nils-herrmann/clean_scirepeval huggingface dataset:
Pixel Level Characterization of Pinned Photodiode and Transfer Gate Physical Parameters in CMOS Image Sensors

A method to extract the pinned photodiode (PPD) physical parameters inside a CMOS image sensor pixel array is presented. The proposed technique is based on the Tan et al. pinning voltage characteristic. This pixel device characterization can be performed directly at the solid-state circuit output without the need of any external test structure. The presented study analyzes the different injection mechanisms involved in the different regimes of the characteristic. It is demonstrated that in addition to the pinning voltage, this fast measurement can be used to retrieve the PPD capacitance, the pixel equilibrium full well capacity, and both the transfer gate threshold voltage and its channel potential at a given gate voltage. An alternative approach is also proposed to extract an objective pinning voltage value from this measurement.
Model-based fault diagnosis in electric drives using machine learning

Electric motor and power electronics-based inverter are the major components in industrial and automotive electric drives. In this paper, we present a model-based fault diagnostics system developed using a machine learning technology for detecting and locating multiple classes of faults in an electric drive. Power electronics inverter can be considered to be the weakest link in such a system from hardware failure point of view; hence, this work is focused on detecting faults and finding which switches in the inverter cause the faults. A simulation model has been developed based on the theoretical foundations of electric drives to simulate the normal condition, all single-switch and post-short-circuit faults. A machine learning algorithm has been developed to automatically select a set of representative operating points in the (torque, speed) domain, which in turn is sent to the simulated electric drive model to generate signals for the training of a diagnostic neural network, fault diagnostic neural network (FDNN). We validated the capability of the FDNN on data generated by an experimental bench setup. Our research demonstrates that with a robust machine learning approach, a diagnostic system can be trained based on a simulated electric drive model, which can lead to a correct classification of faults over a wide operating domain.
Synthesis of Hecken-tapered microstrip to Paralell-Strip baluns for UHF frequency band

Microstrip to Parallel-Strip transitions are frequently used for feeding balanced antenna structures, such as dipoles and printed spiral antennas. In this paper, we propose an analytical method to compute the gradual taper using a Hecken approach in order to minimize the return losses and to have continuity. The proposed method is verified experimentally with the aid of three transitions including matching capabilities with different ratios, suitable for spiral antenna structures in, at least, the ranges from 450 MHz to 2 GHz.
THE THEORY OF USER ACCEPTANCE AND USE OF TECHNOLOGY ( UTAUT ) : A META-ANALYTIC REVIEW OF EMPIRICAL FINDINGS 1

The unified theory of acceptance and use of technol ogy (UTAUT) proposes that performance expectancy, effort expectancy, and social influence predict beh avioral intention towards the acceptance of informa tion technology. The theory further proposes that facili t t ng conditions and behavioural intention predict s use behavior in the acceptance of information technolog y. Ever since its inception, the theory has been as sessed using different applications, and it has become a d è factor model of measuring user acceptance. Nonetheless, in terms of statistical significant ma gnitude and direction, reports on the model are div rse. Therefore, in this study, based on 37 selected empi rical studies, a meta-analysis was conducted in ord er to harmonize the empirical evidence. The outcome of the study suggests that only the rel ationship between performance expectancy and behavioural intention is strong, while the relation ships between effort expectation, social influence and behavioural intention are weak. Similarly, the rela tionship between facilitating condition, behavioura l intention and use behaviour is also weak. Furthermo re, the significance of the relationship between facilitating condition and use behaviour does not p ass the fail safe test while the significance of th e relationship between behavioural intention and use behaviour does not pass the fail safe test satisfac torily. Implications for further studies are also discussed .
AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis

Predicting the a↵ective valence of unknown multiword expressions is key for concept-level sentiment analysis. A↵ectiveSpace 2 is a vector space model, built by means of random projection, that allows for reasoning by analogy on natural language concepts. By reducing the dimensionality of a↵ective common-sense knowledge, the model allows semantic features associated with concepts to be generalized and, hence, allows concepts to be intuitively clustered according to their semantic and a↵ective relatedness. Such an a↵ective intuition (so called because it does not rely on explicit features, but rather on implicit analogies) enables the inference of emotions and polarity conveyed by multi-word expressions, thus achieving e cient concept-level sentiment analysis.
Complementary and alternative treatments for late-life depression, anxiety, and sleep disturbance: a review of randomized controlled trials.

We reviewed randomized controlled trials of complementary and alternative medicine (CAM) treatments for depression, anxiety, and sleep disturbance in nondemented older adults.



