nils-herrmann/clean_scirepeval huggingface dataset:
CubeSat deployable Ka-band reflector antenna for Deep Space missions

While CubeSats have thus far been used exclusively in Low Earth Orbit (LEO), NASA is now investigating the possibility to deploy CubeSats beyond LEO to carry out scientific experiments in Deep Space. Such CubeSats require a high-gain antenna that fits in a constrained and limited volume. This paper introduces a 42.8 dBi gain deployable Ka-band antenna folding in a 1.5U stowage volume suitable for 3U and 6U class CubeSats.
Lattice-based training of bottleneck feature extraction neural networks

This paper investigates a method for training bottleneck (BN) features in a more targeted manner for their intended use in GMM-HMM based ASR. Our approach adds a GMM acoustic model activation layer to a standard BN feature extraction (FE) neural network and performs lattice-based MMI training on the resulting network. After training, the network is reverted back into a working BN FE network by removing the GMM activation layer, and we then train a GMM system on top of the bottleneck features in the normal way. Our results show that this approach can significantly improve recognition accuracy when compared to a baseline system that uses standard BN features. Further, we show that our approach can be used to perform unsupervised speaker adaptation, yielding significantly improved results compared to global cMLLR adaptation.
Interactive Visualization of Large Data Sets

Visualization provides a powerful means for data analysis. But to be practical, visual analytics tools must support smooth and flexible use of visualizations at a fast rate. This becomes increasingly onerous with the ever-increasing size of real-world datasets. First, large databases make interaction more difficult once query response time exceeds several seconds. Second, any attempt to show all data points will overload the visualization, resulting in chaos that will only confuse the user. Over the last few years, substantial effort has been put into addressing both of these issues and many innovative solutions have been proposed. Indeed, data visualization is a topic that is too large to be addressed in a single survey paper. Thus, we restrict our attention here to interactive visualization of large data sets. Our focus then is skewed in a natural way towards query processing problem-provided by an underlying database system-rather than to the actual data visualization problem.
Augmented Complex Common Spatial Patterns for Classification of Noncircular EEG From Motor Imagery Tasks

A novel augmented complex-valued common spatial pattern (CSP) algorithm is introduced in order to cater for general complex signals with noncircular probability distributions. This is a typical case in multichannel electroencephalogram (EEG), due to the power difference or correlation between the data channels, yet current methods only cater for a very restrictive class of circular data. The proposed complex-valued CSP algorithms account for the generality of complex noncircular data, by virtue of the use of augmented complex statistics and the strong-uncorrelating transform (SUT). Depending on the degree of power difference of complex signals, the analysis and simulations show that the SUT based algorithm maximizes the inter-class difference between two motor imagery tasks. Simulations on both synthetic noncircular sources and motor imagery experiments using real-world EEG support the approach.
BYSTANDER INTERVENTION IN EMERGENCIES : DIFFUSION OF RESPONSIBILITY

Ss overheard an epileptic seizure. They believed either that they alone heard the emergency, or that 1 or 4 unseen others were also present. As predicted the presence of other bystanders reduced the individual's feelings of personal responsibility and lowered his speed of reporting (p < .01). In groups of size 3, males reported no faster than females, and females reported no slower when the 1 other bystander was a male rather than a female. In general, personality and background measures were not predictive of helping. Bystander inaction in real-life emergencies is often explained by "apathy," "alienation," and "anomie." This experiment suggests that the explanation may lie more in the bystander's response to other observers than in his indifference to the victim.
Optimal mechanism design of in-pipe cleaning robot

Recently, interests on cleaning robots workable in pipes (termed as in-pipe cleaning robot) are increasing because Garbage Automatic Collection Facilities (i.e, GACF) are widely being installed in Seoul metropolitan area of Korea. So far research on in-pipe robot has been focused on inspection rather than cleaning. In GACF, when garbage is moving, we have to remove the impurities which are stuck to the inner face of the pipe (diameter: 300mm or 400mm). Thus, in this paper, by using TRIZ ( Solving in Russian abbreviation), we will propose an in-pipe cleaning robot of GACF with the 6-link sliding mechanism which can be adjusted to fit into the inner face of pipe using pneumatic pressure(not spring). The proposed in-pipe cleaning robot for GACF can have forward/backward movement itself as well as rotation of brush in cleaning. The robot body should have the limited size suitable for the smaller pipe with diameter of 300mm. In addition, for the pipe with diameter of 400mm, the links of robot should stretch to fit into the diameter of the pipe by using the sliding mechanism. Based on the conceptual design using TRIZ, we will set up the initial design of the robot in collaboration with a field engineer of Robot Valley, Inc. in Korea. For the optimal design of in-pipe cleaning robot, the maximum impulsive force of collision between the robot and the inner face of pipe is simulated by using RecurDyn® when the link of sliding mechanism is stretched to fit into the 400mm diameter of the pipe. The stresses exerted on the 6 links of sliding mechanism by the maximum impulsive force will be simulated by using ANSYS® Workbench based on the Design Of Experiment(in short DOE). Finally the optimal dimensions including thicknesses of 4 links will be decided in order to have the best safety factor as 2 in this paper as well as having the minimum mass of 4 links. It will be verified that the optimal design of 4 links has the best safety factor close to 2 as well as having the minimum mass of 4 links, compared with the initial design performed by the expert of Robot Valley, Inc. In addition, the prototype of in-pipe cleaning robot will be stated with further research.
The Mario AI Championship 2009-2012

