nils-herrmann/clean_scirepeval huggingface dataset:
USING THE JOB DEMANDS-RESOURCES MODEL TO PREDICT BURNOUT AND

characteristics, burnout, and (other-ratings of) performance (N 146). We hypothesized that job demands (e.g., work pressure and emotional demands) would be the most important antecedents of the exhaustion component of burnout, which, in turn, would predict in-role performance (hypothesis 1). In contrast, job resources (e.g., autonomy and social support) were hypothesized to be the most important predictors of extra-role performance, through their relationship with the disengagement component of burnout (hypothesis 2). In addition, we predicted that job resources would buffer the relationship between job demands and exhaustion (hypothesis 3), and that exhaustion would be positively related to disengagement (hypothesis 4). The results of structural equation modeling analyses provided strong support for hypotheses 1, 2, and 4, but rejected hypothesis 3. These findings support the JD-R model’s claim that job demands and job resources initiate two psychological processes, which eventually affect organizational outcomes.
Generative Adversarial Active Learning

We propose a new active learning by query synthesis approach using Generative Adversarial Networks (GAN). Different from regular active learning, the resulting algorithm adaptively synthesizes training instances for querying to increase learning speed. We generate queries according to the uncertainty principle, but our idea can work with other active learning principles. We report results from various numerical experiments to demonstrate the effectiveness the proposed approach. In some settings, the proposed algorithm outperforms traditional poolbased approaches. To the best our knowledge, this is the first active learning work using GAN.
Catastrophic forgetting in connectionist networks

All natural cognitive systems, and, in particular, our own, gradually forget previously learned information. Plausible models of human cognition should therefore exhibit similar patterns of gradual forgetting of old information as new information is acquired. Only rarely does new learning in natural cognitive systems completely disrupt or erase previously learned information; that is, natural cognitive systems do not, in general, forget 'catastrophically'. Unfortunately, though, catastrophic forgetting does occur under certain circumstances in distributed connectionist networks. The very features that give these networks their remarkable abilities to generalize, to function in the presence of degraded input, and so on, are found to be the root cause of catastrophic forgetting. The challenge in this field is to discover how to keep the advantages of distributed connectionist networks while avoiding the problem of catastrophic forgetting. In this article the causes, consequences and numerous solutions to the problem of catastrophic forgetting in neural networks are examined. The review will consider how the brain might have overcome this problem and will also explore the consequences of this solution for distributed connectionist networks.
Explore Multi-Step Reasoning in Video Question Answering

Video question answering (VideoQA) always involves visual reasoning. When answering questions composing of multiple logic correlations, models need to perform multi-step reasoning. In this paper, we formulate multi-step reasoning in VideoQA as a new task to answer compositional and logical structured questions based on video content. Existing VideoQA datasets are inadequate as benchmarks for the multi-step reasoning due to limitations such as lacking logical structure and having language biases. Thus we design a system to automatically generate a large-scale dataset, namely SVQA (Synthetic Video Question Answering). Compared with other VideoQA datasets, SVQA contains exclusively long and structured questions with various spatial and temporal relations between objects. More importantly, questions in SVQA can be decomposed into human readable logical tree or chain layouts, each node of which represents a sub-task requiring a reasoning operation such as comparison or arithmetic. Towards automatic question answering in SVQA, we develop a new VideoQA model. Particularly, we construct a new attention module, which contains spatial attention mechanism to address crucial and multiple logical sub-tasks embedded in questions, as well as a refined GRU called ta-GRU (temporal-attention GRU) to capture the long-term temporal dependency and gather complete visual cues. Experimental results show the capability of multi-step reasoning of SVQA and the effectiveness of our model when compared with other existing models.
CS 229 , Autumn 2010 Practice Midterm

1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The midterm is meant to be educational, and as such some questions could be quite challenging. Use your time wisely to answer as much as you can! 3. For additional practice, please see CS 229 extra problem sets available at 1.  Generalized Linear Models Recall that generalized linear models assume that the response variable y (conditioned on x) is distributed according to a member of the exponential family: p(y; η) = b(y) exp(ηT (y) − a(η)), where η = θ T x. For this problem, we will assume η ∈ R. (a)  Given a training set {(x (i) , y (i))} m i=1 , the loglikelihood is given by (θ) = m i=1 log p(y (i) | x (i) ; θ). Give a set of conditions on b(y), T (y), and a(η) which ensure that the loglikelihood is a concave function of θ (and thus has a unique maximum). Your conditions must be reasonable, and should be as weak as possible. (E.g., the answer " any b(y), T (y), and a(η) so that (θ) is concave " is not reasonable. Similarly, overly narrow conditions, including ones that apply only to specific GLMs, are also not reasonable.) (b)  When the response variable is distributed according to a Normal distribution (with unit variance), we have b(y) = 1 √ 2π e −y 2 2 , T (y) = y, and a(η) = η 2 2. Verify that the condition(s) you gave in part (a) hold for this setting.
THE STAKEHOLDER MODEL REFINED

