nils-herrmann/clean_scirepeval huggingface dataset:
Mapping interference resolution across task domains: A shared control process in left inferior frontal gyrus

Work in functional neuroimaging has mapped interference resolution processing onto left inferior frontal regions for both verbal working memory and a variety of semantic processing tasks. The proximity of the identified regions from these different tasks suggests the existence of a common, domain-general interference resolution mechanism. The current research specifically tests this idea in a within-subject design using fMRI to assess the activation associated with variable selection requirements in a semantic retrieval task (verb generation) and a verbal working memory task with a trial-specific proactive interference manipulation (recent-probes). High interference trials on both tasks were associated with activity in the midventrolateral region of the left inferior frontal gyrus, and the regions activated in each task strongly overlapped. The results indicate that an elemental component of executive control associated with interference resolution during retrieval from working memory and from semantic memory can be mapped to a common portion of the left inferior frontal gyrus.
100kHz IGBT inverter use of LCL topology for high power induction heating

A power electronic inverter is developed for a high frequency induction heating application. This equipment is available for the implementation of ZVS LCL resonance method was used. Using IGBT 20MVA, 100kHz LCL resonant induction heating equipment during the development process, 100kHz IGBT module development process and understanding gained from the design of LCL resonant Zero Crossing through the PLL control over operation of the IGBT Soft Switching, and focused on minimizing losses. In addition, through the actual design and implementation of devices using the area to test drive the development of large inverter technology based on the test
Clinical Concepts Emerging from fMRI Functional Connectomics

Recent advances in connectomics have led to a synthesis of perspectives regarding the brain's functional organization that reconciles classical concepts of localized specialization with an appreciation for properties that emerge from interactions across distributed functional networks. This provides a more comprehensive framework for understanding neural mechanisms of normal cognition and disease. Although fMRI has not become a routine clinical tool, research has already had important influences on clinical concepts guiding diagnosis and patient management. Here we review illustrative examples. Studies demonstrating the network plasticity possible in adults and the global consequences of even focal brain injuries or disease both have had substantial impact on modern concepts of disease evolution and expression. Applications of functional connectomics in studies of clinical populations are challenging traditional disease classifications and helping to clarify biological relationships between clinical syndromes (and thus also ways of extending indications for, or "re-purposing," current treatments). Large datasets from prospective, longitudinal studies promise to enable the discovery and validation of functional connectomic biomarkers with the potential to identify people at high risk of disease before clinical onset, at a time when treatments may be most effective. Studies of pain and consciousness have catalyzed reconsiderations of approaches to clinical management, but also have stimulated debate about the clinical meaningfulness of differences in internal perceptual or cognitive states inferred from functional connectomics or other physiological correlates. By way of a closing summary, we offer a personal view of immediate challenges and potential opportunities for clinically relevant applications of fMRI-based functional connectomics.
Wavelet based real-time smoke detection in video

A method for smoke detection in video is proposed. It is assumed the camera monitoring the scene is stationary. Since the smoke is semi-transparent, edges of image frames start loosing their sharpness and this leads to a decrease in the high frequency content of the image. To determine the smoke in the field of view of the camera, the background of the scene is estimated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the current and the background images. Edges of the scene are especially important because they produce local extrema in the wavelet domain. A decrease in values of local extrema is also an indicator of smoke. In addition, scene becomes grayish when there is smoke and this leads to a decrease in chrominance values of pixels. Periodic behavior in smoke boundaries and convexity of smoke regions are also analyzed. All of these clues are combined to reach a final decision.
The Virtual Human Interface: A Photorealistic Digital Human