We give a brief overview of the Mario AI Championship, a series of competitions based on an open source clone of the seminal platform game Super Mario Bros. The competition has four tracks. The gameplay and learning tracks resemble traditional reinforcement learning competitions, the Level generation track focuses on the generation of entertaining game levels, and the Turing Test track focuses on human-like game-playing behaviour. We also outline some lessons learned from the competition and its future. The paper is written by the four organisers of the competition.
Brahmastra: Driving Apps to Test the Security of Third-Party Components

We present an app automation tool called Brahmastra for helping app stores and security researchers to test thirdparty components in mobile apps at runtime. The main challenge is that call sites that invoke third-party code may be deeply embedded in the app, beyond the reach of traditional GUI testing tools. Our approach uses static analysis to construct a page transition graph and discover execution paths to invoke third-party code. We then perform binary rewriting to “jump start” the third-party code by following the execution path, efficiently pruning out undesired executions. Compared with the state-of-theart GUI testing tools, Brahmastra is able to successfully analyse third-party code in 2.7×more apps and decrease test duration by a factor of 7. We use Brahmastra to uncover interesting results for two use cases: 175 out of 220 children’s apps we tested display ads that point to web pages that attempt to collect personal information, which is a potential violation of the Children’s Online Privacy Protection Act (COPPA); and 13 of the 200 apps with the Facebook SDK that we tested are vulnerable to a known access token attack.
Staffing Call Centers with Uncertain Demand Forecasts: A Chance-Constrained Optimization Approach

We consider the problem of staffing call-centers with multip le customer classes and agent types operating under quality-of-service (QoS) constraints and demand rate uncertainty. We introduce a formulation of the staffing problem that requires that the Q oS constraints are met with high probability with respect to the uncertainty in the demand ra te. We contrast this chance-constrained formulation with the average-performance constraints tha t have been used so far in the literature. We then propose a two-step solution for the staffing problem u nder chance constraints. In the first step, we introduce a Random Static Planning Problem (RSPP) a nd discuss how it can be solved using two different methods. The RSPP provides us with a first -order (or fluid) approximation for the true optimal staffing levels and a staffing frontier. In the second step, we solve a finite number of staffing problems with known arrival rates–the arrival rate s on the optimal staffing frontier. Hence, our formulation and solution approach has the important pro perty that it translates the problem with uncertain demand rates to one with known arrival rates. The o utput of our procedure is a solution that is feasible with respect to the chance constraint and ne arly optimal for large call centers.
Autonomous Multisensor Calibration and Closed-loop Fusion for SLAM

The vast majority of current robot mapping and navigation systems require specific, well-characterized sensors that may require human-supervised calibration and are applicable only in one type of environment. Furthermore, if a sensor degrades in performance, either through damage to itself or changes in environmental conditions, the effect on the mapping system is usually catastrophic. In contrast, the natural world presents robust, reasonably well-characterized solutions to these problems. Using simple movement behaviors and neural learning mechanisms, rats calibrate their sensors for mapping and navigation in an incredibly diverse range of environments and then go on to adapt to sensor damage and changes in the environment over their lifetimes. In this paper, we introduce similar movement-based autonomous calibration techniques that calibrate place recognition and self-motion processes as well as methods for online multi-sensor weighting and fusion. We present calibration and mapping results from multiple robot platforms and multisensory configurations in an office building, university campus and forest. With moderate assumptions and almost no prior knowledge of the robot, sensor suite or environment, the methods enable the bio-inspired RatSLAM system to generate topologically correct maps in the majority of experiments.
"Jumping Through Hoops": Why do Java Developers Struggle with Cryptography APIs?

