nils-herrmann/clean_scirepeval huggingface dataset:
Psychophysiological stress and EMG activity of the trapezius muscle.

Although it is generally assumed that mental stress induces muscular tension, the experimental data have, so far, been inconclusive. Likely explanations for these inconsistent findings are (a) too small subject samples in some experiments, (b) the use of only one type of stress stimulation, and (c) the lack of objective (physiological) measurements documenting the stress-inducing properties of the experimental treatment. Furthermore. the effect of mental stress and physical load separately, versus the combined influence of physical and mental load on muscular tension, has not been investigated earlier. Therefore, the aim o f the present experiment was lo examine the effects of mental stress as well as of physical load, separately and in combination, on perceived stress, physiological stress responses, and on muscular tension as reflected in electromyographical (EMG) activity of the trapezius muscle. Sixty two female subjects were individually exposed to mental arithmetic, the Stroop color word test (CWT), the cold pressor test, standardized test contractions (TCs), and the CWT combined with a TC. Compared to baseline, the stress session induced significant increases in systolic and diastolic blood pressure, heart rate, urinary catecholamines, salivary cortisol, and self-reported stress. Each of the two mental stress tests induced a significant increase in EMG activity. The CWT caused a rise in EMG activity also during the TC, which was significantly more pronounced than the increase induced by the CWT alone. Blood pressure responses and self-reported stress followed the same pattern as the EMG activity. The results are consistent with the assumption that psychological stress plays a role in musculoskeletal disorders by increasing muscular tension both in low-load work situations and in the absence of physical load. It is also indicated that the stress-induced increase in muscular tension is accentuated on top of a physical load.
Imitation Learning of Agenda-based Semantic Parsers

Semantic parsers conventionally construct logical forms bottom-up in a fixed order, resulting in the generation of many extraneous partial logical forms. In this paper, we combine ideas from imitation learning and agenda-based parsing to train a semantic parser that searches partial logical forms in a more strategic order. Empirically, our parser reduces the number of constructed partial logical forms by an order of magnitude, and obtains a 6x-9x speedup over fixed-order parsing, while maintaining comparable accuracy.
Fast Random Walk Graph Kernel

Random walk graph kernel has been used as an important tool for various data mining tasks including classification and similarity computation. Despite its usefulness, however, it suffers from the expensive computational cost which is at least O(n) or O(m) for graphs with n nodes and m edges. In this paper, we propose Ark, a set of fast algorithms for random walk graph kernel computation. Ark is based on the observation that real graphs have much lower intrinsic ranks, compared with the orders of the graphs. Ark exploits the low rank structure to quickly compute random walk graph kernels in O(n) or O(m) time. Experimental results show that our method is up to 97,865× faster than the existing algorithms, while providing more than 91.3% of the accuracies.
A Comparison of Commercial and Military Computer Security Policies

Most discussions of computer security focus on control of disclosure. In Particular, the U.S. Department of Defense has developed a set of criteria for computer mechanisms to provide control of classified information. However, for that core of data processing concerned with business operation and control of assets, the primary security concern is data integrity. This paper presents a policy for data integrity based on commercial data processing practices, and compares the mechanisms needed for this policy with the mechanisms needed to enforce the lattice model for information security. We argue that a lattice model is not sufficient to characterize integrity policies, and that distinct mechanisms are needed to Control disclosure and to provide integrity.
Data Compression and Harmonic Analysis

In this article we review some recent interactions between harmonic analysis and data compression. The story goes back of course to Shannon’s R(D) theory in the case of Gaussian stationary processes, which says that transforming into a Fourier basis followed by block coding gives an optimal lossy compression technique; practical developments like transformbased image compression (JPEG) have been inspired by this result. In this article we also discuss connections perhaps less familiar to the Information Theory community, growing out of the field of harmonic analysis. Recent harmonic analysis constructions, such as wavelet transforms and Gabor transforms, are essentially optimal transforms for transform coding in certain settings. Some of these transforms are under consideration for future compression standards, like JPEG-2000. We discuss some of the lessons of harmonic analysis in this century. Typically, the problems and achievements of this field have involved goals that were not obviously related to practical data compression, and have used a language not immediately accessible to outsiders. Nevertheless, through a extensive generalization of what Shannon called the ‘sampling theorem’, harmonic analysis has succeeded in developing new forms of functional representation which turn out to have significant data compression interpretations. We explain why harmonic analysis has interacted with data compression, and we describe some interesting recent ideas in the field that may affect data compression in the future.
Self-awareness and transgression in children: two field studies.

