nils-herrmann/clean_scirepeval huggingface dataset:
Reflecting on reflections: enhancement of medical education curriculum with structured field notes and guided feedback.

The promotion of reflective capacity within the teaching of clinical skills and professionalism is posited as fostering the development of competent health practitioners. An innovative approach combines structured reflective writing by medical students and individualized faculty feedback to those students to augment instruction on reflective practice. A course for preclinical students at the Warren Alpert Medical School of Brown University, entitled "Doctoring," combined reflective writing assignments (field notes) with instruction in clinical skills and professionalism and early clinical exposure in a small-group format. Students generated multiple e-mail field notes in response to structured questions on course topics. Individualized feedback from a physician-behavioral scientist dyad supported the students' reflective process by fostering critical-thinking skills, highlighting appreciation of the affective domain, and providing concrete recommendations. The development and implementation of this innovation are presented, as is an analysis of the written evaluative comments of students taking the Doctoring course. Theoretical and clinical rationales for features of the innovation and supporting evidence of their effectiveness are presented. Qualitative analyses of students' evaluations yielded four themes of beneficial contributions to their learning experience: promoting deeper and more purposeful reflection, the value of (interdisciplinary) feedback, the enhancement of group process, and personal and professional development. Evaluation of the innovation was the fifth theme; some limitations are described, and suggestions for improvement are provided. Issues of the quality of the educational paradigm, generalizability, and sustainability are addressed.
Cognitive processes mediating behavioral change.

The present experiment was designed to test the theory that psychological procedures achieve changes in behavior by altering the level and strength of self-efficacy. In this formulation, perceived self-efficacy. In this formulation, perceived self-efficacy influences level of performance by enhancing intensity and persistence of effort. Adult phobics were administered treatments based upon either performance mastery experiences, vicarious experiences., or they received no treatment. Their efficacy expectations and approach behavior toward threats differing on a similarity dimension were measured before and after treatment. In accord with our prediction, the mastery-based treatment produced higher, stronger, and more generalized expectations of personal efficacy than did the treatment relying solely upon vicarious experiences. Results of a microanalysis further confirm the hypothesized relationship between self-efficacy and behavioral change. Self-efficacy was a uniformly accurate predictor of performance on tasks of varying difficulty with different threats regardless of whether the changes in self-efficacy were produced through enactive mastery or by vicarious experience alone.
Tiqr: A Novel Take on Two-Factor Authentication

Authentication is of paramount importance for all modern networked applications. The username/password paradigm is ubiquitous. This paradigm suffices for many applications that require BLOCKIN BLOCKIN a BLOCKIN BLOCKIN relatively BLOCKIN BLOCKIN low BLOCKIN BLOCKIN level BLOCKIN BLOCKIN of BLOCKIN BLOCKIN assurance BLOCKIN BLOCKIN about BLOCKIN BLOCKIN the BLOCKIN BLOCKIN identity BLOCKIN BLOCKIN of BLOCKIN BLOCKIN the BLOCKIN BLOCKIN end BLOCKIN BLOCKIN user, BLOCKIN BLOCKIN but BLOCKIN BLOCKIN it BLOCKIN BLOCKIN quickly BLOCKIN BLOCKIN breaks down when a stronger assertion of the user's identity is required. Traditionally, this is where two-­‐ or multi-­‐factor authentication comes in, providing a higher level of assurance. There is a multitude of BLOCKIN BLOCKIN two-­‐factor BLOCKIN BLOCKIN authentication BLOCKIN BLOCKIN solutions BLOCKIN BLOCKIN available, BLOCKIN BLOCKIN but BLOCKIN BLOCKIN we BLOCKIN BLOCKIN feel BLOCKIN BLOCKIN that BLOCKIN BLOCKIN many BLOCKIN BLOCKIN solutions BLOCKIN BLOCKIN do BLOCKIN BLOCKIN not BLOCKIN BLOCKIN meet BLOCKIN BLOCKIN the needs of our community. They are invariably expensive, difficult to roll out in heterogeneous user groups (like student populations), often closed source and closed technology and have usability problems that make them hard to use. In this paper we will give an overview of the two-­‐factor au-­‐ thentication landscape and address the issues of closed versus open solutions. We will introduce a novel open standards-­‐based authentication technology that we have developed and released in open source. We will then provide a classification of two-­‐factor authentication technologies, and we will finish with an overview of future work.
MAttNet: Modular Attention Network for Referring Expression Comprehension

