nils-herrmann/clean_scirepeval huggingface dataset:
Social media as a measurement tool of depression in populations

Depression is a serious and widespread public health challenge. We examine the potential for leveraging social media postings as a new type of lens in understanding depression in populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements over time while radically expanding population sample sizes. We present work on using a crowdsourcing methodology to build a large corpus of postings on Twitter that have been shared by individuals diagnosed with clinical depression. Next, we develop a probabilistic model trained on this corpus to determine if posts could indicate depression. The model leverages signals of social activity, emotion, and language manifested on Twitter. Using the model, we introduce a social media depression index that may serve to characterize levels of depression in populations. Geographical, demographic and seasonal patterns of depression given by the measure confirm psychiatric findings and correlate highly with depression statistics reported by the Centers for Disease Control and Prevention (CDC).
INDUCING THE MORPHOLOGICAL LEXICON OF A NATURAL LANGUAGE FROM UNANNOTATED TEXT

This work presents an algorithm for the unsupervised learning, or induction, of a simple morphology of a natural language. A probabilistic maximum a posteriori model is utilized, which builds hierarchical representations for a set of morphs, which are morpheme-like units discovered from unannotated text corpora. The induced morph lexicon stores parameters related to both the “meaning” and “form” of the morphs it contains. These parameters affect the role of the morphs in words. The model is implemented in a task of unsupervised morpheme segmentation of Finnish and English words. Very good results are obtained for Finnish and almost as good results are obtained in the English task.
Energy and Performance Characterization of Mobile Heterogeneous Computing

A modern mobile application processor is a heterogeneous multi-core SoC which integrates CPU and application-specific accelerators such as GPU and DSP. It provides opportunity to accelerate other compute-intensive applications, yet mapping an algorithm to such a heterogeneous platform is not a straightforward task and has many design decisions to make. In this paper, we evaluate the performance and energy benefits of utilizing the integrated GPU and DSP cores to offload or share CPU's compute-intensive tasks. The evaluation is conducted on three representative mobile platforms, TI's OMAP3530, Qualcomn's Snapdragon S2, and Nvidia's Tegra2, using common computation tasks in mobile applications. We identify key factors that should be considered in energy-optimized mobile heterogeneous computing. Our evaluation results show that, by effectively utilizing all the computing cores concurrently, an average of 3.7X performance improvement can be achieved with the cost of 33% more power consumption, in comparison with the case of utilizing CPU only. This stands for 2.8X energy saving.
USim: a user behavior simulation framework for training and testing IDSes in GUI based systems

Anomaly detection systems largely depend on user profile data to be able to detect deviations from normal activity. Most of this profile data is currently based on command-line instructions/directives executed by users on a system. With the advent and extensive usage of graphical user interfaces (GUIs), command-line data can no longer fully represent user's complete behavior which is essential for effectively detecting the anomalies in these GUI based systems. Collection of user behavior data is a slow and time consuming process. In this paper, we present a new approach to automate the generation of user data by parameterizing user behavior in terms of user intention (malicious/normal), user skill level, set of applications installed on a machine, mouse movement and keyboard activity. The user behavior parameters are used to generate templates, which can be further customized. The framework is called USim which can achieve rapid generation of user behavior data based on these templates for GUI based systems. The data thus generated can be utilized for rapidly training and testing intrusion detection systems (IDSes) and improving their detection precision.
Virtual network embedding through topology-aware node ranking

Virtualizing and sharing networked resources have become a growing trend that reshapes the computing and networking architectures. Embedding multiple virtual networks (VNs) on a shared substrate is a challenging problem on cloud computing platforms and large-scale sliceable network testbeds. In this paper we apply the Markov Random Walk (RW) model to rank a network node based on its resource and topological attributes. This novel topology-aware node ranking measure reflects the relative importance of the node. Using node ranking we devise two VN embedding algorithms. The first algorithm maps virtual nodes to substrate nodes according to their ranks, then embeds the virtual links between the mapped nodes by finding shortest paths with unsplittable paths and solving the multi-commodity flow problem with splittable paths. The second algorithm is a backtracking VN embedding algorithm based on breadth-first search, which embeds the virtual nodes and links during the same stage using node ranks. Extensive simulation experiments show that the topology-aware node rank is a better resource measure and the proposed RW-based algorithms increase the long-term average revenue and acceptance ratio compared to the existing embedding algorithms.
Algorithms on strings, trees, and sequences