We searched PubMed (1966-September 2006) and PsycINFO (1984-September 2006) databases using combinations of terms including depression, anxiety, and sleep; older adult/elderly; randomized controlled trial; and a list of 56 terms related to CAM.



Of the 855 studies identified by database searches, 29 met our inclusion criteria: sample size >or= 30, treatment duration >or= 2 weeks, and publication in English. Four additional articles from manual bibliography searches met inclusion criteria, totaling 33 studies.



We reviewed identified articles for methodological quality using a modified Scale for Assessing Scientific Quality of Investigations (SASQI). We categorized a study as positive if the CAM therapy proved significantly more effective than an inactive control (or as effective as active control) on at least 1 primary psychological outcome. Positive and negative studies were compared on the following characteristics: CAM treatment category, symptom(s) assessed, country where the study was conducted, sample size, treatment duration, and mean sample age.



67% of the 33 studies reviewed were positive. Positive studies had lower SASQI scores for methodology than negative studies. Mind-body and body-based therapies had somewhat higher rates of positive results than energy- or biologically-based therapies.



Most studies had substantial methodological limitations. A few well-conducted studies suggested therapeutic potential for certain CAM interventions in older adults (e.g., mind-body interventions for sleep disturbances and acupressure for sleep and anxiety). More rigorous research is needed, and suggestions for future research are summarized.
Design of Resistive Loading Vivaldi Antenna

A novel Vivaldi antenna structure is proposed to broaden the bandwidth of the conventional ones. The theory of the equivalent circuit is adopted, and it is deduced that the bandwidth of the antenna can be enhanced by the high chip resistor and short pin in the new structure. An antenna of 62 mm (length) times 70 mm (width)times 0.5 mm (thickness) is designed and fabricated. The measure results show that the bandwidth is 1~ 20 GHz ( VSWR les2), while the gain varies between 0.9 and 7.8 dB. It is indicated that the antenna can be reduced to about a half of the conventional ones.
Transactional versus transformational leadership : An analysis of the MLQ

A questionnaire used often to measure transformational, transactional and laissez-faire leadership is che Multifactor Leadership Questionnaire developed by Bass (Bass & Avolio, 1989). This study aims to test the factor structure of the MLQ as developed by Bass & Avolio. The MLQ-8Y was analysed using data collected in Dutch organizations. Seven hundred employees from eight organizations rated their leader's behaviour with the MLQ. First, an indication of the internal consistency of the scales developed by Bass is reported. The results of subsequent factor analyses show that the three types of leadership can be found in the data; however, the scales found here are slightly different from Bass' scales. Especially, the transactional and laissez-faire scales have been altered on theoretical and empirical grounds. The adapted version of the MLQ covers the domain with fewer items.
Social Image Captioning: Exploring Visual Attention and User Attention

Image captioning with a natural language has been an emerging trend. However, the social image, associated with a set of user-contributed tags, has been rarely investigated for a similar task. The user-contributed tags, which could reflect the user attention, have been neglected in conventional image captioning. Most existing image captioning models cannot be applied directly to social image captioning. In this work, a dual attention model is proposed for social image captioning by combining the visual attention and user attention simultaneously.Visual attention is used to compress a large mount of salient visual information, while user attention is applied to adjust the description of the social images with user-contributed tags. Experiments conducted on the Microsoft (MS) COCO dataset demonstrate the superiority of the proposed method of dual attention.
Accurate Human Navigation Using Wearable Monocular Visual and Inertial Sensors

This paper presents a novel visual-inertial integration system for human navigation in free-living environments, where the measurements from wearable inertial and monocular visual sensors are integrated. The preestimated orientation, obtained from magnet, angular rate, and gravity sensors, is used to estimate the translation based on the data from the visual and inertial sensors. This has a significant effect on the performance of the fusion sensing strategy and makes the fusion procedure much easier, because the gravitational acceleration can be correctly removed from the accelerometer measurements before the fusion procedure, where a linear Kalman filter is selected as the fusion estimator. Furthermore, the use of preestimated orientation can help to eliminate erroneous point matches based on the properties of the pure camera translation and thus the computational requirements can be significantly reduced compared with the RANdom SAmple Consensus algorithm. In addition, an adaptive-frame rate single camera is selected to not only avoid motion blur based on the angular velocity and acceleration after compensation, but also to make an effect called visual zero-velocity update for the static motion. Thus, it can recover a more accurate baseline and meanwhile reduce the computational requirements. In particular, an absolute scale factor, which is usually lost in monocular camera tracking, can be obtained by introducing it into the estimator. Simulation and experimental results are presented for different environments with different types of movement and the results from a Pioneer robot are used to demonstrate the accuracy of the proposed method.
rication methods , properties and solar energy applications