In this paper, we address referring expression comprehension: localizing an image region described by a natural language expression. While most recent work treats expressions as a single unit, we propose to decompose them into three modular components related to subject appearance, location, and relationship to other objects. This allows us to flexibly adapt to expressions containing different types of information in an end-to-end framework. In our model, which we call the Modular Attention Network (MAttNet), two types of attention are utilized: language-based attention that learns the module weights as well as the word/phrase attention that each module should focus on; and visual attention that allows the subject and relationship modules to focus on relevant image components. Module weights combine scores from all three modules dynamically to output an overall score. Experiments show that MAttNet outperforms previous state-of-the-art methods by a large margin on both bounding-box-level and pixel-level comprehension tasks. Demo1 and code2 are provided.
UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization

This paper studies a new unmanned aerial vehicle (UAV)-enabled wireless power transfer system, where a UAV-mounted mobile energy transmitter is dispatched to deliver wireless energy to a set of energy receivers (ERs) at known locations on the ground. We investigate how the UAV should optimally exploit its mobility via trajectory design to maximize the amount of energy transferred to all ERs during a finite charging period. First, we consider the maximization of the sum energy received by all ERs by optimizing the UAV’s trajectory subject to its maximum speed constraint. Although this problem is non-convex, we obtain its optimal solution, which shows that the UAV should hover at one single fixed location during the whole charging period. However, the sum-energy maximization incurs a “near-far” fairness issue, where the received energy by the ERs varies significantly with their distances to the UAV’s optimal hovering location. To overcome this issue, we consider a different problem to maximize the minimum received energy among all ERs, which, however, is more challenging to solve than the sum-energy maximization. To tackle this problem, we first consider an ideal case by ignoring the UAV’s maximum speed constraint, and show that the relaxed problem can be optimally solved via the Lagrange dual method. The obtained trajectory solution implies that the UAV should hover over a set of fixed locations with optimal hovering time allocations among them. Then, for the general case with the UAV’s maximum speed constraint considered, we propose a new successive hover-and-fly trajectory motivated by the optimal trajectory in the ideal case and obtain efficient trajectory designs by applying the successive convex programing optimization technique. Finally, numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmark schemes.
Network attributes for segregation and integration in the human brain

Network studies of large-scale brain connectivity have begun to reveal attributes that promote the segregation and integration of neural information: communities and hubs. Network communities are sets of regions that are strongly interconnected among each other while connections between members of different communities are less dense. The clustered connectivity of network communities supports functional segregation and specialization. Network hubs link communities to one another and ensure efficient communication and information integration. This review surveys a number of recent reports on network communities and hubs, and their role in integrative processes. An emerging focus is the shifting balance between segregation and integration over time, which manifest in continuously changing patterns of functional interactions between regions, circuits and systems.
Meta-path based multi-network collective link prediction

Online social networks offering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simultaneously to enjoy specific services provided by different networks. Formally, social networks that share some common users are named as partially aligned networks. In this paper, we want to predict the formation of social links in multiple partially aligned social networks at the same time, which is formally defined as the multi-network link (formation) prediction problem. In multiple partially aligned social networks, users can be extensively correlated with each other by various connections. To categorize these diverse connections among users, 7 "intra-network social meta paths" and 4 categories of "inter-network social meta paths" are proposed in this paper. These "social meta paths" can cover a wide variety of connection information in the network, some of which can be helpful for solving the multi-network link prediction problem but some can be not. To utilize useful connection, a subset of the most informative "social meta paths" are picked, the process of which is formally defined as "social meta path selection" in this paper. An effective general link formation prediction framework, Mli (Multi-network Link Identifier), is proposed in this paper to solve the multi-network link (formation) prediction problem. Built with heterogenous topological features extracted based on the selected "social meta paths" in the multiple partially aligned social networks, Mli can help refine and disambiguate the prediction results reciprocally in all aligned networks. Extensive experiments conducted on real-world partially aligned heterogeneous networks, Foursquare and Twitter, demonstrate that Mli can solve the multi-network link prediction problem very well.
Using the Student ’ s t-test with extremely small sample sizes