Two field studies explored the relationship between self-awareness and transgressive behavior. In the first study, 363 Halloween trick-or-treaters were instructed to only take one candy. Self-awareness induced by the presence of a mirror placed behind the candy bowl decreased transgression rates for children who had been individuated by asking them their name and address, but did not affect the behavior of children left anonymous. Self-awareness influenced older but not younger children. Naturally occurring standards instituted by the behavior of the first child to approach the candy bowl in each group were shown to interact with the experimenter's verbally stated standard. The behavior of 349 subjects in the second study replicated the findings in the first study. Additionally, when no standard was stated by the experimenter, children took more candy when not self-aware than when self-aware.
Combining unit-level symbolic execution and system-level concrete execution for testing NASA software

We describe an approach to testing complex safety critical software that combines unit-level symbolic execution and system-level concrete execution for generating test cases that satisfy user-specified testing criteria. We have developed Symbolic Java PathFinder, a symbolic execution framework that implements a non-standard bytecode interpreter on top of the Java PathFinder model checking tool. The framework propagates the symbolic information via attributes associated with the program data. Furthermore, we use two techniques that leverage system-level concrete program executions to gather information about a unit's input to improve the precision of the unit-level test case generation. We applied our approach to testing a prototype NASA flight software component. Our analysis helped discover a serious bug that resulted in design changes to the software. Although we give our presentation in the context of a NASA project, we believe that our work is relevant for other critical systems that require thorough testing.
SOPS: Stock Prediction Using Web Sentiment

Recently, the web has rapidly emerged as a great source of financial information ranging from news articles to per- sonal opinions. Data mining and analysis of such financial information can aid stock market predictions. Traditional approaches have usually relied on predictions based on past performance of the stocks. In this paper, we introduce a novel way to do stock market prediction based on sentiments of web users. Our method involves scanning for financial message boards and extracting sentiments expressed by in- dividual authors. The system then learns the correlation between the sentiments and the stock values. The learned model can then be used to make future predictions about stock values. In our experiments, we show that our method is able to predict the sentiment with high precision and we also show that the stock performance and its recent web sentiments are also closely correlated.
Zero-Shot Learning via Joint Latent Similarity Embedding

Zero-shot recognition (ZSR) deals with the problem of predicting class labels for target domain instances based on source domain side information (e.g. attributes) of unseen classes. We formulate ZSR as a binary prediction problem. Our resulting classifier is class-independent. It takes an arbitrary pair of source and target domain instances as input and predicts whether or not they come from the same class, i.e. whether there is a match. We model the posterior probability of a match since it is a sufficient statistic and propose a latent probabilistic model in this context. We develop a joint discriminative learning framework based on dictionary learning to jointly learn the parameters of our model for both domains, which ultimately leads to our class-independent classifier. Many of the existing embedding methods can be viewed as special cases of our probabilistic model. On ZSR our method shows 4.90% improvement over the state-of-the-art in accuracy averaged across four benchmark datasets. We also adapt ZSR method for zero-shot retrieval and show 22.45% improvement accordingly in mean average precision (mAP).
MPPT Method for PV Systems Under Partially Shaded Conditions by Approximating I–V Curve

Partially shaded (PS) photovoltaic (PV) arrays have multiple peaks at their P–V characteristic. Although conventional maximum power point tracking (MPPT) algorithms are successful when PV arrays are under uniform irradiance conditions (UICs), their tracking speeds are low and may fail to track global maximum power point (GMPP) for PS arrays. Several MPPT algorithms have been proposed for PS arrays. Most of them require numerous samplings which decreases MPPT speed and increases energy loss. The proposed method in this paper gets the GMPP deterministically and very fast. It intelligently takes some samples from the array's P–V curve and divides the search voltage range into small subregions. Then, it approximates the I–V curve of each subregion with a simple curve, and accordingly estimates an upper limit for the array power in that subregion. Next, by comparing the measured real power values with the estimated upper limits, the search region of GMPP is limited, and based on some defined criteria, the vicinity of GMPP is determined. Simulation and experimental results and comparisons are presented to highlight the performance and superiority of the proposed approach.
A modified adaptive hill climbing MPPT method for photovoltaic power systems

