Miracle-dz/UniFAR-FTUs huggingface dataset:
Fédération et amélioration des activités documentaires par la pratique d'annotation collective. (Federating and Improving Document-related Activities Through Collective Annotation Practice)

Les activites documentaires couramment realisees sur les documents papier sont aujourd'hui transposees sur leurs homologues electroniques. Ainsi, une kyrielle de systemes permet de mener a bien les activites liees aux documents. Ils permettent notamment de rechercher de l'information utilisee pour rediger un document qui peut etre ensuite diffuse, exploite et organise par ses lecteurs dans leur espace documentaire. Notre etude des systemes existants a permis de reveler deux limites principales. Premierement, un systeme ne repond generalement qu'a une seule, voire a deux activites. Ce cloisonnement des activites est prejudiciable a la fois pour les usagers (qui doivent maitriser et jongler entre de nombreux outils) et pour les systemes (qui ne possedent qu'une representation parcellaire des besoins des usagers). Deuxiemement, les systemes n'exploitent pas les resultats des activites documentaires des membres organisationnels. Notre contribution comprend deux volets. Premierement, nous proposons un modele federant les activites documentaires autour de la pratique d'annotation collective. Des processus collectifs y sont associes afin d'exploiter chaque activite documentaire pour enrichir les autres, apportant ainsi une assistance a chaque individu en tirant parti du groupe, et vice versa. Le but de cette approche originale est double : simplifier l'acces et l'appropriation des documents tout en anticipant les besoins de l'usager pour lui offrir une assistance non intrusive. Deuxiemement, nous proposons d'exploiter les espaces documentaires des membres organisationnels. Bien qu'ils contiennent des informations a haute valeur pour l'organisation, collectees au prix de couteux efforts, ces espaces demeurent paradoxalement en sommeil. Afin de tirer parti de ces espaces documentaires, nous proposons une interface multi-facettes d'acces au capital documentaire d'une organisation. Cette interface permet l'exploration des documents et individus de l'organisation selon differents axes et niveaux de granularite. Nos propositions ont ete validees par differentes experimentations ainsi que par le developpement du prototype TafAnnote qui souligne la faisabilite de notre approche federant les activites documentaires autour de l'annotation collective.
Unsupervised outlier detection technique for intrusion detection in cloud computing

Outlier detection is becoming a recent area of research focus in data mining. Here we are proposing an efficient outlier detection concept DenOD (Density Based Outlier Detection) based on unsupervised method for intrusion detection in cloud computing environment. Unsupervised outlier detection techniques are playing big role in a various application domains such as network intrusion detection, fault detection and fraud detection. The beauty of unsupervised method is that, it does not require any training data set or any kind of previous knowledge. This technique can help to detect accurate and novel attacks without any previous knowledge. DenOD will implement on IDCC (Intrusion Detection in Cloud Computing) framework that has three components- Cloud nodes, IDS (Intrusion Detection System) and End User. This technique is capable to detect all kind of attacks as well as detect faulty services in cloud environment.
Isolation and Characterization of an Arterivirus Defective Interfering RNA Genome

ABSTRACT Equine arteritis virus (EAV), the type member of the family Arteriviridae, is a single-stranded RNA virus with a positive-stranded genome of approximately 13 kb. EAV uses a discontinuous transcription mechanism to produce a nested set of six subgenomic mRNAs from which its structural genes are expressed. We have generated the first documented arterivirus defective interfering (DI) RNAs by serial undiluted passaging of a wild-type EAV stock in BHK-21 cells. A cDNA copy of the smallest DI RNA (5.6 kb) was cloned. Upon transfection into EAV-infected BHK-21 cells, transcripts derived from this clone (pEDI) were replicated and packaged. Sequencing of pEDI revealed that the DI RNA was composed of three segments of the EAV genome (nucleotides 1 to 1057, 1388 to 1684, and 8530 to 12704) which were fused in frame with respect to the replicase reading frame. Remarkably, this DI RNA has retained all of the sequences encoding the structural proteins. By insertion of the chloramphenicol acetyltransferase reporter gene in the DI RNA genome, we were able to delimitate the sequences required for replication/DI-based transcription and packaging of EAV DI RNAs and to reduce the maximal size of a replication-competent EAV DI RNA to approximately 3 kb.
Digimouse: a 3D whole body mouse atlas from CT and cryosection data