Linear-Time Construction of Suffix Trees We will present two methods for constructing suffix trees in detail, Ukkonen’s method and Weiner’s method. Weiner was the first to show that suffix trees can be built in linear time, and his method is presented both for its historical importance and for some different technical ideas that it contains. However, lJkkonen’s method is equally fast and uses far less space (i.e., memory) in practice than Weiner’s method Hence Ukkonen is the method of choice for most problems requiring the construction of a suffix tree. We also believe that Ukkonen’s method is easier to understand. Therefore, it will be presented first A reader who wishes to study only one method is advised to concentrate on it. However, our development of Weiner’s method does not depend on understanding Ukkonen’s algorithm, and the two algorithms can be read independently (with one small shared section noted in the description of Weiner’s method).
The theory and practice of corporate finance : Evidence from the field

We survey 392 CFOs about the cost of capital, capital budgeting, and capital structure. Large firms rely heavily on present value techniques and the capital asset pricing model, while small firms are relatively likely to use the payback criterion. A surprising number of firms use firm risk rather than project risk in evaluating new investments. Firms are concerned about financial flexibility and credit ratings when issuing debt, and earnings per share dilution and recent stock price appreciation when issuing equity. We find some support for the pecking-order and trade-off capital structure hypotheses but little evidence that executives are concerned about asset substitution, asymmetric information, transactions costs, free cash flows, or personal taxes.
Low-cost traffic analysis of Tor

Tor is the second generation onion router supporting the anonymous transport of TCP streams over the Internet. Its low latency makes it very suitable for common tasks, such as Web browsing, but insecure against traffic-analysis attacks by a global passive adversary. We present new traffic-analysis techniques that allow adversaries with only a partial view of the network to infer which nodes are being used to relay the anonymous streams and therefore greatly reduce the anonymity provided by Tor. Furthermore, we show that otherwise unrelated streams can be linked back to the same initiator Our attack is feasible for the adversary anticipated by the Tor designers. Our theoretical attacks are backed up by experiments performed on the deployed, albeit experimental, Tor network. Our techniques should also be applicable to any low latency anonymous network. These attacks highlight the relationship between the field of traffic-analysis and more traditional computer security issues, such as covert channel analysis. Our research also highlights that the inability to directly observe network links does not prevent an attacker from performing traffic-analysis: the adversary can use the anonymising network as an oracle to infer the traffic load on remote nodes in order to perform traffic-analysis.
A PIN diode controlled variable attenuator using a 0-dB branch-line coupler

We describe a simple PIN diode controlled variable attenuator that employs a 0-dB branch line directional coupler. The response of the attenuator was measured between 1.3 GHz and 2.6 GHz. At the center frequency, the attenuation monotonically varied from 0.7 dB to 23 dB with the control voltage, and the distributed branch-line coupler structure resulted in low input reflection. Our attenuator is easier to design, smaller in area than a double hybrid coupled attenuator, and has comparable or better reflection and attenuation performance characteristics.
The Homogeneous Chaos Author ( s ) :

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RacerX: effective, static detection of race conditions and deadlocks

It is explicitly designed to find errors in large, complex multithreaded systems. It aggressively infers checking information such as which locks protect which operations, which code contexts are multithreaded, and which shared accesses are dangerous. It tracks a set of code features which it uses to sort errors both from most to least severe. It uses novel techniques to counter the impact of analysis mistakes. The tool is fast, requiring between 2-14 minutes to analyze a 1.8 million line system. We have applied it to Linux, FreeBSD, and a large commercial code base, finding serious errors in all of them. RacerX is a static tool that uses flow-sensitive, interprocedural analysis to detect both race conditions and deadlocks. It uses novel strategies to infer checking information such as which locks protect which operations, which code contexts are multithreaded, and which shared accesses are dangerous. We applied it to FreeBSD, Linux and a large commercial code base and found serious errors in all of them.
Data Mining : Concepts and Techniques

Association rule mining was first proposed by Agrawal, Imielinski, and Swami [AIS93]. The Apriori algorithm discussed in Section 5.2.1 for frequent itemset mining was presented in Agrawal and Srikant [AS94b]. A variation of the algorithm using a similar pruning heuristic was developed independently by Mannila, Tiovonen, and Verkamo [MTV94]. A joint publication combining these works later appeared in Agrawal, Mannila, Srikant, Toivonen, and Verkamo [AMS96]. A method for generating association rules from frequent itemsets is described in Agrawal and Srikant [AS94a].
A Practical Approach to Hard Disk Failure Prediction in Cloud Platforms: Big Data Model for Failure Management in Datacenters

