nils-herrmann/clean_scirepeval huggingface dataset:
Appealing to fear: A meta-analysis of fear appeal effectiveness and theories.

Fear appeals are a polarizing issue, with proponents confident in their efficacy and opponents confident that they backfire. We present the results of a comprehensive meta-analysis investigating fear appeals' effectiveness for influencing attitudes, intentions, and behaviors. We tested predictions from a large number of theories, the majority of which have never been tested meta-analytically until now. Studies were included if they contained a treatment group exposed to a fear appeal, a valid comparison group, a manipulation of depicted fear, a measure of attitudes, intentions, or behaviors concerning the targeted risk or recommended solution, and adequate statistics to calculate effect sizes. The meta-analysis included 127 articles (9% unpublished) yielding 248 independent samples (NTotal = 27,372) collected from diverse populations. Results showed a positive effect of fear appeals on attitudes, intentions, and behaviors, with the average effect on a composite index being random-effects d = 0.29. Moderation analyses based on prominent fear appeal theories showed that the effectiveness of fear appeals increased when the message included efficacy statements, depicted high susceptibility and severity, recommended one-time only (vs. repeated) behaviors, and targeted audiences that included a larger percentage of female message recipients. Overall, we conclude that (a) fear appeals are effective at positively influencing attitude, intentions, and behaviors; (b) there are very few circumstances under which they are not effective; and (c) there are no identified circumstances under which they backfire and lead to undesirable outcomes.
DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades

Online social media remarkably facilitates the production and delivery of information, intensifying the competition among vast information for users' attention and highlighting the importance of predicting the popularity of information. Existing approaches for popularity prediction fall into two paradigms: feature-based approaches and generative approaches. Feature-based approaches extract various features (e.g., user, content, structural, and temporal features), and predict the future popularity of information by training a regression/classification model. Their predictive performance heavily depends on the quality of hand-crafted features. In contrast, generative approaches devote to characterizing and modeling the process that a piece of information accrues attentions, offering us high ease to understand the underlying mechanisms governing the popularity dynamics of information cascades. But they have less desirable predictive power since they are not optimized for popularity prediction. In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process --- a widely-used generative process to model information cascade. DeepHawkes inherits the high interpretability of Hawkes process and possesses the high predictive power of deep learning methods, bridging the gap between prediction and understanding of information cascades. We verify the effectiveness of DeepHawkes by applying it to predict retweet cascades of Sina Weibo and citation cascades of a longitudinal citation dataset. Experimental results demonstrate that DeepHawkes outperforms both feature-based and generative approaches.
Directional prediction of stock prices using breaking news on Twitter

Stock market news and investing tips are popular topics in Twitter. In this dissertation, first I utilize a 5-year financial news corpus comprising over 50,000 articles collected from the NASDAQ website matching the 30 stock symbols in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content. Next, I proceed to show that information in articles indicated by breaking Tweet volumes leads to a statistically significant boost in the hourly directional prediction accuracies for the DJI stock prices mentioned in these articles. Secondly, I show that using document-level sentiment extraction does not yield a statistically significant boost in the directional predictive accuracies in the presence of other 1-gram keyword features. Thirdly I test the performance of the system on several time-frames and identify the 4 hour time-frame for both the price charts and for Tweet breakout detection as the best time-frame combination. Finally, I develop a set of price momentum based trade exit rules to cut losing trades early and to allow the winning trades run longer. I show that the Tweet volume breakout based trading system with the price momentum based exit rules not only improves the winning accuracy and the return on investment, but it also lowers the maximum drawdown and achieves the highest overall return over maximum drawdown.
Light therapy for seasonal and nonseasonal depression: efficacy, protocol, safety, and side effects.

