nils-herrmann/clean_scirepeval huggingface dataset:
Autonomous Vehicle Security: A Taxonomy of Attacks and Defences

In recent years, we have seen significant advancement in technologies to bring about smarter cities worldwide. The interconnectivity of things is the key enabler in these initiatives. An important building block is smart mobility, and it revolves around resolving land transport challenges in cities with dense populations. A transformative direction that global stakeholders are looking into is autonomous vehicles and the transport infrastructure to interconnect them to the traffic management system (that is, vehicle to infrastructure connectivity), as well as to communicate with one another (that is, vehicle to vehicle connectivity) to facilitate better awareness of road conditions. A number of countries had also started to take autonomous vehicles to the roads to conduct trials and are moving towards the plan for larger scale deployment. However, an important consideration in this space is the security of the autonomous vehicles. There has been an increasing interest in the attacks and defences of autonomous vehicles as these vehicles are getting ready to go onto the roads. In this paper, we aim to organize and discuss the various methods of attacking and defending autonomous vehicles, and propose a comprehensive attack and defence taxonomy to better categorize each of them. Through this work, we hope that it provides a better understanding of how targeted defences should be put in place for targeted attacks, and for technologists to be more mindful of the pitfalls when developing architectures, algorithms and protocols, so as to realise a more secure infrastructure composed of dependable autonomous vehicles.
PERSPECTIVE - Absorbing the Concept of Absorptive Capacity: How to Realize Its Potential in the Organization Field

The purpose of this Perspective Paper is to advance understanding of absorptive capacity, its underlying dimensions, its multi-level antecedents, its impact on firm performance and the contextual factors that affect absorptive capacity. Nineteen years after the Cohen and Levinthal 1990 paper, the field is characterized by a wide array of theoretical perspectives and a wealth of empirical evidence. In this paper, we first review these underlying theories and empirical studies of absorptive capacity. Given the size and diversity of the absorptive capacity literature, we subsequently map the existing terrain of research through a bibliometric analysis. The resulting bibliometric cartography shows the major discrepancies in the organization field, namely that (1) most attention so far has been focused on the tangible outcomes of absorptive capacity; (2) organizational design and individual level antecedents have been relatively neglected in the absorptive capacity literature; and (3) the emergence of absorptive capacity from the actions and interactions of individual, organizational and inter-organizational antecedents remains unclear. Building on the bibliometric analysis, we develop an integrative model that identifies the multi-level antecedents, process dimensions, and outcomes of absorptive capacity as well as the contextual factors that affect absorptive capacity. We argue that realizing the potential of the absorptive capacity concept requires more research that shows how “micro antecedents” and “macroantecedents” influence future outcomes such as competitive advantage, innovation, and firm performance. In particular, we identify conceptual gaps that may guide future research to fully exploit the absorptive capacity concept in the organization field and to explore future fruitful extensions of the concept.
THE STAKEHOLDER THEORY OF THE CORPORATION : CONCEPTS , EVIDENCE , AND IMPLICATIONS

The stakeholder theory has been advanced and justified in the management literature on the basis oi its descriptive accuracy, instrumental power, and normative validity. These three aspects of the theory, although interrelated, are quite distinct; they involve diiferent types of evidence and argument and have different implications. In this article, we examine these three aspects of the theory and critique and integrate important contributions to the literature related to each. We conclude that the three aspects of stakeholder theory are mutually supportive and that the normative base of the theory^—which includes the modem theory of property rights—is fundamental.
Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances.