Black silicon (BSi) represents a very active research area in renewable energy materials. The rise of BSi as a focus of study for its fundamental properties and potentially lucrative practical applications is shown by several recent results ranging from solar cells and light-emitting devices to antibacterial coatings and gas-sensors. In this paper, the common BSi fabrication techniques are first reviewed, including electrochemical HF etching, stain etching, metal-assisted chemical etching, reactive ion etching, laser irradiation and the molten salt Fray-Farthing-Chen-Cambridge (FFC-Cambridge) process. The utilization of BSi as an anti-reflection coating in solar cells is then critically examined and appraised, based upon strategies towards higher efficiency renewable solar energy modules. Methods of incorporating BSi in advanced solar cell architectures and the production of ultra-thin and flexible BSi wafers are also surveyed. Particular attention is given to routes leading to passivated BSi surfaces, which are essential for improving the electrical properties of any devices incorporating BSi, with a special focus on atomic layer deposition of Al2O3. Finally, three potential research directions worth exploring for practical solar cell applications are highlighted, namely, encapsulation effects, the development of micro-nano dual-scale BSi, and the incorporation of BSi into thin solar cells. It is intended that this paper will serve as a useful introduction to this novel material and its properties, and provide a general overview of recent progress in research currently being undertaken for renewable energy applications.
Constructing and Embedding Abstract Event Causality Networks from Text Snippets

In this paper, we formally define the problem of representing and leveraging abstract event causality to power downstream applications. We propose a novel solution to this problem, which build an abstract causality network and embed the causality network into a continuous vector space. The abstract causality network is generalized from a specific one, with abstract event nodes represented by frequently co-occurring word pairs. To perform the embedding task, we design a dual cause-effect transition model. Therefore, the proposed method can obtain general, frequent, and simple causality patterns, meanwhile, simplify event matching. Given the causality network and the learned embeddings, our model can be applied to a wide range of applications such as event prediction, event clustering and stock market movement prediction. Experimental results demonstrate that 1) the abstract causality network is effective for discovering high-level causality rules behind specific causal events; 2) the embedding models perform better than state-of-the-art link prediction techniques in predicting events; and 3) the event causality embedding is an easy-to-use and sophisticated feature for downstream applications such as stock market movement prediction.
Probabilistic Image Colorization

We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous desirable properties. In particular, our model is able to produce multiple plausible and vivid colorizations for a given grayscale image and is one of the first colorization models to provide a proper stochastic sampling scheme. Moreover, our training procedure is supported by a rigorous theoretical framework that does not require any ad hoc heuristics and allows for efficient modeling and learning of the joint pixel color distribution. We demonstrate strong quantitative and qualitative experimental results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset.
DevOps in Regulated Software Development: Case Medical Devices

DevOps and continuous development are getting popular in the software industry. Adopting these modern approaches in regulatory environments, such as medical device software, is not straightforward because of the demand for regulatory compliance. While DevOps relies on continuous deployment and integration, regulated environments require strict audits and approvals before releases. Therefore, the use of modern development approaches in regulatory environments is rare, as is the research on the topic. However, as software is more and more predominant in medical devices, modern software development approaches become attractive. This paper discusses the fit of DevOps for regulated medical device software development. We examine two related standards, IEC 62304 and IEC 82304-1, for obstacles and benefits of using DevOps for medical device software development. We found these standards to set obstacles for continuous delivery and integration. Respectively, development tools can help fulfilling the requirements of traceability and documentation of these standards.
A Credit Scoring Model Based on Alternative Mobile Data for Financial Inclusion

Credit is becoming more and more important for consumers in China as both Internet Economy and consumer finance are booming in the recent years. However, more than 2/3 people in China do not have credit records because they have never been to banks, thus leading to the difficulty of making credit risk evaluation for them. With big data time coming, a lot of alternative data shed light on the financial inclusion for these unbanked people. Mobile phone data is deemed to be a good type of alternative data for credit scoring. So in this research, collaborating with China Telecom and Cignifi, we develop a new credit score based on mobile phone data, which is believed to be widely used in China in the near future.
Co-word maps and topic modeling: A comparison using small and medium-sized corpora (N < 1, 000)

