Miracle-dz/UniFAR-FTUs huggingface dataset:
Nepenthes Honeypots based Botnet Detection

the numbers of the botnet attacks are increasing day by day and the detection of botnet spreading in the network has become very challenging. Bots are having specific characteristics in comparison of normal malware as they are controlled by the remote master server and usually don't show their behavior like normal malware until they don't receive any command from their master server. Most of time bot malware are inactive, hence it is very difficult to detect. Further the detection or tracking of the network of theses bots requires an infrastructure that should be able to collect the data from a diverse range of data sources and correlate the data to bring the bigger picture in view.In this paper, we are sharing our experience of botnet detection in the private network as well as in public zone by deploying the nepenthes honeypots. The automated framework for malware collection using nepenthes and analysis using anti- virus scan are discussed. The experimental results of botnet detection by enabling nepenthes honeypots in network are shown. Also we saw that existing known bots in our network can be detected.
Serial Alberta Stroke Program Early CT Score From Baseline to 24 Hours in Solitaire Flow Restoration With the Intention for Thrombectomy Study: A Novel Surrogate End Point for Revascularization in Acute Stroke

Background and Purpose— The Alberta Stroke Program Early CT Score (ASPECTS) on baseline imaging is an established predictor of acute ischemic stroke outcomes. We analyzed change on serial ASPECTS at baseline and 24-hour imaging in the Solitaire Flow Restoration with the Intention for Thrombectomy (SWIFT) study to determine prognostic value and to identify subgroups with extensive injury after intervention. Methods— ASPECTS at baseline and 24 hours was independently scored in all anterior circulation SWIFT cases, blinded to all other trial data. ASPECTS at baseline, at 24 hours, and serial changes were analyzed with univariate and multivariate approaches. Results— One hundred thirty-nine patients (mean age, 67 [SD, 12] years; 52% women; median National Institutes of Health Stroke Scale, 18 [interquartile range, 8–28]) with complete data at both time points were studied. Multivariate analyses showed that higher 24-hour ASPECTS predicted good clinical outcome (day 90 modified Rankin Scale, 0–2; odds ratio, 1.67; P<0.001). Among patients with high baseline ASPECTS (8–10; n=109), dramatic infarct progression (decrease in ASPECTS ≥6 points at 24 hours) was noted in 31 of 109 (28%). Such serial ASPECTS change was predicted by higher baseline systolic blood pressure (P=0.019), higher baseline blood glucose (P=0.133), and failure to achieve Thrombolysis in Cerebral Infarction score of 2b/3 reperfusion (P<0.001), culminating in worse day 90 modified Rankin Scale outcomes (mean modified Rankin Scale, 4.4 versus 2.7; P<0.001). Conclusions— Twenty-four-hour ASPECTS provides better prognostic information compared with baseline ASPECTS. Predictors of dramatic infarct progression on ASPECTS are hyperglycemia, hypertension, and nonreperfusion. Serial ASPECTS change from baseline to 24 hours predicts clinical outcome, providing an early surrogate end point for thrombectomy trials. Clinical Trial Registration— URL: http://www.clinicaltrials. gov. Unique identifier: NCT01054560.
The Impact of Mental Illness Stigma on Seeking and Participating in Mental Health Care

Treatments have been developed and tested to successfully reduce the symptoms and disabilities of many mental illnesses. Unfortunately, people distressed by these illnesses often do not seek out services or choose to fully engage in them. One factor that impedes care seeking and undermines the service system is mental illness stigma. In this article, we review the complex elements of stigma in order to understand its impact on participating in care. We then summarize public policy considerations in seeking to tackle stigma in order to improve treatment engagement. Stigma is a complex construct that includes public, self, and structural components. It directly affects people with mental illness, as well as their support system, provider network, and community resources. The effects of stigma are moderated by knowledge of mental illness and cultural relevance. Understanding stigma is central to reducing its negative impact on care seeking and treatment engagement. Separate strategies have evolved for counteracting the effects of public, self, and structural stigma. Programs for mental health providers may be especially fruitful for promoting care engagement. Mental health literacy, cultural competence, and family engagement campaigns also mitigate stigma’s adverse impact on care seeking. Policy change is essential to overcome the structural stigma that undermines government agendas meant to promote mental health care. Implications for expanding the research program on the connection between stigma and care seeking are discussed.
The Comparison of Expressed Candidate Secreted Proteins from Two Arbuscular Mycorrhizal Fungi Unravels Common and Specific Molecular Tools to Invade Different Host Plants

