nils-herrmann/clean_scirepeval huggingface dataset:
Thin-Ideal Internalization : Mounting Evidence for a New Risk Factor for Body-Image Disturbance and Eating Pathology

Body-image disturbance and eating disorders are a significant physical and mental health problem in Western countries. We describe emerging work on one newly identified variable that appears to be a potent risk factor for the development of these problems— internalization of societal standards of attractiveness. Work conducted independently in our labs over the past decade has included scale development, correlational studies, prospective risk-factor studies, randomized experiments, and randomized prevention trials. Findings collectively suggest that internalization is a causal risk factor for body-image and eating disturbances, and that it appears to operate in conjunction with other established risk factors for these outcomes, including dieting and negative affect. Future research is needed to examine the specific familial, peer, and media influences that promote internalization and to replicate and extend our prospective and experimental studies.
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling

We present a general framework for semantic role labeling. The framework combines a machine-learning technique with an integer linear programming-based inference procedure, which incorporates linguistic and structural constraints into a global decision process. Within this framework, we study the role of syntactic parsing information in semantic role labeling. We show that full syntactic parsing information is, by far, most relevant in identifying the argument, especially, in the very first stagethe pruning stage. Surprisingly, the quality of the pruning stage cannot be solely determined based on its recall and precision. Instead, it depends on the characteristics of the output candidates that determine the difficulty of the downstream problems. Motivated by this observation, we propose an effective and simple approach of combining different semantic role labeling systems through joint inference, which significantly improves its performance. Our system has been evaluated in the CoNLL-2005 shared task on semantic role labeling, and achieves the highest F1 score among 19 participants.
Techniques for Maximizing Efficiency of Solar Energy Harvesting Systems ( Invited Paper )

Energy harvesting capabilities enable totally untethered operation of mobile and ubiquitous systems for extended periods of time without requiring battery replacement. This paper examines technical issues with solar energy harvesting. First, maximum power point tracking (MPPT) techniques are compared in terms of solar cell model, tracking source, and controller style. For energy harvesting in conjunction with energy storage, this paper compares batteries and supercapacitors, and discusses trade-offs between complexity of charging circuitry and efficiency. Recent techniques for handling cold booting are also examined in terms of both hardware and software solutions. This paper assumes mainly small-scale photovoltaic sources, although many techniques apply to other sources as well. Together, the increase efficiency is expected to enable more compact, lower cost energy harvesters to bring longer, more stable operation to the systems.
Digital image smoothing and the sigma filter

A conceptually simple but effective noise smoothing algorithm is described. This filter is motivated by the sigma probability of the Gaussian distribution, and it smooths the image noise by averaging only those neighborhood pixels which have the intensities within a fixed sigma range of the center pixel. Consequently, image edges are preserved, and subtle details and thin tines such as roads are retained. The characteristics of this smoothing algorithm are analyzed and compared with several other known filtering algorithms by their ability to retain subtle details, preserving edge shapes, sharpening ramp edges, etc. The comparison also indicates that the sigma filter is the most computationally efficient filter among those evaluated. The filter can be easily extended into several forms which can be used in contrast enhancement, image segmentation, and smoothing signal-dependent noisy images. Several test images 128 X 128 and 256 X 256 pixels in size are used to substantiate its characteristics. The algorithm can be easily extended to 3-D image smoothing.
Efficient Learning of Timeseries Shapelets

In timeseries classification, shapelets are subsequences of timeseries with high discriminative power. Existing methods perform a combinatorial search for shapelet discovery. Even with speedup heuristics such as pruning, clustering, and dimensionality reduction, the search remains computationally expensive. In this paper, we take an entirely different approach and reformulate the shapelet discovery task as a numerical optimization problem. In particular, the shapelet positions are learned by combining the generalized eigenvector method and fused lasso regularizer to encourage a sparse and blocky solution. Extensive experimental results show that the proposed method is orders of magnitudes faster than the state-of-the-art shapelet-based methods, while achieving comparable or even better classification accuracy.
Socially-Aware Animated Intelligent Personal Assistant Agent

