nils-herrmann/clean_scirepeval huggingface dataset:
Exploring end-user smartphone security awareness within a South African context

International research has shown that users are complacent when it comes to smartphone security behaviour. This is contradictory, as users perceive data stored on the `smart' devices to be private and worth protecting. Traditionally less attention is paid to human factors compared to technical security controls (such as firewalls and antivirus), but there is a crucial need to analyse human aspects as technology alone cannot deliver complete security solutions. Increasing a user's knowledge can improve compliance with good security practices, but for trainers and educators to create meaningful security awareness materials they must have a thorough understanding of users' existing behaviours, misconceptions and general attitude towards smartphone security.
A Deep Recurrent Framework for Cleaning Motion Capture Data

We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data. It exploits temporal coherence and joint correlations to infer adaptive filters for each joint in each frame. A single model can be trained to denoise a heterogeneous mix of action types, under substantial amounts of noise. A signal that has both noise and gaps is preprocessed with a second bidirectional network that synthesizes missing frames from surrounding context. The approach handles a wide variety of noise types and long gaps, does not rely on knowledge of the noise distribution, and operates in a streaming setting. We validate our approach through extensive evaluations on noise both in joint angles and in joint positions, and show that it improves upon various alternatives.
Modeling the role of salience in the allocation of overt visual attention

A biologically motivated computational model of bottom-up visual selective attention was used to examine the degree to which stimulus salience guides the allocation of attention. Human eye movements were recorded while participants viewed a series of digitized images of complex natural and artificial scenes. Stimulus dependence of attention, as measured by the correlation between computed stimulus salience and fixation locations, was found to be significantly greater than that expected by chance alone and furthermore was greatest for eye movements that immediately follow stimulus onset. The ability to guide attention of three modeled stimulus features (color, intensity and orientation) was examined and found to vary with image type. Additionally, the effect of the drop in visual sensitivity as a function of eccentricity on stimulus salience was examined, modeled, and shown to be an important determiner of attentional allocation. Overall, the results indicate that stimulus-driven, bottom-up mechanisms contribute significantly to attentional guidance under natural viewing conditions.
The Work Life of Developers: Activities, Switches and Perceived Productivity

Many software development organizations strive to enhance the productivity of their developers. All too often, efforts aimed at improving developer productivity are undertaken without knowledge about how developers spend their time at work and how it influences their own perception of productivity. To fill in this gap, we deployed a monitoring application at 20 computers of professional software developers from four companies for an average of 11 full work day in situ. Corroborating earlier findings, we found that developers spend their time on a wide variety of activities and switch regularly between them, resulting in highly fragmented work. Our findings extend beyond existing research in that we correlate developers’ work habits with perceived productivity and also show productivity is a personal matter. Although productivity is personal, developers can be roughly grouped into morning, low-at-lunch and afternoon people. A stepwise linear regression per participant revealed that more user input is most often associated with a positive, and emails, planned meetings and work unrelated websites with a negative perception of productivity. We discuss opportunities of our findings, the potential to predict high and low productivity and suggest design approaches to create better tool support for planning developers’ work day and improving their personal productivity.
Efficiency improvement in charge pump circuits

Conventional charge pump circuits use a fixed switching frequency that leads to power efficiency degradation for loading less than the rated loading. This paper proposes a level shifter design that also functions as a frequency converter to automatically vary the switching frequency of a dual charge pump circuit according to the loading. The switching frequency is designed to be 25 kHz with 12 mA loading on both inverting and noninverting outputs. The switching frequency is automatically reduced when loading is lighter to improve the power efficiency. The frequency tuning range of this circuit is designed to be from 100 Hz to 25 kHz. A start-up circuit is included to ensure proper pumping action and avoid latch-up during power-up. A slow turn-on, fast turn-off driving scheme is used in the clock buffer to reduce power dissipation. The new dual charge pump circuit was fabricated in a 3m p-well double-poly single-metal CMOS technology with breakdown voltage of 18 V, the die size is 4.7 4.5 mm2. For comparison, a charge pump circuit with conventional level shifter and clock buffer was also fabricated. The measured results show that the new charge pump has two advantages: 1) the power dissipation of the charge pump is improved by a factor of 32 at no load and by 2% at rated loading of 500 and 2) the breakdown voltage requirement is reduced from 19.2 to 17 V.
Agrobacterium-mediated transformation of kabocha squash (Cucurbita moschata Duch) induced by wounding with aluminum borate whiskers

