nils-herrmann/clean_scirepeval huggingface dataset:
Inlined Information Flow Monitoring for JavaScript

Extant security mechanisms for web apps, notably the "same-origin policy", are not sufficient to achieve confidentiality and integrity goals for the many apps that manipulate sensitive information. The trend in web apps is "mashups" which integrate JavaScript code from multiple providers in ways that can undercut existing security mechanisms. Researchers are exploring dynamic information flow controls (IFC) for JavaScript, but there are many challenges to achieving strong IFC without excessive performance cost or impractical browser modifications. This paper presents an inlined IFC monitor for ECMAScript 5 with web support, using the no-sensitive-upgrade (NSU) technique, together with experimental evaluation using synthetic mashups and performance benchmarks. On this basis it should be possible to conduct experiments at scale to evaluate feasibility of both NSU and inlined monitoring.
Time dependent adaptive pricing for mobile internet access

Aiming to meet the explosive growth of mobile data traffic and reduce the network congestion, we study Time Dependent Adaptive Pricing (TDAP) with threshold policies to motivate users to shift their Internet access from peak hours to off-peak hours. With the proposed TDAP scheme, Internet Service Providers (ISPs) will be able to use less network capacity to provide users Internet access service with the same QoS. Simulation and analysis are carried out to investigate the performance of the proposed TDAP scheme based on the real Internet traffic pattern.
A neural substrate of prediction and reward.

The capacity to predict future events permits a creature to detect, model, and manipulate the causal structure of its interactions with its environment. Behavioral experiments suggest that learning is driven by changes in the expectations about future salient events such as rewards and punishments. Physiological work has recently complemented these studies by identifying dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events. Taken together, these findings can be understood through quantitative theories of adaptive optimizing control.
Quality-biased ranking of web documents

Many existing retrieval approaches do not take into account the content quality of the retrieved documents, although link-based measures such as PageRank are commonly used as a form of document prior. In this paper, we present the quality-biased ranking method that promotes documents containing high-quality content, and penalizes low-quality documents. The quality of the document content can be determined by its readability, layout and ease-of-navigation, among other factors. Accordingly, instead of using a single estimate for document quality, we consider multiple content-based features that are directly integrated into a state-of- the-art retrieval method. These content-based features are easy to compute, store and retrieve, even for large web collections. We use several query sets and web collections to empirically evaluate the performance of our quality-biased retrieval method. In each case, our method consistently improves by a large margin the retrieval performance of text-based and link-based retrieval methods that do not take into account the quality of the document content.
Bimodal Multicast

There are many methods for making a multicast protocol “reliable.” At one end of the spectrum, a reliable multicast protocol might offer tomicity guarantees, such as all-or-nothing delivery, delivery ordering, and perhaps additional properties such as virtually synchronous addressing. At the other are protocols that use local repair to overcome transient packet loss in the network, offering “best effort” reliability. Yet none of this prior work has treated stability of multicast delivery as a basic reliability property, such as might be needed in an internet radio, television, or conferencing application. This article looks at reliability with a new goal: development of a multicast protocol which is reliable in a sense that can be rigorously quantified and includes throughput stability guarantees. We characterize this new protocol as a “bimodal multicast” in reference to its reliability model, which corresponds to a family of bimodal probability distributions. Here, we introduce the protocol, provide a theoretical analysis of its behavior, review experimental results, and discuss some candidate applications. These confirm that bimodal multicast is reliable, scalable, and that the protocol provides remarkably stable delivery throughput.
Scaling life-long off-policy learning

In this paper we pursue an approach to scaling life-long learning using parallel off-policy reinforcement learning algorithms. In life-long learning a robot continually learns from a life-time of experience, slowly acquiring and applying skills and knowledge to new situations. Many of the benefits of life-long learning are a results of scaling the amount of training data, processed by the robot, to long sensorimotor streams. Another dimension of scaling can be added by allowing off-policy sampling from the unending stream of sensorimotor data generated by a long-lived robot. Recent algorithmic developments have made it possible to apply off-policy algorithms to life-long learning, in a sound way, for the first time. We assess the scalability of these off-policy algorithms on a physical robot. We show that hundreds of accurate multi-step predictions can be learned about several policies in parallel and in realtime. We present the first online measures of off-policy learning progress. Finally we demonstrate that our robot, using the new off-policy measures, can learn 8000 predictions about 300 distinct policies, a substantial increase in scale compared to previous simulated and robotic life-long learning systems.
DECISION BOUNDARY ANALYSIS OF ADVERSARIAL EXAMPLES

