Miracle-dz/UniFAR-FTUs huggingface dataset:
Bactericidal/Permeability-Increasing Protein Is an Enhancer of Bacterial Lipoprotein Recognition

Adequate perception of immunologically important pathogen-associated molecular patterns like lipopolysaccharide and bacterial lipoproteins is essential for efficient innate and adaptive immune responses. In the context of Gram-negative infection, bactericidal/permeability-increasing protein (BPI) neutralizes endotoxic activity of lipopolysaccharides, and thus prohibits hyperactivation. So far, no immunological function of BPI has been described in Gram-positive infections. Here, we show a significant elevation of BPI in Gram-positive meningitis and, surprisingly, a positive correlation between BPI and pro-inflammatory markers like TNFα. To clarify the underlying mechanisms, we identify BPI ligands of Gram-positive origin, specifically bacterial lipopeptides and lipoteichoic acids, and determine essential structural motifs for this interaction. Importantly, the interaction of BPI with these newly defined ligands significantly enhances the immune response in peripheral blood mononuclear cells (PBMCs) mediated by Gram-positive bacteria, and thereby ensures their sensitive perception. In conclusion, we define BPI as an immune enhancing pattern recognition molecule in Gram-positive infections.
Now You See Me: Real-time Dynamic Function Call Detection

Efficient detection and instrumentation of function calls is fundamental for a variety of dynamic analysis techniques, including dynamic callgraph construction, control-flow integrity, and automatic vulnerability discovery. A common way of detecting calls at the machine code level is to look for CALL instructions. However, optimizing compilers frequently implement function tail calls with JMP instructions instead, and distinguishing an intra-procedural jump from a JMP-based function call is not straightforward. Despite the importance of making this distinction, prior research has not produced a reliable solution. In this paper, we address the problem of dynamic function call detection in real-time. We propose a heuristic-based approach named iCi to efficiently and automatically instrument calls, including conventional CALLs and JMP-based calls, at runtime. iCi does not rely on source code, debug information, symbol tables or static analysis. We show that iCi achieves an f-score of 0.95 in the worst case, regardless of optimization level. We open-source our implementation as well as the oracle we used for our evaluation.1
The Clinical Utility of Virtual Reality in Neurorehabilitation: A Systematic Review

Background: Virtual reality (VR) experiences (through games and virtual environments) are increasingly being used in physical, cognitive, and psychological interventions. However, the impact of VR as an approach to rehabilitation is not fully understood, and its advantages over traditional rehabilitation techniques are yet to be established. Method: We present a systematic review which was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). During February and March of 2018, we conducted searches on PubMed (Medline), Virtual Health Library Search Portal databases (BVS), Web of Science (WOS), and Embase for all VR-related publications in the past 4 years (2015, 2016, 2017, and 2018). The keywords used in the search were “neurorehabilitation” AND “Virtual Reality” AND “devices. ” Results: We summarize the literature which highlights that a range of effective VR approaches are available. Studies identified were conducted with poststroke patients, patients with cerebral palsy, spinal cord injuries, and other pathologies. Healthy populations have been used in the development and testing of VR approaches meant to be used in the future by people with neurological disorders. A range of benefits were associated with VR interventions, including improvement in motor functions, greater community participation, and improved psychological and cognitive function. Conclusions: The results from this review provide support for the use of VR as part of a neurorehabilitation program in maximizing recovery.
Karriereforventninger under glasstaket:

Ingeniørutdanningen utgjør en av de viktigste lederutdanningene i Norge. Tidligere forskning viser at innenfor ingeniøryrket har kvinner lavere sannsynlighet for å få lederstillinger enn sine mannlige kolleger. I denne studien undersøkes kvinnelige og mannlige ingeniørstudenters karriereambisjoner og forventinger til fremtidig karriere. Spørsmålet som tas opp, er om kjønnsforskjellene i lederstillinger kan knyttes til kvinner og menns ambisjoner og fremtidige karriereforventninger. Resultatene viser at kvinnelige studenter har minst like høye karriereambisjoner som sine mannlige medstudenter, men oppgir likevel lavere forventninger om lederansvar. Ulike forklaringer på disse funnene diskuteres, og noe overraskende gir analysene ingen empirisk støtte til de vanligste forklaringene på kjønnsforskjeller i karriereutvikling. Kvinnelige og mannlige studenter har like preferanser og kvinnene har ikke lavere selvtillit enn menn. Videre har omsorg for barn lik betydning for kvinner og menn for utvikling av karriereambisjoner og lederforventinger. Det som derimot synes å forklare kvinners lavere forventinger om lederstillinger, er forestillingen om et glasstak. Studien er basert på en spørreskjemaundersøkelse (StudData) blant ingeniørstudenter i deres siste studiesemester.
Time-Lag in Feeding Schedule Acts as a Stressor That Alters Circadian Oscillators in Goldfish