Researchers occasionally have to work with an extremely small sample size, defined herein as N ≤ 5. Some methodologists have cautioned against using the t-test when the sample size is extremely small, whereas others have suggested that using the t-test is feasible in such a case. The present simulation study estimated the Type I error rate and statistical power of the oneand two-sample ttests for normally distributed populations and for various distortions such as unequal sample sizes, unequal variances, the combination of unequal sample sizes and unequal variances, and a lognormal population distribution. Ns per group were varied between 2 and 5. Results show that the t-test provides Type I error rates close to the 5% nominal value in most of the cases, and that acceptable power (i.e., 80%) is reached only if the effect size is very large. This study also investigated the behavior of the Welch test and a rank-transformation prior to conducting the t-test (t-testR). Compared to the regular t-test, the Welch test tends to reduce statistical power and the t-testR yields false positive rates that deviate from 5%. This study further shows that a paired t-test is feasible with extremely small Ns if the within-pair correlation is high. It is concluded that there are no principal objections to using a t-test with Ns as small as 2. A final cautionary note is made on the credibility of research findings when sample sizes are small.
Security analysis on consumer and industrial IoT devices

The fast development of Internet of Things (IoT) and cyber-physical systems (CPS) has triggered a large demand of smart devices which are loaded with sensors collecting information from their surroundings, processing it and relaying it to remote locations for further analysis. The wide deployment of IoT devices and the pressure of time to market of device development have raised security and privacy concerns. In order to help better understand the security vulnerabilities of existing IoT devices and promote the development of low-cost IoT security methods, in this paper, we use both commercial and industrial IoT devices as examples from which the security of hardware, software, and networks are analyzed and backdoors are identified. A detailed security analysis procedure will be elaborated on a home automation system and a smart meter proving that security vulnerabilities are a common problem for most devices. Security solutions and mitigation methods will also be discussed to help IoT manufacturers secure their products.
Latent Structures for Coreference Resolution

Machine learning approaches to coreference resolution vary greatly in the modeling of the problem: while early approaches operated on the mention pair level, current research focuses on ranking architectures and antecedent trees. We propose a unified representation of different approaches to coreference resolution in terms of the structure they operate on. We represent several coreference resolution approaches proposed in the literature in our framework and evaluate their performance. Finally, we conduct a systematic analysis of the output of these approaches, highlighting differences and similarities.
An analysis of model-based Interval Estimation for Markov Decision Processes

Several algorithms for learning near-optimal policies in Markov Decision Processes have been analyzed and proven efficient. Empirical results have suggested that Model-based Interval Estimation (MBIE) learns efficiently in practice, effectively balancing exploration and exploitation. This paper presents a theoretical analysis of MBIE and a new variation called MBIE-EB, proving their efficiency even under worst-case conditions. The paper also introduces a new performance metric, average loss, and relates it to its less “online” cousins from the literature.
Inferring networks of diffusion and influence

Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.
Indoor Frame Recovery from Refined Line Segments

An important yet challenging problem in understanding indoor scene is recovering indoor frame structure from a monocular image. It is more difficult when occlusions and illumination vary, and object boundaries are weak. To overcome these difficulties, a new approach based on line segment refinement with two constraints is proposed. First, the line segments are refined by four consecutive operations, i.e., reclassifying, connecting, fitting, and voting. Specifically, misclassified line segments are revised by the reclassifying operation; some short line segments are joined by the connecting operation; the undetected key line segments are recovered by the fitting operation with the help of the vanishing points; the line segments converging on the frame are selected by the voting operation. Second, we construct four frame models ∗Luanzheng Guo is the corresponding author Email addresses: azguolu@gmail.com (Luanzheng Guo), chujun99602@163.com (Jun Chu)  2017 ar X iv :1 70 5. 00 27 9v 1 [ cs .C V ] 3 0 A pr 2 01 7 according to four classes of possible shooting angles of the monocular image, the natures of all frame models are introduced via enforcing the cross ratio and depth constraints. The indoor frame is then constructed by fitting those refined line segments with related frame model under the two constraints, which jointly advance the accuracy of the frame. Experimental results on a collection of over 300 indoor images indicate that our algorithm has the capability of recovering the frame from complex indoor scenes.
Modelling and model predictive control of a DC-DC Boost converter

In this paper we develop a model predictive control (MPC) approach for regulating DC-DC Boost converters. The proposed control strategy is implemented and tested using two models: an averaged non-linear model for control purposes and a switched Buck-Boost circuit model as the controlled plant. The main objective of the paper is to design a Fuzzy Predictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. The proposed algorithm of fuzzy model predictive controller is done by simulation using MATLAB. Comparative analysis and results of FMPC and MPC control is observed.
SUN1 Regulates HIV-1 Nuclear Import in a Manner Dependent on the Interaction between the Viral Capsid and Cellular Cyclophilin A.