Induced by “big data,” “topic modeling” has become an attractive alternative to mapping cowords in terms of co-occurrences and co-absences using network techniques. Does topic modeling provide an alternative for co-word mapping in research practices using moderately sized document collections? We return to the word/document matrix using first a single text with a strong argument (“The Leiden Manifesto”) and then upscale to a sample of moderate size (n = 687) to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co-word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the topic models provide sets of words that are very differently organized. In these samples, the topic models seem to reveal similarities other than semantic ones (e.g., linguistic ones). In other words, topic modeling does not replace co-word mapping in small and medium-sized sets; but the paper leaves open the possibility that topic modeling would work well for the semantic mapping of large sets.
Rennick Concept Map of Adult Perspectives Psychosocial Experiences Associated With Confirmed and Self-Identified Dyslexia : A Participant-Driven

Concept mapping (a mixed qualitative–quantitative methodology) was used to describe and understand the psychosocial experiences of adults with confirmed and self-identified dyslexia. Using innovative processes of art and photography, Phase 1 of the study included 15 adults who participated in focus groups and in-depth interviews and were asked to elucidate their experiences with dyslexia. On index cards, 75 statements and experiences with dyslexia were recorded. The second phase of the study included 39 participants who sorted these statements into self-defined categories and rated each statement to reflect their personal experiences to produce a visual representation, or concept map, of their experience. The final concept map generated nine distinct cluster themes: Organization Skills for Success; Finding Success; A Good Support System Makes the Difference; On Being Overwhelmed; Emotional Downside; Why Can’t They See It?; Pain, Hurt, and Embarrassment From Past to Present; Fear of Disclosure; and Moving Forward. Implications of these findings are discussed.
How scientists think : On-line creativity and conceptual change in science

This is an investigation of " Online Creativity. " I will present a new account of the cognitive and social mechanisms underlying complex thinking of creative scientists as they work on significant problems in contemporary science. I will lay out an innovative methodology that I have developed for investigating creative and complex thinking in a real-world context. Using this method, I have discovered that there are a number of strategies that are used in contemporary science that increase the likelihood of scientists making discoveries. The findings reported in this chapter provide new insights into complex scientific thinking and will dispel many of the myths surrounding the generation of new concepts and scientific discoveries. InVivo cognition: A new way of investigating cognition There is a large background in cognitive research on thinking, reasoning and problem solving processes that form the foundation for creative cognition (see Dunbar, in press, Holyoak 1996 for recent reviews). However, to a large extent, research on reasoning has demonstrated that subjects in psychology experiments make vast numbers of thinking and reasoning errors even in the most simple problems. How is creative thought even possible if people make so many reasoning errors? One problem with research on reasoning is that the concepts and stimuli that the subjects are asked to use are often arbitrary and involve no background knowledge (cf. Dunbar, 1995; Klahr & Dunbar, 1988). I have proposed that one way of determining what reasoning errors are specific and which are general is to investigate cognition in the cognitive laboratory and the real world (Dunbar, 1995). Psychologists should conduct both InVitro and InVivo research to understand thinking. InVitro research is the standard psychological experiment where subjects are brought into the laboratory and controlled experiments are conducted. As can be seen from the research reported in this volume, this approach yields many insights into the psychological mechanisms underlying complex thinking. The use of an InVivo methodology in which online thinking and reasoning are investigated in a real-world context yields fundamental insights into the basic cognitive mechanisms underlying complex cognition and creativity. The results of InVivo cognitive research can then be used as a basis for further InVitro work in which controlled experiments are conducted. In this chapter, I will outline some of the results of my ongoing InVivo research on creative scientific thinking and relate this research back to the more common InVitro research and show that the …
Smart exploration in reinforcement learning using absolute temporal difference errors

Exploration is still one of the crucial problems in reinforcement learning, especially for agents acting in safety-critical situations. We propose a new directed exploration method, based on a notion of state controlability. Intuitively, if an agent wants to stay safe, it should seek out states where the effects of its actions are easier to predict; we call such states more controllable. Our main contribution is a new notion of controlability, computed directly from temporaldifference errors. Unlike other existing approaches of this type, our method scales linearly with the number of state features, and is directly applicable to function approximation. Our method converges to correct values in the policy evaluation setting. We also demonstrate significantly faster learning when this exploration strategy is used in large control problems.
Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries

In this study, we explored application of Word2Vec and Doc2Vec for sentiment analysis of clinical discharge summaries. We applied unsupervised learning since the data sets did not have sentiment annotations. Note that unsupervised learning is a more realistic scenario than supervised learning which requires an access to a training set of sentiment-annotated data. We aim to detect if there exists any underlying bias towards or against a certain disease. We used SentiWordNet to establish a gold sentiment standard for the data sets and evaluate performance of Word2Vec and Doc2Vec methods. We have shown that the Word2vec and Doc2Vec methods complement each other’s results in sentiment analysis of the data sets.