nils-herrmann/clean_scirepeval huggingface dataset:
A General Agnostic Active Learning Algorithm

We present a simple, agnostic active learning algorithm that works for any hypothesis class of bounded VC dimension, and any data distribution. Our algorithm extends a scheme of Cohn, Atlas, and Ladner  to the agnostic setting, by (1) reformulating it using a reduction to supervised learning and (2) showing how to apply generalization bounds even for the non-i.i.d. samples that result from selective sampling. We provide a general characterization of the label complexity of our algorithm. This quantity is never more than the usual PAC sample complexity of supervised learning, and is exponentially smaller for some hypothesis classes and distributions. We also demonstrate improvements experimentally.
The impact of a flipped classroom design on learning performance in higher education: Looking for the best "blend" of lectures and guiding questions with feedback

The present study examines the differential impact of studying in a Flipped Classroom (FC) setting, as compared to a Blended Learning (BL), a Traditional Learning (TL), and an E-Learning (EL) setting on learning performance, self-efficacy beliefs, intrinsic motivation, and perceived flexibility. Participants were second year undergraduate students (N = 90), enrolled in the “Invertebrates” course in Can Tho University (Vietnam). Participants were randomly assigned to one of the four experimental conditions (TL n = 22, BL n = 22, FC n = 23, EL n = 23). Two instructional elements (1) lectures and (2) guiding questions were presented through two different modes (online and face-to-face). In the blended conditions (BL and FC) the mode of these elements were altered. The results show that learning performance was superior in the FC setting as compared to other learning settings TL (Cohens’ d = 1.58), EL (Cohens’ d = 1.01) and BL (Cohens’ d = 0.71). Students in the BL setting had a higher learning performance as compared to the EL setting. In addition, we observed that studying in a FC setting had a positive effect on self-efficacy beliefs and intrinsic motivation, but not on perceived flexibility. These findings suggest that the FC setting could be a promising way of enhancing students’ learning performance.
An Error-Oriented Approach to Word Embedding Pre-Training

We propose a novel word embedding pretraining approach that exploits writing errors in learners’ scripts. We compare our method to previous models that tune the embeddings based on script scores and the discrimination between correct and corrupt word contexts in addition to the generic commonly-used embeddings pre-trained on large corpora. The comparison is achieved by using the aforementioned models to bootstrap a neural network that learns to predict a holistic score for scripts. Furthermore, we investigate augmenting our model with error corrections and monitor the impact on performance. Our results show that our error-oriented approach outperforms other comparable ones which is further demonstrated when training on more data. Additionally, extending the model with corrections provides further performance gains when data sparsity is an issue.
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework

The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in the KEEL format and shows some results of algorithms in these data sets; some guidelines for including new algorithms in KEEL, helping the researchers to make their methods easily accessible to other authors and to compare the results of many approaches already included within the KEEL software; and a module of statistical procedures developed in order to provide to the researcher a suitable tool to contrast the results obtained in any experimental study. A case of study is given to illustrate a complete case of application within this experimental analysis framework.
Augmenting web pages and search results to support credibility assessment

The presence (and, sometimes, prominence) of incorrect and misleading content on the Web can have serious consequences for people who increasingly rely on the internet as their information source for topics such as health, politics, and financial advice. In this paper, we identify and collect several page features (such as popularity among specialized user groups) that are currently difficult or impossible for end users to assess, yet provide valuable signals regarding credibility. We then present visualizations designed to augment search results and Web pages with the most promising of these features. Our lab evaluation finds that our augmented search results are particularly effective at increasing the accuracy of users'" credibility assessments, highlighting the potential of data aggregation and simple interventions to help people make more informed decisions as they search for information online.
Centroid-Based Document Classification: Analysis and Experimental Results

In recent years we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intrane ts. Automatic text categorization, which is the task of assigning text documents to pre-specified classes (topics o r themes) of documents, is an important task that can help both in organizing as well as in finding information on these h uge resources. Text categorization presents unique challenges due to the large number of attributes present in t he data set, large number of training samples, and attribute dependencies. In this paper we focus on a simple linear-time centroid-based document classification algorithm, that despite its simplicity and robust performance, has not been extensively studied and analyzed. Our extensive experiments show that this centroid-based classifier consi stently and substantially outperforms other algorithms su ch as Naive Bayesian, k-nearest-neighbors, and C4.5, on a wide range of datasets. O ur analysis shows that the similarity measure used by the centroid-based scheme allows it to class ify new document based on how closely its behavior matches the behavior of the documents belonging to differen t classes, as measured by the average similarity between the documents. This matching allows it to dynamically adjus t for classes with different densities. Furthermore, our analysis shows that the similarity measure of the centroidbased scheme accounts for dependencies between the terms in the different classes. We believe that this feature is the reason why it consistently outperforms other classifiers th at cannot take these dependencies into account.
Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior

An important challenge in neuroevolution is to evolve complex neural networks with multiple modes of behavior. Indirect encodings can potentially answer this challenge. Yet in practice, indirect encodings do not yield effective multimodal controllers. Thus, this paper introduces novel multimodal extensions to HyperNEAT, a popular indirect encoding. A previous multimodal HyperNEAT approach called situational policy geometry assumes that multiple brains benefit from being embedded within an explicit geometric space. However, experiments here illustrate that this assumption unnecessarily constrains evolution, resulting in lower performance. Specifically, this paper introduces HyperNEAT extensions for evolving many brains without assuming geometric relationships between them. The resulting MultiBrain HyperNEAT can exploit human-specified task divisions to decide when each brain controls the agent, or can automatically discover when brains should be used, by means of preference neurons. A further extension called module mutation allows evolution to discover the number of brains, enabling multimodal behavior with even less expert knowledge. Experiments in several multimodal domains highlight that multi-brain approaches are more effective than HyperNEAT without multimodal extensions, and show that brains without a geometric relation to each other outperform situational policy geometry. The conclusion is that Multi-Brain HyperNEAT provides several promising techniques for evolving complex multimodal behavior.
A scale space approach for automatically segmenting words from historical handwritten documents

Many libraries, museums, and other organizations contain large collections of handwritten historical documents, for example, the papers of early presidents like George Washington at the Library of Congress. The first step in providing recognition/retrieval tools is to automatically segment handwritten pages into words. State of the art segmentation techniques like the gap metrics algorithm have been mostly developed and tested on highly constrained documents like bank checks and postal addresses. There has been little work on full handwritten pages and this work has usually involved testing on clean artificial documents created for the purpose of research. Historical manuscript images, on the other hand, contain a great deal of noise and are much more challenging. Here, a novel scale space algorithm for automatically segmenting handwritten (historical) documents into words is described. First, the page is cleaned to remove margins. This is followed by a gray-level projection profile algorithm for finding lines in images. Each line image is then filtered with an anisotropic Laplacian at several scales. This procedure produces blobs which correspond to portions of characters at small scales and to words at larger scales. Crucial to the algorithm is scale selection that is, finding the optimum scale at which blobs correspond to words. This is done by finding the maximum over scale of the extent or area of the blobs. This scale maximum is estimated using three different approaches. The blobs recovered at the optimum scale are then bounded with a rectangular box to recover the words. A post processing filtering step is performed to eliminate boxes of unusual size which are unlikely to correspond to words. The approach is tested on a number of different data sets and it is shown that, on 100 sampled documents from the George Washington corpus of handwritten document images, a total error rate of 17 percent is observed. The technique outperforms a state-of-the-art gap metrics word-segmentation algorithm on this collection.
Resting state networks and consciousness Alterations of multiple resting state network connectivity in physiological , pharmacological , and pathological consciousness states

Athena Demertzi , Coma Science Group, Cyclotron Research Center, Allée du 6 août no 8, Sart Tilman B30, University of Liège, 4000 Liège, Belgium.  In order to better understand the functional contribution of resting state activity to conscious cognition, we aimed to review increases and decreases in functional magnetic resonance imaging (fMRI) functional connectivity under physiological (sleep), pharmacological (anesthesia), and pathological altered states of consciousness, such as brain death, coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state.The reviewed resting state networks were the DMN, left and right executive control, salience, sensorimotor, auditory, and visual networks. We highlight some methodological issues concerning resting state analyses in severely injured brains mainly in terms of hypothesis-driven seed-based correlation analysis and data-driven independent components analysis approaches. Finally, we attempt to contextualize our discussion within theoretical frameworks of conscious processes.We think that this “lesion” approach allows us to better determine the necessary conditions under which normal conscious cognition takes place. At the clinical level, we acknowledge the technical merits of the resting state paradigm. Indeed, fast and easy acquisitions are preferable to activation paradigms in clinical populations. Finally, we emphasize the need to validate the diagnostic and prognostic value of fMRI resting state measurements in non-communicating brain damaged patients.
Training a Fully Convolutional Neural Network to Route Integrated Circuits

