nils-herrmann/clean_scirepeval huggingface dataset:
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation

We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.
Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis

Technology has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. Much of the content being posted and consumed online is multimodal. With billions of phones, tablets and PCs shipping today with built-in cameras and a host of new video-equipped wearables like Google Glass on the horizon, the amount of video on the Internet will only continue to increase. It has become increasingly difficult for researchers to keep up with this deluge of multimodal content, let alone organize or make sense of it. Mining useful knowledge from video is a critical need that will grow exponentially, in pace with the global growth of content. This is particularly important in sentiment analysis, as both service and product reviews are gradually shifting from unimodal to multimodal. We present a novel method to extract features from visual and textual modalities using deep convolutional neural networks. By feeding such features to a multiple kernel learning classifier, we significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets.
Design and Implementation of Robot Arm Control Using LabVIEW and ARM Controller

The design, analysis and implementation of a robot arm system, which is expressed towards its performance with an analytical model by using LabVIEW and embedded system tools was presented in this article. Mathematical modeling of kinematics plays an essential role in design and implementation of a robot arm control. The projected work was focused to control the end effector of the robot arm to achieve any accessible point in an amorphous region using LabVIEW, ARM (Advanced RISC (Reduced Instruction Set Computing) Machine) microcontroller and Dexter ER2 robotic arm. LabVIEW uses analytical method to design the inverse kinematic model of the robot arm. The inverse kinematic model and robot arm control was implemented using LabVIEW and ARM microcontroller. LabVIEW uses the parallel communication to send the joint angles of the robot arm to the ARM. ARM microcontroller uses five PWM (Pulse Width Modulation) signals in order to control the robot arm, which was geared up with servo motors. Robot arm was controlled manually through the LabVIEW GUI (Graphical User Interface) controls. The present paper discussed about the mechanical configuration, analytical modeling, software and hardware of the above said work.
Hierarchical Spatial Sum–Product Networks for Action Recognition in Still Images

Recognizing actions from still images has been popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial sum–product network (SPN). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside subimages and models the correlation of subimages via extra layers of SPN. Our method is shown to be effective on two benchmark data sets.
An Actor-Critic Algorithm for Sequence Prediction

We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Current log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models must generate tokens conditioned on their previous guesses rather than the ground-truth tokens. We address this problem by introducing a critic network that is trained to predict the value of an output token, given the policy of an actor network. This results in a training procedure that is much closer to the test phase, and allows us to directly optimize for a task-specific score such as BLEU. Crucially, since we leverage these techniques in the supervised learning setting rather than the traditional RL setting, we condition the critic network on the ground-truth output. We show that our method leads to improved performance on both a synthetic task, and for German-English machine translation. Our analysis paves the way for such methods to be applied in natural language generation tasks, such as machine translation, caption generation, and dialogue modelling.
Supply responses to digital distribution: Recorded music and live performances

Changes in technologies for reproducing and redistributing digital goods (e.g., music, movies, software, books) have dramatically affected profitability of these goods, and raised concerns for future development of socially valuable digital products. However, broader illegitimate distribution of digital goods may have offsetting demand implications for legitimate sales of complementary non-digital products. We examine the negative impact of file-sharing on recorded music sales and offsetting implications for live concert performances. We find that file-sharing reduces album sales but increases live performance revenues for small artists, perhaps through increased awareness. The impact on live performance revenues for large, well-known artists is negligible. ∗The data for this study were provided by Pollstar and Nielsen SoundScan, and we thank Chris Muratore, Gary Bongiovanni, and Alan Krueger for their help in assembling the data. Elias Bruegmann, Natalie Chun, Yani Ioannou, Anna Levine, Maisy Samuelson, Matt Schefer, and Hassan Sultan provided outstanding research assistance. We are thankful to Tim Bresnahan, Austan Goolsbee, and Rob Porter for helpful comments. Any errors are ours.
Reinforcement Learning Algorithms for MDPs

