nils-herrmann/clean_scirepeval huggingface dataset:
The Multidimensional Work Motivation Scale : Validation evidence in seven languages and nine countries

The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries Marylène Gagné, Jacques Forest, Maarten Vansteenkiste, Laurence Crevier-Braud, Anja Van den Broeck,  Charles Benabou, Emanuela Chemolli, Stefan Tomas Güntert, Hallgeir Halvari, Devani Laksmi Indiyastuti, Peter A. Johnson, Marianne Hauan Molstad, Mathias Naudin, Assane Ndao, Anja Hagen Olafsen, Patrice Roussel, Zheni Wang & Cathrine Westbye a School of Psychology, University of Western Australia, Perth, Australia b Department of Organization and Human Resource Management, UQAM School of Management Sciences, Université du Québec à Montréal, Montréal, Canada c Department of Developmental, Personality and Social Psychology, University of Gent, Gent, Belgium d Department of Psychology, Université du Québec à Montréal, Montréal, Canada e Department of Economy and Management, Hogeschool Universiteit Brussel, Brussels, and KU Leuven, Leuven, Belgium f Faculty of Economics and Social Science, Høgskolen i Buskerud og Vestfold, Kongsberg, Norway g Department of Psychology, Université de Montréal, Montréal, Canada h Psychology of Work in Organization and Society Research Group, Swiss Federal Institute of Technology, Zurich, Switzerland i Faculty of Economics and Business, Universitas Gadjah Mada, Yogyakarta, Indonesia j Institute of Work Psychology, University of Sheffield, Sheffield, United Kingdom k International Institute of Commerce and Development, Université Panthéon Assas — Paris II, Paris, France l Faculty of Economics and Management Sciences, Université Cheikh Anta DIOP, Dakkar, Sénégal m Department of Management and Human Resources, Université Toulouse 1, Toulouse, France Published online: 05 Feb 2014.
Writing for synthesis of evidence in empirical software engineering

Systematic literature reviews have become common in software engineering in the last decade, but challenges remain.
 Goal: Given the challenges, the objective is to describe improvement areas in writing primary studies, and hence provide a good basis for researchers aiming at synthesizing research evidence in a specific area.
 Method: The results presented are based on a literature review with respect to synthesis of research results in software engineering with a particular focus on empirical software engineering. The literature review is complemented and exemplified with experiences from conducting systematic literature reviews and working with research methodologies in empirical software engineering.
 Results: The paper presents three areas where improvements are needed to become more successful in synthesizing empirical evidence. These three areas are: terminology, paper content and reviewing.
  It is concluded that it must be possible to improve the primary studies, but it requires that researchers start having synthesis in mind when writing their research papers.
NetworkProfiler: Towards automatic fingerprinting of Android apps

Network operators need to have a clear visibility into the applications running in their network. This is critical for both security and network management. Recent years have seen an exponential growth in the number of smart phone apps which has complicated this task. Traditional methods of traffic classification are no longer sufficient as the majority of this smart phone app traffic is carried over HTTP/HTTPS. Keeping up with the new applications that come up everyday is very challenging and time-consuming. We present a novel technique for automatically generating network profiles for identifying Android apps in the HTTP traffic. A network profile consists of fingerprints, i.e., unique characteristics of network behavior, that can be used to identify an app. To profile an Android app, we run the app automatically in an emulator and collect the network traces. We have developed a novel UI fuzzing technique for running the app such that different execution paths are exercised, which is necessary to build a comprehensive network profile. We have also developed a light-weight technique, for extracting fingerprints, that is based on identifying invariants in the generated traces. We used our technique to generate network profiles for thousands of apps. Using our network profiles we were able to detect the presence of these apps in real-world network traffic logs from a cellular provider.
Toward Integrated Scene Text Reading

The growth in digital camera usage combined with a worldly abundance of text has translated to a rich new era for a classic problem of pattern recognition, reading. While traditional document processing often faces challenges such as unusual fonts, noise, and unconstrained lexicons, scene text reading amplifies these challenges and introduces new ones such as motion blur, curved layouts, perspective projection, and occlusion among others. Reading scene text is a complex problem involving many details that must be handled effectively for robust, accurate results. In this work, we describe and evaluate a reading system that combines several pieces, using probabilistic methods for coarsely binarizing a given text region, identifying baselines, and jointly performing word and character segmentation during the recognition process. By using scene context to recognize several words together in a line of text, our system gives state-of-the-art performance on three difficult benchmark data sets.
“Mask-Bot 2i”: An active customisable robotic head with interchangeable face