o mimic the quality of everyday human communication, future computer interfaces must combine the benefits of high visual fidelity with conversational intelligence and—above all—the ability to modulate the emotions of their users. From a computer graphics or visual perspective, achieving this goal requires creating synthetic digital humans that have photorealistic faces capable of expressing the finest shades of emotion. From a perceptual point of view, the same system must also read users' emotional reactions and adapt the behavior of the digital human accordingly. Our research focuses on implementing this concept to create a face-to-face system called the Virtual Human Interface (VHI). Photorealistic virtual humans— in contrast to nonrealistic virtual characters—make a more effective communication interface between computers and humans because they can stimulate declarative and procedural memory in the human brain. Declarative memory is our memory of information and events. Procedural memory, on the other hand, is the memory of how to do something, such as riding a bike. Most computer interfaces primarily access users' declarative memory; the person learns information about a particular topic. The VHI system, however, engages the user through entertaining emotional responses that help engrave knowledge in the user's mind and even modify the user's behavior through procedural memory. During the past several decades, researchers have conducted countless studies on agents and human animation. Early work mainly addressed low-polygon virtual humans that users could animate in real time. 1 Many of these animation systems initially addressed purposes other than the needs of human–computer interaction. However, the requirements of facial ani-mation—and especially speech synthesis—demand a different underlying architecture that can effectively model how real faces move and change. 2-4 As a result, research began to focus on creating autonomous agents that could exhibit rich personalities and interact in virtual worlds inhabited by other characters. To provide the illusion of a lifelike character, researchers have developed detailed emotional and personality models that can control the animation channels as a function of the virtual human's personality, mood, and emotions. 7-9 The real-time interaction with these virtual characters poses an extra set of technical challenges in terms of the speed, computational power, and visual quality required to make the user believe that he or she is interacting with a living creature. To achieve this goal, researchers eventually replaced precrafted animated actions with intelligent behavior modules that could control speech, locomotion, gaze, blinks, gestures (including various postures), and interaction with the …
Techniques for Assessing Polygonal Approximations of Curves

Given the enormous number of available methods for finding po lygonal approximations to curves techniques are required to assess different algorithms. Some of the standard approaches are shown to be unsuitable if the approximations contain varying numbers of lines. Ins tead, we suggest assessing an algorithm’s results relative to an optimal polygon, and describe a measure which combines the relative fidelity and efficiency of a curve segmentation. We use this measure to compare the appl ication of 23 algorithms to a curve first used by Teh and Chin [37]; their ISEs are assessed relative to the o ptimal ISE. In addition, using an example of pose estimation, it is shown how goal-directed evaluation c a be used to select an appropriate assessment criterion.
Speech Feature Extraction and Classification: A Comparative Review

This paper gives a brief survey on speech recognition and presents an overview for various techniques used at various stages of speech recognition systems. Researchers has been working in this research area for many years however accuracy for speech recognition still attention for variation of context, speaker&apos;s variability, environment conditions . The development of speech recognition system requires certain concepts to be included-Defining different classes of speech, techniques for speech feature extraction, speech classification modeling and measuring system performance . The main aim of this paper is to discuss and compare different approaches used for feature extraction and classification stages in speech recognition system.
Sensorless optimization of dead times in dc–dc converters with synchronous rectifiers

This paper introduces an approach to achieve optimum dead times in dc–dc converters with synchronous rectifiers without sensing any of the power-stage signals other than the output voltage. The dead times are adjusted adaptively to minimize the duty-cycle command, which results in maximization of the converter efficiency. The method is particularly well suited for digital controller implementation, requiring no additional analog components or modifications of standard gate-drive circuitry. Experimental results for a digitally controlled 5 V-to-1 V, 5-A synchronous buck converter demonstrate practical implementation of the sensorless dead-time optimization algorithm.
Dispersionless phase discontinuities for controlling light propagation.

Ultrathin metasurfaces consisting of a monolayer of subwavelength plasmonic resonators are capable of generating local abrupt phase changes and can be used for controlling the wavefront of electromagnetic waves. The phase change occurs for transmitted or reflected wave components whose polarization is orthogonal to that of a linearly polarized (LP) incident wave. As the phase shift relies on the resonant features of the plasmonic structures, it is in general wavelength-dependent. Here, we investigate the interaction of circularly polarized (CP) light at an interface composed of a dipole antenna array to create spatially varying abrupt phase discontinuities. The phase discontinuity is dispersionless, that is, it solely depends on the orientation of dipole antennas, but not their spectral response and the wavelength of incident light. By arranging the antennas in an array with a constant phase gradient along the interface, the phenomenon of broadband anomalous refraction is observed ranging from visible to near-infrared wavelengths. We further design and experimentally demonstrate an ultrathin phase gradient interface to generate a broadband optical vortex beam based on the above principle.
A Multi-scale Approach to Gesture Detection and Recognition