With consumer concerns increasing over food quality and safety, the food industry has begun to pay much more attention to the development of rapid and reliable food-evaluation systems over the years. As a result, there is a great need for manufacturers and retailers to operate effective real-time assessments for food quality and safety during food production and processing. Computer vision, comprising a nondestructive assessment approach, has the aptitude to estimate the characteristics of food products with its advantages of fast speed, ease of use, and minimal sample preparation. Specifically, computer vision systems are feasible for classifying food products into specific grades, detecting defects, and estimating properties such as color, shape, size, surface defects, and contamination. Therefore, in order to track the latest research developments of this technology in the agri-food industry, this review aims to present the fundamentals and instrumentation of computer vision systems with details of applications in quality assessment of agri-food products from 2007 to 2013 and also discuss its future trends in combination with spectroscopy.
A Comparative Study of Rule Extraction for Recurrent Neural Networks

Understanding recurrent networks through rule extraction has a long history. This has taken on new interests due to the need for interpreting or verifying neural networks. One basic form for representing stateful rules is deterministic finite automata (DFA). Previous research shows that extracting DFAs from trained second-order recurrent networks is not only possible but also relatively stable. Recently, several new types of recurrent networks with more complicated architectures have been introduced. These handle challenging learning tasks usually involving sequential data. However, it remains an open problem whether DFAs can be adequately extracted from these models. Specifically, it is not clear how DFA extraction will be affected when applied to different recurrent networks trained on data sets with different levels of complexity. Here, we investigate DFA extraction on several widely adopted recurrent networks that are trained to learn a set of seven regular Tomita grammars. We first formally analyze the complexity of Tomita grammars and categorize these grammars according to that complexity. Then we empirically evaluate different recurrent networks for their performance of DFA extraction on all Tomita grammars. Our experiments show that for most recurrent networks, their extraction performance decreases as the complexity of the underlying grammar increases. On grammars of lower complexity, most recurrent networks obtain desirable extraction performance. As for grammars with the highest level of complexity, while several complicated models fail with only certain recurrent networks having satisfactory extraction performance.
Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations

Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract humanunderstandable representations of network activations. We then build a bayesian causal model using these extracted concepts as variables in order to explain image classification. Finally, we use this causal model to identify and visualize features with significant causal influence on final classification.
Robust Statistical Methods in R Using the WRS 2 Package

In this manuscript we present various robust statistical methods popular in the social sciences, and show how to apply them in R using the WRS2 package available on CRAN. We elaborate on robust location measures, and present robust t-test and ANOVA versions for independent and dependent samples, including quantile ANOVA. Furthermore, we present on running interval smoothers as used in robust ANCOVA, strategies for comparing discrete distributions, robust correlation measures and tests, and robust mediator models.
Automated Grammatical Error Correction for Language Learners

A fast growing area in Natural Language Processing is the use of automated tools for identifying and correcting grammatical errors made by language learners. This growth, in part, has been fueled by the needs of a large number of people in the world who are learning and using a second or foreign language. For example, it is estimated that there are currently over one billion people who are non-native writers of English. These numbers drive the demand for accurate tools that can help learners to write and speak proficiently in another language. Such demand also makes this an exciting time for those in the NLP community who are developing automated methods for grammatical error correction (GEC). Our motivation for the COLING tutorial is to make others more aware of this field and its particular set of challenges. For these reasons, we believe that the tutorial will potentially benefit a broad range of conference attendees. In general, there has been a surge in interest in using NLP to address educational needs, which in turn, has spawned the recurring ACL/NAACL workshop “Innovative Use of Natural Language Processing for Building Educational Applications” that had its 9th edition at ACL 2014. The last three years, in particular, have been pivotal for GEC. Papers on the topic have become more commonplace at main conferences such as ACL, NAACL and EMNLP, as well as two editions of a Morgan Claypool Synthesis Series book on the topic (Leacock et al., 2010; Leacock et al., 2014). In 2011 and 2012, the first shared tasks in GEC (Dale and Kilgarriff, 2011; Dale et al., 2012) were created, and dozens of teams from all over the world participated. This was followed by two successful CoNLL Shared Tasks on the topic in 2013 and 2014 (Ng et al., 2013; Ng et al., 2014). While there have been many exciting developments in GEC over the last few years, there is still considerable room for improvement as state-of-the-art performance in detecting and correcting several important error types is still inadequate for real world applications. We hope to engage researchers from other NLP fields to develop novel and effective approaches to these problems. Our tutorial is specifically designed to:
A Nested Attention Neural Hybrid Model for Grammatical Error Correction

Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information, and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography.
How and What Teachers Learn : A Shifting Perspective

