Miracle-dz/UniFAR-FTUs huggingface dataset:
Steal This Movie: Automatically Bypassing DRM Protection in Streaming Media Services

Streaming movies online is quickly becoming the way in which users access video entertainment. This has been powered by the ubiquitous presence of the Internet and the availability of a number of hardware platforms that make access to movies convenient. Often, video-on-demand services use a digital rights management system to prevent the user from duplicating videos because much of the economic model of video stream services relies on the fact that the videos cannot easily be saved to permanent storage and (illegally) shared with other customers. In this paper, we introduce a general memory-based approach that circumvents the protections deployed by popular video-on-demand providers. We apply our approach to four different examples of streaming services: Amazon Instant Video, Hulu, Spotify, and Netflix and we demonstrate that, by using our technique, it is possible to break DRM protection in a semi-automated way.
Unit‐vector RMS (URMS) as a tool to analyze molecular dynamics trajectories

The Unit‐vector RMS (URMS) is a new technique to compare protein chains and to detect similarities of chain segments. It is limited to comparison of Cα chains. However, it has a number of unique features that include exceptionally weak dependence on the length of the chain and efficient detection of substructure similarities. Two molecular dynamics simulations of proteins in the neighborhood of their native states are used to test the performance of the URMS. The first simulation is of a solvated myoglobin and the second is of the protein MHC. In accord with previous studies the secondary structure elements (helices or sheets) are found to be moving relatively rigidly among flexible loops. In addition to these tests, folding trajectories of C peptides are analyzed, revealing a folding nucleus of seven amino acids. Proteins 1999;37:554–564. ©1999 Wiley‐Liss, Inc.
Understanding the Impact of Weather for POI Recommendations

POI recommender systems for location-based social network services, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated into improving recommendation services in such systems by integrating di↵erent features that are assumed to have an impact on people’s preferences for POIs, such as time and geolocation. Yet, little attention has been paid to the impact of weather on the users’ final decision to visit a recommended POI. In this paper we contribute to this area of research by presenting the first results of a study that aims to recommend POIs based on weather data. To this end, we extend the state-of-the-art Rank-GeoFM POI recommender algorithm with additional weather-related features, such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly increases the recommendation accuracy in comparison to the original algorithm, but also outperforms its time-based variant. Furthermore, we present the magnitude of impact of each feature on the recommendation quality, showing the need to study the weather context in more detail in the light of POI recommendation systems.
Context-based ensemble model

The paper represents inference model that combines results from several different data sources. The model makes exclusive use of context properties as context is used to determine the mode different sources are combined. The mode of combining is dynamically set regarding both global and local characteristics of the context. This complicated combination schema is used in order to achieve better accuracy. The model is represented by factor graph with tree structure. That is a flexible implementation, which allows us to incorporate context usage in well-defined manner throughout inference process.
Investigating Model Behavioural Analysis : A Critical Examination of Two Methods

Two methods for model behavioural analysis are implemented on a simple 2 order nonlinear model. The results of applying Ford’s behavioral approach are compared with those obtained using both system-wide and variable-specific loop eigenvalue elasticity analysis. Differences in the division of the time span into analysis intervals are identified as are discrepancies in the outcomes. The effort required for implementation and the necessity for automation also differ substantially. We consider Ford’s method readily understandable, whereas the mathematically more powerful eigenvalue elasticity analysis poses difficulties
Linking Genetics of Brain Changes to Alzheimer's Disease: Sparse Whole Genome Association Scan of Regional MRI Volumes in the ADNI and AddNeuroMed Cohorts.

BACKGROUND Alzheimer's disease (AD) is a highly heritable disease, but until recently few replicated genetic markers have been identified. Markers identified so far are likely to account for only a tiny fraction of the heritability of AD and many more genetic risk alleles are thought to be undiscovered. OBJECTIVE Identifying genetic markers for AD using combined analysis of genetics and brain imaging data. METHODS Imaging quantitative trait loci (iQTLs) has recently emerged as an interesting research area for linking genetics of brain changes to AD. We consider a genome-wide association scan of 109 brain-wide regional imaging phenotypes to identify genetic susceptibility loci for AD from a combined set of 1,045 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the AddNeuroMed studies. We use one-SNP-at-a-time as well as multi-SNP Hyperlasso based iQTL methods for the analysis. RESULTS We identified several novel markers associated with AD, namely HOMER2 (rs1256429; intronic, p = 8.7 × 10⁻¹⁰), EOMES (rs2724509; flanking), JAM2 (rs2829841; intronic), and WEE1 (rs10770042; coding). The SNP rs1256429 (HOMER2) was one of the top hits in Hyperlasso as well as in the single-SNP analysis showing an association with the volume of the right thalamus and AD, a brain region reported to be linked with AD in several studies. CONCLUSION We believe that the markers identified in this study are novel additions to the existing list of genetic variants associated with AD which can be validated in future replicated studies.
Signature of structure failure using asymmetric and broadening factors of Brillouin spectrum