We propose a generalized approach to human gesture recognition based on multiple data modalities such as depth video, articulated pose and speech. In our system, each gesture is decomposed into large-scale body motion and local subtle movements such as hand articulation. The idea of learning at multiple scales is also applied to the temporal dimension, such that a gesture is considered as a set of characteristic motion impulses, or dynamic poses. Each modality is first processed separately in short spatio-temporal blocks, where discriminative data-specific features are either manually extracted or learned. Finally, we employ a Recurrent Neural Network for modeling large-scale temporal dependencies, data fusion and ultimately gesture classification. Our experiments on the 2013 Challenge on Multimodal Gesture Recognition dataset have demonstrated that using multiple modalities at several spatial and temporal scales leads to a significant increase in performance allowing the model to compensate for errors of individual classifiers as well as noise in the separate channels.
Surgical Correction of Posttraumatic Scapulothoracic Bursitis, Rhomboid Major Muscle Injury, Ipsilateral Glenohumeral Instability, and Headaches Resulting from Circus Acrobatic Maneuvers

We report the case of a 28-year-old transgender (male-to-female) patient that had a partial tear of the rhomboid major tendon, scapulothoracic bursitis, and glenohumeral instability on the same side. These conditions resulted from traumatic events during circus acrobatic maneuvers. Additional aspects of this case that make it unique include (1) the main traumatic event occurred during a flagpole exercise, where the patient's trunk was suspended horizontally while a vertical pole was grasped with both hands, (2) headaches were associated with the periscapular injury and they improved after scapulothoracic bursectomy and rhomboid tendon repair, (3) surgical correction was done during the same operation with an open anterior capsular-labral reconstruction, open scapulothoracic bursectomy without bone resection, and rhomboid tendon repair, (4) a postoperative complication of tearing of the serratus anterior and rhomboid muscle attachments with recurrent scapulothoracic pain occurred from patient noncompliance, and (5) the postoperative complication was surgically corrected and ultimately resulted in an excellent outcome at the one-year final follow-up.
Analyzing students’ performance using multi-criteria classification

Education is a key factor for achieving long-term economic progress. During the last decades, higher standards in education have become easier to attain due to the availability of knowledge and resources worldwide. With the emergence of new technology enhanced by using data mining it has become easier to dig into data and extract useful knowledge from data. In this research, we use data analytic techniques applied to real case studies to predict students’ performance using their past academic experience. We introduce a new hybrid classification technique which utilize decision tree and fuzzy multi-criteria classification. The technique is used to predict students’ performance based on several criteria such as age, school, address, family size, evaluation in previous grades, and activities. To check the accuracy of the model, our proposed method is compared with other well-known classifiers. This study on existing student data showed that this method is a promising classification tool.
Based Emotion Recognition : A Survey

Human Computer interaction is a very powerful and most current area of research because the human world is getting more digitize. This needs the digital systems to imitate the human behaviour correctly. Emotion is one aspect of human behaviour which plays an important role in human computer interaction, the computer interfaces need to recognize the emotion of the users in order to exhibit a truly intelligent behaviour. Human express the emotion in the form facial expression, speech, and writing text. Every day, massive amount of textual data is gathered into internet such as blogs, social media etc. This comprise a challenging style as it is formed with both plaint text and short messaging language. This paper is mainly focused on an overview of emotion detection from text and describes the emotion detection methods. These methods are divided into the following four main categories: keyword-based, Lexical Affinity method, learning based, and hybrid based approach. Limitations of these emotion recognition methods are presented in this paper and also, addresses the text normalization using different handling techniques for both plaint text and short messaging language.
EVE: explainable vector based embedding technique using Wikipedia

We present an unsupervised explainable vector embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a concept using human-readable labels, thereby it is readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed model, we consider its usefulness in three fundamental tasks: 1) intruder detection—to evaluate its ability to identify a non-coherent vector from a list of coherent vectors, 2) ability to cluster—to evaluate its tendency to group related vectors together while keeping unrelated vectors in separate clusters, and 3) sorting relevant items first—to evaluate its ability to rank vectors (items) relevant to the query in the top order of the result. For each task, we also propose a strategy to generate a task-specific human-interpretable explanation from the model. These demonstrate the overall effectiveness of the explainable embeddings generated by EVE. Finally, we compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across the three tasks, and report improvements over the state-of-the-art.
Investigation of the single case in neuropsychology: confidence limits on the abnormality of test scores and test score differences

