nils-herrmann/clean_scirepeval huggingface dataset:
Crowd Map: Accurate Reconstruction of Indoor Floor Plans from Crowdsourced Sensor-Rich Videos

Lack of an accurate and low-cost method to reconstruct indoor maps is the main reason behind the current sporadic availability of digital building floor plans. The conventional approach using professional equipment is very costly and only available in the most popular areas. In this paper, we propose and demonstrate CrowdMap, a crowd sourcing system utilizing sensor-rich video data from mobile users for indoor floor plan reconstruction with low-cost. The key idea of CrowdMap is to first jointly leverage crowd sourced sensory and video data to track user movements, then use the inferred user motion traces and context of the image to produce an accurate floor plan. In particular, we exploit the sequential relationship between each consecutive frame abstracted from the video to improve system performance. Our experiments in three college buildings show that CrowdMap achieves a precision of hallway shape around 88%, a recall around 93% and a F-measure around 90%. In addition, we achieve on average 9.8% room area error and on average 6.5% room aspect ratio error. The evaluation result demonstrates a significant improvement of accuracy compared with other crowd sourcing floor plan reconstruction systems.
Quantifying visual preferences around the world

Website aesthetics have been recognized as an influential moderator of people's behavior and perception. However, what users perceive as "good design" is subject to individual preferences, questioning the feasibility of universal design guidelines. To better understand how people's visual preferences differ, we collected 2.4 million ratings of the visual appeal of websites from nearly 40 thousand participants of diverse backgrounds. We address several gaps in the knowledge about design preferences of previously understudied groups. Among other findings, our results show that the level of colorfulness and visual complexity at which visual appeal is highest strongly varies: Females, for example, liked colorful websites more than males. A high education level generally lowers this preference for colorfulness. Russians preferred a lower visual complexity, and Macedonians liked highly colorful designs more than any other country in our dataset. We contribute a computational model and estimates of peak appeal that can be used to support rapid evaluations of website design prototypes for specific target groups.
Unsupervised Learning from URL Corpora

This paper illustrates the utility of URL information in unsupervised learning. We outline the motivation behind the usage of URL information upfront, and present two techniques for unsupervised learning from URL corpora. First, we devise a similarity measure for URL pairs putting down the intuitions behind the same and verify its goodness by using it for clustering. Further, we outline a method for keyword identification using the similarity measure. Then, we explore the usage of character N-grams for flat clustering of URL corpora. The motivation to keep URLs compact forces the usage of a lot of variations of the same word, which is a very unique kind of forcibly inserted noise. N-gram based models are very tolerant to such noise and are very compact too. We then compare the two similarity measures using a rank correlation measure, which reveals that their similarity is very subjective on the corpus used and depends on the distinctiveness of the clusters in the corpus. Given that URLs are small entities, our techniques are magnitudes faster than unsupervised techniques on full-text corpora and require far less information than the latter. To the best of our knowledge, this is the first attempt on unsupervised learning from URL information.
The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research

Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Clinical and clinimetric properties of the PSQI were assessed over an 18-month period with "good" sleepers (healthy subjects, n = 52) and "poor" sleepers (depressed patients, n = 54; sleep-disorder patients, n = 62). Acceptable measures of internal homogeneity, consistency (test-retest reliability), and validity were obtained. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89.6% and specificity of 86.5% (kappa = 0.75, p less than 0.001) in distinguishing good and poor sleepers. The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.
An event-based framework for characterizing the evolutionary behavior of interaction graphs

Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and mining these graphs. However, almost all of them have studied these graphs from a static point of view. The study of the evolution of these graphs over time can provide tremendous insight on the behavior of entities, communities and the flow of information among them. In this work, we present an event-based characterization of critical behavioral patterns for temporally varying interaction graphs. We use non-overlapping snapshots of interaction graphs and develop a framework for capturing and identifying interesting events from them. We use these events to characterize complex behavioral patterns of individuals and communities over time. We demonstrate the application of behavioral patterns for the purposes of modeling evolution, link prediction and influence maximization. Finally, we present a diffusion model for evolving networks, based on our framework.
Benchmarking and Comparison of the Task Graph Scheduling Algorithms