The popularity of the stakeholder model has been achieved thanks to its powerful visual scheme and its very simplicity. Stakeholder management has become an important tool to transfer ethics to management practice and strategy. Nevertheless, legitimate criticism continues to insist on clarification and emphasises on the perfectible nature of the model. Here, rather than building on the discussion from a philosophical or theoretical point of view, a different and innovative approach has been chosen: the analysis will return to the origin of stakeholder theory and will keep the graphical framework firmly in perspective. It will confront the stakeholder model’s graphical representation to the discussion on stakeholder definition, stakeholder identification and categorisation, to re-centre the debate to the strategic origin of the stakeholder model. The ambiguity and the vagueness of the stakeholder concept are discussed from managerial and legal approaches. The impacts of two major shortcomings of the popular stakeholder framework are examined: the boundaries and the level of the firm’s environment, and the ambivalent position of pressure groups and regulators. Working pragmatically, with a focus on the managerial and organisational perspective, an attempt is made to clarify the categorisations and classifications by introducing new terminology with a distinction between stakeholders, stakewatchers and stakekeepers. The analysis will finally lead to a proposed upgraded and refined version of the stakeholder model, with incremental ameliorations close to Freeman’s original model and a return of focus to its essence, the managerial implications in a strategic approach.
STRATEGIC ENTERPRISE MANAGEMENT FOR INNOVATIVE COMPANIES : THE LAST DECADE OF THE BALANCED SCORECARD

Setting corporate vision, strategy, and associated unit-based strategic goals are crucial for any company to create and to sustain the competitive advantage. This concept has been frequently discussed in theory and practice. The case is no different from the innovation perspective of enterprises. Therefore, this article is aimed at exploring the integration of balanced scorecard into innovative strategic management, providing a framework of interrelated issues based on the existing literature and application areas in this research field. Along with the brief review of innovation based strategic management, which includes 13 peer reviewed journal articles; 8 book chapters, 41 peerreviewed scientific journal articles, including a content analysis, was conducted to show the last ten years of the balanced scorecard approach from the innovation perspective. The literature review revealed that: (1) the number of related publications on the topic is showing similarity over the last few years; (2) the explored research field has been mainly grounded on balanced scorecard research, but has also been enriched by innovation perspective; and (3) there are wide range of industry applications based on innovation conducted for the balanced scorecard approach. The literature review provides a summary of the most important issues that emerged in this field which can shed lights on the new opportunities and challenges for further research.
Abstractive Summarization Improved by WordNet-based Extractive Sentences

ive Summarization Improved by WordNet-based Extractive Sentences Niantao Xie, Sujian Li, Huiling Ren, and Qibin Zhai 1 MOE Key Laboratory of Computational Linguistics, Peking University, China 2 Institute of Medical Information, Chinese Academy of Medical Sciences 3 MOE Information Security Lab, School of Software & Microelectronics, Peking University, China {xieniantao,lisujian}@pku.edu.cn ren.huiling@imicams.ac.cn qibinzhai@ss.pku.edu.cn  Recently, the seq2seq abstractive summarization models have achieved good results on the CNN/Daily Mail dataset. Still, how to improve abstractive methods with extractive methods is a good research direction, since extractive methods have their potentials of exploiting various efficient features for extracting important sentences in one text. In this paper, in order to improve the semantic relevance of abstractive summaries, we adopt the WordNet based sentence ranking algorithm to extract the sentences which are most semantically to one text. Then, we design a dual attentional seq2seq framework to generate summaries with consideration of the extracted information. At the same time, we combine pointer-generator and coverage mechanisms to solve the problems of out-of-vocabulary (OOV) words and duplicate words which exist in the abstractive models. Experiments on the CNN/Daily Mail dataset show that our models achieve competitive performance with the state-of-theart ROUGE scores. Human evaluations also show that the summaries generated by our models have high semantic relevance to the original text. Recently, the seq2seq abstractive summarization models have achieved good results on the CNN/Daily Mail dataset. Still, how to improve abstractive methods with extractive methods is a good research direction, since extractive methods have their potentials of exploiting various efficient features for extracting important sentences in one text. In this paper, in order to improve the semantic relevance of abstractive summaries, we adopt the WordNet based sentence ranking algorithm to extract the sentences which are most semantically to one text. Then, we design a dual attentional seq2seq framework to generate summaries with consideration of the extracted information. At the same time, we combine pointer-generator and coverage mechanisms to solve the problems of out-of-vocabulary (OOV) words and duplicate words which exist in the abstractive models. Experiments on the CNN/Daily Mail dataset show that our models achieve competitive performance with the state-of-theart ROUGE scores. Human evaluations also show that the summaries generated by our models have high semantic relevance to the original text.
Policies and Identity Cues Amplify Opinion Polarization About Climate Mitigation Boomerang Effects in Science Communication : How Motivated Reasoning