Large scale cloud platforms can benefit from a service that runs a machine learning model to predict disk drive failures. Unlike previous studies in this space, we have combined multiple data inputs for the model and obtained a better model performance compared to earlier published models. In this paper we explain how we developed and deployed the predictive model in a large scale cloud service. To build the model, we used a combination of two open data sources - Self-Monitoring, Analysis and Reporting technology (S.M.A.R.T or SMART) data and Windows performance counters. The nature of both these data sources is different and complex. The paper provides unique ways of parsing and transforming the data to make it most suited for a classification problem. Trails with different machine learning (ML) and statistical modeling techniques led us to the best performing two-stage ensemble model. We implemented this model to be configurable such that it could be deployed on large scale distributed cloud management systems and iterated on with minimal code impact. We provide a glimpse of the complex cloud hardware ecosystem and how a predictive model would impact such an ecosystem. Although our study focused on hard disk drives, we believe a similar modeling approach can apply to other hardware components as well. A successfully executed hard disk failure prediction model can pre-empt negative impact to client workloads and improve the economics of running a large scale cloud service. We provide the details of our model as a possible template for future extensions and improvements towards building more robust hardware fault prediction services. Finally we give a staged approach to operationalizing the model in large scale cloud systems.
A persistence landscapes toolbox for topological statistics

Topological data analysis provides a multiscale description of the geometry and topology of quantitative data. The persistence landscape is a topological summary that can be easily combined with tools from statistics and machine learning. We give efficient algorithms for calculating persistence landscapes, their averages, and distances between such averages. We discuss an implementation of these algorithms and some related procedures. These are intended to facilitate the combination of statistics and machine learning with topological data analysis. We present an experiment showing that the low-dimensional persistence landscapes of points sampled from spheres (and boxes) of varying dimensions differ.
Loss Max-Pooling for Semantic Image Segmentation

We introduce a novel loss max-pooling concept for handling imbalanced training data distributions, applicable as alternative loss layer in the context of deep neural networks for semantic image segmentation. Most real-world semantic segmentation datasets exhibit long tail distributions with few object categories comprising the majority of data and consequently biasing the classifiers towards them. Our method adaptively re-weights the contributions of each pixel based on their observed losses, targeting under-performing classification results as often encountered for under-represented object classes. Our approach goes beyond conventional cost-sensitive learning attempts through adaptive considerations that allow us to indirectly address both, inter- and intra-class imbalances. We provide a theoretical justification of our approach, complementary to experimental analyses on benchmark datasets. In our experiments on the Cityscapes and Pascal VOC 2012 segmentation datasets we find consistently improved results, demonstrating the efficacy of our approach.
Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments

Active and passive sensing technologies are providing powerful mechanisms to track, model, and understand a range of health behaviors and well-being states. Despite yielding rich, dense and high fidelity data, current sensing technologies often require highly engineered study designs and persistent participant compliance, making them difficult to scale to large populations and to data acquisition tasks spanning extended time periods. This paper situates social media as a new passive, unobtrusive sensing technology. We propose a semi-supervised machine learning framework to combine small samples of data gathered through active sensing, with large-scale social media data to infer mood instability (MI) in individuals. Starting from a theoretically-grounded measure of MI obtained from mobile ecological momentary assessments (EMAs), we show that our model is able to infer MI in a large population of Twitter users with 96% accuracy and F-1 score. Additionally, we show that, our model predicts self-identifying Twitter users with bipolar and borderline personality disorder to exhibit twice the likelihood of high MI, compared to that in a suitable control. We discuss the implications and the potential for integrating complementary sensing capabilities to address complex research challenges in precision medicine.
Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis

Twitter sentiment analysis offers organizations ability to monitor public feeling towards the products and events related to them in real time. The first step of the sentiment analysis is the text pre-processing of Twitter data. Most existing researches about Twitter sentiment analysis are focused on the extraction of new sentiment features. However, to select the pre-processing method is ignored. This paper discussed the effects of text pre-processing method on sentiment classification performance in two types of classification tasks, and summed up the classification performances of six pre-processing methods using two feature models and four classifiers on five Twitter datasets. The experiments show that the accuracy and F1-measure of Twitter sentiment classification classifier are improved when using the pre-processing methods of expanding acronyms and replacing negation, but barely changes when removing URLs, removing numbers or stop words. The Naive Bayes and Random Forest classifiers are more sensitive than Logistic Regression and support vector machine classifiers when various pre-processing methods were applied.
Understanding User Satisfaction with Intelligent Assistants