The problem of scheduling a parallel program represented by a weighted directed acyclic graph (DAG) to a set of homogeneous processors for minimizing the completion time of the program has been extensively studied. The NP-completeness of the problem has stimulated researchers to propose a myriad of heuristic algorithms. While most of these algorithms are reported to be efficient, it is not clear how they compare against each other. A meaningful performance evaluation and comparison of these algorithms is a complex task and it must take into account a number of issues. First, most scheduling algorithms are based upon diverse assumptions, making the performance comparison rather meaningless. Second, there does not exist a standard set of benchmarks to examine these algorithms. Third, most algorithms are evaluated using small problem sizes, and, therefore, their scalability is unknown. In this paper, we first provide a taxonomy for classifying various algorithms into distinct categories according to their assumptions and functionalities. We then propose a set of benchmarks that are based on diverse structures and are not biased toward a particular scheduling technique. We have implemented 15 scheduling algorithms and compared them on a common platform by using the proposed benchmarks, as well as by varying important problem parameters. We interpret the results based upon the design philosophies and principles behind these algorithms, drawing inferences why some algorithms perform better than others. We Article ID jpdc.1999.1578, available online at http: www.idealibrary.com on
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints

This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present SoPhie; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model physical and social interactions. Our approach blends a social attention mechanism with a physical attention that helps the model to learn where to look in a large scene and extract the most salient parts of the image relevant to the path. Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors. SoPhie also takes advantage of GAN to generates more realistic samples and to capture the uncertain nature of the future paths by modeling its distribution. All these mechanisms enable our approach to predict socially and physically plausible paths for the agents and to achieve state-of-the-art performance on several different trajectory forecasting benchmarks.
Edge Detection and Ridge Detection with Automatic Scale Selection

When computing descriptors of image data, the type of information that can be extracted may be strongly dependent on the scales at which the image operators are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of scale levels when detecting one-dimensional image features, such as edges and ridges. A novel concept of a scale-space edge is introduced, defined as a connected set of points in scale-space at which: (i) the gradient magnitude assumes a local maximum in the gradient direction, and (ii) a normalized measure of the strength of the edge response is locally maximal over scales. An important consequence of this definition is that it allows the scale levels to vary along the edge. Two specific measures of edge strength are analyzed in detail, the gradient magnitude and a differential expression derived from the third-order derivative in the gradient direction. For a certain way of normalizing these differential descriptors, by expressing them in terms of so-called γ-normalized derivatives, an immediate consequence of this definition is that the edge detector will adapt its scale levels to the local image structure. Specifically, sharp edges will be detected at fine scales so as to reduce the shape distortions due to scale-space smoothing, whereas sufficiently coarse scales will be selected at diffuse edges, such that an edge model is a valid abstraction of the intensity profile across the edge. Since the scale-space edge is defined from the intersection of two zero-crossing surfaces in scale-space, the edges will by definition form closed curves. This simplifies selection of salient edges, and a novel significance measure is proposed, by integrating the edge strength along the edge. Moreover, the scale information associated with each edge provides useful clues to the physical nature of the edge. With just slight modifications, similar ideas can be used for formulating ridge detectors with automatic selection, having the characteristic property that the selected scales on a scale-space ridge instead reflect the width of the ridge. It is shown how the methodology can be implemented in terms of straightforward visual front-end operations, and the validity of the approach is supported by theoretical analysis as well as experiments on real-world and synthetic data.
Learning to share visual appearance for multiclass object detection

We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes as unrelated entities, our model learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters. Our experimental results on the challenging object localization and detection task demonstrate that the proposed model substantially improves the accuracy of the standard single object detectors that ignore hierarchical structure altogether.
A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles

This paper presents an easy-to-use battery model applied to dynamic simulation software. The simulation model uses only the battery State-Of-Charge (SOC) as a state variable in order to avoid the algebraic loop problem. It is shown that this model, composed of a controlled voltage source in series with a resistance, can accurately represent four types of battery chemistries. The model's parameters can easily be extracted from the manufacturer's discharge curve, which allows for an easy use of the model. A method is described to extract the model's parameters and to approximate the internal resistance. The model is validated by superimposing the results with the manufacturer's discharge curves. Finally, the battery model is included in the SimPowerSystems (SPS) simulation software and is used in the Hybrid Electric Vehicle (HEV) demo. The results for the battery and for the DC-DC converter are analysed and they show that the model can accurately represent the general behaviour of the battery.
Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization World Mental Health Surveys.