This article presents a survey of reinforcement learning algorithms for Markov Decision Processes (MDP). In the first half of the article, the problem of value estimation is considered. Here we start by describing the idea of bootstrapping and temporal difference learning. Next, we compare incremental and batch algorithmic variants and discuss the impact of the choice of the function approximation method on the success of learning. In the second half, we describe methods that target the problem of learning to control an MDP. Here online and active learning are discussed first, followed by a description of direct and actor-critic methods.
Primary gonadal failure and precocious adrenarche in a boy with Prader-Labhart-Willi syndrome

A 7-year-old boy with Prader-Labhart-Willi syndrome who had precocious adrenarche was found to have primary gonadal failure, as evidenced by appropriate laboratory investigations: elevated basal levels of plasma FSH and LH with exaggerated responses to LH-RH stimulation and unresponsiveness of plasma testosterone to repeated hCG stimulations. The elevated values of plasma DHEA which were found indicate an early activation of the adrenal gland. This patient demonstrates the varibility of pubertal development in the Prader-Labhart-Willi syndrome, with the unusual association of primary gonadal failure and precocious adrenarche.
BigBench: towards an industry standard benchmark for big data analytics

There is a tremendous interest in big data by academia, industry and a large user base. Several commercial and open source providers unleashed a variety of products to support big data storage and processing. As these products mature, there is a need to evaluate and compare the performance of these systems.
 In this paper, we present BigBench, an end-to-end big data benchmark proposal. The underlying business model of BigBench is a product retailer. The proposal covers a data model and synthetic data generator that addresses the variety, velocity and volume aspects of big data systems containing structured, semi-structured and unstructured data. The structured part of the BigBench data model is adopted from the TPC-DS benchmark, which is enriched with semi-structured and unstructured data components. The semi-structured part captures registered and guest user clicks on the retailer's website. The unstructured data captures product reviews submitted online. The data generator designed for BigBench provides scalable volumes of raw data based on a scale factor. The BigBench workload is designed around a set of queries against the data model. From a business prospective, the queries cover the different categories of big data analytics proposed by McKinsey. From a technical prospective, the queries are designed to span three different dimensions based on data sources, query processing types and analytic techniques.
 We illustrate the feasibility of BigBench by implementing it on the Teradata Aster Database. The test includes generating and loading a 200 Gigabyte BigBench data set and testing the workload by executing the BigBench queries (written using Teradata Aster SQL-MR) and reporting their response times.
WebMTD: Defeating Web Code Injection Attacks using Web Element Attribute Mutation

Existing mitigation techniques for Web code injection attacks have not been widely adopted, primarily due to incurring impractical overheads on the developer, Web applications, or Web browsers. They either substantially increase Web server/client execution time, enforce restrictive coding practices on developers, fail to support legacy Web applications, demand browser code modification, or fail to provide browser backward compatibility. Moving Target Defense (MTD) is a novel proactive class of techniques that aim to defeat attacks by imposing uncertainty in attack reconnaissance and planning. This uncertainty is achieved by frequent and random mutation (randomization) of system configuration in a manner that is not traceable (predictable) by attackers. In this paper, we present WebMTD, a proactive moving target defense mechanism that thwarts a broad class of code injection attacks on Web applications, including cross-site scripting (XSS), HTML code injection, and server-side code injection attacks, in a manner that is transparent to developers, Web applications and browsers. Relying on built-in features of modern Web browsers, WebMTD randomizes certain attributes of Web elements to differentiate the application code from the injected code and disallow its execution; this is done without requiring Web developer involvement and browser code modification. Through rigorous evaluation, we show that WebMTD has very low performance overhead. Also, we argue that our technique outperforms all competing approaches due to its broad effectiveness, transparency, and low overhead. We claim that these qualities make WebMTD an ideal technique for defeating Web code injection attacks on real-world production Web applications.
PEBL: positive example based learning for Web page classification using SVM