SARA (Socially-Aware Robot Assistant) is an embodied intelligent personal assistant that analyses the user’s visual (head and face movement), vocal (acoustic features) and verbal (conversational strategies) behaviours to estimate its rapport level with the user, and uses its own appropriate visual, vocal and verbal behaviors to achieve task and social goals. The presented agent aids conference attendees by eliciting their preferences through building rapport, and then making informed personalized recommendations about sessions to attend and people to meet.
Gesture recognition using recurrent neural networks

A gesture recognition method for Japanese sign language is presented. We have developed a posture recognition system using neural networks which could recognize a finger alphabet of 42 symbols. We then developed a gesture recognition system where each gesture specifies a word. Gesture recognition is more difficult than posture recognition because it has to handle dynamic processes. To deal with dynamic processes we use a recurrent neural network. Here, we describe a gesture recognition method which can recognize continuous gesture. We then discuss the results of our research.
Predicting Valence-Arousal Ratings of Words Using a Weighted Graph Method

Compared to the categorical approach that represents affective states as several discrete classes (e.g., positive and negative), the dimensional approach represents affective states as continuous numerical values on multiple dimensions, such as the valence-arousal (VA) space, thus allowing for more fine-grained sentiment analysis. In building dimensional sentiment applications, affective lexicons with valence-arousal ratings are useful resources but are still very rare. Therefore, this study proposes a weighted graph model that considers both the relations of multiple nodes and their similarities as weights to automatically determine the VA ratings of affective words. Experiments on both English and Chinese affective lexicons show that the proposed method yielded a smaller error rate on VA prediction than the linear regression, kernel method, and pagerank algorithm used in previous studies.
Comparison of Jaccard , Dice , Cosine Similarity Coefficient To Find Best Fitness

A similarity coefficient represents the similarity between two documents, two queries, or one document and one query. The retrieved documents can also be ranked in the order of presumed importance. A similarity coefficient is a function which computes the degree of similarity between a pair of text objects. There are a large number of similarity coefficients proposed in the literature, because the best similarity measure doesn't exist (yet !). In this paper we do a comparative analysis for finding out the most relevant document for the given set of keyword by using three similarity coefficients viz Jaccard, Dice and Cosine coefficients. This we perform using genetic algorithm approach. Due to the randomized nature of genetic algorithm the best fitness value is the average of 10 runs of the same code for a fixed number of iterations.The similarity coefficient for a set of documents retrieved for a given query from Google are find out then average relevancy in terms of fitness values using similarity coefficients is calculated. In this paper we have averaged 10 different generations for each query by running the program 10 times for the fixed value of Probability of Crossover Pc=0.7 and Probability of Mutation Pm=0.01. The same experiment was conducted for 10 queries.
A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks

Wireless sensor networks (WSNs) use the unlicensed industrial, scientific, and medical (ISM) band for transmissions. However, with the increasing usage and demand of these networks, the currently available ISM band does not suffice for their transmissions. This spectrum insufficiency problem has been overcome by incorporating the opportunistic spectrum access capability of cognitive radio (CR) into the existing WSN, thus giving birth to CR sensor networks (CRSNs). The sensor nodes in CRSNs depend on power sources that have limited power supply capabilities. Therefore, advanced and intelligent radio resource allocation schemes are very essential to perform dynamic and efficient spectrum allocation among sensor nodes and to optimize the energy consumption of each individual node in the network. Radio resource allocation schemes aim to ensure QoS guarantee, maximize the network lifetime, reduce the internode and internetwork interferences, etc. In this paper, we present a survey of the recent advances in radio resource allocation in CRSNs. Radio resource allocation schemes in CRSNs are classified into three major categories, i.e., centralized, cluster-based, and distributed. The schemes are further divided into several classes on the basis of performance optimization criteria that include energy efficiency, throughput maximization, QoS assurance, interference avoidance, fairness and priority consideration, and hand-off reduction. An insight into the related issues and challenges is provided, and future research directions are clearly identified.
An Ultrawideband Conformal Capsule Antenna With Stable Impedance Matching