Bright light therapy for seasonal affective disorder (SAD) has been investigated and applied for over 20 years. Physicians and clinicians are increasingly confident that bright light therapy is a potent, specifically active, nonpharmaceutical treatment modality. Indeed, the domain of light treatment is moving beyond SAD, to nonseasonal depression (unipolar and bipolar), seasonal flare-ups of bulimia nervosa, circadian sleep phase disorders, and more. Light therapy is simple to deliver to outpatients and inpatients alike, although the optimum dosing of light and treatment time of day requires individual adjustment. The side-effect profile is favorable in comparison with medications, although the clinician must remain vigilant about emergent hypomania and autonomic hyperactivation, especially during the first few days of treatment. Importantly, light therapy provides a compatible adjunct to antidepressant medication, which can result in accelerated improvement and fewer residual symptoms.
Reference-Dependent Job Search : Evidence from Hungary ∗

We propose a model of job search with reference-dependent preferences, where the reference point is given by recent income. Newly unemployed individuals search hard given that they are at a loss, but over time they get used to lower income, and thus search less. They search harder again in anticipation of a benefit cut, only to ultimately get used to it. The model fits the typical shape of the exit from unemployment, including the spike at the UI exhaustion point. The model also makes unique predictions on the response to benefit changes. We provide evidence using a reform in the unemployment system in Hungary. In November 2005, Hungary switched from a single-step UI system to a two-step system, with unchanged overall generosity. We show that the system generated increased hazard rates in anticipation of, and especially following, benefit cuts in ways the standard model has a hard time fitting, even when allowing for unobserved heterogeneity. We structurally estimate the model and estimate a weight on gain-loss utility comparable to the weight on the standard utility term, and a speed of adjustment of the reference point in the order of eight months.The results suggest that a revenue-neutral shift to multiple-step UI systems can speed exit from unemployment. ∗We would like to thank Lajos Bódis, David Card, Sebastian Findeisen, Gábor Kézdi, Patrick Kline, Matthew Rabin, Hedvig Horváth, Edward O’Donoghue, Alex Rees-Jones, Emmanuel Saez, Mihály Szoboszlai, Owen Zidar and audiences at Boston University, at the UCSB Berhavioral Conference, and at the 2014 BEAM conference for very helpful comments. We are grateful for János Köllő, Kitti Varadovics, Mónika Bálint, Dániel Biró for giving us access to the administrative data and providing continuous help throughout the project. We also thank Tristan Gagnon-Bartsch, Jessica Shui, and Ferenc Szucs for excellent research assistance and Gautam Rao for sharing his code. Financial support from support from the Center for Equitable Growth at UC Berkeley is gratefully acknowledged. All errors are our own. § sdellavi@econ.berkeley.edu, ‡ lindner@econ.berkeley.edu, §§reizer_balazs@ceu-budapest.edu,†
An efficient error correction interface for speech recognition on mobile touchscreen devices

Correcting speech recognition errors on a mobile touchscreen device is an unavoidable but time-consuming task that requires a lot of user effort. To reduce this user effort, we previously proposed an error correction method using long context match with Web N-gram, which we combined with a simple gesture-based user interface. This method automatically replaces an error word with its corresponding correct word. However, it was evaluated only substitution errors in sentences, each of which involves only one error. In this paper, we extend this method to be used for more general cases when a sentence has more than one error. It recovers not only substitution errors but also deletion errors and insertion errors. For recovering deletion errors, it predicts a deleted word based on the phonemes and the part-of-speech tags of its surrounding words. Our experimental results show that the proposed method recovered the errors more accurately with less user effort than the conventional Word Confusion Network based error correction interface.
Let the music play or not : The influence of background music on consumer behavior

This study concerns the effect that music has on consumer behavior in two different retail contexts during regular opening hours. Two studies were conducted in a field setting with consumers (N1⁄4550). Consumers were recruited to answer questions regarding behavioral measures, attitudes, and mood during days when background music was played. The conclusions from the two studies are that music affects consumer behavior, but also that the type of retail store and gender influences both the strength and direction of the effect. & 2012 Elsevier Ltd. All rights reserved.
Which Tumblr Post Should I Read Next?

Microblogging sites have emerged as major platforms for bloggers to create and consume posts as well as to follow other bloggers and get informed of their updates. Due to the large number of users, and the huge amount of posts they create, it becomes extremely difficult to identify relevant and interesting blog posts. In this paper, we propose a novel convex collective matrix completion (CCMC) method that effectively utilizes user-item matrix and incorporates additional user activity and topic-based signals to recommend relevant content. The key advantage of CCMC over existing methods is that it can obtain a globally optimal solution and can easily scale to large-scale matrices using Hazan’s algorithm. To the best of our knowledge, this is the first work which applies and studies CCMC as a recommendation method in social media. We conduct a large scale study and show significant improvement over existing state-ofthe-art approaches.
A Quantization-Friendly Separable Convolution for MobileNets