Neuropsychologists often need to estimate the abnormality of an individual patient's test score, or test score discrepancies, when the normative or control sample against which the patient is compared is modest in size. Crawford and Howell [The Clinical Neuropsychologist 12 (1998) 482] and Crawford et al.  Although these limits can be used with data from normative or control samples of any size, they will be most useful when the sample sizes are modest. We also develop a method for obtaining point estimates and confidence limits on the abnormality of a discrepancy between a patient's mean score on k-tests and a test entering into that mean. Computer programs that implement the formulae for the confidence limits (and point estimates) are described and made available.
Investigating the Potential of the Flipped Classroom Model in K-12 ICT Teaching and Learning: An Action Research Study

The emerging Flipped Classroom approach has been widely used to enhance teaching practices in many subject domains and educational levels, reporting promising results for enhancing student learning experiences. However, despite this encouraging body of research, the subject domain of Information and Communication Technologies (ICT) teaching at K-12 education has not yet been explicitly researched. This is a considerable shortcoming given (a) the globally acknowledged need to effectively design and deliver ICT curricula to students at all levels of school education (primary, junior and senior secondary) towards cultivation of digital competences, and (b) the recurring research evidence that the Flipped Classroom approach can enhance students’ learning experiences in Science, Technology, Engineering and Math (STEM) K-12 education. Therefore, the contribution of this paper is the design and implementation of an action research for studying the effect of the Flipped Classroom approach in K-12 ICT teaching and learning. The action research employed a quasi-experimental design using experimental-control groups, from two classes (a total of 46 students) for an entire semester of the school year following the National (Greek) Curriculum in junior high school ICT studies. The results of this study provide evidence for potential advantages in students’ cognitive learning outcomes related to subject domain knowledge, the exploitation of teaching time during the classroom face-to-face sessions, the students’ level of motivation, as well as their level of engagement.
Visual Saliency Detection Based on Multiscale Deep CNN Features

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving the state-of-the-art performance on all public benchmarks, improving the F-measure by 6.12% and 10%, respectively, on the DUT-OMRON data set and our new data set (HKU-IS), and lowering the mean absolute error by 9% and 35.3%, respectively, on these two data sets.
The effects of fear and anger facial expressions on approach- and avoidance-related behaviors.

The facial expressions of fear and anger are universal social signals in humans. Both expressions have been frequently presumed to signify threat to perceivers and therefore are often used in studies investigating responses to threatening stimuli. Here the authors show that the anger expression facilitates avoidance-related behavior in participants, which supports the notion of this expression being a threatening stimulus. The fear expression, on the other hand, facilitates approach behaviors in perceivers. This contradicts the notion of the fear expression as predominantly threatening or aversive and suggests it may represent an affiliative stimulus. Although the fear expression may signal that a threat is present in the environment, the effect of the expression on conspecifics may be in part to elicit approach.
Towards Neural Network-based Reasoning

We propose Neural Reasoner , a framework for neural network-based reasoning over natural language sentences. Given a question, Neural Reasoner can infer over multiple supporting facts and find an answer to the question in specific forms. Neural Reasoner has 1) a specific interaction-pooling mechanism, allowing it to examine multiple facts, and 2) a deep architecture, allowing it to model the complicated logical relations in reasoning tasks. Assuming no particular structure exists in the question and facts, Neural Reasoner is able to accommodate different types of reasoning and different forms of language expressions. Despite the model complexity, Neural Reasoner can still be trained effectively in an end-to-end manner. Our empirical studies show that Neural Reasoner can outperform existing neural reasoning systems with remarkable margins on two difficult artificial tasks (Positional Reasoning and Path Finding) proposed in . For example, it improves the accuracy on Path Finding(10K) from 33.4% [6] to over 98%.
Positive affect facilitates creative problem solving.

Four experiments indicated that positive affect, induced by means of seeing a few minutes of a comedy film or by means of receiving a small bag of candy, improved performance on two tasks that are generally regarded as requiring creative ingenuity: Duncker's (1945) candle task and M. T. Mednick, S. A. Mednick, and E. V. Mednick's (1964) Remote Associates Test. One condition in which negative affect was induced and two in which subjects engaged in physical exercise (intended to represent affectless arousal) failed to produce comparable improvements in creative performance. The influence of positive affect on creativity was discussed in terms of a broader theory of the impact of positive affect on cognitive organization.