nils-herrmann/clean_scirepeval huggingface dataset:
Comparing Website Fingerprinting Attacks and Defenses

Website fingerprinting attacks allow a local, passive eavesdropper to identify a web browsing client’s destination web page by extracting noticeable and unique features from her traffic. Such attacks magnify the gap between privacy and security — a client who encrypts her communication traffic may still have her browsing behaviour exposed to lowcost eavesdropping. Previous authors have shown that privacysensitive clients who use anonymity technologies such as Tor are susceptible to website fingerprinting attacks, and some attacks have been shown to outperform others in specific experimental conditions. However, as these attacks differ in data collection, feature extraction and experimental setup, they cannot be compared directly. On the other side of the coin, proposed website fingerprinting defenses (countermeasures) are generally designed and tested only against specific attacks. Some defenses have been shown to fail against more advanced attacks, and it is unclear which defenses would be effective against all attacks. In this paper, we propose a feature-based comparative methodology that allows us to systematize attacks and defenses in order to compare them. We analyze attacks for their sensitivity to different packet sequence features, and analyze the effect of proposed defenses on these features by measuring whether or not the features are hidden. If a defense fails to hide a feature that an attack is sensitive to, then the defense will not work against this attack. Using this methodology, we propose a new network layer defense that can more effectively hide all of the features we consider.
2D/3D image registration using regression learning

In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object's 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projec-tion(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region's motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method's application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof.
Surround video: a multihead camera approach

We describe algorithms for creating, storing and viewing high-resolution immersive surround videos. Given a set of unit cameras designed to be almost aligned at a common nodal point, we first present a versatile process for stitching seamlessly synchronized streams of videos into a single surround video corresponding to the video of the multihead camera. We devise a general registration process onto raymaps based on minimizing a tailored objective function. We review and introduce new raymaps with good sampling properties. We then give implementation details on the surround video viewer and present experimental results on both real-world acquired and computer-graphics rendered full surround videos. We conclude by mentioning potential applications and discuss ongoing related activities. Video supplements: http://www.csl.sony.co.jp/person/nielsen
A Divided Regression Analysis for Big Data

Statistics is an important part in big data because many statistical methods are used for big data analysis. The aim of statistics is to estimate population using the sample extracted from the population, so statistics is to analyze not the population but the sample. But in big data environment, we can get the big data set closed to the population by the advanced computing systems such as cloud computing and high-speed internet. According to the circumstances, we can analyze entire part of big data like the population of statistics. But we may be impossible to analyze the entire data because of its huge data volume. So, in this paper, we propose a new analytical methodology for big data analysis in regression problem for reducing the computing burden. We call this a divided regression analysis. To verify the performance of our divided regression model, we carry out experiment and simulation.
Mechanism Design via Differential Privacy

We study the role that privacy-preserving algorithms, which prevent the leakage of specific information about participants, can play in the design of mechanisms for strategic agents, which must encourage players to honestly report information. Specifically, we show that the recent notion of differential privacv, in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie. More precisely, mechanisms with differential privacy are approximate dominant strategy under arbitrary player utility functions, are automatically resilient to coalitions, and easily allow repeatability. We study several special cases of the unlimited supply auction problem, providing new results for digital goods auctions, attribute auctions, and auctions with arbitrary structural constraints on the prices. As an important prelude to developing a privacy-preserving auction mechanism, we introduce and study a generalization of previous privacy work that accommodates the high sensitivity of the auction setting, where a single participant may dramatically alter the optimal fixed price, and a slight change in the offered price may take the revenue from optimal to zero.
Meta optimization and its application to portfolio selection

Several data mining algorithms use iterative optimization methods for learning predictive models. It is not easy to determine upfront which optimization method will perform best or converge fast for such tasks. In this paper, we analyze Meta Algorithms (MAs) which work by adaptively combining iterates from a pool of base optimization algorithms. We show that the performance of MAs are competitive with the best convex combination of the iterates from the base algorithms for online as well as batch convex optimization problems. We illustrate the effectiveness of MAs on the problem of portfolio selection in the stock market and use several existing ideas for portfolio selection as base algorithms. Using daily S\&P500 data for the past 21 years and a benchmark NYSE dataset, we show that MAs outperform existing portfolio selection algorithms with provable guarantees by several orders of magnitude, and match the performance of the best heuristics in the pool.
The impact of the transient response of organic light emitting diodes on the design of active matrix OLED displays

