nils-herrmann/clean_scirepeval huggingface dataset:
The Molecular and Cellular Effect of Homocysteine Metabolism Imbalance on Human Health
Venue: International journal of molecular sciences

Homocysteine (Hcy) is a sulfur containing non proteinogenic amino acid derived in methionine metabolism. The increased level of Hcy in plasma, hyperhomocysteinemia, is considered to be an independent risk factor for cardio and cerebrovascular diseases. However, it is still not clear if Hcy is a marker or a causative agent of diseases. More and more research data suggest that Hcy is an important indicator for overall health status. This review represents the current understanding of molecular mechanism of Hcy metabolism and its link to hyperhomocysteinemia related pathologies in humans. The aberrant Hcy metabolism could lead to the redox imbalance and oxidative stress resulting in elevated protein, nucleic acid and carbohydrate oxidation and lipoperoxidation, products known to be involved in cytotoxicity. Additionally, we examine the role of Hcy in thiolation of proteins, which results in their molecular and functional modifications. We also highlight the relationship between the imbalance in Hcy metabolism and pathogenesis of diseases, such as cardiovascular diseases, neurological and psychiatric disorders, chronic kidney disease, bone tissue damages, gastrointestinal disorders, cancer, and congenital defects.
The integration of visual context information in facial emotion recognition in 5 to 15 year olds.
Venue: Journal of experimental child psychology

The current study investigated the role of congruent visual context information in the recognition of facial emotional expression in 190 participants from 5 to 15years of age. Children performed a matching task that presented pictures with different facial emotional expressions (anger, disgust, happiness, fear, and sadness) in two conditions: with and without a visual context. The results showed that emotions presented with visual context information were recognized more accurately than those presented in the absence of visual context. The context effect remained steady with age but varied according to the emotion presented and the gender of participants. The findings demonstrated for the first time that children from the age of 5years are able to integrate facial expression and visual context information, and this integration improves facial emotion recognition.
Limitations of emotion recognition in software user experience evaluation context
Venue: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS)

This paper concerns how an affective behavioural cognitive approach applies to the evaluation of the software user experience. Although it may seem that affect recognition solutions are accurate in determining the user experience, there are several challenges in practice. This paper aims to explore the limitations of the automatic affect recognition applied in the usability context as well as to propose a set of criteria to select input channels for affect recognition. The results are revealed via a semi experiment based on the case study of an educational game. As a result, a number of concerns were identified, providing a list of pros and cons for affective computing methods applied in the usability testing context. The lessons learned might be interesting for both researchers that develop emotion recognition algorithms and for practitioners, who apply them to diverse areas.
Emotion in Context: Deep Semantic Feature Fusion for Video Emotion Recognition
Venue: ACM Multimedia

Human emotions demonstrate high correlations with certain events that consist of the interaction of objects, and are usually constrained by particular scenes. In this paper, we exploit these abundant context clues for video emotion recognition. We first compute event, object and scene scores with state of the art detectors based on deep neural networks. The extracted high level features serve as effective contextual information demonstrating what is occurring in the video, and are further integrated in a context fusion network to generate a unified representation aiming to bridge the affective gap. The contribution of this paper is three fold: (a) we are the first to incorporate event as context for emotion recognition, and we demonstrate its superiority for emotion understanding, (b) we utilize a context fusion network to exploit a comprehensive set of high level semantics features as contextual clues and (c) the proposed framework can realize recognition in real time and achieves state of the art performance on two challenging emotion recognition benchmarks, 50.6 on VideoEmotion and 51.8 on Ekman.
On Business Process Redesign and Configuration: Leveraging Data Mining Classification Outliers and Artifact Centric Process Modeling
Venue: 2016 International Conference on Advanced Computing and Applications (ACOMP)

In today's e business, data increasingly capture many behavioral aspects of our daily life, e.g. operational patterns in production lines, human shopping habit, medical treatment, supply chain management. We formally define the behavior of tasks (of the business process in question) in connection with conceptually described business artifacts that are manipulated by the tasks. We relate this representation to data mining and nonfunctional requirements to better define the context for process redesign (i.e, design time changes) and process configuration (i.e, run time changes) Furthermore, we propose algorithms for business process redesign and configuration based on the above context. Our research is positioned in the field of business process flexibility a term coined by van der Aalst  in his thorough literature survey on business processes. We validate our framework using real life data provided by our industrial partners.
Dental plaque bacteria with reduced susceptibility to chlorhexidine are multidrug resistant
Venue: BMC Microbiology