We have constructed a three-dimensional (3D) whole body mouse atlas from coregistered x-ray CT and cryosection data of a normal nude male mouse. High quality PET, x-ray CT and cryosection images were acquired post mortem from a single mouse placed in a stereotactic frame with fiducial markers visible in all three modalities. The image data were coregistered to a common coordinate system using the fiducials and resampled to an isotropic 0.1 mm voxel size. Using interactive editing tools we segmented and labelled whole brain, cerebrum, cerebellum, olfactory bulbs, striatum, medulla, masseter muscles, eyes, lachrymal glands, heart, lungs, liver, stomach, spleen, pancreas, adrenal glands, kidneys, testes, bladder, skeleton and skin surface. The final atlas consists of the 3D volume, in which the voxels are labelled to define the anatomical structures listed above, with coregistered PET, x-ray CT and cryosection images. To illustrate use of the atlas we include simulations of 3D bioluminescence and PET image reconstruction. Optical scatter and absorption values are assigned to each organ to simulate realistic photon transport within the animal for bioluminescence imaging. Similarly, 511 keV photon attenuation values are assigned to each structure in the atlas to simulate realistic photon attenuation in PET. The Digimouse atlas and data are available at http://neuroimage.usc. edu/Digimouse.html.
Rabies Group-Specific Ribonucleoprotein Antigen and a Test System for Grouping and Typing of Rhabdoviruses

Cell-associated ribonucleoprotein (RNP) was isolated from BHK-21 cells infected with several strains of rabies and rabies-related viruses. The RNP-antigen from rabies and related viruses induced the formation of complement-fixing, precipitating, and immunofluorescent antibodies, and proved to be the group-specific antigen common to all rabies viruses. Antigens of the envelope which induce virus-neutralizing antibodies are apparently determinative for the serotype of a virus as evidenced by two-way neutralization tests. A combination of these methods seems to be a useful approach to the serological grouping and typing of rhabdoviruses.
Inhibition of TNF‐α reduces transplant arteriosclerosis in a murine aortic transplant model

Experimental and clinical data provide evidence that TNF‐α contributes to acute and chronic allograft rejection. In this study, we explored the effect of TNF‐α blockade using the chimeric monoclonal antibody infliximab on the development of transplant arterisoclerosis in a fully mismatched aortic allograft model. Post‐transplant treatment of CBA (H2k) recipients with 250 μg infliximab (cumulative dose 1.25 mg) reduced luminal occlusion of C57Bl/6 (H2b) aortic grafts on day 30 from 77 ± 5% in untreated controls to 52 ± 6%. Increasing the dose of anti‐TNF‐α antibody had no further beneficial effect. Treatment with human control immunoglobulin had no effect on intima proliferation. Under TNF‐α blockade, ICAM‐1 and PDGF mRNA expression within the grafts was strongly reduced, whereas iNOS expression was enhanced. The data show that TNF‐α blockade using infliximab can reduce the development of transplant arteriosclerosis in fully mismatched murine aortic grafts.
DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides

