Miracle-dz/UniFAR-FTUs huggingface dataset:
Invader Job: A Kind of Malicious Failure Job on Hadoop YARN

In distributed computing platform, it's possible to occur unexpected job failure. Normally, the system performance will not be affected obviously. But, in Hadoop YARN, we find Invader Job, a kind of inappropriate user-definable parameter caused malicious failure job, may cut down the system performance greatly. In this paper, we find in Hadoop YARN, there are two vulnerabilities that can be used to construct invader job. First, it's easy to cause job failure by modifying the user-definable parameters inappropriately. Second, YARN doesn't check on the job before execution, and also doesn't check the failure reason before re-attempt. So that, invader job could fail as much as possible to occupy the scheduling resource over and over again. Thus, we propose a detection framework called InTect which employs SVM to predict invader jobs. Finally, we verify our findings using the cluster of our lab and Amazon EMR respectively. As a result, the cluster performance degrades 4 times than normal case. Moreover, the recall rate of our detection framework is more than 90%, which means the SVM model has a good discrimination for invader jobs.
La localisation forte d'Anderson

Article proposé par : Dominique Delande, delande@spectro.jussieu. fr Laboratoire Kastler Brossel, UMR 8552, UPMC / CNRS / ENS, Paris Jean-Claude Garreau, jean-claude.garreau@univ-lille1.fr Laboratoire de Physiques des Lasers, Atomes et Molécules, UMR 8523, CNRS / Univ. Lille 1, Villeneuve-d’Ascq Laurent Sanchez-Palencia, laurent.sanchez-palencia@institutoptique.fr Laboratoire Charles Fabry de l’Institut d’Optique, Institut d’optique / CNRS / Univ. Paris-Sud, Palaiseau Bart van Tiggelen, bart.van-tiggelen@grenoble.cnrs. fr Laboratoire de Physique et de Modélisation des Milieux Condensés, UMR 5493, Univ. Joseph Fourier / CNRS, Grenoble En 1958, Philip W. Anderson a étudié l’effet des impuretés sur les propriétés de transport électronique dans les cristaux imparfaits. Il a prédit l’existence d’un régime localisé, correspondant à une absence totale de diffusion quand le désordre est suffisamment fort, et d’une transition délocalisé-localisé (métal-isolant) avec des caractéristiques bien particulières quand on augmente la force du désordre. Cet effet, dû à des interférences quantiques, est difficile à mettre en évidence expérimentalement, car les effets parasites tels que la diffusion inélastique et les interactions électron-électron perturbent fortement le scénario d’Anderson. Aujourd’hui, la localisation forte d’Anderson a été observée dans la conductance électronique, dans la transmission des ondes électromagnétiques et des ultrasons, et directement dans l’expansion d’ondes de matière atomique (gaz atomiques froids). Les articles qui suivent présentent trois avancées importantes sur ce qui est devenu un phénomène majeur en physique : la transition d’Anderson, une transition de phase gouvernée par des effets quantiques.
Lasserre Hierarchy for Large Scale Polynomial Optimization in Real and Complex Variables

We propose general notions to deal with large scale polynomial optimization problems and demonstrate their efficiency on a key industrial problem of the 21st century, namely the optimal power flow problem. These notions enable us to find global minimizers on instances with up to 4,500 variables and 14,500 constraints. First, we generalize the Lasserre hierarchy from real to complex numbers in order to enhance its tractability when dealing with complex polynomial optimization. Complex numbers are typically used to represent oscillatory phenomena, which are omnipresent in physical systems. Using the notion of hyponormality in operator theory, we provide a finite convergence criterion which generalizes the Curto--Fialkow conditions of the real Lasserre hierarchy. Second, we introduce the multi-ordered Lasserre hierarchy in order to exploit sparsity in polynomial optimization problems (in real or complex variables) while preserving global convergence. It is based on two ideas: (1) to use a different relaxatio.
Накопление новых знаний о внутреннем устройстве рассеянных звездных скоплений на основе интенсивного использования данных (Accumulation of New Knowledge about the Internal Structure of an Open Star Clusters on the Basis of Intensive Use of Data)