We introduce a novel data analysis approach based on the extraction of peak strain, asymmetric and broadening factors of the Brillouin spectrum measured with the distributed Brillouin sensor (DBS). Such an approach provides simultaneously global and local strains, describing the status of the structure, at the contrary of average strain measurements. These results are confirmed by a trial on a composite column subjected to vertical and bending loads. This demonstrates that the DBS is a powerful tool to give the signature of the structure failure and then to identify early problems in structures that none of existing point sensors can detect.
Acoustic event detection for spotting "hot spots" in podcasts

This paper presents a method to detect acoustic events that can be used to find “hot spots” in podcast programs. We focus on meaningful non-verbal audible reactions which suggest hot spots such as laughter and reactive tokens. In order to detect this kind of short events and segment the counterpart utterances, we need accurate audio segmentation and classification, dealing with various recording environments and background music. Thus, we propose a method for automatically estimating and switching penalty weights for the BIC-based segmentation depending on background environments. Experimental results show significant improvement in detection accuracy by proposed method compared to when using a constant penalty weight.
Microstructural evolution during direct laser sintering of multi-component Cu-based metal powder

A multi-component Cu-based metal powder was chosen for direct laser sintering. The powder consists of a mixture of high-purity Cu powder, pre-alloyed CuSn and CuP powder. Liquid phase sintering with complete melting of the binder (CuSn) but non-melting of the cores of structural metal (Cu) proves to be a feasible mechanism for laser sintering of this powder system. The microstructural evolution of the sintered powder with variation of laser processing parameters was presented. High sintering activities and sound densification response were obtained by optimizing the laser powers and scan speeds. Using a high laser power accompanied by a high scan speed gives rise to balling effect. At a high laser power with a slow scan speed the sintering mechanism may change into complete melting/solidification, which decreases the obtainable sintered density. The role of additive phosphorus in the laser sintering process is addressed. Phosphorus can act as a fluxing agent and has a preferential reaction with oxygen to form phosphatic slag, protecting the Cu particles from oxidation. The phosphatic slag shows a concentration along grain boundaries due to its light mass as well as the short thermal cycle of SLS.
Enhancing data availability in disk drives through background activities

Latent sector errors in disk drives affect only a few data sectors. They occur silently and are detected only when the affected area is accessed again. If a latent error is detected while the storage system is operating under reduced redundancy, i.e., during a RAID rebuild, then data loss may occur. Various features such as scrubbing and intra-disk data redundancy are proposed to detect and/or recover from latent errors and avoid data loss. While such features enhance data availability in the storage system, their execution may cause performance degradation. In this paper, we evaluate the effectiveness of scrubbing and intra-disk data redundancy in improving data availability while the overall goal is to maintain user performance within predefined bounds. We show that by treating them as low priority background activities and scheduling them efficiently during idle times, these features remain performance-wise transparent to the storage system user while still improving data reliability. Detailed trace-driven simulations show that the mean time to data loss (MTTDL) improves by up to 5 orders of magnitude if these features are implemented independently. By scheduling concurrently both scrubbing and intra-disk parity updates during idle times in disk drives, MTTDL improves by as much as 8 orders of magnitude.
Word Clustering Approach to Bilingual Document Alignment (WMT 2016 Shared Task)

Our participation in Bilingual Document Alignment shared task at WMT16 focuses on building a language-independent, scalable system for aligning documents based on content as opposed to using webpage meta information. The resulting system is capable of producing scored n-best lists of candidate pages and can therefore be adapted to tasks where either precision or recall is maximized. We conduct a series of experiments that show the effectiveness of the system without any speciﬁc tuning.
Tool-use practice induces changes in intrinsic functional connectivity of parietal areas