To protect sensitive data processed by current applications, developers, whether security experts or not, have to rely on cryptography. While cryptography algorithms have become increasingly advanced, many data breaches occur because developers do not correctly use the corresponding APIs. To guide future research into practical solutions to this problem, we perform an empirical investigation into the obstacles developers face while using the Java cryptography APIs, the tasks they use the APIs for, and the kind of (tool) support they desire. We triangulate data from four separate studies that include the analysis of 100 StackOverflow posts, 100 GitHub repositories, and survey input from 48 developers. We find that while developers find it difficult to use certain cryptographic algorithms correctly, they feel surprisingly confident in selecting the right cryptography concepts (e.g., encryption vs. signatures). We also find that the APIs are generally perceived to be too low-level and that developers prefer more task-based solutions.
Monocular Scene Reconstruction for Reliable Obstacle Detection and Robot Navigation

In this paper, we present a feature based approach for monocular scene reconstruction based on extended Kalman filters (EKF). Our method processes a sequence of images taken by a single camera mounted frontal on a mobile robot. Using different techniques, we are able to produce a precise reconstruction that is free from outliers and therefore can be used for reliable obstacle detection. In real-world field-tests we show that the presented approach is able to detect obstacles that are not seen by other sensors, such as laser-range-finder s. Furthermore, we show that visual obstacle detection combined with a laser-range-finder can increase the detection rate of obstacles considerably allowing the autonomous use of mobile robots in complex public environments.
Real-Time Volume Rendering in Dynamic Lighting Environments Using Precomputed Photon Mapping

We present a framework for precomputed volume radiance transfer that achieves real-time rendering of global illumination effects for volume data sets such as multiple scattering, volumetric shadows, and so on. Our approach incorporates the volumetric photon mapping method into the classical precomputed radiance transfer pipeline. We contribute several techniques for light approximation, radiance transfer precomputation, and real-time radiance estimation, which are essential to make the approach practical and to achieve high frame rates. For light approximation, we propose a new discrete spherical function that has better performance for construction and evaluation when compared with existing rotational invariant spherical functions such as spherical harmonics and spherical radial basis functions. In addition, we present a fast splatting-based radiance transfer precomputation method and an early evaluation technique for real-time radiance estimation in the clustered principal component analysis space. Our techniques are validated through comprehensive evaluations and rendering tests. We also apply our rendering approach to volume visualization.
Extract Me If You Can: Abusing PDF Parsers in Malware Detectors

Owing to the popularity of the PDF format and the continued exploitation of Adobe Reader, the detection of malicious PDFs remains a concern. All existing detection techniques rely on the PDF parser to a certain extent, while the complexity of the PDF format leaves an abundant space for parser confusion. To quantify the difference between these parsers and Adobe Reader, we create a reference JavaScript extractor by directly tapping into Adobe Reader at locations identified through a mostly automatic binary analysis technique. By comparing the output of this reference extractor against that of several opensource JavaScript extractors on a large data set obtained from VirusTotal, we are able to identify hundreds of samples which existing extractors fail to extract JavaScript from. By analyzing these samples we are able to identify several weaknesses in each of these extractors. Based on these lessons, we apply several obfuscations on a malicious PDF sample, which can successfully evade all the malware detectors tested. We call this evasion technique a PDF parser confusion attack. Lastly, we demonstrate that the reference JavaScript extractor improves the accuracy of existing JavaScript-based classifiers and how it can be used to mitigate these parser limitations in a real-world setting.
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks

Recurrent neural networks (RNNs) have drawn interest from machine learning researchers because of their effectiveness at preserving past inputs for time-varying data processing tasks. To understand the success and limitations of RNNs, it is critical that we advance our analysis of their fundamental memory properties. We focus on echo state networks (ESNs), which are RNNs with simple memoryless nodes and random connectivity. In most existing analyses, the short-term memory (STM) capacity results conclude that the ESN network size must scale linearly with the input size for unstructured inputs. The main contribution of this paper is to provide general results characterizing the STM capacity for linear ESNs with multidimensional input streams when the inputs have common low-dimensional structure: sparsity in a basis or significant statistical dependence between inputs. In both cases, we show that the number of nodes in the network must scale linearly with the information rate and poly-logarithmically with the input dimension. The analysis relies on advanced applications of random matrix theory and results in explicit non-asymptotic bounds on the recovery error. Taken together, this analysis provides a significant step forward in our understanding of the STM properties in RNNs.
Cross Lingual Sentiment Analysis using Modified BRAE