A technique for studying the motions of stars inside open star clusters is proposed. It allows revealing the details of the cluster device on the basis of precise measurements of the astrometric parameters of the stars. Successively scanning a lot of clusters and applying a uniform technique, a processing pipeline is built. Analysis of the results of mass processing will reveal the patterns and relationships of the internal arrangement of clusters with their parameters and position in the Galaxy. Actuality of the problem is added by the fact that the recently launched space telescope GAIA investigates, in particular, star clusters.
ROPNN: Detection of ROP Payloads Using Deep Neural Networks

Return-oriented programming (ROP) is a code reuse attack that chains short snippets of existing code (known as gadgets) to perform arbitrary operations on target machines. Existing detection mechanisms against ROP often rely on certain heuristic rules and/or require instrumentations to the program or the compiler. As a result, they exhibit low detection efficiency and/or have high runtime overhead. In this paper, we present ROPNN, which innovatively combines address space layout guided disassembly and deep neural networks, to detect ROP payloads in HTTP requests, PDF files, and images, etc. The disassembler treats application input data as code pointers to potential gadgets and aims to find any potential gadget chains. The identified potential gadget chains are then classified by the deep neural network as benign or malicious. We propose novel methods to generate the two training datasets, respectively, and process huge amount (TB-level) of raw input data to obtain sufficient training data. Our experiments show that ROPNN has high detection rate (98.3%) while maintaining very low false positive rate (0.01%). To show ROPNN is usable in practical scenario, we also test it against ROP exploits that are collected in-the-wild, created manually or created by ROP exploit generation tools Ropper and ROPC. ROPNN successfully detects all of the 80 exploits. Meanwhile, ROPNN is completely non-intrusive and does not incur any runtime overhead to the protected program.
Relative Roles of Listeriolysin O, InlA, and InlB in Listeria monocytogenes Uptake by Host Cells

Listeria monocytogenes is a facultative intracellular pathogen that infects a wide variety of cells, causing the life-threatening disease listeriosis. L. monocytogenes virulence factors include two surface invasins, InlA and InlB, known to promote bacterial uptake by host cells, and the secreted pore-forming toxin listeriolysin O (LLO), which disrupts the phagosome to allow bacterial proliferation in the cytosol. ABSTRACT Listeria monocytogenes is a facultative intracellular pathogen that infects a wide variety of cells, causing the life-threatening disease listeriosis. L. monocytogenes virulence factors include two surface invasins, InlA and InlB, known to promote bacterial uptake by host cells, and the secreted pore-forming toxin listeriolysin O (LLO), which disrupts the phagosome to allow bacterial proliferation in the cytosol. In addition, plasma membrane perforation by LLO has been shown to facilitate L. monocytogenes internalization into epithelial cells. In this work, we tested the host cell range and importance of LLO-mediated L. monocytogenes internalization relative to the canonical invasins, InlA and InlB. We measured the efficiencies of L. monocytogenes association with and internalization into several human cell types (hepatocytes, cytotrophoblasts, and endothelial cells) using wild-type bacteria and isogenic single, double, and triple deletion mutants for the genes encoding InlA, InlB and LLO. No role for InlB was detected in any tested cells unless the InlB expression level was substantially enhanced, which was achieved by introducing a mutation (prfA*) in the gene encoding the transcription factor PrfA. In contrast, InlA and LLO were the most critical invasion factors, although they act in a different manner and in a cell-type-dependent fashion. As expected, InlA facilitates both bacterial attachment and internalization in cells that express its receptor, E-cadherin. LLO promotes L. monocytogenes internalization into hepatocytes, but not into cytotrophoblasts and endothelial cells. Finally, LLO and InlA cooperate to increase the efficiency of host cell invasion by L. monocytogenes.
Socioeconomic Factors, Perioperative Complications, and 30-Day Readmission Rates Associated With Delayed Cranial Vault Reconstruction for Craniosynostosis