Voice-controlled intelligent personal assistants, such as Cortana, Google Now, Siri and Alexa, are increasingly becoming a part of users' daily lives, especially on mobile devices. They introduce a significant change in information access, not only by introducing voice control and touch gestures but also by enabling dialogues where the context is preserved. This raises the need for evaluation of their effectiveness in assisting users with their tasks. However, in order to understand which type of user interactions reflect different degrees of user satisfaction we need explicit judgements. In this paper, we describe a user study that was designed to measure user satisfaction over a range of typical scenarios of use: controlling a device, web search, and structured search dialogue. Using this data, we study how user satisfaction varied with different usage scenarios and what signals can be used for modeling satisfaction in the different scenarios. We find that the notion of satisfaction varies across different scenarios, and show that, in some scenarios (e.g. making a phone call), task completion is very important while for others (e.g. planning a night out), the amount of effort spent is key. We also study how the nature and complexity of the task at hand affects user satisfaction, and find that preserving the conversation context is essential and that overall task-level satisfaction cannot be reduced to query-level satisfaction alone. Finally, we shed light on the relative effectiveness and usefulness of voice-controlled intelligent agents, explaining their increasing popularity and uptake relative to the traditional query-response interaction.
deepViz : Visualizing Convolutional Neural Networks for Image Classification

Deep convolutional neural networks have recently shown state of the art performance on image classification problems. However, their inner workings remain a mystery to machine learning experts, particularly when compared to better studied and less complex algorithms such as SVM and Logistic Regression. As a result, constructing and debugging effective convolutional neural networks is time-consuming and error-prone, as it often involves a substantial amount of trial and error. We introduce deepViz, a system designed to allow experts to understand their models and diagnose issues with the model structure, enabling more rapid iteration during the model construction process and faster convergence to a suitable model for the task at hand.
Multi-Chart Geometry Images

We introduce multi-chart geometry images, a new representation for arbitrary surfaces. It is created by resampling a surface onto a regular 2D grid. Whereas the original scheme of Gu et al. maps the entire surface onto a single square, we use an atlas construction to map the surface piecewise onto charts of arbitrary shape. We demonstrate that this added flexibility reduces parametrization distortion and thus provides greater geometric fidelity, particularly for shapes with long extremities, high genus, or disconnected components. Traditional atlas constructions suffer from discontinuous reconstruction across chart boundaries, which in our context create unacceptable surface cracks. Our solution is a novel zippering algorithm that creates a watertight surface. In addition, we present a new atlas chartification scheme based on clustering optimization.
Scene text detection using graph model built upon maximally stable extremal regions

Scene text detection could be formulated as a bi-label (text and non-text regions) segmentation problem. However, due to the high degree of intraclass variation of scene characters as well as the limited number of training samples, single information source or classifier is not enough to segment text from non-text background. Thus, in this paper, we propose a novel scene text detection approach using graph model built upon Maximally Stable Extremal Regions (MSERs) to incorporate various information sources into one framework. Concretely, after detecting MSERs in the original image, an irregular graph whose nodes are MSERs, is constructed to label MSERs as text regions or non-text ones. Carefully designed features contribute to the unary potential to assess the individual penalties for labeling a MSER node as text or non-text, and color and geometric features are used to define the pairwise potential to punish the likely discontinuities. By minimizing the cost function via graph cut algorithm, different information carried by the cost function could be optimally balanced to get the final MSERs labeling result. The proposed method is naturally context-relevant and scale-insensitive. Experimental results on the ICDAR 2011 competition dataset show that the proposed approach outperforms state-of-the-art methods both in recall and
Green Queue: Customized Large-Scale Clock Frequency Scaling

We examine the scalability of a set of techniques related to Dynamic Voltage-Frequency Scaling (DVFS) on HPC systems to reduce the energy consumption of scientific applications through an application-aware analysis and runtime framework, Green Queue. Green Queue supports making CPU clock frequency changes in response to intra-node and inter-node observations about application behavior. Our intra-node approach reduces CPU clock frequencies and therefore power consumption while CPUs lacks computational work due to inefficient data movement. Our inter-node approach reduces clock frequencies for MPI ranks that lack computational work. We investigate these techniques on a set of large scientific applications on 1024 cores of Gordon, an Intel Sandy bridge-based supercomputer at the San Diego Supercomputer Center. Our optimal intra-node technique showed an average measured energy savings of 10.6% and a maximum of 21.0% over regular application runs. Our optimal inter-node technique showed an average 17.4% and a maximum of 31.7% energy savings.