This paper describes the development of “Mask-Bot 2i” (codenamed: Kabuto), a robotic head designed for research into Human-Robot-Interactions. The uniqueness of our new robotic head is that the appearance of its face can be alter on-the-fly. Different faces can be projected onto the active head system. The head is fully active with a 3-DOF neck, with support for biannual hearing as well as video camera for seeing. An interchangeable face is the main feature of this new Mask-Bot, the head can be equipped with an average face mask as well as a highly customised individualised face that can be easily exchanged. Additionally, the actuation of the head has been designed to match the natural head movements of an average human. Thus, enabling the head and face to be articulated synchronously to the speech production while the natural head movement matches that of the animated face. The design and realisation of this new system is presented in details in this paper.
Combining Labeled and Unlabeled Data for MultiClass Text Categorization

Supervised learning techniques for text classi cation often require a large number of labeled examples to learn accurately. One way to reduce the amount of labeled data required is to develop algorithms that can learn e ectively from a small number of labeled examples augmented with a large number of unlabeled examples. Current text learning techniques for combining labeled and unlabeled, such as EM and Co-Training, are mostly applicable for classi cation tasks with a small number of classes and do not scale up well for large multiclass problems. In this paper, we develop a framework to incorporate unlabeled data in the Error-Correcting Output Coding (ECOC) setup by rst decomposing multiclass problems into multiple binary problems and then using Co-Training to learn the individual binary classi cation problems. We show that our method is especially useful for text classi cation tasks involving a large number of categories and outperforms other semi-supervised learning techniques such as EM and Co-Training. In addition to being highly accurate, this method utilizes the hamming distance from ECOC to provide high-precision results. We also present results with algorithms other than co-training in this framework and show that co-training is uniquely suited to work well within ECOC.
Improved keyword spotting based on keyword/garbage models

We propose two simple methods to improve the performance of a keyword spotting system. In our application, the users are allowed to change the keywords anytime if they want. Thus we focused on phone-based GMM-HMM models since they do not require keyword-specific training data. However, the GMM-HMM based models usually have very high false alarm rate, i.e., a keyword is not present but the system gives a positive decision. We found that we can utilize the uncertainty of the system when a non-keyword is presented. Two simple methods are proposed to incorporate the uncertainty into the confidence measure. Our experiments show that these two methods can substantially reduce the false alarm rate from 75.05% to 5.71%. Meanwhile, the false reject rate increases from 1.04% to 5.71%.
Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait

An active ankle-foot orthoses (AAFO) is presented where the impedance of the orthotic joint is modulated throughout the walking cycle to treat drop-foot gait. During controlled plantar flexion, a biomimetic torsional spring control is applied where orthotic joint stiffness is actively adjusted to minimize forefoot collisions with the ground. Throughout late stance, joint impedance is minimized so as not to impede powered plantar flexion movements, and during the swing phase, a torsional spring-damper control lifts the foot to provide toe clearance. To assess the clinical effects of variable-impedance control, kinetic and kinematic gait data were collected on two drop-foot participants wearing the AAFO. For each participant, zero, constant, and variable impedance control strategies were evaluated and the results were compared to the mechanics of three age, weight, and height matched normals. We find that actively adjusting joint impedance reduces the occurrence of slap foot allows greater powered plantar flexion and provides for less kinematic difference during swing when compared to normals. These results indicate that a variable-impedance orthosis may have certain clinical benefits for the treatment of drop-foot gait compared to conventional ankle-foot orthoses having zero or constant stiffness joint behaviors.
Vehicle tracking system for intelligent and connected vehicle based on radar and V2V fusion

