nils-herrmann/clean_scirepeval huggingface dataset:
How are campus students using social media to support their studies? An explorative interview study

Social media hype has created a lot of speculation among educators on how these media can be used to support learning, but there have been rather few studies so far. Our explorative interview study contributes by critically exploring how campus students perceive using social media to support their studies and the perceived benefits and limitations compared with other means. Although the vast majority of the respondents use social media frequently, a “digital dissonance” can be noted, because few of them feel that they use such media to support their studies. The interviewees mainly put forth e-mail and instant messaging, which are used among students to ask questions, coordinate group work and share files. Some of them mention using Wikipedia and YouTube for retrieving content and Facebook to initiate contact with course peers. Students regard social media as one of three key means of the educational experience, alongside face-to-face meetings and using the learning management systems, and are mainly used for brief questions and answers, and to coordinate group work. In conclusion, we argue that teaching strategy plays a key role in supporting students in moving from using social media to support coordination and information retrieval to also using such media for collaborative learning, when appropriate.
Consumers’ purchase of organic food products. A matter of convenience and reflexive practices

The aim of this study was to gain insight into the purchase of organic food products by consumers and to explore the main factors driving this process. This paper uses evidence from 16 in-depth interviews with consumers in Denmark carried out in 2008-2009. On the basis of the analysis two broad concepts are suggested: convenience behaviours and reflexive practices. Convenience behaviours are characteristic of pragmatic organic consumers. This type of shopping behaviour requires organic foods to be available in the local supermarket, they have to be clearly visible (preferably with an eco-label), and the price differential vis-à-vis conventional products have to be minimal. The analysis also showed that politically/ethically minded consumers have reflexive practices when purchasing organic food products: health considerations, ethical considerations (animal welfare), political considerations (environmentalism) and quality considerations (taste) play an important part for these consumers. Reflexive shopping practices can be sparked by life events (e.g. having children), "shocking" news about conventional food products and similar events, and news capable of creating a "cognitive dissonance" among consumers. The Danish case illustrates that the government needs to actively implement reforms and promote activities which make organic products a convenient choice for the pragmatic oriented consumer if their market share is to increase substantially.
Simple and inexpensive incorporation of 19F-tryptophan for protein NMR spectroscopy.

Fluorine-containing amino acids are valuable probes for the biophysical characterization of proteins. Current methods for (19)F-labeled protein production involve time-consuming genetic manipulation, compromised expression systems and expensive reagents. We show that Escherichia coli BL21, the workhorse of protein production, can utilise fluoroindole for the biosynthesis of proteins containing (19)F-tryptophan.
Aggregating and Predicting Sequence Labels from Crowd Annotations

Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant. To predict sequences in unannotated text, we propose a neural approach using Long Short Term Memory. We evaluate a suite of methods across two different applications and text genres: Named-Entity Recognition in news articles and Information Extraction from biomedical abstracts. Results show improvement over strong baselines. Our source code and data are available online.
FuSeO: Fuzzy semantic overlapping community detection

Finding community structures in online social networks is an important methodology for understanding the internal organization of users and actions. Most previous studies have focused on structural properties to detect communities. They do not analyze the information gathered from the posting activities of members of social networks, nor do they consider overlapping communities. To tackle these two drawbacks, a new overlapping community detection method involving social activities and semantic analysis is proposed. This work applies a fuzzy membership to detect overlapping communities with different extent and run semantic analysis to include information contained in posts. The available resource description format contributes to research in social networks. Based on this new understanding of social networks, this approach can be adopted for large online social networks and for social portals, such as forums, that are not based on network topology. The efficiency and feasibility of this method is verified by the available experimental analysis. The results obtained by the tests on real networks indicate that the proposed approach can be effective in discovering labelled and overlapping communities with a high amount of modularity. This approach is fast enough to process very large and dense social networks. 6
Redressing First Nations historical trauma: theorizing mechanisms for indigenous culture as mental health treatment.