BackgroundChlorhexidine (CHX) is used in oral care products to help control dental plaque. In this study dental plaque bacteria were grown on media containing 2 mg/ml chlorhexidine gluconate to screen for bacteria with reduced CHX susceptibility. The isolates were characterized by 16S rRNA gene sequencing and antibiotic resistance profiles were determined using the disc diffusion method.ResultsThe isolates were variably resistant to multiple drugs including ampicillin, kanamycin, gentamicin and tetracycline. Two species, Chryseobacterium culicis and Chryseobacterium indologenes were able to grow planktonically and form biofilms in the presence of 32 mg/ml CHX. In the CHX and multidrug resistant C. indologenes we demonstrated a 19 fold up regulation of expression of the HlyD like periplasmic adaptor protein of a tripartite efflux pump upon exposure to 16 mg/ml CHX suggesting that multidrug resistance may be mediated by this system. Exposure of biofilms of these resistant species to undiluted commercial CHX mouthwash for intervals from 5 to 60 s indicated that the mouthwash was unlikely to eliminate them from dental plaque in vivo.ConclusionsThe study highlights the requirement for increased vigilance of the presence of multidrug resistant bacteria in dental plaque and raises a potential risk of long term use of oral care products containing antimicrobial agents for the control of dental plaque.
Oral health practices and prevalence of dental plaque and gingivitis among Indian adults
Venue: Clinical and experimental dental research

This cross sectional survey study evaluated oral hygiene habits in conjunction with whole mouth examinations for dental plaque and gingivitis among adults in India. Subjects across several age groups who provided informed consent [220 male and 158 female (mean age 30.9 years) were enrolled. All enrolled subjects were interviewed for oral hygiene practices and evaluated by the Turesky modification of the Quigley Hein and the Loe Silness methods for dental plaque and gingivitis, respectively. Evaluations included oral hygiene parameters, prevalence of dental plaque and gingivitis, and regional differences within the dentition for dental plaque and gingivitis. Results from this study indicate that most subjects (97% utilized a toothbrush and toothpaste for oral hygiene with a majority (92% using their right hand to brush their teeth. While 29% reported two or more episodes of daily oral hygiene, a majority (53% brushed their teeth once daily. Utilization of dental floss and mouthwashes were reported by approximately 1% of this population, and most (73% reported no dental visits in the preceding 5 years. Whole mouth plaque and gingival scores (average standard deviation) for this population were 2.47 0.55 and 1.19 0.31, respectively, with no significant differences between either gender (P 0.05) Significant correlations (r 0.44) were observed between plaque and gingival scores for the entire sample, either gender or between age groups (P 0.001) Analyses indicate that anterior teeth demonstrated lower average scores for dental plaque and gingivitis than posterior and molar regions (P 0.05) Education was associated with higher plaque and gingival scores: plaque scores [odds ratios; 95% confidence interval; 1.23; 1.01 1.50 and gingival scores odds ratios 1.25; 1.02 1.54] In summary, results from this study demonstrate the prevalence of dental plaque and gingivitis in the general population and their relationships with demographic characteristics. They reinforce examinations of posterior regions that consistently harbor more plaque and corresponding gingivitis in evaluations of oral health.
Innovation and technology transfer through global value chains: Evidence from China's PV industry

China's success as a rapid innovation follower in the infant Photovoltaic (PV) industry surprised many observers. This paper explores how China inserted itself into global clean energy innovation systems by examining the case of the solar PV industry. The paper decomposes the global PV industrial value chain, and determines the main factors shaping PV technology transfer and diffusion. Chinese firms first entered PV module manufacturing through technology acquisition, and then gradually built their global competitiveness by utilizing a vertical integration strategy within segments of the industry as well as the broader PV value chain. The main drivers for PV technology transfer from the global innovation system to China are global market formation policy, international mobilization of talent, the flexibility of manufacturing in China, and belated policy incentives from China's government. The development trajectory of the PV industry in China indicates that innovation in cleaner energy technologies can occur through both global and national innovation processes, and knowledge exchange along the global PV value chain.
New Backpropagation Algorithm with Type 2 Fuzzy Weights for Neural Networks