Little is known about the extent or severity of untreated mental disorders, especially in less-developed countries.



To estimate prevalence, severity, and treatment of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) mental disorders in 14 countries (6 less developed, 8 developed) in the World Health Organization (WHO) World Mental Health (WMH) Survey Initiative.



Face-to-face household surveys of 60 463 community adults conducted from 2001-2003 in 14 countries in the Americas, Europe, the Middle East, Africa, and Asia.



The DSM-IV disorders, severity, and treatment were assessed with the WMH version of the WHO Composite International Diagnostic Interview (WMH-CIDI), a fully structured, lay-administered psychiatric diagnostic interview.



The prevalence of having any WMH-CIDI/DSM-IV disorder in the prior year varied widely, from 4.3% in Shanghai to 26.4% in the United States, with an interquartile range (IQR) of 9.1%-16.9%. Between 33.1% (Colombia) and 80.9% (Nigeria) of 12-month cases were mild (IQR, 40.2%-53.3%). Serious disorders were associated with substantial role disability. Although disorder severity was correlated with probability of treatment in almost all countries, 35.5% to 50.3% of serious cases in developed countries and 76.3% to 85.4% in less-developed countries received no treatment in the 12 months before the interview. Due to the high prevalence of mild and subthreshold cases, the number of those who received treatment far exceeds the number of untreated serious cases in every country.



Reallocation of treatment resources could substantially decrease the problem of unmet need for treatment of mental disorders among serious cases. Structural barriers exist to this reallocation. Careful consideration needs to be given to the value of treating some mild cases, especially those at risk for progressing to more serious disorders.
Object-Level Video Advertising: An Optimization Framework

In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are inserted in a video. For human clothing advertising, we design a deep convolutional neural network using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothing retrieval. Second, we develop a heuristic algorithm to solve the proposed optimization problem. For comparison, we also employ the genetic algorithm to find solutions approaching the global optimum. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object-level video advertising.
Adaptive online scheduling in storm

Today we are witnessing a dramatic shift toward a data-driven economy, where the ability to efficiently and timely analyze huge amounts of data marks the difference between industrial success stories and catastrophic failures. In this scenario Storm, an open source distributed realtime computation system, represents a disruptive technology that is quickly gaining the favor of big players like Twitter and Groupon. A Storm application is modeled as a topology, i.e. a graph where nodes are operators and edges represent data flows among such operators. A key aspect in tuning Storm performance lies in the strategy used to deploy a topology, i.e. how Storm schedules the execution of each topology component on the available computing infrastructure.
 In this paper we propose two advanced generic schedulers for Storm that provide improved performance for a wide range of application topologies. The first scheduler works offline by analyzing the topology structure and adapting the deployment to it; the second scheduler enhance the previous approach by continuously monitoring system performance and rescheduling the deployment at run-time to improve overall performance. Experimental results show that these algorithms can produce schedules that achieve significantly better performances compared to those produced by Storm's default scheduler.
An index finger exoskeleton with series elastic actuation for rehabilitation: Design, control and performance characterization

Rehabilitation of the hands is critical for the restoration of independence in activities of daily living for individuals exhibiting disabilities of the upper extremities. There is initial evidence that robotic devices with force-control-based strategies can help in effective rehabilitation of human limbs. However, to the best of our knowledge, none of the existing hand exoskeletons allow for accurate force or torque control. In this work, we present a novel index finger exoskeleton with Bowden-cable-based series elastic actuation allowing for bidirectional torque control of the device with high backdrivability and low reflected inertia. We present exoskeleton and finger joint torque controllers along with an optimization-based offline parameter estimator. Finally, we carry out tests with the developed prototype to characterize its kinematics, dynamics, and controller performance. Results show that the device preserves the characteristics of natural motion of finger and can be controlled to achieve both exoskeleton and finger joint torque control. Finally, dynamic transparency tests show that the device can be controlled to offer minimal resistance to finger motion. Beyond the present application of the device as a hand rehabilitation exoskeleton, it has the potential to be used as a haptic device for teleoperation.
Simplicity and Representation Change in Grammar Induction