Maximum power point tracking (MPPT) must usually be integrated with photovoltaic (PV) power systems so that the photovoltaic arrays are able to deliver maximum available power. In this paper, a modified adaptive hill climbing (MAHC) MPPT method is introduced. It can be treated as an extension of the traditional hill climbing algorithm. The simulation and experimental results show that the proposed MPPT control can avoid tracking deviation and result in improved performance in both dynamic response and steady-state.
Experimental characterization of components for active soft orthotics

In this paper, we present the characterization of soft pneumatic actuators currently under development for active soft orthotics. The actuators are tested statically and dynamically to characterize force and displacement properties needed for use in system design. They are shown to demonstrate remarkably repeatable performance, and very predictable behavior as actuator size (initial length) is varied to adjust total actuation. The results of the characterization are then used to inform design rules for selecting the size and number of actuators for a soft leg orthotic that can produce the force/moment/velocity properties required for a target wearer. The device was tested on a silicone model with inner frame and articulated ankle. Additionally, a portable pneumatic system was developed and characterized, that can actuate the pneumatic actuator over 800 times before refilling the gas supply.
Revisiting unsupervised learning for defect prediction

Collecting quality data from software projects can be time-consuming and expensive. Hence, some researchers explore âunsupervisedâ approaches to quality prediction that does not require labelled data. An alternate technique is to use âsupervisedâ approaches that learn models from project data labelled with, say, âdefectiveâ or ânot-defectiveâ. Most researchers use these supervised models since, it is argued, they can exploit more knowledge of the projects. 
At FSEâ16, Yang et al. reported startling results where unsupervised defect predictors outperformed supervised predictors for effort-aware just-in-time defect prediction. If confirmed, these results would lead to a dramatic simplification of a seemingly complex task (data mining) that is widely explored in the software engineering literature. 
This paper repeats and refutes those results as follows. (1) There is much variability in the efficacy of the Yang et al. predictors so even with their approach, some supervised data is required to prune weaker predictors away. (2) Their findings were grouped across N projects. When we repeat their analysis on a project-by-project basis, supervised predictors are seen to work better. 
Even though this paper rejects the specific conclusions of Yang et al., we still endorse their general goal. In our our experiments, supervised predictors did not perform outstandingly better than unsupervised ones for effort-aware just-in-time defect prediction. Hence, they may indeed be some combination of unsupervised learners to achieve comparable performance to supervised ones. We therefore encourage others to work in this promising area.
Lift-Based Bidding in Ad Selection

Real-time bidding (RTB) has become one of the largest online advertising markets in the world. Today the bid price per ad impression is typically decided by the expected value of how it can lead to a desired action event (e.g., registering an account or placing a purchase order) to the advertiser. However, this industry standard approach to decide the bid price does not consider the actual effect of the ad shown to the user, which should be measured based on the performance lift among users who have been or have not been exposed to a certain treatment of ads. In this paper, we propose a new bidding strategy and prove that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events. We describe the modeling methodology to predict the performance lift and demonstrate the actual performance gain through blind A/B test with real ad campaigns in an industry-leading Demand-Side Platform (DSP). We also discuss the relationship between attribution models and bidding strategies. We prove that, to move the DSPs to bid based on performance lift, they should be rewarded according to the relative performance lift they contribute.
A Cognitive Process Theory of Writing

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Customer Relationship Management : Implementation Process Perspective

Customer relationship management (CRM) can help organizations manage customer interactions more effectively to maintain competitiveness in the present economy. As more and more organizations realize the significance of becoming customer-centric in today's competitive era, they adopted CRM as a core business strategy and invested heavily. CRM, an integration of information technology and relationship marketing, provides the infrastructure that facilitates long-term relationship building with customers at an enterprise-wide level. Successful CRM implementation is a complex, expensive and rarely technical projects. This paper presents the successful implementation of CRM from process perspective in a trans-national organization with operations in different segments. This study will aid in understanding transition, constraints and the implementation process of CRM in such organizations.
A Fast Algorithm for the Minimum Covariance Determinant Estimator