Arbuscular mycorrhizal fungi (AMF), belonging to the fungal phylum Glomeromycota, form mutualistic symbioses with roots of almost 80% of land plants. The release of genomic data from the ubiquitous AMF Rhizophagus irregularis revealed that this species possesses a large set of putative secreted proteins (RiSPs) that could be of major importance for establishing the symbiosis. In the present study, we aimed to identify SPs involved in the establishment of AM symbiosis based on comparative gene expression analyses. We first curated the secretome of the R. irregularis DAOM 197198 strain based on two available genomic assemblies. Then we analyzed the expression patterns of the putative RiSPs obtained from the fungus in symbiotic association with three phylogenetically distant host plants—a monocot, a dicot and a liverwort—in comparison with non-symbiotic stages. We found that 33 out of 84 RiSPs induced in planta were commonly up-regulated in these three hosts. Most of these common RiSPs are small proteins of unknown function that may represent putative host non-specific effector proteins. We further investigated the expressed secretome of Gigaspora rosea, an AM fungal species phylogenetically distant from R. irregularis. G. rosea also presents original symbiotic features, a narrower host spectrum and a restrictive geographic distribution compared to R. irregularis. Interestingly, when analyzing up-regulated G. rosea SPs (GrSPs) in different hosts, a higher ratio of host-specific GrSPs was found compared to RiSPs. Such difference of expression patterns may mirror the restrained host spectrum of G. rosea compared to R. irregularis. Finally, we identified a set of conserved SPs, commonly up-regulated by both fungi in all hosts tested, that could correspond to common keys of AMF to colonize host plants. Our data thus highlight the specificities of two distant AM fungi and help in understanding their conserved and specific strategies to invade different hosts.
A Friendly Relationship between Endophytic Fungi and Medicinal Plants: A Systematic Review

Endophytic fungi or endophytes exist widely inside the healthy tissues of living plants, and are important components of plant micro-ecosystems. Over the long period of evolution, some co-existing endophytes and their host plants have established a special relationship with one and another, which can significantly influence the formation of metabolic products in plants, then affect quality and quantity of crude drugs derived from medicinal plants. This paper will focus on the increasing knowledge of relationships between endophytic fungi and medicinal plants through reviewing of published research data obtained from the last 30 years. The analytical results indicate that the distribution and population structure of endophytes can be considerably affected by factors, such as the genetic background, age, and environmental conditions of their hosts. On the other hand, the endophytic fungi can also confer profound impacts on their host plants by enhancing their growth, increasing their fitness, strengthening their tolerances to abiotic and biotic stresses, and promoting their accumulation of secondary metabolites. All the changes are very important for the production of bioactive components in their hosts. Hence, it is essential to understand such relationships between endophytic fungi and their host medicinal plants. Such knowledge can be well exploited and applied for the production of better and more drugs from medicinal plants.
Structural health monitoring robot using paired structured light