Antimicrobial peptides (AMPs) are anti-infectives that may represent a novel and untapped class of biotherapeutics. Increasing interest in AMPs means that new peptides (natural and synthetic) are discovered faster than ever before. We describe herein a new version of the Database of Antimicrobial Activity and Structure of Peptides (DBAASPv.2, which is freely accessible at http://dbaasp.org). This iteration of the database reports chemical structures and empirically-determined activities (MICs, IC50, etc.) against more than 4200 specific target microbes for more than 2000 ribosomal, 80 non-ribosomal and 5700 synthetic peptides. Of these, the vast majority are monomeric, but nearly 200 of these peptides are found as homo- or heterodimers. More than 6100 of the peptides are linear, but about 515 are cyclic and more than 1300 have other intra-chain covalent bonds. More than half of the entries in the database were added after the resource was initially described, which reflects the recent sharp uptick of interest in AMPs. New features of DBAASPv.2 include: (i) user-friendly utilities and reporting functions, (ii) a ‘Ranking Search’ function to query the database by target species and return a ranked list of peptides with activity against that target and (iii) structural descriptions of the peptides derived from empirical data or calculated by molecular dynamics (MD) simulations. The three-dimensional structural data are critical components for understanding structure–activity relationships and for design of new antimicrobial drugs. We created more than 300 high-throughput MD simulations specifically for inclusion in DBAASP. The resulting structures are described in the database by novel trajectory analysis plots and movies. Another 200+ DBAASP entries have links to the Protein DataBank. All of the structures are easily visualized directly in the web browser.
Pattern-Based Vulnerability Discovery

With our increasing reliance on the correct functioning of computer systems, identifying and eliminating vulnerabilities in program code is gaining in importance. To date, the vast majority of these flaws are found by tedious manual auditing of code conducted by experienced security analysts. Unfortunately, a single missed flaw can suffice for an attacker to fully compromise a system, and thus, the sheer amount of code plays into the attacker’s cards. On the defender’s side, this creates a persistent demand for methods that assist in the discovery of vulnerabilities at scale. This thesis introduces pattern-based vulnerability discovery, a novel approach for identifying vulnerabilities which combines techniques from static analysis, machine learning, and graph mining to augment the analyst’s abilities rather than trying to replace her. The main idea of this approach is to leverage patterns in the code to narrow in on potential vulnerabilities, where these patterns may be formulated manually, derived from the security history, or inferred from the code directly. We base our approach on a novel architecture for robust analysis of source code that enables large amounts of code to be mined for vulnerabilities via traversals in a code property graph, a joint representation of a program’s syntax, control flow, and data flow. While useful to identify occurrences of manually defined patterns in its own right, we proceed to show that the platform offers a rich data source for automatically discovering and exposing patterns in code. To this end, we develop different vectorial representations of source code based on symbols, trees, and graphs, allowing it to be processed with machine learning algorithms. Ultimately, this enables us to devise three unique pattern-based techniques for vulnerability discovery, each of which address a different task encountered in day-to-day auditing by exploiting a different of the three main capabilities of unsupervised learning methods. In particular, we present a method to identify vulnerabilities similar to a known vulnerability, a method to uncover missing checks linked to security critical objects, and finally, a method that closes the loop by automatically generating traversals for our code analysis platform to explicitly express and store vulnerable programming patterns. We empirically evaluate our methods on the source code of popular and widely-used open source projects, both in controlled settings and in real world code audits. In controlled settings, we find that all methods considerably reduce the amount of code that needs to be inspected. In real world audits, our methods allow us to expose many previously unknown and often critical vulnerabilities, including vulnerabilities in the VLC media player, the instant messenger Pidgin, and the Linux kernel.
Motion artifact reduction in photoplethysmogram signals based on Periodic Component Factorization

Continuous blood pressure measurement based on pulse transit time (PTT) is a deeply research topic over recent decades. Advanced algorithms have been proposed by scholars to give satisfactory estimation in stationary position. Nevertheless, pulse transit time (PTT) is shown to be strongly affected by hand movement and the estimation of blood pressure is no longer accurate under strenuous exercise. Because of this, a novel algorithm called Periodic Component Factorization (PCF), which is an extension of Independent Component Analysis (ICA), for better removal of motion artifact (MA) from photoplethysmography (PPG) signals is proposed in this paper. Compared to FastICA algorithm based on nongaussianity such as kurtosis and skewness, PCF is able to extract dependent source components from noisy signals when the PPG signal shows quasi-periodicity or periodicity. This newly proposed algorithm undoubtedly shows its practicality and effectiveness in removing motion artifact of PPG signals.
Local Area Prediction-Based Mobile Target Tracking in Wireless Sensor Networks