Indigenous "First Nations" communities have consistently associated their disproportionate rates of psychiatric distress with historical experiences of European colonization. This emphasis on the socio-psychological legacy of colonization within tribal communities has occasioned increasingly widespread consideration of what has been termed historical trauma within First Nations contexts. In contrast to personal experiences of a traumatic nature, the concept of historical trauma calls attention to the complex, collective, cumulative, and intergenerational psychosocial impacts that resulted from the depredations of past colonial subjugation. One oft-cited exemplar of this subjugation--particularly in Canada--is the Indian residential school. Such schools were overtly designed to "kill the Indian and save the man." This was institutionally achieved by sequestering First Nations children from family and community while forbidding participation in Native cultural practices in order to assimilate them into the lower strata of mainstream society. The case of a residential school "survivor" from an indigenous community treatment program on a Manitoba First Nations reserve is presented to illustrate the significance of participation in traditional cultural practices for therapeutic recovery from historical trauma. An indigenous rationale for the postulated efficacy of "culture as treatment" is explored with attention to plausible therapeutic mechanisms that might account for such recovery. To the degree that a return to indigenous tradition might benefit distressed First Nations clients, redressing the socio-psychological ravages of colonization in this manner seems a promising approach worthy of further research investigation.
Fundamental development of a virtual reality simulator for four-arm disaster rescue robot OCTOPUS

This paper presents a virtual reality (VR) simulator for four-arm disaster response robot OCTOPUS, which has high capable of both mobility and workability. OCTOPUS has 26 degrees of freedom (DOF) and is currently teleoperated by two operators, so it is quite difficult to operate OCTOPUS. Thus, we developed a VR simulator for training operation, developing operator support system and control strategy. Compared with actual robot and environment, VR simulator can reproduce them at low cost and high efficiency. The VR simulator consists of VR environment and human-machine interface such as operation-input and video- and sound-output, based on robot operation system (ROS) and Gazebo. To enhance work performance, we implement indicators and data collection functions. Four tasks such as rough terrain passing, high-step climbing, obstacle stepping over, and object transport were conducted to evaluate OCTOPUS itself and our VR simulator. The results indicate that operators could complete all the tasks but the success rate differed in tasks. Smooth and stable operations increased the work performance, but sudden change and oscillation of operation degraded it. Cooperating multi-joint adequately is quite important to execute task more efficiently.
Electronic document management in construction - research issues and results

Electronic document management (EDM) technology has the potential to enhance the information management in construction projects considerably, without radical changes to current practice. Over the past fifteen years this topic has been overshadowed by building product modelling in the construction IT research world, but at present EDM is quickly being introduced in practice, in particular in bigger projects. Often this is done in the form of third party services available over the World Wide Web. In the paper, a typology of research questions and methods is presented, which can be used to position the individual research efforts which are surveyed in the paper. Questions dealt with include: What features should EMD systems have? How much are they used? Are there benefits from use and how should these be measured? What are the barriers to wide-spread adoption? Which technical questions need to be solved? Is there scope for standardisation? How will the market for such systems evolve?
An Integrated Telepresence Environment for Collaboration in Construction

For construction projects involving transient ‘virtual organisations’ composed of non-collocated team-members, the adoption of concurrent engineering principles is seen as vital. An important aspect of concurrent engineering in construction is the need for an effective communications infrastructure between team members. Traditionally, such communication has been handled through person-to-person meetings, however the complexity of construction projects has grown and, as a result, reliance on new information and communications technologies is becoming increasingly necessary. Hence, within a concurrent engineering setting, there is the need for an integrated information and collaboration environment that will create a persistent space to support interaction between project personnel throughout all phases of construction projects. This joint initiative between the Massachusetts Institute of Technology (MIT), Loughborough University, British Telecommunications plc. (BT) and Kajima Corporation explores computer-supported mechanisms for enhancing distributed engineering collaboration. The goal of this paper is to develop a set of requirements, a system architecture and a system prototype to facilitate computer-supported collaboration among distributed teams. The prototype consists of a comprehensive working collaborative system built from the integration of complementary standalone applications. These applications are the CAIRO system, developed at the Massachusetts Institute of Technology and the Telepresence system developed by Loughborough University and BT.
Using Pulse Density Modulation to Improve the Efficiency of IGBT Inverters in Induction Heating Applications