Web page classification is one of the essential techniques for Web mining. Specifically, classifying Web pages of a user-interesting class is the first step of mining interesting information from the Web. However, constructing a classifier for an interesting class requires laborious pre-processing such as collecting positive and negative training examples. For instance, in order to construct a "homepage" classifier, one needs to collect a sample of homepages (positive examples) and a sample of non-homepages (negative examples). In particular, collecting negative training examples requires arduous work and special caution to avoid biasing them. We introduce in this paper the Positive Example Based Learning (PEBL) framework for Web page classification which eliminates the need for manually collecting negative training examples in pre-processing. We present an algorithm called Mapping-Convergence (M-C) that achieves classification accuracy (with positive and unlabeled data) as high as that of traditional SVM (with positive and negative data). Our experiments show that when the M-C algorithm uses the same amount of positive examples as that of traditional SVM, the M-C algorithm performs as well as traditional SVM.
Large age-gap face verification by feature injection in deep networks

This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task. Finetuning is performed in a Siamese architecture using a contrastive loss function. A feature injection layer is introduced to boost verification accuracy, showing the ability of the DCNN to learn a similarity metric leveraging external features. Experimental results on the LAG dataset show that our method is able to outperform the face verification solutions in the state of the art considered.
The BOBYQA algorithm for bound constrained optimization without derivatives

BOBYQA is an iterative algorithm for finding a minimum of a function F (x), x∈R, subject to bounds a≤x≤b on the variables, F being specified by a “black box” that returns the value F (x) for any feasible x. Each iteration employs a quadratic approximation Q to F that satisfies Q(y j ) = F (y j ), j = 1, 2, . . . , m, the interpolation points y j being chosen and adjusted automatically, but m is a prescribed constant, the value m= 2n+1 being typical. These conditions leave much freedom in Q, taken up when the model is updated by the highly successful technique of minimizing the Frobenius norm of the change to the second derivative matrix of Q. Thus no first derivatives of F are required explicitly. Most changes to the variables are an approximate solution to a trust region subproblem, using the current quadratic model, with a lower bound on the trust region radius that is reduced cautiously, in order to keep the interpolation points well separated until late in the calculation, which lessens damage from computer rounding errors. Some other changes to the variables are designed to improve the model without reducing F . These techniques are described. Other topics include the starting procedure that is given an initial vector of variables, the value of m and the initial trust region radius. There is also a new device called RESCUE that tries to restore normality if severe loss of accuracy occurs in the matrix calculations of the updating of the model. Numerical results are reported and discussed for two test problems, the numbers of variables being between 10 and 320. Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, England.
PACMan: Coordinated Memory Caching for Parallel Jobs

Data-intensive analytics on large clusters is important for modern Internet services. As machines in these clusters have large memories, in-memory caching of inputs is an effective way to speed up these analytics jobs. The key challenge, however, is that these jobs run multiple tasks in parallel and a job is sped up only when inputs of all such parallel tasks are cached. Indeed, a single task whose input is not cached can slow down the entire job. To meet this “all-or-nothing” property, we have built PACMan, a caching service that coordinates access to the distributed caches. This coordination is essential to improve job completion times and cluster efficiency. To this end, we have implemented two cache replacement policies on top of PACMan’s coordinated infrastructure – LIFE that minimizes average completion time by evicting large incomplete inputs, and LFU-F that maximizes cluster efficiency by evicting less frequently accessed inputs. Evaluations on production workloads from Facebook and Microsoft Bing show that PACMan reduces average completion time of jobs by 53% and 51% (small interactive jobs improve by 77%), and improves efficiency of the cluster by 47% and 54%, respectively.
Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting

