nils-herrmann/clean_scirepeval huggingface dataset:
Proportional-resonant controllers and filters for grid-connected voltage-source converters

The recently introduced proportional-resonant (PR) controllers and filters, and their suitability for current/voltage control of grid-connected converters, are described. Using the PR controllers, the converter reference tracking performance can be enhanced and previously known shortcomings associated with conventional PI controllers can be alleviated. These shortcomings include steady-state errors in single-phase systems and the need for synchronous d–q transformation in three-phase systems. Based on similar control theory, PR filters can also be used for generating the harmonic command reference precisely in an active power filter, especially for single-phase systems, where d–q transformation theory is not directly applicable. Another advantage associated with the PR controllers and filters is the possibility of implementing selective harmonic compensation without requiring excessive computational resources. Given these advantages and the belief that PR control will find wide-ranging applications in grid-interfaced converters, PR control theory is revised in detail with a number of practical cases that have been implemented previously, described clearly to give a comprehensive reference on PR control and filtering.
Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets

Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime.
Application of Infrared Thermography to Failure Detection in Industrial Induction Motors: Case Stories

Infrared thermography has been extensively applied over decades to areas such as maintenance of electrical installations. Its use in electrical machinery has been mainly circumscribed to the detection of faults in static machines, such as power transformers. However, with regard to the predictive maintenance of rotating electrical machines, its use has been much more limited. In spite of this fact, the potential of this tool, together with the progressive decrease in the price of infrared cameras, makes this technique a very interesting option to at least complement the diagnosis provided by other well-known techniques, such as current or vibration data analysis. In this context, infrared thermography has recently shown potential for the detection of motor failures including misalignments, cooling problems, bearing damages, or connection defects. This work presents several industrial cases that help to illustrate the effectiveness of this technique for the detection of a wide range of faults in field induction motors. The data obtained with this technique made it possible to detect the presence of faults of diverse nature (electrical, mechanical, thermal, and environmental); these data were very useful to either diagnose or complement the diagnosis provided by other tools.
Denial-of-Service detection in 6LoWPAN based Internet of Things

Smart objects connected to the Internet, constituting the so called Internet of Things (IoT), are revolutionizing human beings' interaction with the world. As technology reaches everywhere, anyone can misuse it, and it is always essential to secure it. In this work we present a denial-of-service (DoS) detection architecture for 6LoWPAN, the standard protocol designed by IETF as an adaptation layer for low-power lossy networks enabling low-power devices to communicate with the Internet. The proposed architecture integrates an intrusion detection system (IDS) into the network framework developed within the EU FP7 project ebbits. The aim is to detect DoS attacks based on 6LoWPAN. In order to evaluate the performance of the proposed architecture, preliminary implementation was completed and tested against a real DoS attack using a penetration testing system. The paper concludes with the related results proving to be successful in detecting DoS attacks on 6LoWPAN. Further, extending the IDS could lead to detect more complex attacks on 6LoWPAN.
Learning Robot Skill Embeddings

We present a method for reinforcement learning of closely related skills that are parameterized via a skill embedding space. We learn such skills by taking advantage of latent variables and exploiting a connection between reinforcement learning and variational inference. The main contribution of our work is an entropyregularized policy gradient formulation for hierarchical policies, and an associated, data-efficient and robust off-policy gradient algorithm based on stochastic value gradients. We demonstrate the effectiveness of our method on several simulated robotic manipulation tasks. We find that our method allows for discovery of multiple solutions and is capable of learning the minimum number of distinct skills that are necessary to solve a given set of tasks. In addition, our results indicate that the hereby proposed technique can interpolate and/or sequence previously learned skills in order to accomplish more complex tasks, even in the presence of sparse rewards.
Is there universal recognition of emotion from facial expression? A review of the cross-cultural studies.

Emotions are universally recognized from facial expressions--or so it has been claimed. To support that claim, research has been carried out in various modern cultures and in cultures relatively isolated from Western influence. A review of the methods used in that research raises questions of its ecological, convergent, and internal validity. Forced-choice response format, within-subject design, preselected photographs of posed facial expressions, and other features of method are each problematic. When they are altered, less supportive or nonsupportive results occur. When they are combined, these method factors may help to shape the results. Facial expressions and emotion labels are probably associated, but the association may vary with culture and is loose enough to be consistent with various alternative accounts, 8 of which are discussed.
A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 × 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image.
Work climate, work values and professional commitment as predictors of job satisfaction in nurses.

To investigate the effect of some psychosocial variables on nurses' job satisfaction.



Nurses' job satisfaction is one of the most important factors in determining individuals' intention to stay or leave a health-care organisation. Literature shows a predictive role of work climate, professional commitment and work values on job satisfaction, but their conjoint effect has rarely been considered.



A cross-sectional questionnaire survey was adopted. Participants were hospital nurses and data were collected in 2011.