The circadian system controls temporal homeostasis in all vertebrates. The light-dark (LD) cycle is the most important zeitgeber (“time giver”) of circadian system, but feeding time also acts as a potent synchronizer in the functional organization of the teleost circadian system. In mammals is well known that food intake during the rest phase promotes circadian desynchrony which has been associated with metabolic diseases. However, the impact of a misalignment of LD and feeding cycles in the entrainment of fish circadian oscillators is largely unknown. The objective of this work was to investigate how a time-lag feeding alters temporal homeostasis and if this could be considered a stressor. To this aim, goldfish maintained under a 12 h light-12 h darkness were fed at mid-photophase (SF6) or mid-scotophase (SF18). Daily rhythms of locomotor activity, clock genes expression in hypothalamus, liver, and head kidney, and circulating cortisol were studied. Results showed that SF6 fish showed daily rhythms of bmal1a and clock1a in all studied tissues, being in antiphase with rhythms of per1 genes, as expected for proper functioning clocks. The 12 h shift in scheduled feeding induced a short phase advance (4–5-h) of the clock genes daily rhythms in the hypothalamus, while in the liver the shift for clock genes expression rhythms was the same that the feeding time shift (∼12 h). In head kidney, acrophases of per genes underwent a 12-h shift in SF18 animals, but only 6 h shift for clock1a. Plasma cortisol levels showed a significant daily rhythm in animals fed at SF6, but not in SF18 fish fed, which displayed higher cortisol values throughout the 24-h. Altogether, results indicate that hypothalamus, liver, and head kidney oscillate in phase in SF6 fish, but these clocks are desynchronized in SF18 fish, which could explain cortisol alterations. These data reinforce the hypothesis that the misalignment of external cues (daily photocycle and feeding time) alters fish temporal homeostasis and it might be considered a stressor for the animals.
Tracking the Pixels: Detecting Web Trackers via Analyzing Invisible Pixels

Web tracking has been extensively studied over the last decade. To detect tracking, most of the research studies and user tools rely on consumer protection lists. However, there was always a suspicion that lists miss unknown trackers. In this paper, we propose an alternative solution to detect trackers by analyzing behavior of invisible pixels that are perfect suspects for tracking. By crawling 829,349 webpages, we detect that third-party invisible pixels are widely deployed: they are present on more than 83% of domains and constitute 37.22% of all third-party images. We then propose a fine-grained classification of tracking based on the analysis of invisible pixels and use this classification to detect new categories of tracking and uncover new collaborations between domains on the full dataset of 34,952,217 third-party requests. We demonstrate that two blocking strategies – based on EasyList&EasyPrivacy and on Disconnect lists – each miss 22% of the trackers that we detect. Moreover, we find that if we combine both strategies, 238,439 requests (11%) originated from 7,773 domains that still track users on 5,098 websites.
Interaction Technology Based on 3D printing topographic sand table for Emergency Management

Enhance Virtual Reality is an innovative technique to perform dynamic 3D visualization and emergency management based on sand table, which needs convenient human-computer interaction techniques. Traditional interaction techniques such as mouse and keyboard cannot realize convenient interaction between human and the dynamic virtual scene of sand table. Gesture interaction method has received increasing attention in human-computer interaction. This paper researches interaction techniques based on computer vision and deep learning, and convolutional neural network was used in gesture recognition. A prototype system based on 3D printing sand table is proposed to achieve dynamic scene demonstration. The experiment shows that gesture recognition accuracy is 99.51%, and the visualization system can perform convenient operations on 3D printing sand table.
Implementing arbitrarily High-order symplectic Methods via Krylov Deferred correction Technique

In this paper, an efficient numerical procedure is presented to implement the Gaussian Runge–Kutta (GRK) methods (also called Gauss methods). The GRK technique first discretizes each marching step of the initial value problem using collocation formulations based on Gaussian quadrature. As is well known, it preserves the geometric structures of Hamiltonian systems. Existing analysis shows that the GRK discretization with s nodes is of order 2s, A-stable, B-stable, symplectic and symmetric, and hence "optimal" for solving initial value problems of general ordinary differential equations (ODEs). However, as the unknowns at different collocation points are coupled in the discretized system, direct solution of the resulting algebraic equations is in general inefficient. Instead, we use the Krylov deferred correction (KDC) method in which the spectral deferred correction (SDC) scheme is applied as a preconditioner to decouple the original system, and the resulting preconditioned nonlinear system is solved efficiently using Newton–Krylov schemes such as Newton–GMRES method. The KDC accelerated GRK methods have been applied to several Hamiltonian systems and preliminary numerical results are presented to show the accuracy, stability, and efficiency features of these methods for different accuracy requirements in short- and long-time simulations.
Towards Automated Generation of Exploitation Primitives for Web Browsers