The web has become an essential part of our society and is currently the main medium of information delivery. Billions of users browse the web on a daily basis, and there are single websites that have reached over one billion user accounts. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. In this paper, we examine how web-based device fingerprinting currently works on the Internet. By analyzing the code of three popular browser-fingerprinting code providers, we reveal the techniques that allow websites to track users without the need of client-side identifiers. Among these techniques, we show how current commercial fingerprinting approaches use questionable practices, such as the circumvention of HTTP proxies to discover a user's real IP address and the installation of intrusive browser plugins. At the same time, we show how fragile the browser ecosystem is against fingerprinting through the use of novel browser-identifying techniques. With so many different vendors involved in browser development, we demonstrate how one can use diversions in the browsers' implementation to distinguish successfully not only the browser-family, but also specific major and minor versions. Browser extensions that help users spoof the user-agent of their browsers are also evaluated. We show that current commercial approaches can bypass the extensions, and, in addition, take advantage of their shortcomings by using them as additional fingerprinting features.
Fully Convolutional Crowd Counting On Highly Congested Scenes

In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016). Producing an accurate and robust crowd count estimator using computer vision techniques has attracted significant research interest in recent years. Applications for crowd counting systems exist in many diverse areas including city planning, retail, and of course general public safety. Developing a highly generalised counting model that can be deployed in any surveillance scenario with any camera perspective is the key objective for research in this area. Techniques developed in the past have generally performed poorly in highly congested scenes with several thousands of people in frame (Rodriguez et al., 2011). Our approach, influenced by the work of (Zhang et al., 2016), consists of the following contributions: (1) A training set augmentation scheme that minimises redundancy among training samples to improve model generalisation and overall counting performance; (2) a deep, single column, fully convolutional network (FCN) architecture; (3) a multi-scale averaging step during inference. The developed technique can analyse images of any resolution or aspect ratio and achieves state-of-the-art counting performance on the Shanghaitech Part B and UCF CC 50 datasets as well as competitive performance on Shanghaitech Part A.
A Convergent O ( n ) Algorithm for Off-policy Temporal-difference Learning with Linear Function Approximation

We introduce the first temporal-difference learning algorithm that is stable with linear function approximation and off-policy training, for any finite Markov decision process, behavior policy, and target policy, and whose complexity scales linearly in the number of parameters. We consider an i.i.d. policy-evaluation setting in which the data need not come from on-policy experience. The gradient temporal-difference (GTD) algorithm estimates the expected update vector of the TD(0) algorithm and performs stochastic gradient descent on its L2 norm. We prove that this algorithm is stable and convergent under the usual stochastic approximation conditions to the same least-squares solution as found by the LSTD, but without LSTD’s quadratic computational complexity. GTD is online and incremental, and does not involve multiplying by products of likelihood ratios as in importance-sampling methods. 1 Off-policy learning methods Off-policy methods have an important role to play in the larger ambitions of modern reinforcement learning. In general, updates to a statistic of a dynamical process are said to be “off-policy” if their distribution does not match the dynamics of the process, particularly if the mismatch is due to the way actions are chosen. The prototypical example in reinforcement learning is the learning of the value function for one policy, the target policy, using data obtained while following another policy, the behavior policy. For example, the popular Q-learning algorithm (Watkins 1989) is an offpolicy temporal-difference algorithm in which the target policy is greedy with respect to estimated action values, and the behavior policy is something more exploratory, such as a corresponding greedy policy. Off-policy methods are also critical to reinforcement-learning-based efforts to model human-level world knowledge and state representations as predictions of option outcomes (e.g., Sutton, ; Sutton, Rafols & Koop 2006). Unfortunately, off-policy methods such as Q-learning are not sound when used with approximations that are linear in the learned parameters—the most popular form of function approximation in reinforcement learning. Counterexamples have been known for many years (e.g., Baird 1995) in which Q-learning’s parameters diverge to infinity for any positive step size. This is a severe problem in so far as function approximation is widely viewed as necessary for large-scale applications of reinforcement learning. The need is so great that practitioners have often simply ignored the problem and continued to use Q-learning with linear function approximation anyway. Although no instances of absolute divergence in applications have been reported in the literature, the potential for instability is disturbing and probably belies real but less obvious problems.