Cross-Lingual Learning provides a mechanism to adapt NLP tools available for label rich languages to achieve similar tasks for label-scarce languages. An efficient cross-lingual tool significantly reduces the cost and effort required to manually annotate data. In this paper, we use the Recursive Autoencoder architecture to develop a Cross Lingual Sentiment Analysis (CLSA) tool using sentence aligned corpora between a pair of resource rich (English) and resource poor (Hindi) language. The system is based on the assumption that semantic similarity between different phrases also implies sentiment similarity in majority of sentences. The resulting system is then analyzed on a newly developed Movie Reviews Dataset in Hindi with labels given on a rating scale and compare performance of our system against existing systems. It is shown that our approach significantly outperforms state of the art systems for Sentiment Analysis, especially when labeled data is scarce.
Matching as an Econometric Evaluation Estimator

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Learning-based optimization of cache content in a small cell base station

Optimal cache content placement in a wireless small cell base station (sBS) with limited backhaul capacity is studied. The sBS has a large cache memory and provides content-level selective offloading by delivering high data rate contents to users in its coverage area. The goal of the sBS content controller (CC) is to store the most popular contents in the sBS cache memory such that the maximum amount of data can be fetched directly form the sBS, not relying on the limited backhaul resources during peak traffic periods. If the popularity profile is known in advance, the problem reduces to a knapsack problem. However, it is assumed in this work that, the popularity profile of the files is not known by the CC, and it can only observe the instantaneous demand for the cached content. Hence, the cache content placement is optimised based on the demand history. By refreshing the cache content at regular time intervals, the CC tries to learn the popularity profile, while exploiting the limited cache capacity in the best way possible. Three algorithms are studied for this cache content placement problem, leading to different exploitation-exploration trade-offs. We provide extensive numerical simulations in order to study the time-evolution of these algorithms, and the impact of the system parameters, such as the number of files, the number of users, the cache size, and the skewness of the popularity profile, on the performance. It is shown that the proposed algorithms quickly learn the popularity profile for a wide range of system parameters.
Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12

In June 2016, Apple made a bold announcement that it will deploy local differential privacy for some of their user data collection in order to ensure privacy of user data, even from Apple . The details of Apple’s approach remained sparse. Although several patents [17–19] have since appeared hinting at the algorithms that may be used to achieve differential privacy, they did not include a precise explanation of the approach taken to privacy parameter choice. Such choice and the overall approach to privacy budget use and management are key questions for understanding the privacy protections provided by any deployment of differential privacy. In this work, through a combination of experiments, static and dynamic code analysis of macOS Sierra (Version 10.12) implementation, we shed light on the choices Apple made for privacy budget management. We discover and describe Apple’s set-up for differentially private data processing, including the overall data pipeline, the parameters used for differentially private perturbation of each piece of data, and the frequency with which such data is sent to Apple’s servers. We find that although Apple’s deployment ensures that the (differential) privacy loss per each datum submitted to its servers is 1 or 2, the overall privacy loss permitted by the system is significantly higher, as high as 16 per day for the four initially announced applications of Emojis, New words, Deeplinks and Lookup Hints . Furthermore, Apple renews the privacy budget available every day, which leads to a possible privacy loss of 16 times the number of days since user opt-in to differentially private data collection for those four applications. We applaud Apple’s deployment of differential privacy for its bold demonstration of feasibility of innovation while guaranteeing rigorous privacy. However, we argue that in order to claim the full benefits of differentially private data collection, Apple must give full transparency of its implementation and privacy loss choices, enable user choice in areas related to privacy loss, and set meaningful defaults on the daily and device lifetime privacy loss permitted.  Jun Tang, Aleksandra Korolova, Xiaolong Bai, XueqiangWang, and Xiaofeng Wang.
The Lack of A Priori Distinctions Between Learning Algorithms

This is the first of two papers that use off-training set (OTS) error to investigate the assumption-free relationship between learning algorithms. This first paper discusses the senses in which there are no a priori distinctions between learning algorithms. (The second paper discusses the senses in which there are such distinctions.) In this first paper it is shown, loosely speaking, that for any two algorithms A and B, there are as many targets (or priors over targets) for which A has lower expected OTS error than B as vice versa, for loss functions like zero-one loss. In particular, this is true if A is cross-validation and B is anti-cross-validation (choose the learning algorithm with largest cross-validation error). This paper ends with a discussion of the implications of these results for computational learning theory. It is shown that one cannot say: if empirical misclassification rate is low, the Vapnik-Chervonenkis dimension of your generalizer is small, and the training set is large, then with high probability your OTS error is small. Other implications for membership queries algorithms and punting algorithms are also discussed.