Tracking mobile targets in wireless sensor networks (WSNs) has many important applications. As it is often the case in prior work that the quality of tracking (QoT) heavily depends on high accuracy in localization or distance estimation, which is never perfect in practice. These bring a cumulative effect on tracking, e.g., target missing. Recovering from the effect and also frequent interactions between nodes and a central server result in a high energy consumption. We design a tracking scheme, named t-Tracking, aiming to achieve two major objectives: high QoT and high energy efficiency of the WSN. We propose a set of fully distributed tracking algorithms, which answer queries like whether a target remains in a “specific area” (called a “face” in localized geographic routing, defined in terms of radio connectivity and local interactions of nodes). When a target moves across a face, the nodes of the face that are close to its estimated movements compute the sequence of the target's movements and predict when the target moves to another face. The nodes answer queries from a mobile sink called the “tracker”, which follows the target along with the sequence. t-Tracking has advantages over prior work as it reduces the dependency on requiring high accuracy in localization and the frequency of interactions. It also timely solves the target missing problem caused by node failures, obstacles, etc., making the tracking robust in a highly dynamic environment. We validate its effectiveness considering the objectives in extensive simulations and in a proof-of-concept system implementation.
Optical Verification of Mouse Event Accuracy

Optical verification of mouse movements can independently verify the integrity of mouse event data without the overhead of software instrumentation. We propose a methodology to accurately validate mouse sensor values utilizing objecttracking techniques on physical mouse movements. By capturing mouse movements with a camera, applying morphological image processing operators, and a probabilistic Hough transform, we demonstrate a proof of concept that compares the mouse movement observed by the camera verified by mouse event data captured on a computer. Our software utilizes OpenCV libraries and can run real time on an Android device or on a computer for post-hoc analysis. Potential use cases for tracking mouse data includes verifying sensor problems, for example, poor tracking or skipping and calibration of a mouse. This data can also be used to determine the validity of mouse event data as an input to various software anti-cheat methods.
Staying Safe While Consuming Alcohol: A Qualitative Study of the Protective Strategies and Informational Needs of College Freshmen

Objective: In this qualitative study, the authors examined how students attempt to minimize harm to themselves and others when drinking. Participants: The authors recruited freshmen at a large, mid-Atlantic US public university during the fall semester of 2005 to participate in 8 focus groups. Methods: The moderator's guide was developed through an iterative process that included input from experts and pilot testing. The researchers audiotaped focus group conversations, transcribed them, and subjected them to an interrater reliability check. Analysis was based on the framework of Information-Motivation-Behavioral Skills Model and a phenomenological approach. Results: College students have a repertoire of coping strategies they use in an attempt to safeguard themselves and their friends from harm when drinking. Strategies encompass planning a safe context for drinking, using safety measures to minimize harm when drinking, and taking care of someone who has consumed too much alcohol. Conclusions: A harm-reduction focus that acknowledges and builds on existing protective strategies may be a promising avenue for alcohol interventions.
Analyzing hardware based malware detectors

Detection of malicious software at the hardware level is emerging as an effective solution to increasing security threats. Hardware based detectors rely on Machine Learning(ML) classifiers to detect malware-like execution pattern based on Hardware Performance Counters(HPC) information at run-time. The effectiveness of these learning methods mainly relies on the information provided by expensive-to-implement limited number of HPC. This paper is the first attempt to thoroughly analyze various robust machine learning methods to classify benign and malware applications. Given the limited availability of HPC the analysis results help guiding architectural decision on what hardware performance counters are needed most to effectively improve ML classification accuracy. For software implementation we fully implemented these classifier at OS Kernel to understand various software overheads. The software implementation of these classifiers are found to be relatively slow with the execution time in the range of milliseconds, order of magnitude higher than the latency needed to capture malware at run-time. This is calling for hardware accelerated implementation of these algorithms. For hardware implementation, we have synthesized the studied classifier models on FPGA to compare various design parameters including logic area, power, and latency. The results show that while complex ML classifier such as MultiLayerPerceptron and logistics are achieving close to 90% accuracy, after taking into consideration their implementation overheads, they perform worst in terms of PDP, accuracy/area and latency compared to simpler but slightly less accurate rule based and tree based classifiers. Our results further show OneR to be the most cost-effective classifier with more than 80% accuracy and fast execution time of less than 10ns, achieving highest accuracy per logic area, while mainly relying on only a single branch-instruction HPC information.
On the Cover Time for Random Walks on Random Graphs