Background: Premature fusion of the cranial sutures can lead to significant neurocognitive, developmental, and esthetic consequences, especially if not corrected within the first year of life. This study aimed to identify the drivers of delayed cranial vault reconstruction (CVR) and its impact on complication and 30-day readmission rates among craniosynostosis patients. Methods: The medical records of all children who underwent CVR for craniosynostosis between 2005 and 2017 at an academic institution were retrospectively reviewed. A delay in operation was defined by surgery performed >12 months of age. Patient demographics, comorbidities, perioperative complication rates, and 30-day readmission rates were collected. Results: A total of 96 patients underwent primary CVR, with 79 (82.3%) patients undergoing nondelayed surgery and 17 (17.7%) patients undergoing surgery >12 months of age. Children undergoing delayed surgery were significantly more likely to be non-White (P < 0.0001), have Medicaid insurance (P = 0.023), and have a non-English primary language (P < 0.005). There was increased incidence of developmental disability identified at first consult (no-delay: 3.9% vs delay: 41.2%, P < 0.0001) and increased intracranial pressure (no-delay: 6.3% vs delay: 29.4%, P < 0.005) among children undergoing delayed surgery. The delayed cohort had a significantly higher unplanned 30-day readmission rate (no-delay: 0.0% vs delay: 5.9%, P = 0.03). Conclusion: Our study suggests that craniosynostosis patients who are non-White, have a non-English primary language, and have Medicaid insurance are at risk for delayed primary surgery, which may lead to increased 30-day readmission. Interventions are necessary to reduce craniosynostosis patients’ barriers to care to minimize the sequelae associated with delayed surgery.
Deceitful Attacks in Security Games

Given recent applications of defender-attacker Stackelberg Security Games in real-world domains such as wildlife protection, a majority of research has focused on addressing uncertainties regarding the attacker in these games based on the exploitation of attack data. However, there is an important challenge of deceitful attacks; the attacker can manipulate his attacks to mislead the defender, leading her to conduct ineffective patrolling strategies. In this work, we focus on addressing this challenge while providing the following main contributions. First, we introduce a new game model with uncertainty about the attacker type and repeated interactions between the players. In our game model, the defender attempts to collect attack data over time to learn about the attacker type while the attacker aims at playing deceitfully. Second, based on the new game model, we propose new game-theoretic algorithms to compute optimal strategies for both players. Third, we present preliminary experiment results to evaluate our proposed algorithms, showing that our defense solutions can effectively address deceitful attacks.
An Iterative, Sketching-based Framework for Ridge Regression

Ridge regression is a variant of regularized least squares regression that is particularly suitable in settings where the number of predictor variables greatly exceeds the number of observations. We present a simple, iterative, sketching-based algorithm for ridge regression that guarantees highquality approximations to the optimal solution vector. Our analysis builds upon two simple structural results that boil down to randomized matrix multiplication, a fundamental and wellunderstood primitive of randomized linear algebra. An important contribution of our work is the analysis of the behavior of sub-sampled ridge regression problems when the ridge leverage scores are used: we prove that accurate approximations can be achieved by a sample whose size depends on the degrees of freedom of the ridge-regression problem rather than the dimensions of the design matrix. Our empirical evaluations verify our theoretical results on both real and synthetic data.
Investigating Social Distances between Humans, Virtual Humans and Virtual Robots in Mixed Reality

Mixed reality environments offer new potentials for the design of compelling social interaction experiences with virtual characters. In this paper, we summarise initial experiments we are conducting in which we measure comfortable social distances between humans, virtual humans and virtual robots in mixed reality environments. We consider a scenario in which participants walk within a comfortable distance of a virtual character that has its appearance varied between a male and female human, and a standard- and human-height virtual Pepper robot. Our studies in mixed reality thus far indicate that humans adopt social zones with artificial agents that are similar in manner to human-human social interactions and interactions in virtual reality.
Impact of Contact Resistance on 2D Negative-Capacitance FETs