Much of the organic light emitting diode (OLED) characterization published to date addresses the high current regime encountered in the operation of passively addressed displays. Higher efficiency and brightness can be obtained by driving with an active matrix, but the lower instantaneous pixel currents place the OLEDs in a completely different operating mode. Results at these low current levels are presented and their impact on active matrix display design is discussed.
SDN AND ITS USE-CASES-NV AND NFV A State-ofthe-Art Survey

Three concepts (a) network programmability by clear separation of data and control planes and (b) sharing of network infrastructure to provide multitenancy, including traffic and address isolation, in large data center networks and (c) replacing the functions that traditionally run on a specialized hardware, with the software-realizations that run on commodity servers have gained lot of attention by both Industry and research-community over past few years. These three concepts are broadly referred as software defined networking (SDN), network virtualization (NV) and network functions virtualization (NFV). This paper presents a thorough study of these three concepts, including how SDN technology can complement the network virtualization and network functions virtualization. SDN, is about applying modularity to network control, which gives network designer the freedom to re-factor the control plane. This modularity has found its application in various areas including network virtualization. This work begins with the survey of software defined networking, considering various perspectives. The survey of SDN is followed by discussing how SDN plays a significant role in NV and NFV. Finally, this work also attempts to explore future directions in SDN based on current trends. Software defined networking, Network Virtualization, Network Functions Virtualization, OpenFlow, Data Center, Overlay, Underlay, Network Planes, Programmable networks.
Information technology and economic performance: A critical review of the empirical evidence

For many years, there has been considerable debate about whether the IT revolution was paying off in higher productivity. Studies in the 1980s found no connection between IT investment and productivity in the U.S. economy, a situation referred to as the productivity paradox. Since then, a decade of studies at the firm and country level has consistently shown that the impact of IT investment on labor productivity and economic growth is significant and positive. This article critically reviews the published research, more than 50 articles, on computers and productivity. It develops a general framework for classifying the research, which facilitates identifying what we know, how well we know it, and what we do not know. The framework enables us to systematically organize, synthesize, and evaluate the empirical evidence and to identify both limitations in existing research and data and substantive areas for future research.The review concludes that the productivity paradox as first formulated has been effectively refuted. At both the firm and the country level, greater investment in IT is associated with greater productivity growth. At the firm level, the review further concludes that the wide range of performance of IT investments among different organizations can be explained by complementary investments in organizational capital such as decentralized decision-making systems, job training, and business process redesign. IT is not simply a tool for automating existing processes, but is more importantly an enabler of organizational changes that can lead to additional productivity gains.In mid-2000, IT capital investment began to fall sharply due to slowing economic growth, the collapse of many Internet-related firms, and reductions in IT spending by other firms facing fewer competitive pressures from Internet firms. This reduction in IT investment has had devastating effects on the IT-producing sector, and may lead to slower economic and productivity growth in the U.S. economy. While the turmoil in the technology sector has been unsettling to investors and executives alike, this review shows that it should not overshadow the fundamental changes that have occurred as a result of firms' investments in IT. Notwithstanding the demise of many Internet-related companies, the returns to IT investment are real, and innovative companies continue to lead the way.
New building blocks for modular design of elliptic and self-equalized filters