The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of multivariate location and scatter. Its objective is to nd h observations (out of n) whose covariance matrix has the lowest determinant. Until now applications of the MCD were hampered by the computation time of existing algorithms, which were limited to a few hundred objects in a few dimensions. We discuss two important applications of larger size: one about a production process at Philips with n = 677 objects and p = 9 variables, and a data set from astronomy with n =137,256 objects and p = 27 variables. To deal with such problems we have developed a new algorithm for the MCD, called FAST-MCD. The basic ideas are an inequality involving order statistics and determinants, and techniques which we calìselective iteration' and`nested extensions'. For small data sets FAST-MCD typically nds the exact MCD, whereas for larger data sets it gives more accurate results than existing algorithms and is faster by orders of magnitude. Moreover, FAST-MCD is able to detect an exact t, i.e. a hyperplane containing h or more observations. The new algorithm makes the MCD method available as a routine tool for analyzing multivariate data. We also propose the distance-distance plot (or`D-D plot') which displays MCD-based robust distances versus Mahalanobis distances, and illustrate it with some examples. We wish to thank Doug Hawkins and Jos e Agulll o for making their programs available to us. We also want to dedicate special thanks to Gertjan Otten, Frans Van Dommelen en Herman Veraa for giving us access to the Philips data, and to S.C. Odewahn and his research group at the California Institute of Technology for allowing us to analyze their Digitized Palomar data.
Grammatical form and semantic context in verb learning.

Decades of research have documented that young word learners have more difficulty learning verbs than nouns. Nonetheless, recent evidence has uncovered conditions under which children as young as 24 months succeed. Here, we focus in on the kind of linguistic information that undergirds 24-month-olds' success. We introduced 24-month-olds to novel words (either nouns or verbs) as they watched dynamic scenes (e.g., a man waving a balloon); the novel words were presented in semantic contexts that were either rich (e.g., The man is pilking a balloon), or more sparse (e.g., He's pilking it). Toddlers successfully learned nouns in both the semantically rich and sparse contexts, but learned verbs only in the rich context. This documents that to learn the meaning of a novel verb, English-acquiring toddlers take advantage of the semantically rich information provided in lexicalized noun phrases. Implications for cross-linguistic theories of acquisition are discussed.
Associating neural word embeddings with deep image representations using Fisher Vectors

In recent years, the problem of associating a sentence with an image has gained a lot of attention. This work continues to push the envelope and makes further progress in the performance of image annotation and image search by a sentence tasks. In this work, we are using the Fisher Vector as a sentence representation by pooling the word2vec embedding of each word in the sentence. The Fisher Vector is typically taken as the gradients of the log-likelihood of descriptors, with respect to the parameters of a Gaussian Mixture Model (GMM). In this work we present two other Mixture Models and derive their Expectation-Maximization and Fisher Vector expressions. The first is a Laplacian Mixture Model (LMM), which is based on the Laplacian distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution. Finally, by using the new Fisher Vectors derived from HGLMMs to represent sentences, we achieve state-of-the-art results for both the image annotation and the image search by a sentence tasks on four benchmarks: Pascal1K, Flickr8K, Flickr30K, and COCO.
An empirical study of the impact of modern code review practices on software quality

Software code review, i.e., the practice of having other team members critique changes to a software system, is a well-established best practice in both open source and proprietary software domains. Prior work has shown that formal code inspections tend to improve the quality of delivered software. However, the formal code inspection process mandates strict review criteria (e.g., in-person meetings and reviewer checklists) to ensure a base level of review quality, while the modern, lightweight code reviewing process does not. Although recent work explores the modern code review process, little is known about the relationship between modern code review practices and long-term software quality. Hence, in this paper, we study the relationship between post-release defects (a popular proxy for long-term software quality) and: (1) code review coverage, i.e., the proportion of changes that have been code reviewed, (2) code review participation, i.e., the degree of reviewer involvement in the code review process, and (3) code reviewer expertise, i.e., the level of domain-specific expertise of the code reviewers. Through a case study of the Qt, VTK, and ITK projects, we find that code review coverage, participation, and expertise share a significant link with software quality. Hence, our results empirically confirm the intuition that poorly-reviewed code has a negative impact on software quality in large systems using modern reviewing tools.
Power allocation strategies for Non-Orthogonal Multiple Access

Non-Orthogonal Multiple Access (NOMA) is considered as a promising downlink Multiple Access (MA) scheme for future radio access. In this paper two power allocation strategies for NOMA are proposed. The first strategy is based on channel state information experienced by NOMA users. The other strategy is based on pre-defined QoS per NOMA user. In this paper we develop mathematical models for the proposed strategies. Also we clarify the potential gains of NOMA using proposed power allocation strategies over Orthogonal Multiple Access (OMA). Simulation results showed that NOMA performance using the proposed strategies achieves superior performance compared to that for OMA.