Intrinsic functional connectivity from resting state functional magnetic resonance imaging (rsfMRI) has increasingly received attention as a possible predictor of cognitive function and performance. In this study, we investigated the influence of practicing skillful tool manipulation on intrinsic functional connectivity in the resting brain. Acquisition of tool-use skill has two aspects such as formation of motor representation for skillful manipulation and acquisition of the tool concept. To dissociate these two processes, we chose chopsticks-handling with the non-dominant hand. Because participants were already adept at chopsticks-handling with their dominant hand, practice with the non-dominant hand involved only acquiring the skill for tool manipulation with existing knowledge. Eight young participants practiced chopsticks-handling with their non-dominant hand for 8 weeks. They underwent functional magnetic resonance imaging (fMRI) sessions before and after the practice. As a result, functional connectivity among tool-use-related regions of the brain decreased after practice. We found decreased functional connectivity centered on parietal areas, mainly the supramarginal gyrus (SMG) and superior parietal lobule (SPL) and additionally between the primary sensorimotor area and cerebellum. These results suggest that the parietal lobe and cerebellum purely mediate motor learning for skillful tool-use. This decreased functional connectivity may represent increased efficiency of functional network.
RAIN: A Reliable Wireless Network Architecture

Despite years of research and development, pioneering deployments of multihop wireless networks have not proven successful. The performance of routing and transport is often unstable due to contention-induced packet losses, especially when the network is large and the offered load is high. In this paper we propose RAIN, a reliable wireless network architecture for large-scale multihop wireless networks. A RAIN network enforces contention control by limiting the queue length at intermediate wireless routers to the minimum. To keep the queue short a RAIN network enforces congestion control through in-network implicit back-pressure. RAIN congestion control is built on wireless datalink layer mechanisms, e.g., mandatory per-frame acknowledgement and inter-frame backoff in popular CSMA/CA wireless transceivers, therefore very efficient and effective compared with those defined at the network or transport layer for the wired Internet. As a result of the built-in contention and congestion control, RAIN presents the end hosts a highly reliable network service model, even more reliable than that of the wired Internet. The end hosts only need to deal with packet losses due to router or routing failures. Therefore, the transport protocol can be significantly simplified. This is in stark contrast to the existing approach of adding more and more complexity to adapt TCP for multihop wireless networks. We propose the details of RAIN datalink layer protocol, and a simple transport protocol at the end hosts. Performance evaluation through intensive simulations shows that RAIN improves the throughput by up to 92% and fairness by up to 48%, with packet losses due to contention and congestion significantly reduced.
The Prognostic Value of Fasting Plasma Glucose, Two-Hour Postload Glucose, and HbA1c in Patients With Coronary Artery Disease: A Report From EUROASPIRE IV

OBJECTIVE Three tests are recommended for identifying dysglycemia: fasting glucose (FPG), 2-h postload glucose (2h-PG) from an oral glucose tolerance test (OGTT), and glycated hemoglobin A1c (HbA1c). This study explored the prognostic value of these screening tests in patients with coronary artery disease (CAD). RESEARCH DESIGN AND METHODS FPG, 2h-PG, and HbA1c were used to screen 4,004 CAD patients without a history of diabetes (age 18–80 years) for dysglycemia. The prognostic value of these tests was studied after 2 years of follow-up. The primary end point included cardiovascular mortality, nonfatal myocardial infarction, stroke, or hospitalization for heart failure and a secondary end point of incident diabetes. RESULTS Complete information including all three glycemic parameters was available in 3,775 patients (94.3%), of whom 246 (6.5%) experienced the primary end point. Neither FPG nor HbA1c predicted the primary outcome, whereas the 2h-PG, dichotomized as <7.8 vs. ≥7.8 mmol/L, was a significant predictor (hazard ratio 1.38, 95% CI 1.07–1.78; P = 0.01). During follow-up, diabetes developed in 78 of the 2,609 patients (3.0%) without diabetes at baseline. An FPG between 6.1 and 6.9 mmol/L did not predict incident diabetes, whereas HbA1c 5.7–6.5% and 2h-PG 7.8–11.0 mmol/L were both significant independent predictors. CONCLUSIONS The 2h-PG, in contrast to FPG and HbA1c, provides significant prognostic information regarding cardiovascular events in patients with CAD. Furthermore, elevated 2h-PG and HbA1c are significant prognostic indicators of an increased risk of incident diabetes.
Whole-program optimization for time and space efficient threads

Modern languages and operating systems often encourage programmers to use threads, or independent control streams, to mask the overhead of some operations and simplify program structure. Multitasking operating systems use threads to mask communication latency, either with hardwares devices or users. Client-server applications typically use threads to simplify the complex control-flow that arises when multiple clients are used. Recently, the scientific computing community has started using threads to mask network communication latency in massively parallel architectures, allowing computation and communication to be overlapped. Lastly, some architectures implement threads in hardware, using those threads to tolerate memory latency.In general, it would be desirable if threaded programs could be written to expose the largest degree of parallelism possible, or to simplify the program design. However, threads incur time and space overheads, and programmers often compromise simple designs for performance. In this paper, we show how to reduce time and space thread overhead using control flow and register liveness information inferred after compilation. Our techniques work on binaries, are not specific to a particular compiler or thread library and reduce the the overall execution time of fine-grain threaded programs by &asymp; 15-30%. We use execution-driven analysis and an instrumented operating system to show why the execution time is reduced and to indicate areas for future work.
Robust Geometry-Based User Scheduling for Large MIMO Systems Under Realistic Channel Conditions