The growing dependence on software and the increasing complexity of such systems builds and feeds the attack surface for exploitable vulnerabilities. Security researchers put up a lot of effort to develop exploits and analyze existing exploits with the goal of staying ahead of the state-of-the-art in attacks and defenses. The urge for automated systems that operate at scale, speed and efficiency is therefore undeniable. Given their complexity and large user base, web browsers pose an attractive target. Due to various mitigation strategies, the exploitation of a browser vulnerability became a time consuming, multi-step task: creating a working exploit even from a crashing input is a resource-intensive task that can take a substantial amount of time to complete. In many cases, the input, which triggers a vulnerability follows a crashing path but does not enter an exploitable state. In this paper, we introduce novel methods to significantly improve and partially automate the development process for browser exploits. Our approach is based on the observation that an analyst typically performs certain manual analysis steps that can be automated. This serves the purpose to propagate the bug-induced, controlled data to a specific program location to carry out a desired action. These actions include achieving write-what-where or control over the instruction pointer primitives. These are useful to extend control over the target program and are necessities towards successful code execution, the ultimate goal of the adversary. We implemented a prototype of our approach called PrimGen. For a given browser vulnerability, it is capable of automatically crafting data objects that lead the execution to a desired action. We show in our evaluation that our approach is able to generate new and previously unknown exploitation opportunities for real-world vulnerabilities in Mozilla Firefox, Internet Explorer, and Google Chrome. Using small templates, PrimGen generates inputs that conducts specific primitives. In total, PrimGen has found 48 JavaScript inputs which conduct the desired primitives when fed into the target browsers.
Map Matching Algorithm: Trajectory and Sequential Map Analysis on Road Network

The Global Positioning System (GPS) tracking data is essential for sensor data sources. It plays an important role for various systems like Traffic assessment and Prediction, routing and navigation, Fleet management etc. Trajectory data accuracy is key factor for sampling based vehicle movement using existing GPS alerting systems. GPS navigation process is not reliable because of weak signaling transmission, weather scenario , specially, tall buildings area and drass sectors in Indian scenario. Map matching finding a path between available points on the active road segment, enhance road data accuracy through minimize frechet distance for future purpose. Therefore, accurate road data, become necessary for fast map matching outcomes. This work provides to locate the frechet distance on available free space for accurate path finding. This work also contributes to measuring frechet distance, trajectory data error estimation and finding free space surface on road network with sequential map computational method.
New Phone, Who Dis? Modeling Millennials’ Backup Behavior

Given the ever-rising frequency of malware attacks and other problems leading people to lose their files, backups are an important proactive protective behavior in which users can engage. Backing up files can prevent emotional and financial losses and improve overall user experience. Yet, we find that less than half of young adults perform mobile or computer backups regularly. To understand why, we model the factors that drive mobile and computer backup behavior, and changes in that behavior over time, using data from a panel survey of 384 diverse young adults. We develop a set of models that explain 37% and 38% of the variance in reported mobile and computer backup behaviors, respectively. These models show consistent relationships between Internet skills and backup frequency on both mobile and computer devices. We find that this relationship holds longitudinally: increases in Internet skills lead to increased frequency of computer backups. This article provides a foundation for understanding what drives young adults’ backup behavior. It concludes with recommendations for motivating people to back up, and for future work, modeling similar user behaviors.
Elias Revisited: Group Elias SIMD Coding

The prior belief that the Elias gamma and delta coding are slow because of the bit-wise manipulations is examined in the light of new CPU instructions that perform those manipulations. It is shown that despite using those instructions, Elias gamma and Elias delta remain slow compared to SIMD codecs such as QMX. We provide a theoretical basis on which to bit-wise encode data, and show that it is equivalent to SIMD extensions to Elias gamma that others have already introduced. Extending this we introduce a new SIMD Elias delta variant. Experiments comparing these two codecs to QMX on public data, and in the JASSv2 search engine show that although the index is slightly larger than QMX, search throughput is increased and latency is decreased.
Bad practices in evaluation methodology relevant to class-imbalanced problems

For research to go in the right direction, it is essential to be able to compare and quantify performance of different algorithms focused on the same problem. Choosing a suitable evaluation metric requires deep understanding of the pursued task along with all of its characteristics. We argue that in the case of applied machine learning, proper evaluation metric is the basic building block that should be in the spotlight and put under thorough examination. Here, we address tasks with class imbalance, in which the class of interest is the one with much lower number of samples. We encountered non-insignificant amount of recent papers, in which improper evaluation methods are used, borrowed mainly from the field of balanced problems. Such bad practices may heavily bias the results in favour of inappropriate algorithms and give false expectations of the state of the field.
A Heuristic Framework to Detect Concurrency Vulnerabilities