We explore our efforts to create a conceptual framework to describe and analyse the challenges around preparing teachers to create, sustain, and educate in a "community of learners." In particular, we offer a new frame for conceptualizing teacher learning and development within communities and contexts. This conception allows us to understand the variety of ways in which teachers respond in the process of learning lo teach in the manner described by the "Fostering a Community of Learners" (FCL) programme. The model illustrates the ongoing interaction among individual student and teacher learning, institutional or programme learning, and the characteristics of the policy environment critical to the success of theory-intensive reform efforts such as FCL.
Facial Expression Recognition via Deep Learning

Automated Facial Expression Recognition has remained a challenging and interesting problem in computer vision. The recognition of facial expressions is difficult problem for machine learning techniques, since people can vary significantly in the way they show their expressions. Deep learning is a new area of research within machine learning method which can classify images of human faces into emotion categories using Deep Neural Networks (DNN). Convolutional neural networks (CNN) have been widely used to overcome the difficulties in facial expression classification. In this paper, we present a new architecture network based on CNN for facial expressions recognition. We fine tuned our architecture with Visual Geometry Group model (VGG) to improve results. To evaluate our architecture we tested it with many largely public databases (CK+, MUG, and RAFD). Obtained results show that the CNN approach is very effective in image expression recognition on many public databases which achieve an improvements in facial expression analysis.
Least Squares Support Vector Machines : an Overview

Support Vector Machines is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation which has also led recently to many new developments in kernel based learning in general. In these methods one solves convex optimization problems, typically quadratic programs. We focus on Least Squares Support Vector Machines which are reformulations to standard SVMs that lead to solving linear KKT systems. Least squares support vector machines are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primaldual interpretations from optimization theory. In view of interior point algorithms such LS-SVM KKT systems can be considered as a core problem. Where needed the obtained solutions can be robustified and/or sparsified. As an alternative to a top-down choice of the cost function, methods from robust statistics are employed in a bottom-up fashion for further improving the estimates. We explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel canonical correlation analysis. Furthermore, LS-SVM formulations are mentioned towards recurrent networks and control, thereby extending the methods from static to dynamic problems. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, we propose a method of Fixed Size LS-SVM where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors and we discuss extensions to committee networks. The methods will be illustrated by several benchmark and real-life applications.
Looking through the Glass Ceiling: A Qualitative Study of STEM Women’s Career Narratives

Although efforts have been directed toward the advancement of women in science, technology, engineering, and mathematics (STEM) positions, little research has directly examined women's perspectives and bottom-up strategies for advancing in male-stereotyped disciplines. The present study utilized Photovoice, a Participatory Action Research method, to identify themes that underlie women's experiences in traditionally male-dominated fields. Photovoice enables participants to convey unique aspects of their experiences via photographs and their in-depth knowledge of a community through personal narrative. Forty-six STEM women graduate students and postdoctoral fellows completed a Photovoice activity in small groups. They presented photographs that described their experiences pursuing leadership positions in STEM fields. Three types of narratives were discovered and classified: career strategies, barriers to achievement, and buffering strategies or methods for managing barriers. Participants described three common types of career strategies and motivational factors, including professional development, collaboration, and social impact. Moreover, the lack of rewards for these workplace activities was seen as limiting professional effectiveness. In terms of barriers to achievement, women indicated they were not recognized as authority figures and often worked to build legitimacy by fostering positive relationships. Women were vigilant to other people's perspectives, which was costly in terms of time and energy. To manage role expectations, including those related to gender, participants engaged in numerous role transitions throughout their day to accommodate workplace demands. To buffer barriers to achievement, participants found resiliency in feelings of accomplishment and recognition. Social support, particularly from mentors, helped participants cope with negative experiences and to envision their future within the field. Work-life balance also helped participants find meaning in their work and have a sense of control over their lives. Overall, common workplace challenges included a lack of social capital and limited degrees of freedom. Implications for organizational policy and future research are discussed.
Running Head : PERSUASION THROUGH FICTION Persuasive Effects of Fictional Narratives Increase Over Time