Professional commitment and work climate positively predicted nurses' job satisfaction. The effect of intrinsic vs. extrinsic work value orientation on job satisfaction was completely mediated by professional commitment.



Nurses' job satisfaction is influenced by both contextual and personal variables, in particular work climate and professional commitment. According to a more recent theoretical framework, work climate, work values and professional commitment interact with each other in determining nurses' job satisfaction.



Nursing management must be careful to keep the context of work tuned to individuals' attitude and vice versa. Improving the work climate can have a positive effect on job satisfaction, but its effect may be enhanced by favouring strong professional commitment and by promoting intrinsic more than extrinsic work values.
Choosing an NLP Library for Analyzing Software Documentation: A Systematic Literature Review and a Series of Experiments

To uncover interesting and actionable information from natural language documents authored by software developers, many researchers rely on "out-of-the-box" NLP libraries. However, software artifacts written in natural language are different from other textual documents due to the technical language used. In this paper, we first analyze the state of the art through a systematic literature review in which we find that only a small minority of papers justify their choice of an NLP library. We then report on a series of experiments in which we applied four state-of-the-art NLP libraries to publicly available software artifacts from three different sources. Our results show low agreement between different libraries (only between 60% and 71% of tokens were assigned the same part-of-speech tag by all four libraries) as well as differences in accuracy depending on source: For example, spaCy achieved the best accuracy on Stack Overflow data with nearly 90% of tokens tagged correctly, while it was clearly outperformed by Google's SyntaxNet when parsing GitHub ReadMe files. Our work implies that researchers should make an informed decision about the particular NLP library they choose and that customizations to libraries might be necessary to achieve good results when analyzing software artifacts written in natural language.
Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course

Internet of Things (IoT) is rapidly emerging as the next generation of communication infrastructure, where myriad of multi-scale sensors and devices are seamlessly blended for ubiquitous computing and communication. The rapid growth of IoT applications has increased the demand for experienced professionals in the area. Since few, if any, dedicated IoT courses are currently offered, most Science, Technology, Engineering, and Mathematics (STEM) students will have limited or no exposure to IoT development until after graduation and entrance into the workforce. Moreover, there is a little room for adding additional courses into existing STEM curriculum. Therefore, we propose to transform STEM core courses by integrating IoT-based learning framework into their corresponding lab projects. The design challenges of the new learning framework is summarized in the paper. Subsequently, we propose the effective learning approaches to address those challenges. Moreover, in this paper, we present a case study by incorporating IoT-based learning framework into a Software Engineering (SWE) embedded system analysis & design course. Specifically, we introduce a lab development kit composed of Raspberry Pi/Arduino boards and a set of sensors with Zigbee supporting to provide wireless communication in the class lab section. We adopt module design method to design the course labware. Well-developed modules are presented and one sample module is illustrated in the paper. The labware is evaluated through survey questions. The majority of the students provided positive feedback and enjoyed the IoT-based lab development kit.
Chunking mechanisms in human learning

Pioneering work in the 1940s and 1950s suggested that the concept of 'chunking' might be important in many processes of perception, learning and cognition in humans and animals. We summarize here the major sources of evidence for chunking mechanisms, and consider how such mechanisms have been implemented in computational models of the learning process. We distinguish two forms of chunking: the first deliberate, under strategic control, and goal-oriented; the second automatic, continuous, and linked to perceptual processes. Recent work with discrimination-network computational models of long- and short-term memory (EPAM/CHREST) has produced a diverse range of applications of perceptual chunking. We focus on recent successes in verbal learning, expert memory, language acquisition and learning multiple representations, to illustrate the implementation and use of chunking mechanisms within contemporary models of human learning.
Wormhole attacks in wireless networks

As mobile ad hoc network applications are deployed, security emerges as a central requirement. In this paper, we introduce the wormhole attack, a severe attack in ad hoc networks that is particularly challenging to defend against. The wormhole attack is possible even if the attacker has not compromised any hosts, and even if all communication provides authenticity and confidentiality. In the wormhole attack, an attacker records packets (or bits) at one location in the network, tunnels them (possibly selectively) to another location, and retransmits them there into the network. The wormhole attack can form a serious threat in wireless networks, especially against many ad hoc network routing protocols and location-based wireless security systems. For example, most existing ad hoc network routing protocols, without some mechanism to defend against the wormhole attack, would be unable to find routes longer than one or two hops, severely disrupting communication. We present a general mechanism, called packet leashes, for detecting and, thus defending against wormhole attacks, and we present a specific protocol, called TIK, that implements leashes. We also discuss topology-based wormhole detection, and show that it is impossible for these approaches to detect some wormhole topologies.
KBGAN: Adversarial Learning for Knowledge Graph Embeddings

We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a nontrivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts, but the majority of the generated negative facts can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TRANSE and TRANSD, each with assistance from one of the two probability-based models, DISTMULT and COMPLEX. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.
Increasing the Use of Student-Centered Pedagogies from Moderate to High Improves Student Learning and Attitudes about Biology