In this work, we investigate and analyze the impact of contact resistance (R<inf>SD</inf>) on the negative-capacitance FET with 2D transition-metal-dichalco-genide channel (NC-TMDFET). We report a ferroelectric capacitance (C<inf>FE</inf>) feedback mechanism of the NC-TMDFET that can be used to suppress the impact of R<inf>SD</inf>. Our study indicates that low remnant polarization (P<inf>r</inf>) ferroelectric material can be utilized to leverage this effect for NC-TMDFETs under the presence of significant R<inf>SD</inf>.
Concept-Aware Feature Extraction for Knowledge Transfer in Reinforcement Learning

We introduce a novel mechanism for knowledge transfer via concept formation to augment reinforcement learning agents operating in complex, uncertain domains. Based on their observations, agents form concepts and associate them with actions to generalize their decisions at higher levels of abstraction. Concepts serve as simple, portable, efﬁcient packets of hierarchical information that can be learned in parallel. The use of conceptual knowledge simultaneously provides an interpretable, semantic explanation of an agent’s decisions, making the techniques promising for human-interaction domains such as games, where human observers wish to in-spect an agent’s rationale. This technique extends previous work on probabilistic learning with Markov decision processes (MDPs) by introducing rich hierarchical feature structures that can be learned from experience, enabling more effective learning transfer to new, related tasks.
PerfIso: Performance Isolation for Commercial Latency-Sensitive Services

Large commercial latency-sensitive services, such as web search, run on dedicated clusters provisioned for peak load to ensure responsiveness and tolerate data center outages. As a result, the average load is far lower than the peak load used for provisioning, leading to resource under-utilization. The idle resources can be used to run batch jobs, completing useful work and reducing overall data center provisioning costs. However, this is challenging in practice due to the complexity and stringent tail-latency requirements of latency-sensitive services. Left unmanaged, the competition for machine resources can lead to severe response-time degradation and unmet service-level objectives (SLOs). This work describes PerfIso, a performance isolation framework which has been used for nearly three years in Microsoft Bing, a major search engine, to colocate batch jobs with production latency-sensitive services on over 90,000 servers. We discuss the design and implementation of PerfIso, and conduct an experimental evaluation in a production environment. We show that colocating CPU-intensive jobs with latency-sensitive services increases average CPU utilization from 21% to 66% for off-peak load without impacting tail latency.
Reliable Topology Design of Wireless Networks under Correlated Failures

Inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel complicates the design of reliable backhaul networks. In the presence of such disturbances, network reliability can be improved by providing redundant paths between given source and destination. Many studies deal with modifying and designing the network topology to meet the reliability requirements in a cost- efficient manner. However, these studies ignore the correlation among link failures, such as those caused by rain. Consequently, the resulting topology design solutions may fail to satisfy the network reliability requirements under correlated failure scenarios. To address this issue, this paper studies the design of reliable wireless backhaul networks under correlated failures with focus on rain fading. We consider green-field topology design and brown-field topology upgrade scenarios with the objective to minimize the total cost of wireless links added to meet the target reliability requirement in the presence of correlated link failures. We propose a new model to formulate the spatial correlation using pairwise joint probability distribution of rain attenuation between different links. This model is applied to consider the link- wise correlation along individual paths, as well as the correlation among the multiple redundant paths from the source to the destination node of a traffic flow. We formulate the problem as a quadratic integer program, which is NP-hard, and develop a heuristic algorithm to find near-optimal solutions. Performance evaluation shows that correlation-aware design improves the resiliency under rain disturbance at a slightly increased cost.
Data Mining and Visualization: Meteorological Parameters and Gas Concentration Use Case