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are carefully crafted instances aiming to cause prediction errors for DNNs. Recent research on adversarial examples has examined local neighborhoods in the input space of DNN models. However, previous work has limited what regions to consider, focusing either on low-dimensional subspaces or small balls. In this paper, we argue that information from larger neighborhoods, such as from more directions and from greater distances, will better characterize the relationship between adversarial examples and the DNN models. First, we introduce an attack, OPTMARGIN, which generates adversarial examples robust to small perturbations. These examples successfully evade a defense that only considers a small ball around an input instance. Second, we analyze a larger neighborhood around input instances by looking at properties of surrounding decision boundaries, namely the distances to the boundaries and the adjacent classes. We find that the boundaries around these adversarial examples do not resemble the boundaries around benign examples. Finally, we show that, under scrutiny of the surrounding decision boundaries, our OPTMARGIN examples do not convincingly mimic benign examples. Although our experiments are limited to a few specific attacks, we hope these findings will motivate new, more evasive attacks and ultimately, effective defenses.
Locally Linear Support Vector Machines

Linear support vector machines (svms) have become popular for solving classification tasks due to their fast and simple online application to large scale data sets. However, many problems are not linearly separable. For these problems kernel-based svms are often used, but unlike their linear variant they suffer from various drawbacks in terms of computational and memory efficiency. Their response can be represented only as a function of the set of support vectors, which has been experimentally shown to grow linearly with the size of the training set. In this paper we propose a novel locally linear svm classifier with smooth decision boundary and bounded curvature. We show how the functions defining the classifier can be approximated using local codings and show how this model can be optimized in an online fashion by performing stochastic gradient descent with the same convergence guarantees as standard gradient descent method for linear svm. Our method achieves comparable performance to the state-of-the-art whilst being significantly faster than competing kernel svms. We generalise this model to locally finite dimensional kernel svm.
Fear and Misperception of Los Angeles Urban Space

Imagining urban space as being comfortable or fearful is studied as an effect of people’s connections to their residential area communication infrastructure. Geographic Information System (GIS) modeling and spatial-statistical methods are used to process 215 mental maps obtained from respondents to a multilingual survey of seven ethnically marked residential communities of Los Angeles. Spatial-statistical analyses reveal that fear perceptions of Los Angeles urban space are not associated with commonly expected causes of fear, such as high crime victimization likelihood. The main source of discomfort seems to be presence of non-White and non-Asian populations. Respondents more strongly connected to television and interpersonal communication channels are relatively more fearful of these populations than those less strongly connected. Theoretical, methodological, and community-building policy implications are discussed.
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. To our best knowledge, this is the first publication on LiDAR point cloud simulation framework for autonomous driving. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic registration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed.
Open-market innovation.

Companies in many industries are feeling immense pressure to improve their ability to innovate. Even in these tough economic times, executives have pushed innovation initiatives to the top of their priority lists, but they know that the best ideas aren't always coming out of their own R&D labs. That's why a growing number of companies are exploring the idea of open-market innovation--an approach that uses tools such as licensing, joint ventures, and strategic alliances to bring the benefits of free trade to the flow of new ideas. For instance, when faced with the unanticipated anthrax scare last fall, Pitney Bowes had nothing in its R&D pipeline to help its customers combat the deadly spores. So it sought help from outside innovators to come up with scanning and imaging technologies that could alert its customers to tainted letters and packages. And Dow Chemical and Cargill jointly produced a new form of plastic derived from plant starches--a breakthrough product that neither company could have created on its own. In this article, Bain consultants Darrell Rigby and Chris Zook describe the advantages and disadvantages of open-market innovation and the ways some companies are using it to gain competitive advantage. By importing ideas from the outside, the authors say, companies can collect more and better ideas from different kinds of experts. Creative types within a company will stick around longer if they know their ideas will eventually find a home--as internal R&D projects or as concepts licensed to outside buyers. Exporting ideas also gives companies a way to measure an innovation's real value. However, the authors warn against entering into open-market innovation without properly structuring deals: Xerox and TRW virtually gave away their innovations and had to stand by while other companies capitalized on them.
Compressed Sensing Image Reconstruction Via Recursive Spatially Adaptive Filtering

We introduce a new approach to image reconstruction from highly incomplete data. The available data are assumed to be a small collection of spectral coefficients of an arbitrary linear transform. This reconstruction problem is the subject of intensive study in the recent field of "compressed sensing" (also known as "compressive sampling"). Our approach is based on a quite specific recursive filtering procedure. At every iteration the algorithm is excited by injection of random noise in the unobserved portion of the spectrum and a spatially adaptive image denoising filter, working in the image domain, is exploited to attenuate the noise and reveal new features and details out of the incomplete and degraded observations. This recursive algorithm can be interpreted as a special type of the Robbins-Monro stochastic approximation procedure with regularization enabled by a spatially adaptive filter. Overall, we replace the conventional parametric modeling used in CS by a nonparametric one. We illustrate the effectiveness of the proposed approach for two important inverse problems from computerized tomography: Radon inversion from sparse projections and limited-angle tomography. In particular we show that the algorithm allows to achieve exact reconstruction of synthetic phantom data even from a very small number projections. The accuracy of our reconstruction is in line with the best results in the compressed sensing field.
Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue

This article serves as an introduction to the Special Issue on Metalearning and Algorithm Selection. The introduction is divided into two parts. In the the first section, we give an overview of how the field of metalearning has evolved in the last 1–2 decades and mention how some of the papers in this special issue fit in. In the second section, we discuss the contents of this special issue. We divide the papers into thematic subgroups, provide information about each subgroup, as well as about the individual papers. Our main aim is to highlight how the papers selected for this special issue contribute to the field of metalearning.
Deep Learning with Dynamic Computation Graphs

Neural networks that compute over graph structures are a natural fit for problems in a variety of domains, including natural language (parse trees) and cheminformatics (molecular graphs). However, since the computation graph has a different shape and size for every input, such networks do not directly support batched training or inference. They are also difficult to implement in popular deep learning libraries, which are based on static data-flow graphs. We introduce a technique called dynamic batching, which not only batches together operations between different input graphs of dissimilar shape, but also between different nodes within a single input graph. The technique allows us to create static graphs, using popular libraries, that emulate dynamic computation graphs of arbitrary shape and size. We further present a high-level library1 of compositional blocks that simplifies the creation of dynamic graph models. Using the library, we demonstrate concise and batch-wise parallel implementations for a variety of models from the literature.
3-D Eddy Current Analysis of Induction Heating Apparatus Considering Heat Emission, Heat Conduction, and Temperature Dependence of Magnetic Characteristics

The main issue of a billet heater using induction heating is to avoid billets that were not heated at a desired temperature. In order to improve the induction heating system, it is necessary to clarify the heating property of an object due to eddy current loss and to investigate the temperature distribution in an object by the magneto-thermal coupled analysis. In this paper, the eddy current and temperature distribution of a billet heater is analyzed considering the heat emission, heat conduction, and temperature dependence of magnetic characteristics of the billet. It is shown that the calculated values of temperature in the center and surface of a billet are in good agreement with measured values. The precise analysis is possible by considering the temperature dependence of magnetic characteristics, heat conductivity, etc. The detailed behavior of the heat generation in the billet is clarified. The skin depth is increased because the resistivity of the billet is increased and the permeability is decreased at high temperature. As a result, the flux in the billet is reduced, and then the power (eddy current loss) in the billet is decreased.
Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM.

Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology.The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T(2) two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives.
Trust in IoT: dynamic remote attestation through efficient behavior capture

The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier.
Sybot: an adaptive and mobile spectrum survey system for wifi networks

Information on site-specific spectrum characteristics is essential to evaluate and improve the performance of wireless networks. However, it is usually very costly to obtain accurate spectrum-condition information in heterogeneous wireless environments. This paper presents a novel spectrum-survey system, called Sybot (Spectrum survey robot), that guides network engineers to efficiently monitor the spectrum condition (e.g., RSS) of WiFi networks. Sybot effectively controls mobility and employs three disparate monitoring techniques - complete, selective, and diagnostic - that help produce and maintain an accurate spectrum-condition map for challenging indoor WiFi networks. By adaptively triggering the most suitable of the three techniques, Sybot captures spatio-temporal changes in spectrum condition. Moreover, based on the monitoring results, Sybot automatically determines several key survey parameters, such as site-specific measurement time and space granularities. Sybot has been prototyped with a commodity IEEE 802.11 router and Linux OS, and experimentally evaluated, demonstrating its ability to generate accurate spectrum-condition maps while reducing the measurement effort (space, time) by more than 56%.
Automated WordNet Construction Using Word Embeddings

We present a fully unsupervised method for automated construction of WordNets based upon recent advances in distributional representations of sentences and word-senses combined with readily available machine translation tools. The approach requires very few linguistic resources and is thus extensible to multiple target languages. To evaluate our method we construct two 600-word test sets for word-to-synset matching in French and Russian using native speakers and evaluate the performance of our method along with several other recent approaches. Our method exceeds the best language-specific and multi-lingual automated WordNets in F-score for both languages. The databases we construct for French and Russian, both languages without large publicly available manually constructed WordNets, will be publicly released along with the test sets.
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Wireless local area networks (WLANs) have become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLANs have been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently garnered great attention due to its promising performance. Notwithstanding, WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and corresponding state-of-the-art solutions. This paper consists of three main parts: 1) conventional localization schemes; 2) state-of-the-art approaches; and 3) practical deployment challenges. Since all proposed methods in the WLAN literature have been conducted and tested in different settings, the reported results are not readily comparable. So, we compare some of the representative localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of the approaches in the literature and guide to future improvement opportunities.