With a growing demand of concurrent software to exploit multi-core hardware capability, concurrency vulnerabilities have become an inevitable threat to the security of today's IT industry. Existing concurrent program detection schemes focus mainly on detecting concurrency errors such as data races, atomicity violation, etc., with little attention paid to detect concurrency vulnerabilities that may be exploited to infringe security. In this paper, we propose a heuristic framework that combines both static analysis and fuzz testing to detect targeted concurrency vulnerabilities such as concurrency buffer overflow, double free, and use-after-free. The static analysis locates sensitive concurrent operations in a concurrent program, categorizes each finding into a potential type of concurrency vulnerability, and determines the execution order of the sensitive operations in each finding that would trigger the suspected concurrency vulnerability. The results are then plugged into the fuzzer with the execution order fixed by the static analysis in order to trigger the suspected concurrency vulnerabilities. In order to introduce more variance which increases possibility that the concurrency errors can be triggered, we also propose manipulation of thread scheduling priority to enable a fuzzer such as AFL to effectively explore thread interleavings in testing a concurrent program. To the best of our knowledge, this is the first fuzzer that is capable of effectively exploring concurrency errors. In evaluating the proposed heuristic framework with a benchmark suit of six real-world concurrent C programs, the framework detected two concurrency vulnerabilities for the proposed concurrency vulnerability detection, both being confirmed to be true positives, and produced three new crashes for the proposed interleaving exploring fuzzer that existing fuzzers could not produce. These results demonstrate the power and effectiveness of the proposed heuristic framework in detecting concurrency errors and vulnerabilities.
TIFF: Using Input Type Inference To Improve Fuzzing

Developers commonly use fuzzing techniques to hunt down all manner of memory corruption vulnerabilities during the testing phase. Irrespective of the fuzzer, input mutation plays a central role in providing adequate code coverage, as well as in triggering bugs. However, each class of memory corruption bugs requires a different trigger condition. While the goal of a fuzzer is to find bugs, most existing fuzzers merely approximate this goal by targeting their mutation strategies toward maximizing code coverage. In this work, we present a new mutation strategy that maximizes the likelihood of triggering memory-corruption bugs by generating fewer, but better inputs. In particular, our strategy achieves bug-directed mutation by inferring the type of the input bytes. To do so, it tags each offset of the input with a basic type (e.g., 32-bit integer, string, array etc.), while deriving mutation rules for specific classes of bugs. We infer types by means of in-memory data-structure identification and dynamic taint analysis, and implement our novel mutation strategy in a fully functional fuzzer which we call TIFF (Type Inference-based Fuzzing Framework). Our evaluation on real-world applications shows that type-based fuzzing triggers bugs much earlier than existing solutions, while maintaining high code coverage. For example, on several real-world applications and libraries (e.g., poppler, mpg123 etc.), we find real bugs (with known CVEs) in almost half of the time and upto an order of magnitude fewer inputs than state-of-the-art fuzzers.
Unleashing the Power of Hashtags in Tweet Analytics with Distributed Framework on Apache Storm

Twitter is a popular social network platform where users can interact and post texts of up to 280 characters called tweets. Hashtags, hyperlinked words in tweets, have increasingly become crucial for tweet retrieval and search. Using hashtags for tweet topic classification is a challenging problem because of context dependent among words, slangs, abbreviation and emoticons in a short tweet along with evolving use of hashtags. Since Twitter generates millions of tweets daily, tweet analytics is a fundamental problem of Big data stream that often requires a real-time Distributed processing. This paper proposes a distributed online approach to tweet topic classification with hashtags. Being implemented on Apache Storm, a distributed real time framework, our approach incrementally identifies and updates a set of strong predictors in the Naïve Bayes model for classifying each incoming tweet instance. Preliminary experiments show promising results with up to 97% accuracy and 37% increase in throughput on eight processors.
Adversarial Example Decomposition

Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer across models. We hypothesize that adversarial weakness is composed of three sources of bias: architecture, dataset, and random initialization. We show that one can decompose adversarial examples into an architecture-dependent component, data-dependent component, and noise-dependent component and that these components behave intuitively. For example, noise-dependent components transfer poorly to all other models, while architecture-dependent components transfer better to retrained models with the same architecture. In addition, we demonstrate that these components can be recombined to improve transferability without sacrificing efficacy on the original model.
From biological vision to unsupervised hierarchical sparse coding

The formation of connections between neural cells is emerging essentially from an unsupervised learning process. For instance, during the development of the primary visual cortex of mammals (V1), we observe the emergence of cells selective to localized and oriented features. This leads to the development of a rough contour-based representation of the retinal image in area V1. We propose a biological model of the formation of this representation along the thalamo-cortical pathway. To achieve this goal, we replicated the Multi-Layer Convolutional Sparse Coding (ML-CSC) algorithm developed by Michael Elad's group.