In this book a neural network learning method with type 2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type 2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type 1 fuzzy weights and neural network with type 2 fuzzy weights are presented to illustrate the advantages of the proposed method. The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type 1 and type 2. The proposed approach is applied to a cases of prediction for the Mackey Glass (for =17) and Dow Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey Glass time series for showing that the type 2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods. The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type 2 fuzzy inference systems for the neural network with type 2 fuzzy weights that permit to obtain the lowest prediction error.
Using SPSS for Windows and Macintosh, Books a la Carte (8th Edition)

For courses in Political and Social Statistics Using the popular SPSS software to de mystify statistics Using SPSS for Windows and Macintosh guides students through basic SPSS techniques, using step by step descriptions and explaining in detail how to avoid common pitfalls in the study of statistics. Authors Samuel Green and Neil Salkind provide extensive substantive information about each statistical technique, including a brief discussion of the technique, examples of how the statistic is applied, a sample data set that can be analyzed with the statistic, a discussion of the analysis results, practice exercises, and more. The Eighth Edition has been updated for SPSS version 23 (Windows/Mac) and now offers added accessibility and increased problem solving.  This ISBN is for a Pearson Books a la Carte edition: a convenient, three hole punched, loose leaf text. In addition to the flexibility offered by this format, Books a la Carte editions offer students great value, as they cost significantly less than a bound textbook.
A decision tree logic based recommendation system to select software fault prediction techniques
Venue: Computing

Identifying a reliable fault prediction technique is the key requirement for building effective fault prediction model. It has been found that the performance of fault prediction techniques is highly dependent on the characteristics of the fault dataset. To mitigate this issue, researchers have evaluated and compared a plethora of fault prediction techniques by varying the context in terms of domain information, characteristics of input data, complexity, etc. However, the lack of an accepted benchmark makes it difficult to select fault prediction technique for a particular context of prediction. In this paper, we present a recommendation system that facilitates the selection of appropriate technique(s) to build fault prediction model. First, we have reviewed the literature to elicit the various characteristics of the fault dataset and the appropriateness of the machine learning and statistical techniques for the identified characteristics. Subsequently, we have formalized our findings and built a recommendation system that helps in the selection of fault prediction techniques. We performed an initial appraisal of our presented system and found that proposed recommendation system provides useful hints in the selection of the fault prediction techniques.
Autonomous drifting using simulation aided reinforcement learning
Venue: 2016 IEEE International Conference on Robotics and Automation (ICRA)

We introduce a framework that combines simple and complex continuous state action simulators with a real world robot to efficiently find good control policies, while minimizing the number of samples needed from the physical robot. The framework combines the strengths of various simulation levels by first finding optimal policies in a simple model, and then using that solution to initialize a gradient based learner in a more complex simulation. The policy and transition dynamics from the complex simulation are in turn used to guide the learning in the physical world. A method is developed for transferring information gathered in the physical world back to the learning agent in the simulation. The new information is used to re evaluate whether the original simulated policy is still optimal given the updated knowledge from the real world. This reverse transfer is critical to minimizing samples from the physical world. The new framework is demonstrated on a robotic car learning to perform controlled drifting maneuvers. A video of the car's performance can be found at https: /youtu.be/opsmd5yuBF0.
What Matters in Increasing Community College Students' Upward Transfer to the Baccalaureate Degree: Findings from the Beginning Postsecondary Study 2003 2009

This research uses the nationally representative Beginning Postsecondary Study: 2003 2009 to investigate the relative significance in upward transfer of individual and institutional factors for different groups of students, considering their state policy contexts of variable support for improved articulation and transfer between 2 year and baccalaureate granting colleges. Layered analyses of hierarchical generalized linear model population average results found that a few community college characteristics and state transfer policy components (such as a state articulation policy, cooperative articulation agreements, transfer data reporting, etc. demonstrated a statistically significant association with individual upward transfer probability within 6 years of community college entry. Student characteristics found to be influential and positive for increasing upward transfer probability included: having an intention for upward transfer at entry, attending primarily full time, working between 1 and 19 h per week (not more or less) and declaring a transfer oriented major in STEM (science, technology, engineering, or mathematics) Arts and Social/Behavioral Sciences, or Education.
Mathematics teachers' conceptions and constraints for changing teaching practices in Brazilian higher education: an analysis through activity theory