The environment perception plays a significantly role in intelligent vehicles and the advanced driver assistance system (ADAS), which enhances the driving safety and convenience. Target tracking is one of the key technologies of environment perception. The on-board sensors such as cameras and radar are commonly used for target tracking while they have limitations in terms of detection range and angle of view. One way to overcome the perception limitations of on-board ranging sensors by incorporating the vehicle-to-vehicle (V2V) communication. This paper proposes a vehicle tracking system which fuse the radar and V2V information to improve the target tracking accuracy. The proposed system integrates the radar, GPS and DSRC communication equipment. The GPS and radar are utilized to obtain its own position information and the position information of nearby vehicles. The proposed system also resolves the problem of data association in multiple target tracking measurements by other connected vehicles' identity information. With the association measurements, a Kalman filter is used to improve the accuracy of target tracking. An assessment of tracking system in real road environment shows that the proposed fusion approach for target tracking can reduce the data association error and improve the vehicle target tracking accuracy.
KeLP: a Kernel-based Learning Platform for Natural Language Processing

Kernel-based learning algorithms have been shown to achieve state-of-the-art results in many Natural Language Processing (NLP) tasks. We present KELP, a Java framework that supports the implementation of both kernel-based learning algorithms and kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms: once a new kernel function has been implemented it can be adopted in all the available kernelmachine algorithms. The platform includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. This paper will show the main aspects of the framework by applying it to different NLP tasks.
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations

We describe PARANMT-50M, a dataset of more than 50 million English-English sentential paraphrase pairs. We generated the pairs automatically by using neural machine translation to translate the nonEnglish side of a large parallel corpus, following Wieting et al. (2017). Our hope is that PARANMT-50M can be a valuable resource for paraphrase generation and can provide a rich source of semantic knowledge to improve downstream natural language understanding tasks. To show its utility, we use PARANMT-50M to train paraphrastic sentence embeddings that outperform all supervised systems on every SemEval semantic textual similarity competition, in addition to showing how it can be used for paraphrase
where science and magic meet the illusion of a science

Recent articles calling for a scientific study of magic have been the subject of widespread interest. This article considers the topic from a broader perspective, and argues that to engage in a science of magic, in any meaningful sense, is misguided. It argues that those who have called for a scientific theory of magic have failed to explain either how or why such a theory might be constructed, that a shift of focus to a neuroscience of magic is simply unwarranted, and that a science of magic is itself an inherently unsound idea. It seeks to provide a more informed view of the relationship between science and magic, and suggests a more appropriate way forward for scientists.
Transparent, lightweight application execution replay on commodity multiprocessor operating systems

We present Scribe, the first system to provide transparent, low-overhead application record-replay and the ability to go live from replayed execution. Scribe introduces new lightweight operating system mechanisms, rendezvous and sync points, to efficiently record nondeterministic interactions such as related system calls, signals, and shared memory accesses. Rendezvous points make a partial ordering of execution based on system call dependencies sufficient for replay, avoiding the recording overhead of maintaining an exact execution ordering. Sync points convert asynchronous interactions that can occur at arbitrary times into synchronous events that are much easier to record and replay.
 We have implemented Scribe without changing, relinking, or recompiling applications, libraries, or operating system kernels, and without any specialized hardware support such as hardware performance counters. It works on commodity Linux operating systems, and commodity multi-core and multiprocessor hardware. Our results show for the first time that an operating system mechanism can correctly and transparently record and replay multi-process and multi-threaded applications on commodity multiprocessors. Scribe recording overhead is less than 2.5% for server applications including Apache and MySQL, and less than 15% for desktop applications including Firefox, Acrobat, OpenOffice, parallel kernel compilation, and movie playback.
Bridging a Conceptual Divide: How Peer Collaboration Facilitates Science Learning

Collaboration is generally an effective means of learning new information, but is collaboration productive in domains where collaborators may hold qualitatively different conceptions of the domain’s causal structure? We explored this question in the domain of evolutionary biology, where previous research has shown that most individuals construe evolution as the uniform transformation of an entire population (akin to metamorphosis) rather than the selective survival and reproduction of a subset of the population. College undergraduates (n = 44) completed an assessment of their evolutionary reasoning by themselves (pretest), with a partner (dyad test), and several weeks later (posttest). Collaboration proved ineffective for the higherscoring partner in each dyad, as their scores generally remained unchanged from pretest to dyad test to posttest, but it proved effective for the lower-scoring partner. Not only did lowerscoring partners increase their score from pretest to dyad test, but they maintained higher scores at posttest as well. Followup analyses revealed that participants’ posttest scores were predicted by their partners’ pretest scores but only for lowerscoring partners, and the relation was negative: the smaller the difference between pretest score, the greater the gain from pretest to posttest for lower-scoring partners. These findings indicate that collaboration in domains characterized by conceptual change is possible, but that learning from such collaboration is asymmetric (i.e., individuals with low levels of understanding benefit more than their partners do) and unequal (i.e., individuals with low levels of understanding benefit more if their partner’s understanding is only moderately higher). Thus, bridging the gap between a novice’s view of a conceptually complex domain and an expert’s view appears to require instruction more aligned with the former than the latter.
Treating chronic worry: Psychological and physiological effects of a training programme based on mindfulness.