This paper introduces building blocks for modular design of elliptic, pseudoelliptic, and self-equalized filters. The first building block is of second order and generates two transmission zeros (TZs), which are either on the real or imaginary axis. Moving the zeros from the real axis (linear phase response) to the imaginary axis (attenuation poles) requires changing the sign of one coupling coefficient. The second building block is a structure of order three, called extended doublet, which allows the generation of two TZs practically anywhere in the complex plane. An important property of this block is its ability to move the two TZs from the real axis to the imaginary axis of the complex s-plane without changing the signs of its coupling coefficients. The third building block is of third order and generates two TZs, which can be moved from the real to the imaginary axis by changing the sign of one coupling coefficient. Simple waveguide structures to implement these blocks are introduced for validation, although this general approach is feasible for all resonator filter types. Higher order filters are designed modularly by cascading an arbitrary number of these building blocks. A novel concept, which allows the independent control of each pair of TZs in higher order filters, is then introduced. It is shown that the new concept allows a filter of order N to generate N TZs without directly coupling the source to the load even when the coupling coefficients are all assumed frequency independent. The same approach can be used to design and reduce the sensitivity of higher order elliptic and pseudoelliptic filters using other building blocks such as doublets or a mixture of building blocks of different orders and properties. Measured results and extensive computer simulation are presented to demonstrate the validity of the concept and the performance of the designed filters.
Stable-Aware Evolutionary Routing Protocol for Wireless Sensor Networks

In real life scenario for wireless sensor networks (WSNs), energy heterogeneity among the sensor nodes due to uneven terrain, connectivity failure, and packet dropping is a crucial factor that triggered the race for developing robust and reliable routing protocols. Prolonging the time interval before the death of the first sensor node, viz. the stability period, is critical for many applications where the feedback from the WSN must be reliable. Although Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-like protocols are fundamental and popular clustering protocols to manage the system’s energy and thus to prolong the lifespan of the network, they assume a near to a perfect energy homogeneous system where a node failure, drainage and re-energizing are typically not considered. More recent protocols like Stable Election Protocol (SEP) considers the reverse, i.e., energy heterogeneity, and properly utilizes the extra energy to guarantee a stable and reliable performance of the network system. While paradigms of computational intelligence such as evolutionary algorithms (EAs) have attracted significant attention in recent years to address various WSN’s challenges such as nodes deployment and localization, data fusion and aggregation, security and routing, they did not (to the best of our knowledge) explore the possibility of maintaining heterogeneous-aware energy consumption to guarantee a reliable and robust routing protocol design. By this, a new protocol named stable-aware evolutionary routing protocol (SAERP), is proposed in this paper to ensure maximum stability and minimum instability periods for both homogeneous/heterogeneous WSNs. SAERP introduces an evolutionary modeling, where the cluster head election probability becomes more efficient, to well maintain balanced energy consumption in both energy homogeneous and heterogeneous settings. The performance of SAERP over simulation for 90 WSNs is evaluated and compared to well known LEACH and SEP protocols. We found that SAERP is more robust and always ensures longer stability period and shorter instability period. E. A. Khalil · B. A. Attea (B) Department of Computer Science, Baghdad University, Baghdad, Iraq e-mail: baraaali@yahoo.com E. A. Khalil e-mail: enanameen@yahoo.com
Detection of stationary foreground objects: A survey

Detection of stationary foreground objects (i.e., moving objects that remain static throughout several frames) has attracted the attention of many researchers over the last decades and, consequently, many new ideas have been recently proposed, trying to achieve high-quality detections in complex scenarios with the lowest misdetections, while keeping real-time constraints. Most of these strategies are focused on detecting abandoned objects. However, there are some approaches that also allow detecting partially-static foreground objects (e.g. people remaining temporarily static) or stolen objects (i.e., objects removed from the background of the scene). This paper provides a complete survey of the most relevant approaches for detecting all kind of stationary foreground objects. The aim of this survey is not to compare the existing methods, but to provide the information needed to get an idea of the state of the art in this field: kinds of stationary foreground objects, main challenges in the field, main datasets for testing the detection of stationary foreground, main stages in the existing approaches and algorithms typically used in such stages.
Sexual differentiation of the human brain: Relation to gender identity, sexual orientation and neuropsychiatric disorders

During the intrauterine period a testosterone surge masculinizes the fetal brain, whereas the absence of such a surge results in a feminine brain. As sexual differentiation of the brain takes place at a much later stage in development than sexual differentiation of the genitals, these two processes can be influenced independently of each other. Sex differences in cognition, gender identity (an individual's perception of their own sexual identity), sexual orientation (heterosexuality, homosexuality or bisexuality), and the risks of developing neuropsychiatric disorders are programmed into our brain during early development. There is no evidence that one's postnatal social environment plays a crucial role in gender identity or sexual orientation. We discuss the relationships between structural and functional sex differences of various brain areas and the way they change along with any changes in the supply of sex hormones on the one hand and sex differences in behavior in health and disease on the other.
Continuous Transverse Stub Array for Ka-Band Applications