We present a deep, fully convolutional neural network that learns to route a circuit layout ‘net’ with appropriate choice of metal tracks and wire class combinations. Inputs to the network are the encoded layouts containing spatial location of pins to be routed. After 15 fully convolutional stages followed by a score comparator, the network outputs 8 layout layers (corresponding to 4 route layers, 3 via layers and an identity-mapped pin layer) which are then decoded to obtain the routed layouts. We formulate this as a binary segmentation problem on a per-pixel per-layer basis, where the network is trained to correctly classify pixels in each layout layer to be ‘on’ or ‘off’. To demonstrate learnability of layout design rules, we train the network on a dataset of 50,000 train and 10,000 validation samples that we generate based on certain pre-defined layout constraints. Precision, recall and F1 score metrics are used to track the training progress. Our network achieves F1 ≈ 97% on the train set and F1 ≈ 92% on the validation set. We use PyTorch for implementing our model. Code is made publicly available1.
Academy for Eating Disorders position paper: the role of the family in eating disorders.

It is the position of the Academy for Eating Disorders (AED) that family factors can play a role in the genesis and maintenance of eating disorders; current knowledge refutes the idea that they are either the exclusive or even the primary mechanisms that underlie risk. Thus, the AED stands firmly against any etiologic model of eating disorders in which family influences are seen as the primary cause of anorexia nervosa or bulimia nervosa, and condemns generalizing statements that imply families are to blame for their child’s illness. The AED recommends that families be included in the treatment of younger patients, unless doing so is clearly ill advised on clinical grounds. The position articulated in this article is in line with the World Wide Charter for Action on Eating Disorders.
Multimodal Classification for Analysing Social Media

Classification of social media data is an important approach in understanding user behavior on the Web. Although information on social media can be of different modalities such as texts, images, audio or videos, traditional approaches in classification usually leverage only one prominent modality. Techniques that are able to leverage multiple modalities are often complex and susceptible to the absence of some modalities. In this paper, we present simple models that combine information from different modalities to classify social media content and are able to handle the above problems with existing techniques. Our models combine information from different modalities using a pooling layer and an auxiliary learning task is used to learn a common feature space. We demonstrate the performance of our models and their robustness to the missing of some modalities in the emotion classification domain. Our approaches, although being simple, can not only achieve significantly higher accuracies than traditional fusion approaches but also have comparable results when only one modality is available.
Design and Experimentation of WPT Charger for Electric City Car

Wireless power transfer systems (WPTSs) with inductive coupling are advantageously used to charge the battery pack of electric vehicles. They basically consist of coil coupling, power supply circuitry connected to the transmitting side of the coil coupling, and power-conditioning circuitry connected to the receiving side of the coil coupling. This paper presents the design and implementation of a wireless power transfer (WPT) battery charger for an electric city car. A short overview on the working principles of a series-series resonant WPTS is given before describing in detail the design procedure of the power circuitry needed for its operation, i.e., an alternating-current-direct-current converter cascaded by a high-frequency inverter in the transmitting section and a diode rectifier cascaded by a chopper in the receiving section. The coil coupling with spiral coils is designed with the help of a finite-element-method code. A prototype of the WPT battery charger is built up according to the design results, and experiments that validate the design procedure are carried out.
Direct generation of functional dopaminergic neurons from mouse and human fibroblasts

Transplantation of dopaminergic neurons can potentially improve the clinical outcome of Parkinson’s disease, a neurological disorder resulting from degeneration of mesencephalic dopaminergic neurons. In particular, transplantation of embryonic-stem-cell-derived dopaminergic neurons has been shown to be efficient in restoring motor symptoms in conditions of dopamine deficiency. However, the use of pluripotent-derived cells might lead to the development of tumours if not properly controlled. Here we identified a minimal set of three transcription factors—Mash1 (also known as Ascl1), Nurr1 (also known as Nr4a2) and Lmx1a—that are able to generate directly functional dopaminergic neurons from mouse and human fibroblasts without reverting to a progenitor cell stage. Induced dopaminergic (iDA) cells release dopamine and show spontaneous electrical activity organized in regular spikes consistent with the pacemaker activity featured by brain dopaminergic neurons. The three factors were able to elicit dopaminergic neuronal conversion in prenatal and adult fibroblasts from healthy donors and Parkinson’s disease patients. Direct generation of iDA cells from somatic cells might have significant implications for understanding critical processes for neuronal development, in vitro disease modelling and cell replacement therapies.
Self-optimizing AST interpreters

An abstract syntax tree (AST) interpreter is a simple and natural way to implement a programming language. However, it is also considered the slowest approach because of the high overhead of virtual method dispatch. Language implementers therefore define bytecodes to speed up interpretation, at the cost of introducing inflexible and hard to maintain bytecode formats. We present a novel approach to implementing AST interpreters in which the AST is modified during interpretation to incorporate type feedback. This tree rewriting is a general and powerful mechanism to optimize many constructs common in dynamic programming languages. Our system is implemented in Java and uses the static typing and primitive data types of Java elegantly to avoid the cost of boxed representations of primitive values in dynamic programming languages.
The REM sleep-memory consolidation hypothesis.