This paper analyses a high power (50 kW), high frequency (150 kHz) voltage fed inverter with a series resonant load circuit for industrial induction heating applications which is characterized by a full bridge inverter made with IGBT and the power control based on pulse density modulation (PDM). This power control strategy allows that the inverter works close to the resonance frequency for all output power levels. In this situation zero-voltage switching (ZVS) and zero-current switching (ZCS) conditions are performed and the switching losses are minimized. The results are verified experimentally using a prototype for induction hardening applications. A comparative study between the PDM and the classical frequency power control will be made.
Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control

This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method for training a RNNs model from prerecorded motion data. We implement recurrent neural networks with long short-term memory (LSTM) cells because they are capable of handling nonlinear dynamics and long term temporal dependencies present in human motions. Next, we train a refiner network using an adversarial loss, similar to Generative Adversarial Networks (GANs), such that the refined motion sequences are indistinguishable from real motion capture data using a discriminative network. We embed contact information into the generative deep learning model to further improve the performance of our generative model. The resulting model is appealing to motion synthesis and control because it is compact, contact-aware, and can generate an infinite number of naturally looking motions with infinite lengths. Our experiments show that motions generated by our deep learning model are always highly realistic and comparable to high-quality motion capture data. We demonstrate the power and effectiveness of our models by exploring a variety of applications, ranging from random motion synthesis, online/offline motion control, and motion filtering. We show the superiority of our generative model by comparison against baseline models.
Underspecification for incremental generation

State-of-the-art NLG systems have a mostly static perspective on language generation, i.e. they have a fixed knowledge representation from which they generate language. This is fine for offline working systems, where the language is read only after it has been completely generated. However, systems set in a dynamic environment, e.g. those interacting with a human user, need a dynamic way of processing and, therefore, dynamic representations to exhibit appropriate response behaviour. For such settings we suggest to use an incremental mode of processing in conjunction with underspecified semantic representations. We especially focus on the case where the content to be verbalised changes during language generation.
Video Based Reconstruction of 3D People Models

This paper describes a method to obtain accurate 3D body models and texture of arbitrary people from a single, monocular video in which a person is moving. Based on a parametric body model, we present a robust processing pipeline to infer 3D model shapes including clothed people with 4.5mm reconstruction accuracy. At the core of our approach is the transformation of dynamic body pose into a canonical frame of reference. Our main contribution is a method to transform the silhouette cones corresponding to dynamic human silhouettes to obtain a visual hull in a common reference frame. This enables efficient estimation of a consensus 3D shape, texture and implanted animation skeleton based on a large number of frames. Results on 4 different datasets demonstrate the effectiveness of our approach to produce accurate 3D models. Requiring only an RGB camera, our method enables everyone to create their own fully animatable digital double, e.g., for social VR applications or virtual try-on for online fashion shopping.
Control of Tendon-Driven Soft Foam Robot Hands

This paper presents a series of control strategies for soft compliant manipulators. We provide a novel approach to control multi-fingered tendon-driven foam hands using a CyberGlove and a simple ridge regression model. The results achieved include complex posing, dexterous grasping and in-hand manipulations. To enable efficient data sampling and a more intuitive design process of foam robots, we implement and evaluate a finite element based simulation. The accuracy of this model is evaluated using a Vicon motion capture system. We then use this simulation to solve inverse kinematics and compare the performance of supervised learning, reinforcement learning, nearest neighbor and linear ridge regression methods in terms of their accuracy and sample efficiency.
Early versus late maturation arrest: reproductive outcomes of testicular failure.

There is a paucity of data characterizing infertile men with maturation arrest. We hypothesized that men with early stage maturation arrest could be clinically distinguished from men with late maturation arrest and would have worse reproductive outcomes.



We retrospectively reviewed the records of all patients with nonobstructive azoospermia and cryptozoospermia who underwent testis mapping and sperm extraction from 2002 to 2009 and for whom histopathological findings were available. Patients had uniform maturation arrest if multiple biopsies revealed maturation arrest at the spermatogonia/spermatocyte (early maturation arrest) or the spermatid (late maturation arrest) stage. Clinical parameters and pregnancy outcomes of in vitro fertilization/intracytoplasmic sperm injection were examined. Statistical analysis consisted of univariate and multivariate analysis.