An ultrawideband conformal capsule slot antenna, which has a simple configuration and a stable impedance matching characteristic, is described in this paper. In the past, wideband outer-wall antennas have been proposed for capsule-type applications. However, this paper shows that the outer wall is not a good choice for placing capsule antennas, since such a choice exhibits a high specific absorption ratio, low gain, and low efficiency. Instead, the antenna proposed in this paper is conformal to the inner wall of a capsule shell, and it provides a wide impedance bandwidth ranging from 1.64 to 5.95 GHz (113.6%). Furthermore, the impedance matching remains stable even with a change in the operating environment. Since in a typical application scenario a capsule will move through the digestive system, and thus experience varying environments, a wide bandwidth and stable performance are both very desirable attributes of the antenna, whose design is discussed in this paper. Moreover, the proposed antenna continues to maintain a stable impedance match even when a battery is added inside the capsule, or when there is a change in the battery size and/or its position. Given these advantages, we argue that slot antennas are well suited for incorporation into capsule antennas for the present application.
Optical Flow in Mostly Rigid Scenes

The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPI-Sintel and KITTI-2015 benchmarks.
Boosting Convolutional Features for Robust Object Proposals

Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, but still show room for improvement in object-detection tasks with many categories, in particular for cluttered scenes and occlusion. Modern detection algorithms like Regions with CNNs (Girshick et al., 2014) rely on Selective Search (Uijlings et al., 2013) to propose regions which with high probability represent objects, where in turn CNNs are deployed for classification. Selective Search represents a family of sophisticated algorithms that are engineered with multiple segmentation, appearance and saliency cues, typically coming with a significant runtime overhead. Furthermore, (Hosang et al., 2014) have shown that most methods suffer from low reproducibility due to unstable superpixels, even for slight image perturbations. Although CNNs are subsequently used for classification in top-performing object-detection pipelines, current proposal methods are agnostic to how these models parse objects and their rich learned representations. As a result they may propose regions which may not resemble high-level objects or totally miss some of them. To overcome these drawbacks we propose a boosting approach which directly takes advantage of hierarchical CNN features for detecting regions of interest fast. We demonstrate its performance on ImageNet 2013 detection benchmark and compare it with state-of-the-art methods. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
Robot Task Commander: A framework and IDE for robot application development

This paper introduces the Robot Task Commander (RTC) framework for defining, developing, and deploying robot application software for use in different run-time contexts. RTC was created by NASA-JSC in conjunction with General Motors for use with the Robonaut-2 and Valkyrie humanoid robot platforms. RTC provides a robot programming syntax and an IDE appropriate for use by experts and non-experts for implementation and execution. An expert developer can implement a new application with a combination of scripts, called process nodes, and state machines that set the control mode of the robot. A non-expert developer can assemble process nodes and controller state machines into novel hierarchical applications using a visual programming language (VPL). This VPL also allows developers to interface with other RTC applications or with third-party software packages using a variety of network transport mechanisms (ROS, TCP, shared memory, etc.). RTC represents an advantage over other robot programming frameworks by providing multiple levels of flexibility for development. The efficacy of RTC is demonstrated through examples of sophisticated behaviors, such as programming the Valkyrie robot to grab objects and turn a valve.
Depth of Presence in Virtual Environments

This paper describes a study to assess the influence of a variety of factors on reported level of presence in immersive virtual environments. It introduces the idea of stacking depth, that is, where a participant can simulate the process of entering the virtual environment while already in such an environment, which can be repeated to several levels of depth. An experimental study including 24 subjects was carried out. Half of the subjects were transported between environments by using virtual head-mounted displays, and the other half by going through doors. Three other binary factors were whether or not gravity operated, whether or not the subject experienced a virtual precipice, and whether or not the subject was followed around by a virtual actor. Visual, auditory, and kinesthetic representation systems and egocentric/exocentric perceptual positions were assessed by a preexperiment questionnaire. Presence was assessed by the subjects as their sense of being there, the extent to which they experienced the virtual environments as more the presenting reality than the real world in which the experiment was taking place, and the extent to which the subject experienced the virtual environments as places visited rather than images seen. A logistic regression analysis revealed that subjective reporting of presence was significantly positively associated with visual and kinesthetic representation systems, and negatively with the auditory system. This was not surprising since the virtual reality system used was primarily visual. The analysis also showed a significant and positive association with stacking level depth for those who were transported between environments by using the virtual HMD, and a negative association for those who were transported through doors. Finally, four of the subjects moved their real left arm to match movement of the left arm of the virtual body displayed by the system. These four scored significantly higher on the kinesthetic representation system than the remainder of the subjects.
Using grounded theory to study the experience of software development