The displacement measurement in structural health monitoring (SHM), though important, was not popular due to inaccessibility and the huge size of the civil infrastructures. The frequently employed approaches use accelerometer, strain gauge, PZT, GPS, LVDT(Linear Variable Differential Transformer) etc., where most of them indirectly measure the displacement, require high cost, and are not so practical to install and maintain. Thus the development of SHM system that directly measures the displacement of the structure using low-cost sensors can be very helpful. In this paper, a multiple paired structured light (SL) system is proposed to be used as a structural displacement measurement. The proposed paired SL module is quite cheap to implement and can directly measure the accurate relative displacement between any two locations. Based on some simulations, a minimal configuration of the paired SL module is proposed. And the steepest descent and extended Kalman filtering-based displacement estimation methods are proposed by deriving a kinematic equation and its constraints. After building a prototype of the paired SL module, some real experiments are performed to test the feasibility of the system for the long-span structural displacement monitoring. The proposed system is expected to act also as a visual inspection and active sensing system if the mobility of the robot is utilized.
Outlier Responses Reflect Sensitivity to Statistical Structure in the Human Brain

We constantly look for patterns in the environment that allow us to learn its key regularities. These regularities are fundamental in enabling us to make predictions about what is likely to happen next. The physiological study of regularity extraction has focused primarily on repetitive sequence-based rules within the sensory environment, or on stimulus-outcome associations in the context of reward-based decision-making. Here we ask whether we implicitly encode non-sequential stochastic regularities, and detect violations therein. We addressed this question using a novel experimental design and both behavioural and magnetoencephalographic (MEG) metrics associated with responses to pure-tone sounds with frequencies sampled from a Gaussian distribution. We observed that sounds in the tail of the distribution evoked a larger response than those that fell at the centre. This response resembled the mismatch negativity (MMN) evoked by surprising or unlikely events in traditional oddball paradigms. Crucially, responses to physically identical outliers were greater when the distribution was narrower. These results show that humans implicitly keep track of the uncertainty induced by apparently random distributions of sensory events. Source reconstruction suggested that the statistical-context-sensitive responses arose in a temporo-parietal network, areas that have been associated with attention orientation to unexpected events. Our results demonstrate a very early neurophysiological marker of the brain's ability to implicitly encode complex statistical structure in the environment. We suggest that this sensitivity provides a computational basis for our ability to make perceptual inferences in noisy environments and to make decisions in an uncertain world.
Effects of Intravenous Adenosine on Antegrade Refractoriness of Accessory Atrioventricular Connections

Background Several groups have suggested the use of intravenous adenosine or adenosine triphosphate in the diagnosis of regular broad complex tachycardias. However, the short half-life of these agents has precluded assessment of their effects on refractoriness of accessory connections, and their safety in preexcited arrhythmias has not been demonstrated. Methods and Results We examined the effects of intravenous adenosine on accessory atrioventricular (AV) connections in 30 patients with the Wolff-Parkinson-White syndrome. Intravenous adenosine (12 mg, rapid bolus) was administered to 14 patients (group 1) during continuous atrial pacing at a cycle length 20 msec below that required to cause 2:1 conduction block in the accessory connection (mean pacing cycle length 261+41 msec). After adenosine, transient 1:1 conduction occurred via the accessory connection in 12 of 14 patients, indicating a shortening of antegrade refractoriness. In three of seven patients, this effect was abolished after intravenous propranolol (0.2 mg/kg). Nineteen patients (group 2) received adenosine (0.17 ± 0.04 mg/kg) during induced, preexcited atrial arrhythmias. The minimum RR interval during preexcited atrial fibrillation transiently decreased (252 ± 44 msec to 224 ± 35 msec, p< 0.01) after adenosine, but no change in average RR interval was observed (360 ± 59 msec to 357 ± 60 msec, NS). The preexcited ventricular response to atrial flutter was transiently accelerated in five of eight patients (415 ± 21 msec to 360 ± 49 msec, p< 0.05) due to shortening of flutter cycle length (207 ± 10 msec to 180 ± 24 msec, p< 0.05). However, 2:1 accessory connection conduction was maintained in all eight patients. All effects were short lived, with the decrease in RR interval during atrial fibrillation occurring for a maximum of two RR intervals only. No patient suffered ventricular arrhythmias or hemodynamic deterioration. Conclusions Adenosine shortens antegrade refractoriness of accessory AV connections, and in some patients this action is mediated by f3-adrenergic stimulation. Adenosine may cause acceleration of preexcited atrial arrhythmias, but these effects are transient and should not discourage the use of adenosine as a diagnostic agent in broad complex, regular tachycardias of uncertain origin.
Downstream promoter interactions of TFIID TAFs facilitate transcription reinitiation