An efficient genetic transformation method for kabocha squash (Cucurbita moschata Duch cv. Heiankogiku) was established by wounding cotyledonary node explants with aluminum borate whiskers prior to inoculation with Agrobacterium. Adventitious shoots were induced from only the proximal regions of the cotyledonary nodes and were most efficiently induced on Murashige–Skoog agar medium with 1 mg/L benzyladenine. Vortexing with 1% (w/v) aluminum borate whiskers significantly increased Agrobacterium infection efficiency in the proximal region of the explants. Transgenic plants were screened at the T0 generation by sGFP fluorescence, genomic PCR, and Southern blot analyses. These transgenic plants grew normally and T1 seeds were obtained. We confirmed stable integration of the transgene and its inheritance in T1 generation plants by sGFP fluorescence and genomic PCR analyses. The average transgenic efficiency for producing kabocha squashes with our method was about 2.7%, a value sufficient for practical use.
Engineered Resilient Systems: A DoD Perspective

Department of Defense (DoD) systems are required to be trusted and effective in a wide range of operational contexts with the ability to respond to new or changing conditions through modified tactics, appropriate reconfiguration, or replacement. As importantly, these systems are required to exhibit predictable and graceful degradation outside their designed performance envelope. For these systems to be included in the force structure, they need to be manufacturable, readily deployable, sustainable, easily modifiable, and cost-effective. Collectively, these requirements inform the definition of resilient DoD systems. This paper explores the properties and tradeoffs for engineered resilient systems in the military context. It reviews various perspectives on resilience, overlays DoD requirements on these perspectives, and presents DoD challenges in realizing and rapidly fielding resilient systems. This paper also presents promising research themes that need to be pursued by the research community to help the DoD realize the vision of affordable, adaptable, and effective systems. This paper concludes with a discussion of specific DoD systems that can potentially benefit from resilience and stresses the need for sustaining a community of interest in this important area.  Selection and peer-review under responsibility of the University of Southern California.
Automatic detection and correction of purple fringing using the gradient information and desaturation

This paper proposes a method to automatically detect and correct purple fringing that is one of the color artifacts due to characteristics of charge coupled device sensors in a digital camera. The proposed method consists of two steps. In the first step, we detect purple fringed regions that satisfy specific properties: hue characteristics around highlight regions with large gradient magnitudes. In the second step, color correction of the purple fringed regions is made by desaturating the pixels in the detected regions. The proposed method is able to detect purple fringe artifacts more precisely than Kang's method. It can be used as a post processing in a digital camera.
Expert Cognition for Attributed Network Embedding

Attributed network embedding has been widely used in modeling real-world systems. The obtained low-dimensional vector representations of nodes preserve their proximity in terms of both network topology and node attributes, upon which different analysis algorithms can be applied. Recent advances in explanation-based learning and human-in-the-loop models show that by involving experts, the performance of many learning tasks can be enhanced. It is because experts have a better cognition in the latent information such as domain knowledge, conventions, and hidden relations. It motivates us to employ experts to transform their meaningful cognition into concrete data to advance network embedding. However, learning and incorporating the expert cognition into the embedding remains a challenging task. Because expert cognition does not have a concrete form, and is difficult to be measured and laborious to obtain. Also, in a real-world network, there are various types of expert cognition such as the comprehension of word meaning and the discernment of similar nodes. It is nontrivial to identify the types that could lead to a significant improvement in the embedding. In this paper, we study a novel problem of exploring expert cognition for attributed network embedding and propose a principled framework NEEC. We formulate the process of learning expert cognition as a task of asking experts a number of concise and general queries. Guided by the exemplar theory and prototype theory in cognitive science, the queries are systematically selected and can be generalized to various real-world networks. The returned answers from the experts contain their valuable cognition. We model them as new edges and directly add into the attributed network, upon which different embedding methods can be applied towards a more informative embedding representation. Experiments on real-world datasets verify the effectiveness and efficiency of NEEC.  Xiao Huang, Qingquan Song, Jundong Li, Xia Hu. 2018. Exploring Expert Cognition for Attributed Network Embedding. In Proceedings of WSDM’18. ACM, New York, NY, USA, 9 pages.
Video text detection and recognition: Dataset and benchmark

This paper focuses on the problem of text detection and recognition in videos. Even though text detection and recognition in images has seen much progress in recent years, relatively little work has been done to extend these solutions to the video domain. In this work, we extend an existing end-to-end solution for text recognition in natural images to video. We explore a variety of methods for training local character models and explore methods to capitalize on the temporal redundancy of text in video. We present detection performance using the Video Analysis and Content Extraction (VACE) benchmarking framework on the ICDAR 2013 Robust Reading Challenge 3 video dataset and on a new video text dataset. We also propose a new performance metric based on precision-recall curves to measure the performance of text recognition in videos. Using this metric, we provide early video text recognition results on the above mentioned datasets.
Nonlinear Koopman Modes and Coherency Identification of Coupled Swing Dynamics