Human immunodeficiency virus type 1 (HIV-1) can infect nondividing cells via passing through the nuclear pore complex. The nuclear membrane-imbedded protein SUN2 was recently reported to be involved in the nuclear import of HIV-1. Whether SUN1, which shares many functional similarities with SUN2, is involved in this process remained to be explored. Here we report that overexpression of SUN1 specifically inhibited infection by HIV-1 but not that by simian immunodeficiency virus (SIV) or murine leukemia virus (MLV). Overexpression of SUN1 did not affect reverse transcription but led to reduced accumulation of the 2-long-terminal-repeat (2-LTR) circular DNA and integrated viral DNA, suggesting a block in the process of nuclear import. HIV-1 CA was mapped as a determinant for viral sensitivity to SUN1. Treatment of SUN1-expressing cells with cyclosporine (CsA) significantly reduced the sensitivity of the virus to SUN1, and an HIV-1 mutant containing CA-G89A, which does not interact with cyclophilin A (CypA), was resistant to SUN1 overexpression. Downregulation of endogenous SUN1 inhibited the nuclear entry of the wild-type virus but not that of the G89A mutant. These results indicate that SUN1 participates in the HIV-1 nuclear entry process in a manner dependent on the interaction of CA with CypA.IMPORTANCE HIV-1 infects both dividing and nondividing cells. The viral preintegration complex (PIC) can enter the nucleus through the nuclear pore complex. It has been well known that the viral protein CA plays an important role in determining the pathways by which the PIC enters the nucleus. In addition, the interaction between CA and the cellular protein CypA has been reported to be important in the selection of nuclear entry pathways, though the underlying mechanisms are not very clear. Here we show that both SUN1 overexpression and downregulation inhibited HIV-1 nuclear entry. CA played an important role in determining the sensitivity of the virus to SUN1: the regulatory activity of SUN1 toward HIV-1 relied on the interaction between CA and CypA. These results help to explain how SUN1 is involved in the HIV-1 nuclear entry process.
Tor: The Second-Generation Onion Router

We present Tor, a circuit-based low-latency anonymous communication service. This second-generation Onion Routing system addresses limitations in the original design. Tor adds perfect forward secrecy, congestion control, directory servers, integrity checking, configurable exit policies, and a practical design for rendezvous points. Tor works on the real-world Internet, requires no special privileges or kernel modifications, requires little synchronization or coordination between nodes, and provides a reasonable tradeoff between anonymity, usability, and efficiency. We briefly describe our experiences with an international network of more than a dozen hosts. We close with a list of open problems in anonymous communication.
Review of Tourism Augmented Reality ( AR ) in Urban Heritage Tourism

New technology has been seen as a way for many businesses in the tourism industry to stay competitive and enhance their marketing campaign in various ways. AR has evolved as the buzzword of modern information technology and is gaining increasing attention in the media as well as through a variety of use cases. This trend is highly fostered across mobile applications as well as the hype of wearable computing triggered by Google’s Glass project to be launched in 2014. However, although research on AR has been conducted in various fields including the Urban Tourism industry, the majority of studies focus on technical aspects of AR, while others are tailored to specific applications. Therefore, this paper aims to examine the current implementation of AR in the Urban Tourism context and identifies areas of research and development that is required to guide the early stages of AR implementation in a purposeful way to enhance the tourist experience. The paper provides an overview of AR and examines the impacts AR has made on the economy. Hence, AR applications in Urban Tourism are identified and benefits of AR are discussed. Please cite this article as: Jung, T. and Han, D. (2014). Augmented Reality (AR) in Urban Heritage Tourism. Review of Tourism Research.
Multi-style Generative Reading Comprehension

This study focuses on the task of multipassage reading comprehension (RC) where an answer is provided in natural language. Current mainstream approaches treat RC by extracting the answer span from the provided passages and cannot generate an abstractive summary from the given question and passages. Moreover, they cannot utilize and control different styles of answers, such as concise phrases and well-formed sentences, within a model. In this study, we propose a style-controllable Multi-source Abstractive Summarization model for QUEstion answering, called Masque. The model is an end-toend deep neural network that can generate answers conditioned on a given style. Experiments with MS MARCO 2.1 show that our model achieved state-of-the-art performance on two tasks with different answer styles.