TFIID binds promoter DNA to recruit RNA polymerase II and other basal factors for transcription. Although the TATA-Binding Protein (TBP) subunit of TFIID is necessary and sufficient for in vitro transcription, the TBP-Associated Factor (TAF) subunits recognize downstream promoter elements, act as co-activators, and interact with nucleosomes. Here we show that transcription induces stable TAF binding to downstream promoter DNA, independent of upstream contacts, TBP, or other basal transcription factors. This transcription-dependent TAF complex promotes subsequent activator-independent transcription, and promoter response to TAF mutations in vivo correlates with the level of downstream, rather than overall, Taf1 crosslinking. We propose a new model in which TAFs function as reinitiation factors, accounting for the differential responses of promoters to various transcription factor mutations.
NBCT : Naive Bayes Clustering Toolkit ( version 0 . 4 . 2 )

2 Getting started 2 2.1 What is clustering? 2 2.2 Target data . 2 2.3 Naive Bayes models . 3 2.4 Running NBCT . 4 2.4. 1 Basic usage . 4 2.4. 2 Output files . 4 2.4. 3 Various execution options . 5 2.4. 4 Auxiliary tool for post-processing . 5
Nonnegative/Binary matrix factorization with a D-Wave quantum annealer

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method takes a matrix as input and produces two low-rank matrices as output—one containing latent features in the data and another matrix describing how the features can be combined to approximately reproduce the input matrix. Despite the limited number of bits in the D-Wave hardware, this method is capable of handling a large input matrix. The D-Wave only limits the rank of the two output matrices. We apply this method to learn the features from a set of facial images and compare the performance of the D-Wave to two classical tools. This method is able to learn facial features and accurately reproduce the set of facial images. The performance of the D-Wave shows some promise, but has some limitations. It outperforms the two classical codes in a benchmark when only a short amount of computational time is allowed (200-20,000 microseconds), but these results suggest heuristics that would likely outperform the D-Wave in this benchmark.
High gain and broadband microstrip patch antenna using a superstrate layer

In this paper, the gain of the single patch with the superstrate is compared to that of the single patch without the superstrate and the experimental results of 2/spl times/8 array antenna with the superstrate, and 4/spl times/8 array antenna without the superstrate are shown. Details of the proposed antenna design are presented.
3D tracking of deformable surface by propagating feature correspondences

This paper addresses the problem of 3D tracking deformable surfaces undergoing non-rigid motion from multi-view video sequences. We propose a method that starts from a set of feature points which have been matched across views and time. A data-driven motion propagation technique makes the motion dense enough to give initial guess to the parameter estimation of the vertices of the 3D model. Finally after rejecting some outliers the 3D mesh model is deformed using the estimated vertex motion. Experimental results demonstrate the proposed method is accurate and robust even at a low frame rate and for medium elastic deformations.
Virtual Reality–Induced Cortical Reorganization and Associated Locomotor Recovery in Chronic Stroke: An Experimenter-Blind Randomized Study

