manoeuvre simulation with developed algorithm for motion estimation using image data was succefully tried for the proximity flight around a failed satellite utilizing CG(Computer Graphics).
Modeling Herd Trajectory Data Warehouse

Studding the movement of animals and their behavior become an important subject. Indeed, such study can generate useful information used next to assist the decision makers involved in different domains such as agriculture, security, health care and business. We present in this work a trajectory model related to the displacement of the herd, which allows the generation of trajectory data that is going to be stored into trajectory data warehouse. The animals in the herd change continuously their location, so the collected information varies over the space and the time. To facilitate their study we present the herd by a point so we focus on the moving points. Such supposition facilitates the modeling of the trajectory.