Knowledge extraction from big data is one of the important subjects now and in future. Mining in the big data needs many steps, which must be implemented very carefully. The final step in big data mining is visualizing the results or summarizing the results numerically. This paper aims to mining the big data recorded by environmental station. These stations are recording the concentrations of some gases and meteorological parameters. The 2D and 3D data visualization is used to evaluate the capability of visualization in determining the effect of meteorological parameters on some gases that caused pollution. The results showing the visualization is a very important tool, and visualization can be used in mining big data, by showing the concentrations of gases. The paper recommends using big data visualization periodically as an alarming tool for monitoring the levels of pollution gases concentration.
Channel Estimation and Peak-to-Average Power Ratio Analysis of Narrowband Internet of Things Uplink Systems

Narrowband Internet of Things (NB-IoT) is a cellular based promising low-power wide-area network (LPWN) technology standardized by the 3rd Generation Partnership Project (3GPP) in release-13 as a part of the future 5th Generation (5G) wireless communication systems. The main design target of NB-IoT was to enhance radio coverage by repeating signal over an additional period of time for the ultralow-end IoT devices that would be operated in extreme coverage environments. But the power efficiency of the low-cost NB-IoT user equipment (NB-IoT UE) in the uplink is the major concern. Coverage improvement from signal repetitions depends on the channel estimation quality at extremely bad radio conditions. The typical operating signal-to-noise ratio (SNR) for NB-IoT is expected to be much lower than the zero. In this paper, we have proposed two efficient narrowband demodulation reference signal (NDMRS)-assisted channel estimation algorithms based on the conventional least squares (LS) and minimum mean square error (MMSE) estimation methods. The theoretical analysis and the link-level performance of our proposed estimation methods are presented. Simulation results exhibit that the proposed methods provide better estimation precision compared to the traditional LS and MMSE methods at the low SNR situations. Furthermore, we have analyzed the raised-cosine (RC) and square-root-raised cosine (RRC) pulse shaping to reduce peak-to-average power ratio (PAPR) as an uplink transmit filter. The PAPR values are evaluated through extensive computer simulations for both single-tone and multi-tone transmissions. Our evaluation results vindicate that the RRC pulse shaping with lower PAPR values is feasible to design of practical NB-IoT uplink transmitter and increases power efficiency.
Story Understanding in Video Advertisements

In order to resonate with the viewers, many video advertisements explore creative narrative techniques such as "Freytag's pyramid" where a story begins with exposition, followed by rising action, then climax, concluding with denouement. In the dramatic structure of ads in particular, climax depends on changes in sentiment. We dedicate our study to understand the dynamic structure of video ads automatically. To achieve this, we first crowdsource climax annotations on 1,149 videos from the Video Ads Dataset, which already provides sentiment annotations. We then use both unsupervised and supervised methods to predict the climax. Based on the predicted peak, the low-level visual and audio cues, and semantically meaningful context features, we build a sentiment prediction model that outperforms the current state-of-the-art model of sentiment prediction in video ads by 25%. In our ablation study, we show that using our context features, and modeling dynamics with an LSTM, are both crucial factors for improved performance.
Slicing WiFi WLAN-Sharing Access Infrastructures to Enhance Ultra-Dense 5G Networking

The rise of 5th Generation (5G) based network systems provide the prospect for an unprecedented technological revolution in different aspects of current network infrastructures to fully satisfy the high demands of smart space. This work addresses the challenges that raise in exploiting the potential of WiFi WLAN-sharing technology in 5G Ultra-Dense Networking (UDN) use cases. We investigate new complementary aspects of emerging 5G technologies such as Network Function Virtualization (NFV) and Fog computing to design a unique WiFi WLAN-sharing ecosystem to allow complying with 5G UDN critical requirements. In the resulting approach, we empower WiFi WLAN-sharing infrastructures with Fog computing capabilities and follow a slice-defined approach, aiming to provide differentiated services at unprecedented levels, on top of the same infrastructure through customized, isolated and independent building blocks. The solution also enables slices to accommodate applications besides networking functions, seeking to provide ultra-low latency rates by leveraging direct linkage to data producer entities. A proof of concept was conducted by carrying out experiments in a real laboratory testbed, allowing insights into the feasibility and suitability of slicing WiFi WLAN-sharing systems.