Background and Purpose— Virtual reality (VR) is a new promising computer-assisted technology to promote motor recovery in stroke patients. VR-induced neuroplasticity supporting locomotor recovery is not known. We investigated the effects of VR intervention on cortical reorganization and associated locomotor recovery in stroke patients. Methods— Ten chronic stroke patients were assigned randomly to either the control group or the VR group. VR was designed to provide interactive real-life practice environments in which practice parameters can be individualized to optimize motor relearning. Laterality index (LI) in the regions of interests (ROIs) and locomotor recovery were measured before and after VR using functional MRI (fMRI) and standardized locomotor tests, respectively. The t test and nonparametric test were performed to compare the mean differences at P<0.05. Results— There was a significant difference in the interval change in the LI score for the primary sensorimotor cortex (SMC) between the groups (P<0.05), indicating that VR practice produced a greater increase in LI for the control group. However, the interval changes in the other ROIs were not significantly different (P>0.05). Motor function was significantly improved after VR (P<0.05). Conclusions— Our novel findings suggest that VR could induce cortical reorganization from aberrant ipsilateral to contralateral SMC activation. This enhanced cortical reorganization might play an important role in recovery of locomotor function in patients with chronic stroke. This is the first fMRI study in the literature that provides evidence for neuroplasticity and associated locomotor recovery after VR.
Racial/ethnic variation in clinical presentation, treatment, and survival among breast cancer patients under age 35

The age specific breast cancer incidence rate for African‐American women under age 35 is more than twice the rate for white women of similar age, and the mortality rate is more than three times higher. To determine factors that may explain racial/ethnic variation in outcomes among young women diagnosed with breast cancer, the authors examined the clinical presentation, treatment, and survival of African‐American, Hispanic, and white women under age 35 years.
Addiction and Cue-triggered Decision Processes

We propose a model of addiction based on three premises: (i) use among addicts is frequently a mistake; (ii) experience sensitizes an individual to environmental cues that trigger mistaken usage; (iii) addicts understand and manage their susceptibil-ities. We argue that these premises find support in evidence from psychology, neuroscience, and clinical practice. The model is tractable and generates a plausible mapping between behavior and the characteristics of the user, substance, and environment. It accounts for a number of important patterns associated with addiction, gives rise to a clear welfare standard, and has novel implications for policy. According to clinical definitions, substance addiction occurs when, after significant exposure , users find themselves engaging in compulsive , repeated, and unwanted use despite clearly harmful consequences, and often despite a strong desire to quit unconditionally (see, e.g., the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders , known as DSM-IV). There is widespread agreement that certain substances have addic-tive properties, 1 and there is some debate as to whether formal definitions of addiction should be expanded to include other substances (such as fats and sugars) and activities (such as shopping , shoplifting, sex, television viewing, and internet use). The consumption of addictive substances raises important social issues affecting members of all socioeconomic strata. 2 Tens of millions of Americans use addictive substances. Nearly 25 million adults have a history of alcohol dependence, and more than five million qualify as " hard-core " chronic drug users. Estimates for 1999 place total U.S. expenditures on tobacco products, alcoholic beverages, cocaine, heroin, marijuana, and methamphetamines at more than $150 billion, with still more spent on caffeine and addictive prescription drugs. Estimated social costs (health care, impaired productivity , crime, and so forth) total more than Bank of Minneap-olis, and the McArthur Preferences Network for useful comments and discussions. We also thank Luis Rayo, Daniel Quint, and John Hatfiled for outstanding research assistance. Rangel gratefully acknowledges financial support from the NSF (SES-0134618) and thanks the Hoover Institution for its financial support and stimulating research environment. This paper was prepared in part while B. Douglas Bernheim was a Fellow at the Center for Advanced Study in the Behavioral Sciences (CASBS), where he was supported in part by funds from the William and Flora Hewlett Foundation (Grant No. 2000-5633).
The Materials Science of Cosmetics

The cosmetics industry is increasingly using sophisticated materials and technologies in their products and to measure their effectiveness. Understanding how these products interact with skin, adhere to skin, and deliver a benefit or create a visible effect leads to better and safer products. An understanding of the optics and mechanics of skin and the cosmetics–skin interface is important for achieving various visual and functional effects. This issue reviews the underlying science and the technical challenges behind the development of materials for the personal care and cosmetics industry.