The cover time, C, for a simple random walk on a realization, GN, of [Gscr ](N, p), the random graph on N vertices where each two vertices have an edge between them with probability p independently, is studied. The parameter p is allowed to decrease with N and p is written on the form f(N)/N, where it is assumed that f(N)[ges ]c log N for some c>1 to asymptotically ensure connectedness of the graph. It is shown that if f(N) is of higher order than log N, then, with probability 1−o(1), (1−ε)N log N[les ]E[C[mid ]GN] [les ](1+ε)N log N for any fixed ε>0, whereas if f(N)=O(log N), there exists a constant a>0 such that, with probability 1−o(1), E[C[mid ]GN] [ges ](1+a)N log N. It is furthermore shown that if f(N) is of higher order than (log N)3 then Var(C[mid ]GN)= o((N log N)2) with probability 1−o(1), so that with probability 1−o(1), the stronger statement that (1−ε)N log N[les ]C[les ](1+ε)N log N holds.
Testing for ARCH in the presence of additive outliers

In this paper we investigate the properties of the Lagrange Multiplier (LM) test for autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) in the presence of additive outliers (AOs). We show analytically that both the asymptotic size and power are adversely affected if AOs are neglected: the test rejects the null hypothesis of homoscedasticity too often when it is in fact true, while the test has difficulty detecting genuine GARCH effects. Several Monte Carlo experiments show that these phenomena occur in small samples as well. We design and implement a robust test, which has better size and power properties than the conventional test in the presence of AOs. We apply the tests to a number of US macroeconomic time series, which illustrates the dangers involved when nonrobust tests for ARCH are routinely applied as diagnostic tests for misspecification.
QBD*: A Graphical Query Language with Recursion

A system to query databases using diagrams as a standard user interface is proposed. The system, called Query by Diagram* (QBD*), makes use of a conceptual data model, a query language on this model, and a graphical user interface. The conceptual model is the entity-relationship model. The query language, whose expressive power allows recursive queries, supports visual interaction. The main characteristics of the interface are ease of use and the availability of a rich set of primitives for schema selection and query formulation. The expressive power of QBD* and G/sup +/, which are the only languages allowing recursive queries to be expressed graphically are compared. >
The diagnostic and prognostic value of interleukin-10 in cerebrospinal fluid for central nervous system lymphoma: a meta-analysis

Abstract Central nervous system lymphoma (CNSL) presents diagnostic and prognostic challenges. The aim of this meta-analysis was to evaluate the diagnostic and prognostic value of interleukin (IL)-10 in cerebrospinal fluid (CSF) for CNSL comprehensively. PubMed and Cochrane Library databases were searched through September 2016. Four studies with 212 CNSL patients and 262 control patients were included. The pooled sensitivity and specificity of CSF IL-10 for diagnosing CNSL were 81% (95% CI: 66–91%) and 97% (95% CI: 83–100%), respectively. The summary receiver operating characteristic (SROC) curve indicated that the area under the curve was 0.95 (0.93–0.97). The ROC curve based on extracted individual data showed that the optimal cutoff value was 6.88 pg/ml. Moreover, elevated CSF IL-10 was found to be associated with shorter progression-free survival (hazard ratio: 2.89, 95% CI: 1.13–7.41, p = .027). In conclusion, our meta-analysis showed that CSF IL-10 is an effective diagnostic and prognostic biomarker for CNSL.