This paper presents a flat, high gain, wide scanning, broadband continuous transverse stub (CTS) array. The design procedure, the fabrication, and an exhaustive antenna characterization are described in details. The array comprises 16 radiating slots and is fed by a corporate-feed network in hollow parallel plate waveguide (PPW) technology. A pillbox-based linear source illuminates the corporate network and allows for beam steering. The antenna is designed by using an ad hoc mode matching code recently developed for CTS arrays, providing design guidelines. The assembly technique ensures the electrical contact among the various stages of the network without using any electromagnetic choke and any bonding process. The main beam of the antenna is mechanically steered over ±40° in elevation, by moving a compact horn within the focal plane of the pillbox feeding system. Excellent performances are achieved. The features of the beam are stable within the design 27.5-31 GHz band and beyond, in the entire Ka-band (26.5-40 GHz). An antenna gain of about 29 dBi is measured at broadside at 29.25 GHz and scan losses lower than 2 dB are reported at ±40°. The antenna efficiency exceeds 80% in the whole scan range. The very good agreement between measurements and simulations validates the design procedure. The proposed design is suitable for Satcom Ka-band terminals in moving platforms, e.g., trains and planes, and also for mobile ground stations, as a multibeam sectorial antenna.
Dynamic modelling and control of variable stiffness actuators

After briefly summarizing the mechanical design of the two joint prototypes for the new DLR variable compliance arm, the paper exemplifies the dynamic modelling of one of the prototypes and proposes a generic variable stiffness joint model for nonlinear control design. Based on this model, the design of a simple, gain scheduled state feedback controller for active vibration damping of the mechanically very weakly damped joint is presented. Moreover, the computation of the motor reference values out of the desired stiffness and position is addressed. Finally, simulation and experimental results validate the proposed methods.
Computer vision applied to super resolution

Super-resolution restoration aims to solve the following problem: given a set of observed images, estimate an image at a higher-resolution than is present in any of the individual images. Where the application of this technique differs in Computer Vision from other fields, is in the variety and severity of the registration transformation between the images. In particular this transformation is generally unknown, and a significant component of solving the superresolution problem in Computer Vision is the estimation of the transformation. The transformation may have a simple parametric form, or it may be scene dependent and have to be estimated for every point. In either case the transformation is estimated directly and automatically from the images. Computer Vision techniques applied to the superresolution problem have already yielded several successful products, including Cognitech’s “Video Investigator” software  and Salient Stills’ “Video Focus” . In the latter case, for example, a high resolution still of a face, suitable for printing in a newspaper article, can be constructed from low resolution video news feed. The approach discussed in this article is outlined in figure 1. The input images are first mutually aligned onto a common reference frame. This alignment involves not only a geometric component, but also a photometric component, modelling illumination, gain or colour balance variations among the images. After alignment a composite image mosaic may be rendered and super-resolution restoration may be applied to any chosen region of interest. We shall describe the two key components which are necessary for successful super-resolution restoration : the accurate alignment or registration of the low-resolution images; and the formulation of a super-resolution estimator which utilizes a generative image model together with a prior model of the super-resolved image itself. As with many other problems in computer vision, these different aspects are tackled in a robust, statistical framework.
Sentence Rewriting for Semantic Parsing

A major challenge of semantic parsing is the vocabulary mismatch problem between natural language and target ontology. In this paper, we propose a sentence rewriting based semantic parsing method, which can effectively resolve the mismatch problem by rewriting a sentence into a new form which has the same structure with its target logical form. Specifically, we propose two sentence-rewriting methods for two common types of mismatch: a dictionary-based method for 1N mismatch and a template-based method for N-1 mismatch. We evaluate our sentence rewriting based semantic parser on the benchmark semantic parsing dataset – WEBQUESTIONS. Experimental results show that our system outperforms the base system with a 3.4% gain in F1, and generates logical forms more accurately and parses sentences more robustly.