Student-centered strategies are being incorporated into undergraduate classrooms in response to a call for reform. We tested whether teaching in an extensively student-centered manner (many active-learning pedagogies, consistent formative assessment, cooperative groups; the Extensive section) was more effective than teaching in a moderately student-centered manner (fewer active-learning pedagogies, less formative assessment, without groups; the Moderate section) in a large-enrollment course. One instructor taught both sections of Biology 101 during the same quarter, covering the same material. Students in the Extensive section had significantly higher mean scores on course exams. They also scored significantly higher on a content postassessment when accounting for preassessment score and student demographics. Item response theory analysis supported these results. Students in the Extensive section had greater changes in postinstruction abilities compared with students in the Moderate section. Finally, students in the Extensive section exhibited a statistically greater expert shift in their views about biology and learning biology. We suggest our results are explained by the greater number of active-learning pedagogies experienced by students in cooperative groups, the consistent use of formative assessment, and the frequent use of explicit metacognition in the Extensive section.
Sensory substitution and the human–machine interface

Recent advances in the instrumentation technology of sensory substitution have presented new opportunities to develop systems for compensation of sensory loss. In sensory substitution (e.g. of sight or vestibular function), information from an artificial receptor is coupled to the brain via a human-machine interface. The brain is able to use this information in place of that usually transmitted from an intact sense organ. Both auditory and tactile systems show promise for practical sensory substitution interface sites. This research provides experimental tools for examining brain plasticity and has implications for perceptual and cognition studies more generally.
Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems

Goal-oriented dialogue systems typically rely on components specifically developed for a single task or domain. This limits such systems in two different ways: If there is an update in the task domain, the dialogue system usually needs to be updated or completely re-trained. It is also harder to extend such dialogue systems to different and multiple domains. The dialogue state tracker in conventional dialogue systems is one such component — it is usually designed to fit a welldefined application domain. For example, it is common for a state variable to be a categorical distribution over a manually-predefined set of entities (Henderson et al., 2013), resulting in an inflexible and hard-to-extend dialogue system. In this paper, we propose a new approach for dialogue state tracking that can generalize well over multiple domains without incorporating any domain-specific knowledge. Under this framework, discrete dialogue state variables are learned independently and the information of a predefined set of possible values for dialogue state variables is not required. Furthermore, it enables adding arbitrary dialogue context as features and allows for multiple values to be associated with a single state variable. These characteristics make it much easier to expand the dialogue state space. We evaluate our framework using the widely used dialogue state tracking challenge data set (DSTC2) and show that our framework yields competitive results with other state-of-the-art results despite incorporating little domain knowledge. We also show that this framework can benefit from widely available external resources such as pre-trained word embeddings.
Text/non-text image classification in the wild with convolutional neural networks

Text in natural images is an important source of information, which can be utilized for many real-world applications. This work focuses on a new problem: distinguishing images that contain text from a large volume of natural images. To address this problem, we propose a novel convolutional neural network variant, called Multi-scale Spatial Partition Network (MSP-Net). The network classifies images that contain text or not, by predicting text existence in all image blocks, which are spatial partitions at multiple scales on an input image. The whole image is classified as a text image (an image containing text) as long as one of the blocks is predicted to contain text. The network classifies images very efficiently by predicting all blocks simultaneously in a single forward propagation. Through experimental evaluations and comparisons on public datasets, we demonstrate the effectiveness and robustness of the proposed method.
A hybrid ontology-based information extraction system

Information Extraction is the process of automatically obtaining knowledge from plain text. Because of the ambiguity of written natural language, Information Extraction is a difficult task. Ontology-based Information Extraction (OBIE) reduces this complexity by including contextual information in the form of a domain ontology. The ontology provides guidance to the extraction process by providing concepts and relationships about the domain. However, OBIE systems have not been widely adopted because of the difficulties in deployment and maintenance. The Ontology-based Components for Information Extraction (OBCIE) architecture has been proposed as a form to encourage the adoption of OBIE by promoting reusability through modularity. In this paper, we propose two orthogonal extensions to OBCIE that allow the construction of hybrid OBIE systems with higher extraction accuracy and a new functionality. The first extension utilizes OBCIE modularity to integrate different types of implementation into one extraction system, producing a more accurate extraction. For each concept or relationship in the ontology, we can select the best implementation for extraction, or we can combine both implementations under an ensemble learning schema. The second extension is a novel ontology-based error detection mechanism. Following a heuristic approach, we can identify sentences that are logically inconsistent with the domain ontology. Because the implementation strategy for the extraction of a concept is independent of the functionality of the extraction, we can design a hybrid OBIE system with concepts utilizing different implementation strategies for extracting correct or incorrect sentences. Our evaluation shows that, in the implementation extension, our proposed method is more accurate in terms of correctness and completeness of the extraction. Moreover, our error detection method can identify incorrect statements with a high accuracy.