We perform modal analysis of short-term swing dynamics in multi-machine power systems. The analysis is based on the so-called Koopman operator, a linear, infinite-dimensional operator that is defined for any nonlinear dynamical system and captures full information of the system. Modes derived through spectral analysis of the Koopman operator, called Koopman modes, provide a nonlinear extension of linear oscillatory modes. Computation of the Koopman modes extracts single-frequency, spatial modes embedded in non-stationary data of short-term, nonlinear swing dynamics, and it provides a novel technique for identification of coherent swings and machines.
T-spline simplification and local refinement

A typical NURBS surface model has a large percentage of superfluous control points that significantly interfere with the design process. This paper presents an algorithm for eliminating such superfluous control points, producing a T-spline. The algorithm can remove substantially more control points than competing methods such as B-spline wavelet decomposition. The paper also presents a new T-spline local refinement algorithm and answers two fundamental open questions on T-spline theory.
17-Italian Foot Function Index with numerical rating scale: development, reliability, and validity of a modified version of the original Foot Function Index.

Clinical research quantifies symptoms and signs of pain.



To develop a brief outcome measure to assess foot and ankle conditions, the psychometric properties of a modified version of the original Foot Function Index (FFI) were examined.



Eighty-six subjects with musculoskeletal foot and ankle disorders were enrolled. The internal consistency and test-retest reliability were evaluated by using Cronbach's α and intraclass correlation coefficient (ICC). Criterion validity was tested by Pearson's correlation coefficient between 17 items of the Italian FFI (17-IFFI) and the Lower Extremity Functional Scale (LEFS). The responsiveness was calculated using the receiver operating characteristic curve (ROC).



Cronbach's Alpha was 0.95 (95% CI: 0.92, 0.99). The intra-interviewer and inter-interviewer ICC values were, respectively, 0.92 (95% CI: 0.88-10 0.96) and 0.90 (95% CI: 0.89-0.94). Correlations between the 17-IFFI scores and the LEFS scores were -0.564 and -0.456 at the initial and at the end of the treatment, respectively. The ROC analysis revealed an area under the curve of 0.732 (95% CI: 0.61-0.82) for the 17-IFFI and 0.633 (95% CI: 0.52-0.71) for the LEFS score.



The 17-IFFI is a reliable and valid scale and we recommend its application to evaluate the effectiveness of a treatment in patients with musculoskeletal foot and ankle disorders.
Five dimensions of reasoning in the wild

Reasoning does not work well when done in isolation from its significance, both to the needs and interests of an agent and with respect to the wider world. Moreover, those issues may best be handled with a new sort of data structure that goes beyond the knowledge base and incorporates aspects of perceptual knowledge and even more, in which a kind of anticipatory action may be key. Out of the Ivory Tower Reasoning is one of the oldest topics in artificial intelligence (AI). And it has made lots of progress, in the form of commonsense reasoning (CSR), planning, automated theorem-proving, and more. But I suspect it has hit a barrier that must be surmounted if we are to approach anything like human-level inference. Here I give evidence for such a barrier, and ideas about dealing with it, loosely based on evidence from human behavior. In rough synopsis, reasoning does not work well when done in isolation from its broader significance, both for the needs and interests of an agent and for the wider world. Moreover, those issues may best be handled with a new sort of data structure that goes beyond the knowledge base (KB) and incorporates aspects of perceptual knowledge and even more, in which a kind of anticipatory action many be key. I suspect this has ties with recent calls to “put the Science” back in AI (Levesque 2013, Langley 2012). For what I am arguing, in some sense, is that reasoning should be regarded as “in the wild” as events unfold rather than confined to management of an isolated KB; and that this speaks to an agent interacting with the world, rather than a puzzle in abstract inference (yet I will also argue that even “pure” reasoning as in mathematics hugely benefits from many connections with the world). And finally, we then will end up studying the nature of world-embedded cognitive agents, humans included. But this is very broadbrushed and general, whereas my main point is a technical  One quick example at the outset: The Wason Selection Task (Wason 1968) shows that human inference is strongly aided when the details of the task at hand have real meaning that the subjects can relate to in terms of things that matter to them, helping keep attention on what is relevant; and this holds even when the task in the abstract is a matter of so-called pure logic. While this could be seen as a defect in human reasoning, something computers would never trip up on, I think it points in the opposite direction: inference without broader meaning is not worth much, and not worth being good at. I will illustrate my main points with a series of examples based on the activities of proving, planning, and understanding.
Incidence of chronic thromboembolic pulmonary hypertension after pulmonary embolism.