Uniform maturation arrest was identified in 49 of 219 men (22.3%) undergoing testicular sperm extraction. On multivariate analysis men with maturation arrest had significantly larger testes (p=0.01), decreased follicle-stimulating hormone (p=0.05) and more detectable genetic abnormalities (p=0.01) than men with other histopathological conditions. Men with late maturation arrest had decreased follicle-stimulating hormone (p=0.02), increased testosterone (p=0.03) and a higher sperm retrieval rate at testicular sperm extraction (p=0.01) than men with early maturation arrest. Predictors of successful sperm retrieval were larger testes, cryptozoospermia, late maturation arrest and hypospermatogenesis (each p≤0.05). Pregnancy outcomes for men with maturation arrest were not significantly different from those for men with other histopathological conditions.



Maturation arrest is a common, diverse histopathological subtype of severe male infertility. Compared to men with late maturation arrest those with early maturation arrest have increased follicle-stimulating hormone, decreased testosterone and a decreased probability of mature spermatozoa. In vitro fertilization/intracytoplasmic sperm injection outcomes were similar when spermatozoa were discovered during testicular sperm extraction.
How the probability of a false positive affects the value of DNA evidence.

Errors in sample handling or test interpretation may cause false positives in forensic DNA testing. This article uses a Bayesian model to show how the potential for a false positive affects the evidentiary value of DNA evidence and the sufficiency of DNA evidence to meet traditional legal standards for conviction. The Bayesian analysis is contrasted with the "false positive fallacy," an intuitively appealing but erroneous alternative interpretation. The findings show the importance of having accurate information about both the random match probability and the false positive probability when evaluating DNA evidence. It is argued that ignoring or underestimating the potential for a false positive can lead to serious errors of interpretation, particularly when the suspect is identified through a "DNA dragnet" or database search, and that ignorance of the true rate of error creates an important element of uncertainty about the value of DNA evidence.
Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network

The next generation wireless networks are expected to operate in fully automated fashion to meet the burgeoning capacity demand and to serve users with superior quality of experience. Mobile wireless networks can leverage spatio-temporal information about user and network condition to embed the system with end-to-end visibility and intelligence. Big data analytics has emerged as a promising approach to unearth meaningful insights and to build artificially intelligent models with assistance of machine learning tools. Utilizing aforementioned tools and techniques, this paper contributes in two ways. First, we utilize mobile network data (Big Data)—call detail record—to analyze anomalous behavior of mobile wireless network. For anomaly detection purposes, we use unsupervised clustering techniques namely k-means clustering and hierarchical clustering. We compare the detected anomalies with ground truth information to verify their correctness. From the comparative analysis, we observe that when the network experiences abruptly high (unusual) traffic demand at any location and time, it identifies that as anomaly. This helps in identifying regions of interest in the network for special action such as resource allocation, fault avoidance solution, etc. Second, we train a neural-network-based prediction model with anomalous and anomaly-free data to highlight the effect of anomalies in data while training/building intelligent models. In this phase, we transform our anomalous data to anomaly-free and we observe that the error in prediction, while training the model with anomaly-free data has largely decreased as compared to the case when the model was trained with anomalous data.
Input/output-to-state stability and state-norm estimators for switched nonlinear systems

In this paper, the concepts of input/output-to-state stability (IOSS) and state-norm estimators are considered for switched nonlinear systems under average dwell-time switching signals. We show that when the average dwell-time is large enough, a switched system is IOSS if all of its constituent subsystems are IOSS. Moreover, under the same conditions, a non-switched state-norm estimator exists for the switched system. Furthermore, if some of the constituent subsystems are not IOSS, we show that still IOSS can be established for the switched system, if the activation time of the non-IOSS subsystems is not too big. Again, under the same conditions, a state-norm estimator exists for the switched system. However, in this case, the state-norm estimator is a switched system itself, consisting of two subsystems. We show that this state-norm estimator can be constructed such that its switching times are independent of the switching times of the switched system it is designed for.
Nutrigenomics: goals and strategies

Nutrigenomics is the application of high-throughput genomics tools in nutrition research. Applied wisely, it will promote an increased understanding of how nutrition influences metabolic pathways and homeostatic control, how this regulation is disturbed in the early phase of a diet-related disease and to what extent individual sensitizing genotypes contribute to such diseases. Ultimately, nutrigenomics will allow effective dietary-intervention strategies to recover normal homeostasis and to prevent diet-related diseases.