In recent years, changes in the Brazilian economic and social scenario have generated a growing demand for higher education in the country. In response to this new context, the federal government launched in 2007 a programme aiming at expansion of the enrolment to public higher education. In such environment of changes, several proposals have emerged to adapt the Brazilian federal universities to a new reality. Taking this context into account, the focus of our study is on proposals from the Mathematics Department of the Federal University of Minas Gerais. Their aim is to create a new model of teaching practices for the freshman lessons of the Exact Sciences area, which at first were being experimented in special classrooms of students attending their first course on differential and integral calculus. The data were collected from interviews with students and professors from the mathematics department. They were analyzed and systematized using an activity theory approach. We became instigated by a model developed by Engestrom in his study on the changes in the Finnish public health system, considering it as a test (testbench) for activity theory in its application to a particular case. Following Engestrom's footsteps when developing his research, we arrived at our own model showing the internal tensions in the activity of reformulation of the courses offered by the Department of Mathematics and in the conceptions of teachers that promote and constraint the proposals for change.
THE THEORY OF OBJECTIFICATION AND ITS PLACE AMONG SOCIOCULTURAL RESEARCH IN MATHEMATICS EDUCATION

This article is an attempt at locating the theory of objectification (TO) among sociocultural research in mathematics education. The first part contains a summary of the emergence of sociocultural perspectives in mathematics education research. The second part deals with some of the central ideas that underpin the TO. It begins with a discussion of the concepts of teachers and students. Then, the general trend of language centered types of theorizing in mathematics teaching and learning is discussed. The discussion is followed by a brief presentation of the concept of activity as understood in dialectic materialism. Such a concept is central to the TO. This concept, however, is reformulated as joint labor, which works in tandem with a dialect materialist concept of knowledge and the knower. Through these concepts the TO reformulates teaching and learning as an ethical cultural historical phenomenon, providing the TO with a distinct orientation among sociocultural research.
Biometric Authentication Techniques: A Study on Keystroke Dynamics

Biometrics technologies are gaining popularity today since they provide reliable and efficient means of authentication and verification. Our dependency on electronic devices is growing, and so is our need to secure information on them. Keystroke Dynamics is one of the famous biometric technologies, which identifies the authenticity of a user when the user is working via a keyboard. The authentication process is done by observing the variation in the typing pattern of the user. A comprehensive study of the existing keystroke dynamics methods, metrics, and different approaches are presented. This paper also discusses about the various security issues and challenges faced by keystroke dynamics. KeywordsBiometrics, Keystroke Dynamics, computer Security, Information Security, Biometric Authentication.
Brief Mental Skills Training Improves Memory and Performance in High Stress Police Cadet Training

Psychological performance training has been shown to mitigate the negative effects of stress for police officers. We contributed to this body of evidence using techniques in breathing, mental performance imagery, and attentional focus. One group (experimental) of police academy cadets was trained in these techniques to deal with the stressful event of being sprayed with oleoresin capsicum (OC) Their physiological and behavioral responses to the event were compared to a control group of cadets. The results showed the experimental group demonstrated significantly better memory recall of salient aspects of the OC spray event and that cadets from both groups using controlled breathing during the event scored significantly higher in memory recall. Several trends were found that further suggested the effectiveness of psychological performance training. While heart rate increased from baseline to pre OC and post OC measures across groups, there were no group differences at these time points. Our findings add to the growing body of literature on psychological skill training effects on tactical performance and are notable for results of enhanced performance occurring with relatively brief training in the psychological techniques. Because of the ecological design, the findings should generalize to other high stress encounters in policing.
Multi Modal Bayesian Embeddings for Learning Social Knowledge Graphs
Venue: IJCAI

We study the extent to which online social networks can be connected to open knowledge bases. The problem is referred to as learning social knowledge graphs. We propose a multi modal Bayesian embedding model, GenVector, to learn latent topics that generate word and network embeddings. GenVector leverages large scale unlabeled data with embeddings and represents data of two modalities i.e. social network users and knowledge concepts in a shared latent topic space. Experiments on three datasets show that the proposed method clearly outperforms state of the art methods. We then deploy the method on AMiner, a large scale online academic search system with a network of 38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our method significantly decreases the error rate in an online A/B test with live users.