Grounded Theory is a research method that generates theory from data and is useful for understanding how people resolve problems that are of concern to them. Although the method looks deceptively simple in concept, implementing Grounded Theory research can often be confusing in practice. Furthermore, despite many papers in the social science disciplines and nursing describing the use of Grounded Theory, there are very few examples and relevant guides for the software engineering researcher. This paper describes our experience using classical (i.e., Glaserian) Grounded Theory in a software engineering context and attempts to interpret the canons of classical Grounded Theory in a manner that is relevant to software engineers. We provide model to help the software engineering researchers interpret the often fuzzy definitions found in Grounded Theory texts and share our experience and lessons learned during our research. We summarize these lessons learned in a set of fifteen guidelines.
Perceived information security , financial liability and consumer trust in electronic commerce transactions

Electronic commerce (EC) transactions are subject to multiple information security threats. Proposes that consumer trust in EC transactions is influenced by perceived information security and distinguishes it from the objective assessment of security threats. Proposes mechanisms of encryption, protection, authentication, and verification as antecedents of perceived information security. These mechanisms are derived from technological solutions to security threats that are visible to consumers and hence contribute to actual consumer perceptions. Tests propositions in a study of 179 consumers and shows a significant relationship between consumers’ perceived information security and trust in EC transactions. Explores the role of limited financial liability as a surrogate for perceived security. However, the findings show that there is a minimal effect of financial liability on consumers’ trust in EC. Engenders several new insights regarding the role of perceived security in EC transactions.
Stock Replenishment and Shipment Scheduling for Vendor-Managed Inventory Systems

Vendor-managed inventory (VMI) is a supply-chain initiative where the supplier is authorized to manage inventories of agreed-upon stock-keeping units at retail locations. The benefits of VMI are well recognized by successful retail businesses such as Wal-Mart. In VMI, distortion of demand information (known as bullwhip effect) transferred from the downstream supply-chain member (e.g., retailer) to the upstream member (e.g., supplier) is minimized, stockout situations are less frequent, and inventory-carrying costs are reduced. Furthermore, a VMI supplier has the liberty of controlling the downstream resupply decisions rather than filling orders as they are placed. Thus, the approach offers a framework for synchronizing inventory and transportation decisions. In this paper, we present an analytical model for coordinating inventory and transportation decisions in VMI systems. Although the coordination of inventory and transportation has been addressed in the literature, our particular problem has not been explored previously. Specifically, we consider a vendor realizing a sequence of random demands from a group of retailers located in a given geographical region. Ideally, these demands should be shipped immediately. However, the vendor has the autonomy of holding small orders until an agreeable dispatch time with the expectation that an economical consolidated dispatch quantity accumulates. As a result, the actual inventory requirements at the vendor are partly dictated by the parameters of the shipment-release policy in use. We compute the optimum replenishment quantity and dispatch frequency simultaneously. We develop a renewaltheoretic model for the case of Poisson demands, and present analytical results.
Neural Motifs: Scene Graph Parsing with Global Context

We investigate the problem of producing structured graph representations of visual scenes. Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We present new quantitative insights on such repeated structures in the Visual Genome dataset. Our analysis shows that object labels are highly predictive of relation labels but not vice-versa. We also find that there are recurring patterns even in larger subgraphs: more than 50% of graphs contain motifs involving at least two relations. Our analysis motivates a new baseline: given object detections, predict the most frequent relation between object pairs with the given labels, as seen in the training set. This baseline improves on the previous state-of-the-art by an average of 3.6% relative improvement across evaluation settings. We then introduce Stacked Motif Networks, a new architecture designed to capture higher order motifs in scene graphs that further improves over our strong baseline by an average 7.1% relative gain. Our code is available at github.com/rowanz/neural-motifs.