The problem of user scheduling with reduced overhead of channel estimation in the uplink of Massive multiple-input multiple-output (MIMO) systems has been considered. A geometry-based stochastic channel model (GSCM), called the COST 2100 channel model has been used for realistic analysis of channels. In this paper, we propose a new user selection algorithm based on knowledge of the geometry of the service area and location of clusters, without having full channel state information (CSI) at the base station (BS). The multi-user link correlation in the GSCMs arises from the common clusters in the area. The throughput depends on the position of clusters in the GSCMs and users in the system. Simulation results show that although the BS does not require the channel information of all users, by the proposed geometry-based user scheduling algorithm the sum-rate of the system is only slightly less than the well-known greedy weight clique scheme. Finally, the robustness of the proposed algorithm to the inaccuracy of cluster localization is verified by the simulation results.
A Line of sight integration approach to cosmic microwave background anisotropies

We present a new method for calculating linear cosmic microwave background (CMB) anisotropy spectra based on integration over sources along the photon past light cone. In this approach the temperature anisotropy is written as a time integral over the product of a geometrical term and a source term. The geometrical term is given by radial eigenfunctions which do not depend on the particular cosmological model. The source term can be expressed in terms of photon, baryon and metric perturbations, all of which can be calculated using a small number of differential equations. This split clearly separates between the dynamical and geometrical effects on the CMB anisotropies. More importantly, it allows to significantly reduce the computational time compared to standard methods. This is achieved because the source term, which depends on the model and is generally the most time consuming part of calculation, is a slowly varying function of wavelength and needs to be evaluated only in a small number of points. The geometrical term, which oscillates much more rapidly than the source term, does not depend on the particular model and can be precomputed in advance. Standard methods that do not separate the two terms and require a much higher number of evaluations. The new method leads to about two orders of magnitude reduction in CPU time when compared to standard methods and typically requires a few minutes on a workstation for a single model. The method should be especially useful for accurate determinations of cosmological parameters from CMB anisotropy and polarization measurements that will become possible with the next generation of experiments. A programm implementing this method can be obtained from the authors.
A Connectionist Approach to Human Planning

In the present thesis, a connectionist model (called EVA) was developed for the simulation of human planning behavior in the domain of Plan-A-Day. Plan-A-Day is a diagnostic instrument for the assessment of planning capabilities (Funke & Krüger, 1995), which provides a computer scenario in which the subjects must find an optimal sequence for several scheduled appointments by applying operators from a predefined set. EVA is designed to produce sequences of these operators which are applied within Plan-A-Day. In this way, EVA is intended to show the same kind of operationally defined planning behavior as human subjects do. EVA comprises three sub-networks of the backpropagation-class, two of which are hierarchical Elman networks. Based on the situated action account (Clark, 1997; Suchman, 1987), the input of EVA is for the most part a representation of the current state of the Plan-A-Day scenario. The data obtained by simulation runs is compared to empirical data obtained by a study with 45 human subjects. The comparison is carried out with regard to indicators reflecting planning performance, as well as characteristics of the process of planning. The fit between simulated and human data is good enough to allow certain theoretical claims. EVA provides evidence that simple pattern transformation devices like connectionist networks are capable of performing human-like planning, if these networks are thoroughly embedded into their environment, as it is demanded by the situated action account. EVA demonstrates that even conventional connec-tionist models are applicable to tasks from the field of high-level cognition like planning or problem-solving, which were mostly reserved for symbol-processing models until now.
Tolerating corrupted communication

Consensus encalpsulates the inherent problems of building fault tolerant distributed systems. In this context, the classic model of Byzantine faulty processes can be restated such that messages from a subset of processes can be arbitrarily corrupted (including addition and omission of messages). We consider the case of dynamic and transient faults,that may affect all processes and that are not permanent, and we model them via corrupted communication. For corrupted communication it is natural to distinguish between the safety of communication, which is concerned with the number of altered messages, and the liveness of communication, which restricts message loss. We present two consensus algorithms, together with sufficient conditions on the system to ensure correctness. Our first algorithm needs strong conditions on safety but requires weak conditions on liveness in order to terminate. Our second algorithm tolerates a lower degree of communication safety at the price of stronger liveness conditions. Our algorithms allow us to circumvent the resilience lower bounds from Santoro/Widmayer and Martin/Alvisi.