The present study examines psychological and physiological indices of emotional regulation in non-clinical high worriers after a mindfulness-based training programme aimed at reducing worry. Thirty-six female university students with high Penn State Worry Questionnaire scores were split into two equal intervention groups: (a) mindfulness, and (b) progressive muscle relaxation plus self-instruction to postpone worrying to a specific time of the day. Assessment included clinical questionnaires, daily self-report of number/duration of worry episodes and indices of emotional meta-cognition. A set of somatic and autonomic measures was recorded (a) during resting, mindfulness/relaxation and worrying periods, and (b) during cued and non-cued affective modulation of defence reactions (cardiac defence and eye-blink startle). Both groups showed equal post-treatment improvement in the clinical and daily self-report measures. However, mindfulness participants reported better emotional meta-cognition (emotional comprehension) and showed improved indices of somatic and autonomic regulation (reduced breathing pattern and increased vagal reactivity during evocation of cardiac defense). These findings suggest that mindfulness reduces chronic worry by promoting emotional and physiological regulatory mechanisms contrary to those maintaining chronic worry.
Dynamic Cascades for Face Detection

In this paper, we propose a novel method, called "dynamic cascade", for training an efficient face detector on massive data sets. There are three key contributions. The first is a new cascade algorithm called "dynamic cascade ", which can train cascade classifiers on massive data sets and only requires a small number of training parameters. The second is the introduction of a new kind of weak classifier, called "Bayesian stump", for training boost classifiers. It produces more stable boost classifiers with fewer features. Moreover, we propose a strategy for using our dynamic cascade algorithm with multiple sets of features to further improve the detection performance without significant increase in the detector's computational cost. Experimental results show that all the new techniques effectively improve the detection performance. Finally, we provide the first large standard data set for face detection, so that future researches on the topic can be compared on the same training and testing set.
Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal

We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly sized CNN. Specifically, we train our DerainNet on the detail (high-pass) layer rather than in the image domain. Though DerainNet is trained on synthetic data, we find that the learned network translates very effectively to real-world images for testing. Moreover, we augment the CNN framework with image enhancement to improve the visual results. Compared with the state-of-the-art single image de-raining methods, our method has improved rain removal and much faster computation time after network training.
Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders

RMSProp and ADAM continue to be extremely popular algorithms for training neural nets but their theoretical foundations have remained unclear. In this work we make progress towards that by giving proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives and we give bounds on the running time. We then design experiments to compare the performances of RMSProp and ADAM against Nesterov Accelerated Gradient method on a variety of autoencoder setups. Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter β1. We show that in terms of getting lower training and test losses, at very high values of the momentum parameter (β1 = 0.99) (and large enough nets if using mini-batches) ADAM outperforms NAG at any momentum value tried for the latter. On the other hand, NAG can sometimes do better when ADAM’s β1 is set to the most commonly used value: β1 = 0.9. We also report experiments on different autoencoders to demonstrate that NAG has better abilities in terms of reducing the gradient norms and finding weights which increase the minimum eigenvalue of the Hessian of the loss function.
Building Conference Proceedings Requires Adaptable Workflow and Content Management

ProceedingsBuilder is a system that helps the proceedings chair of a scientific conference to carry out his chores. It has features of both workflow management systems (WFMS) and content management systems (CMS), in order to collect the material for the printed proceedings and other products. ProceedingsBuilder has been operational at several conferences, including VLDB 2005. When using ProceedingsBuilder, we had a very intense lesson which kinds of workflow adaptations may become necessary. Existing WFMS do not offer support for most of them. The concern of this article is to describe and classify these various requirements regarding adaptation. ProceedingsBuilder is an example of a broad class of systems, namely editorial systems that collect content in order to publish it. Our findings are of interest to a broader audience, not only to conference organizers.