As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc. Quantization, as one of the key approaches, can effectively offload GPU, and make it possible to deploy DL on fixed-point pipeline. Unfortunately, not all existing networks design are friendly to quantization. For example, the popular lightweight MobileNetV1, while it successfully reduces parameter size and computation latency with separable convolution, our experiment shows its quantized models have large performance gap against its float point models. To resolve this, we analyzed the root cause of quantization loss and proposed a quantization-friendly separable convolution architecture. By evaluating the image classification task on ImageNet2012 dataset, our modified MobileNetV1 model can archive 8-bit inference top-1 accuracy in 68.03%, almost closed the gap to the float pipeline.
A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams

In recent years, virtual learning environments are gaining more and more momentum, considering both the technologies deployed in their support and the sheer number of terminals directly or indirectly interacting with them. This essentially means that every day, more and more smart devices play an active role in this exemplary Web of Things scenario. This digital revolution, affecting education, appears clearly intertwined with the earliest forecasts of the Internet of Things, envisioning around 50 billions heterogeneous devices and gadgets to be active by 2020, considering also the deployment of the fog computing paradigm, which moves part of the computational power to the edge of the network. Moreover, these interconnected objects are expected to produce more and more significant streams of data, themselves generated at unprecedented rates, sometimes to be analyzed almost in real time. Concerning educational environments, this translates to a new type of big data stream, which can be labeled as educational big data streams. Here, pieces of information coming from different sources (such as communications between students and instructors, as well as students’ tests, etc.) require accurate analysis and mining techniques in order to retrieve fruitful and well-timed insights from them. This article presents an overview of the current state of the art of virtual learning environments and their limitations; then, it explains the main ideas behind the paradigms of big data streams and of fog computing, in order to introduce an e-learning architecture integrating both of them. Such an action aims to enhance the ability of virtual learning environments to be closer to the needs of all the actors in an educational scenario, as demonstrated by a preliminary implementation of the envisioned architecture. We believe that the proposed big stream and fog-based educational framework may pave the way towards a better understanding of students’ educational behaviors and foster new research directions in the field.
Scheduling black-box mutational fuzzing

Black-box mutational fuzzing is a simple yet effective technique to find bugs in software. Given a set of program-seed pairs, we ask how to schedule the fuzzings of these pairs in order to maximize the number of unique bugs found at any point in time. We develop an analytic framework using a mathematical model of black-box mutational fuzzing and use it to evaluate 26 existing and new randomized online scheduling algorithms. Our experiments show that one of our new scheduling algorithms outperforms the multi-armed bandit algorithm in the current version of the CERT Basic Fuzzing Framework (BFF) by finding 1.5x more unique bugs in the same amount of time.
Virtual Reality and Education

One of the challenges in working with instructional media is that developers and educators are confronted with a rapidly moving target in terms of information technology's capabilities. The business and entertainment sectors are driving a fast-paced evolution of the devices people have in their workplaces and homes. Researchers and educators are scrambling to assess the potential, develop pedagogical strategies, create instructional materials, and implement a school-based infrastructure for today's technologies— only to find that computers and communications are " morphing " into new media of even greater power. Students of today experience a society and a workplace that is entirely different from those that their parents faced. To allow educational tools to fall behind the pace of technological advance is to sell out a generation of learners. People's understanding of what computers can do has shifted dramatically as the size and cost of these devices has decreased while their power has grown. First, computers were seen as number-crunching machines, then came data processing, now we live in the age of tools that manipulate symbols and information. Virtual Reality (VR) research is based on the growing certainty that the next evolutionary stage is computers and telecommunications fusing into virtual environments. " Cyberspace " is not simply a channel within which content flows, but a virtual place to live that competes directly with reality for the attention of many, especially new generation of students. For this reason, charting the strengths and limits of virtual reality is vital for educational technology. 2 Educating children now and in the future to live in an information society is critical. There is also a need to provide lifelong education for all citizens and to support a flexible workplace. VR technology has been widely proposed as a major technological advance that has potential to support for such education. There are several ways in which VR technology is expected to assist learning. Most importantly it allows students to visualize abstract concepts, to observe events at atomic or planetary scales, and to visit environments and interact with events that distance, time, or safety factor make unavailable. The types of activities supported by this technology promote current educational thinking that students are better able to master, retain, and generalize new knowledge when they are actively involved in constructing that knowledge in a hand on learning environment. There is evidence that, in suitable application areas, VR can offer an …
A Statistical Semantic Parser that Integrates Syntax and Semantics