The deficit-model of science communication assumes increased communication about science issues will move public opinion toward the scientific consensus. However, in the case of climate change, public polarization about the issue has increased in recent years, not diminished. In this study, we draw from theories of motivated reasoning, social identity, and persuasion to examine how science-based messages may increase public polarization on controversial science issues such as climate change. Exposing 240 adults to simulated news stories about possible climate change health impacts on different groups, we found the influence of identification with potential victims was contingent on participants’ political partisanship. This partisanship increased the degree of political polarization on support for climate mitigation policies and resulted in a boomerang effect among Republican participants. Implications for understanding the role of motivated reasoning within the context of science communication are discussed.
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform

We propose a fast algorithm for ridge regression when the number of features is much larger than the number of observations (p≫n). The standard way to solve ridge regression in this setting works in the dual space and gives a running time of ). Our algorithm Subsampled Randomized Hadamard Transform Dual Ridge Regression (SRHT-DRR) runs in time O(np log(n)) and works by preconditioning the design matrix by a Randomized Walsh-Hadamard Transform with a subsequent subsampling of features. We provide risk bounds for our SRHT-DRR algorithm in the fixed design setting and show experimental results on synthetic and real datasets. Disciplines Computer Sciences | Statistics and Probability | Theory and Algorithms This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/statistics_papers/221 Faster Ridge Regression via the Subsampled Randomized Hadamard Transform Yichao Lu Paramveer S. Dhillon Dean Foster Lyle Ungar Statistics (Wharton School), Computer & Information Science University of Pennsylvania, Philadelphia, PA, U.S.A {dhillon|ungar}@cis.upenn.edu foster@wharton.upenn.edu, yichaolu@sas.upenn.edu
T-SGX: Eradicating Controlled-Channel Attacks Against Enclave Programs

Intel Software Guard Extensions (SGX) is a hardware-based Trusted Execution Environment (TEE) that enables secure execution of a program in an isolated environment, called an enclave. SGX hardware protects the running enclave against malicious software, including the operating system, hypervisor, and even low-level firmware. This strong security property allows trustworthy execution of programs in hostile environments, such as a public cloud, without trusting anyone (e.g., a cloud provider) between the enclave and the SGX hardware. However, recent studies have demonstrated that enclave programs are vulnerable to accurate controlled-channel attacks conducted by a malicious OS. Since enclaves rely on the underlying OS, curious and potentially malicious OSs can observe a sequence of accessed addresses by intentionally triggering page faults. In this paper, we propose T-SGX, a complete mitigation solution to the controlled-channel attack in terms of compatibility, performance, and ease of use. T-SGX relies on a commodity component of the Intel processor (since Haswell), called Transactional Synchronization Extensions (TSX), which implements a restricted form of hardware transactional memory. As TSX is implemented as an extension (i.e., snooping the cache protocol), any unusual event, such as an exception or interrupt, that should be handled in its core component, results in an abort of the ongoing transaction. One interesting property is that the TSX abort suppresses the notification of errors to the underlying OS. This means that the OS cannot know whether a page fault has occurred during the transaction. T-SGX, by utilizing this property of TSX, can carefully isolate the effect of attempts to tap running enclaves, thereby completely eradicating the known controlledchannel attack. We have implemented T-SGX as a compiler-level scheme to automatically transform a normal enclave program into a secured enclave program without requiring manual source code modification or annotation. We not only evaluate the security properties of T-SGX, but also demonstrate that it could be applied to all the previously demonstrated attack targets, such as libjpeg, Hunspell, and FreeType. To evaluate the performance of T-SGX, we ported 10 benchmark programs of nbench to the SGX environment. Our evaluation results look promising. T-SGX is † The two lead authors contributed equally to this work. ⋆ The author did part of this work during an intership at Microsoft Research. an order of magnitude faster than the state-of-the-art mitigation schemes. On our benchmarks, T-SGX incurs on average 50% performance overhead and less than 30% storage overhead.
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts

Sentiment analysis of short texts such as single sentences and Twitter messages is challenging because of the limited contextual information that they normally contain. Effectively solving this task requires strategies that combine the small text content with prior knowledge and use more than just bag-of-words. In this work we propose a new deep convolutional neural network that exploits from characterto sentence-level information to perform sentiment analysis of short texts. We apply our approach for two corpora of two different domains: the Stanford Sentiment Treebank (SSTb), which contains sentences from movie reviews; and the Stanford Twitter Sentiment corpus (STS), which contains Twitter messages. For the SSTb corpus, our approach achieves state-of-the-art results for single sentence sentiment prediction in both binary positive/negative classification, with 85.7% accuracy, and fine-grained classification, with 48.3% accuracy. For the STS corpus, our approach achieves a sentiment prediction accuracy of 86.4%.
Procrastination , Academic Success and the Effectiveness of a Remedial Program

Procrastination, Academic Success and the Effectiveness of a Remedial Program Procrastination produces harmful effects for human capital investments and studying activities. Using data from a large sample of Italian undergraduates, we measure procrastination with the actual behaviour of students, considering the delay in finalizing their university enrolment procedure. We firstly show that procrastination is a strong predictor of students’ educational achievements. This result holds true controlling for quite reliable measures of cognitive abilities, a number of background characteristics and indicators of students’ motivation. Secondly, we investigate, using a Regression Discontinuity Design, the effects of a remedial program in helping students with different propensity to procrastinate. We show that the policy especially advantages students who tend to procrastinate, suggesting that also policies not directly aimed at handling procrastination can help to solve self-control problems.
The development of reading in children who speak English as a second language.

Patterns of reading development were examined in native English-speaking (L1) children and children who spoke English as a second language (ESL). Participants were 978 (790 L1 speakers and 188 ESL speakers) Grade 2 children involved in a longitudinal study that began in kindergarten. In kindergarten and Grade 2, participants completed standardized and experimental measures including reading, spelling, phonological processing, and memory. All children received phonological awareness instruction in kindergarten and phonics instruction in Grade 1. By the end of Grade 2, the ESL speakers' reading skills were comparable to those of L1 speakers, and ESL speakers even outperformed L1 speakers on several measures. The findings demonstrate that a model of early identification and intervention for children at risk is beneficial for ESL speakers and also suggest that the effects of bilingualism on the acquisition of early reading skills are not negative and may be positive.
Real-time Object Detection CS 229 Course Project

Objection detection is a key problem in computer vision. We report our work on object detection using neural network and other computer vision features. We use Faster Regionbased Convolutional Neural Network method (Faster R-CNN) for detection and then match the object with features from both neural network and features like histograms of gradients (HoG). We are able to achieve real-time performance and satisfactory matching results.
Affective Analysis for Video Frames Using ConvLSTM Network

With the rapid development of various online video sharing platforms, large numbers of videos are produced every day. Video affective content analysis has become an active research area in recent years, since emotion plays an important role in the classification and retrieval of videos. In this work, we explore to train very deep convolutional networks using ConvLSTM layers to add more expressive power for video affective content analysis models. Network-in-network principles, batch normalization, and convolution auto-encoder are applied to ensure the effectiveness of the model. Then an extended emotional representation model is used as an emotional annotation. In addition, we set up a database containing two thousand fragments to validate the effectiveness of the proposed model. Experimental results on the proposed data set show that deep learning approach based on ConvLSTM outperforms the traditional baseline and reaches the state-of-the-art system.
Fourier and wavelet descriptors for shape recognition using neural networks - a comparative study

This paper presents the application of three di1erent types of neural networks to the 2-D pattern recognition on the basis of its shape. They include the multilayer perceptron (MLP), Kohonen self-organizing network and hybrid structure composed of the self-organizing layer and the MLP subnetwork connected in cascade. The recognition is based on the features extracted from the Fourier and wavelet transformations of the data, describing the shape of the pattern. Application of di1erent neural network structures associated with di1erent preprocessing of the data results in di1erent accuracy of recognition and classi9cation. The numerical experiments performed for the recognition of simulated shapes of the airplanes have shown the superiority of the wavelet preprocessing associated with the self-organizing neural network structure. The integration of the individual classi9ers based on the weighted summation of the signals from the neural networks has been proposed and checked in numerical experiments. ? 2002 Pattern Recognition Society.
Internet of Things ( IOT ) Standards , Protocols and Security Issues

The internet of things (IOT) is the new revolution of internet after PCS and ServersClients communication now sensors, smart object, wearable devices, and smart phones are able to communicate. Everything surrounding us can talk to each other. life will be easier and smarter with smart environment, smart homes,smart cities and intelligent transport and healthcare.Billions of devices will be communicating wirelessly is a real huge challenge to our security and privacy.IOT requires efficient and effective security solutions which satisfies IOT requirements, The low power, small memory and limited computational capabilities . This paper addresses various standards, protocols and technologies of IOT and different security attacks which may compromise IOT security and privacy.