In this paper we examine the role of a bias toward simplicity in directing the process of representation change. We focus on the task of inducing context-free grammars from sample sentences, and we present a rational reconstruction of Woll's SNPR { the Grids system { that incorporates the simplicity bias. The basic induction method alternates between merging existing nonterminal symbols and creating new symbols, using hill-climbing search to move from complex to simpler grammars. In the process, the algorithm creates word classes, phrases, and recursive rewrite rules that move beyond the training sentences, using the simplicity metric to direct search and negative training cases to eliminate overly general grammars. Experiments reveal that this approach can induce accurate grammars and that it scales reasonably to more diicult domains. Moreover, comparative studies show that both the simplicity bias and negative instances play an important role in constraining search, and that simplicity compares favorably with an alternative scheme that estimates accuracy using a separate evaluation set. We discuss Grids' relation to other work on grammar induction and representation change, and we draw some tentative conclusions about the role of simplicity in such tasks.
Mining Product Features from Online Reviews

With the advance of the Internet, e-commerce systems have become extremely important and convenient to human being. More and more products are sold on the web, and more and more people are purchasing products online. As a result, an increasing number of customers post product reviews at merchant websites and express their opinions and experiences in any network space such as Internet forums, discussion groups, and blogs. So there is a large amount of data records related to products on the Web, which are useful for both manufacturers and customers. Mining product reviews becomes a hot research topic, and prior researches mostly base on product features to analyze the opinions. So mining product features is the first step to further reviews processing. In this paper, we present how to mine product features. The proposed extraction approach is different from the previous methods because we only mine the features of the product in opinion sentences which the customers have expressed their positive or negative experiences on. In order to find opinion sentence, a SentiWordNet-based algorithm is proposed. There are three steps to perform our task: (1) identifying opinion sentences in each review which is positive or negative via SentiWordNet; (2) mining product features that have been commented on by customers from opinion sentences; (3) pruning feature to remove those incorrect features. Compared to previous work, our experimental result achieves higher precision and recall.
Predicting long-term growth in students' mathematics achievement: the unique contributions of motivation and cognitive strategies.

This research examined how motivation (perceived control, intrinsic motivation, and extrinsic motivation), cognitive learning strategies (deep and surface strategies), and intelligence jointly predict long-term growth in students' mathematics achievement over 5 years. Using longitudinal data from six annual waves (Grades 5 through 10; Mage  = 11.7 years at baseline; N = 3,530), latent growth curve modeling was employed to analyze growth in achievement. Results showed that the initial level of achievement was strongly related to intelligence, with motivation and cognitive strategies explaining additional variance. In contrast, intelligence had no relation with the growth of achievement over years, whereas motivation and learning strategies were predictors of growth. These findings highlight the importance of motivation and learning strategies in facilitating adolescents' development of mathematical competencies.
Speed Error Mitigation for a DSP-Based Resolver-to-Digital Converter Using Autotuning Filters

Modern resolver-to-digital converters (RDCs) are typically implemented using DSP techniques to reduce hardware footprint and enhance system accuracy. However, in such implementations, both resolver sensor and ADC channel unbalances introduce significant errors, particularly in the speed output of the tracking loop. The frequency spectrum of the output error is variable depending on the resolver mechanical velocity. This paper presents the design of an autotuning output filter based on the interpolation of precomputed filters for a DSP-based RDC with a type-II tracking loop. A fourth-order peak and a second-order high-pass filter are designed and tested for an experimental RDC. The experimental results demonstrate significant reduction of the peak-to-peak error in the estimated speed.