We introduce a learning semantic parser, SCISSOR, that maps natural-language sentences to a detailed, formal, meaningrepresentation language. It first uses an integrated statistical parser to produce a semantically augmented parse tree, in which each non-terminal node has both a syntactic and a semantic label. A compositional-semantics procedure is then used to map the augmented parse tree into a final meaning representation. We evaluate the system in two domains, a natural-language database interface and an interpreter for coaching instructions in robotic soccer. We present experimental results demonstrating that S CISSOR produces more accurate semantic representations than several previous approaches.
3D Surface Reconstruction by Pointillism

The objective of this work is to infer the 3D shape of an object from a single image. We use sculptures as our training and test bed, as these have great variety in shape and appearance. To achieve this we build on the success of multiple view geometry (MVG) which is able to accurately provide correspondences between images of 3D objects under varying viewpoint and illumination conditions, and make the following contributions: first, we introduce a new loss function that can harness image-to-image correspondences to provide a supervisory signal to train a deep network to infer a depth map. The network is trained end-to-end by differentiating through the camera. Second, we develop a processing pipeline to automatically generate a large scale multi-view set of correspondences for training the network. Finally, we demonstrate that we can indeed obtain a depth map of a novel object from a single image for a variety of sculptures with varying shape/texture, and that the network generalises at test time to new domains (e.g. synthetic images).
Loss Functions for Image Restoration With Neural Networks

Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is $\ell _2$. In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.
CORPORATE GOVERNANCE AND CORPORATE SOCIAL RESPONSIB ILITY : A CRITICAL REVIEW

This paper aims to critically review the existing l iterature on the relationship between Corporate Governance, in particular board diversity , and its influence on both Corporate Social Responsibility (CSR) and Corporate Social Re sponsibility Reporting (CSRR) and suggest some important avenues for future research in this field. Assuming that both CSR and CSRR are outcomes of boards’ decisions, this pa per proposes that examining boards’ decision making process with regard to CSR would pr ovide more insight into the link between board diversity and CSR. Particularly, the paper stresses the importance of studies linking gender diversity and CSR decision making pr ocesses which is quite rare in the existing literature. It also identifies some of th e important methodological drawbacks in the previous literature and highlights the importan ce of rigorous qualitative methods and longitudinal studies for the development of underst anding of the diversity-CSR relationship.
Neural enquirer: learning to query tables in natural language

We propose NEURAL ENQUIRER — a neural network architecture for answering natural language (NL) questions given a knowledge base (KB) table. Unlike previous work on end-to-end training of semantic parsers, NEURAL ENQUIRER is fully “neuralized”: it gives distributed representations of queries and KB tables, and executes queries through a series of differentiable operations. The model can be trained with gradient descent using both endto-end and step-by-step supervision. During training the representations of queries and the KB table are jointly optimized with the query execution logic. Our experiments show that the model can learn to execute complex NL queries on KB tables with rich structures.
Laser grooving profile optimization for chip strength enhancement

Laser grooving process is crucial to thin chip strength. Much of paper has been put on the mechanism of laser grooving, but only few investigations were taken for chip strength enhancement. In this paper, thin chip (less than 100um chip thickness) is adopted, and the experiment of laser grooving process is carried out for mechanical characteristics evaluation. Based on experiment results, the optimal process condition has been determined. A notable improvement for thin chip strength is achieved after process optimization.
Structured Data Fusion

We present structured data fusion (SDF) as a framework for the rapid prototyping of knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set-stored as a dense, sparse, or incomplete tensor-is factorized with a matrix or tensor decomposition. Factorizations can be coupled, or fused, with each other by indicating which factors should be shared between data sets. At the same time, factors may be imposed to have any type of structure that can be constructed as an explicit function of some underlying variables. With the right choice of decomposition type and factor structure, even well-known matrix factorizations such as the eigenvalue decomposition, singular value decomposition and QR factorization can be computed with SDF. A domain specific language (DSL) for SDF is implemented as part of the software package Tensorlab, with which we offer a library of tensor decompositions and factor structures to choose from. The versatility of the SDF framework is demonstrated by means of four diverse applications, which are all solved entirely within Tensorlab's DSL.