Chronic thromboembolic pulmonary hypertension (CTPH) is associated with considerable morbidity and mortality. Its incidence after pulmonary embolism and associated risk factors are not well documented.



We conducted a prospective, long-term, follow-up study to assess the incidence of symptomatic CTPH in consecutive patients with an acute episode of pulmonary embolism but without prior venous thromboembolism. Patients with unexplained persistent dyspnea during follow-up underwent transthoracic echocardiography and, if supportive findings were present, ventilation-perfusion lung scanning and pulmonary angiography. CTPH was considered to be present if systolic and mean pulmonary-artery pressures exceeded 40 mm Hg and 25 mm Hg, respectively; pulmonary-capillary wedge pressure was normal; and there was angiographic evidence of disease.



The cumulative incidence of symptomatic CTPH was 1.0 percent (95 percent confidence interval, 0.0 to 2.4) at six months, 3.1 percent (95 percent confidence interval, 0.7 to 5.5) at one year, and 3.8 percent (95 percent confidence interval, 1.1 to 6.5) at two years. No cases occurred after two years among the patients with more than two years of follow-up data. The following increased the risk of CTPH: a previous pulmonary embolism (odds ratio, 19.0), younger age (odds ratio, 1.79 per decade), a larger perfusion defect (odds ratio, 2.22 per decile decrement in perfusion), and idiopathic pulmonary embolism at presentation (odds ratio, 5.70).



CTPH is a relatively common, serious complication of pulmonary embolism. Diagnostic and therapeutic strategies for the early identification and prevention of CTPH are needed.
Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction

We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based on questions. For the adaptive parameter prediction, we employ a separate parameter prediction network, which consists of gated recurrent unit (GRU) taking a question as its input and a fully-connected layer generating a set of candidate weights as its output. However, it is challenging to construct a parameter prediction network for a large number of parameters in the fully-connected dynamic parameter layer of the CNN. We reduce the complexity of this problem by incorporating a hashing technique, where the candidate weights given by the parameter prediction network are selected using a predefined hash function to determine individual weights in the dynamic parameter layer. The proposed network-joint network with the CNN for ImageQA and the parameter prediction network-is trained end-to-end through back-propagation, where its weights are initialized using a pre-trained CNN and GRU. The proposed algorithm illustrates the state-of-the-art performance on all available public ImageQA benchmarks.
Transitional feedback schedules during computer-based problem-solving practice

Feedback has a strong influence on effective learning from computer-based instruction. Prior research on feedback in computer-based instruction has mainly focused on static feedback schedules that employ the same feedback schedule throughout an instructional session. This study examined transitional feedback schedules in computer-based multimedia instruction on procedural problem-solving in electrical circuit analysis. Specifically, we compared two transitional feedback schedules: the TFS-P schedule switched from initial feedback after each problem step to feedback after a complete problem at later learning states; the TFP-S schedule transitioned from feedback after a complete problem to feedback after each problem step. As control conditions, we also considered two static feedback schedules, namely providing feedback after each practice problem-solving step (SFS) or providing feedback after attempting a complete multi-step practice problem (SFP). Results indicate that the static stepwise (SFS) and transitional stepwise to problem (TFS-P) feedback produce higher problem solving near-transfer post-test performance than static problem (SFP) and transitional problem to step (TFP-S) feedback. Also, TFS-P resulted in higher ratings of program liking and feedback helpfulness than TFP-S. Overall, the study results indicate benefits of maintaining high feedback frequency (SFS) and reducing feedback frequency (TFS-P) compared to low feedback frequency (SFP) or increasing feedback frequency (TFP-S) as novice learners acquire engineering problem solving skills.
Online Kernel-Based Learning for Task-Space Tracking Robot Control

Task-space control of redundant robot systems based on analytical models is known to be susceptive to modeling errors. Data-driven model learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an ill-posed problem. In particular, the same input data point can yield many different output values, which can form a nonconvex solution space. Because the problem is ill-posed, models cannot be learned from such data using common regression methods. While learning of task-space control mappings is globally ill-posed, it has been shown in recent work that it is locally a well-defined problem. In this paper, we use this insight to formulate a local kernel-based learning approach for online model learning for task-space tracking control. We propose a parametrization for the local model, which makes an application in task-space tracking control of redundant robots possible. The model parametrization further allows us to apply the kernel-trick and, therefore, enables a formulation within the kernel learning framework. In our evaluations, we show the ability of the method for online model learning for task-space tracking control of redundant robots.