Fact-related information contained in fictional narratives may induce substantial changes in readers’ real-world beliefs. Current models of persuasion through fiction assume that these effects occur because readers are psychologically transported into the fictional world of the narrative. Contrary to general dual-process models of persuasion, models of persuasion through fiction also imply that persuasive effects of fictional narratives are persistent and even increase over time (absolute sleeper effect). In an experiment designed to test this prediction, 81 participants read either a fictional story that contained true as well as false assertions about realworld topics or a control story. There were large short-term persuasive effects of false information, and these effects were even larger for a group with a two-week assessment delay. Belief certainty was weakened immediately after reading but returned to baseline level after two weeks, indicating that beliefs acquired by reading fictional narratives are integrated into realworld knowledge.
Integrating Context Similarity with Sparse Linear Recommendation Model

Context-aware recommender systems extend traditional recommender systems by adapting their output to users’ specific contextual situations. Most of the existing approaches to context-aware recommendation involve directly incorporating context into standard recommendation algorithms (e.g., collaborative filtering, matrix factorization). In this paper, we highlight the importance of context similarity and make the attempt to incorporate it into context-aware recommender. The underlying assumption behind is that the recommendation lists should be similar if their contextual situations are similar. We integrate context similarity with sparse linear recommendation model to build a similarity-learning model. Our experimental evaluation demonstrates that the proposed model is able to outperform several state-of-the-art context-aware recommendation algorithms for the top-N recommendation task.
New chirp sequence radar waveform

The general requirement in the automotive radar application is to measure the target range R and radial velocity vr simultaneously and unambiguously with high accuracy and resolution even in multitarget situations, which is a matter of the appropriate waveform design. Based on a single continuous wave chirp transmit signal, target range R and radial velocity vr cannot be measured in an unambiguous way. Therefore a so-called multiple frequency shift keying (MFSK) transmit signal was developed, which is applied to measure target range and radial velocity separately and simultaneously. In this case the radar measurement is based on a frequency and additionally on a phase measurement, which suffers from a lower estimation accuracy compared with a pure frequency measurement. This MFSK waveform can therefore be improved and outperformed by a chirp sequences waveform. Each chirp signal has in this case very short time duration Tchirp. Therefore the measured beat frequency fB is dominated by target range R and is less influenced by the radial velocity vr. The range and radial velocity estimation is based on two separate frequency measurements with high accuracy in both cases. Classical chirp sequence waveforms suffer from possible ambiguities in the velocity measurement. It is the objective of this paper to modify the classical chirp sequence to get an unambiguous velocity measurement even in multitarget situations.
Crisis decision theory: decisions in the face of negative events.

How do people respond to negative life events? Crisis decision theory combines the strengths of coping theories with research on decision making to predict the responses people choose under negative circumstances. The theory integrates literatures on coping, health behavior, and decision making, among others, into 3 stages that describe the process of responding to negative events: (a) assessing the severity of the negative event, (b) determining response options, and (c) evaluating response options. The author reviews and organizes the relevant research on factors that shape information processing at each stage and that ultimately predict decisions in the face of negative events. Finally, the author presents a critique of crisis decision theory and discusses areas for future research.
Soft-Margin Softmax for Deep Classification

In deep classification, the softmax loss (Softmax) is arguably one of the most commonly used components to train deep convolutional neural networks (CNNs). However, such a widely used loss is limited due to its lack of encouraging the discriminability of features. Recently, the large-margin softmax loss (L-Softmax ) is proposed to explicitly enhance the feature discrimination, with hard margin and complex forward and backward computation. In this paper, we propose a novel soft-margin softmax (SM-Softmax) loss to improve the discriminative power of features. Specifically, SM-Softamx only modifies the forward of Softmax by introducing a non-negative real number m, without changing the backward. Thus it can not only adjust the desired continuous soft margin but also be easily optimized by the typical stochastic gradient descent (SGD). Experimental results on three benchmark datasets have demonstrated the superiority of our SM-Softmax over the baseline Softmax, the alternative L-Softmax and several state-of-the-art competitors.