It has been hypothesized that REM (rapid eye movement) sleep has an important role in memory consolidation. The evidence for this hypothesis is reviewed and found to be weak and contradictory. Animal studies correlating changes in REM sleep parameters with learning have produced inconsistent results and are confounded by stress effects. Humans with pharmacological and brain lesion-induced suppression of REM sleep do not show memory deficits, and other human sleep-learning studies have not produced consistent results. The time spent in REM sleep is not correlated with learning ability across humans, nor is there a positive relation between REM sleep time or intensity and encephalization across species. Although sleep is clearly important for optimum acquisition and performance of learned tasks, a major role in memory consolidation is unproven.
Distributed and interactive brain mechanisms during emotion face perception: Evidence from functional neuroimaging

Brain imaging studies in humans have shown that face processing in several areas is modulated by the affective significance of faces, particularly with fearful expressions, but also with other social signals such gaze direction. Here we review haemodynamic and electrical neuroimaging results indicating that activity in the face-selective fusiform cortex may be enhanced by emotional (fearful) expressions, without explicit voluntary control, and presumably through direct feedback connections from the amygdala. fMRI studies show that these increased responses in fusiform cortex to fearful faces are abolished by amygdala damage in the ipsilateral hemisphere, despite preserved effects of voluntary attention on fusiform; whereas emotional increases can still arise despite deficits in attention or awareness following parietal damage, and appear relatively unaffected by pharmacological increases in cholinergic stimulation. Fear-related modulations of face processing driven by amygdala signals may implicate not only fusiform cortex, but also earlier visual areas in occipital cortex (e.g., V1) and other distant regions involved in social, cognitive, or somatic responses (e.g., superior temporal sulcus, cingulate, or parietal areas). In the temporal domain, evoked-potentials show a widespread time-course of emotional face perception, with some increases in the amplitude of responses recorded over both occipital and frontal regions for fearful relative to neutral faces (as well as in the amygdala and orbitofrontal cortex, when using intracranial recordings), but with different latencies post-stimulus onset. Early emotional responses may arise around 120ms, prior to a full visual categorization stage indexed by the face-selective N170 component, possibly reflecting rapid emotion processing based on crude visual cues in faces. Other electrical components arise at later latencies and involve more sustained activities, probably generated in associative or supramodal brain areas, and resulting in part from the modulatory signals received from amygdala. Altogether, these fMRI and ERP results demonstrate that emotion face perception is a complex process that cannot be related to a single neural event taking place in a single brain regions, but rather implicates an interactive network with distributed activity in time and space. Moreover, although traditional models in cognitive neuropsychology have often considered that facial expression and facial identity are processed along two separate pathways, evidence from fMRI and ERPs suggests instead that emotional processing can strongly affect brain systems responsible for face recognition and memory. The functional implications of these interactions remain to be fully explored, but might play an important role in the normal development of face processing skills and in some neuropsychiatric disorders.
Arousal-Biased Competition in Perception and Memory.

Our everyday surroundings besiege us with information. The battle is for a share of our limited attention and memory, with the brain selecting the winners and discarding the losers. Previous research shows that both bottom-up and top-down factors bias competition in favor of high priority stimuli. We propose that arousal during an event increases this bias both in perception and in long-term memory of the event. Arousal-biased competition theory provides specific predictions about when arousal will enhance memory for events and when it will impair it, which accounts for some puzzling contradictions in the emotional memory literature.
A Simplified Analytical Model Toward Big Data Analysis using Ridge Regression Method

Ridge Regression is a essential method in linear regression used to analyze multiple regression data which have multicollinearity. For solving highly related multicollinearity problems, Ridge Regression is a better modeling technique than ordinary least square method. The analytical data in modern technology is becoming extremely large in size and the term which describes this large volume of data is “Big Data”, and ordinary tools are insufficient to analyzes big data. In this paper, we are presenting an approach toward big data analysis through ridge regression method. Our simulation result represents a mapping model of Gaussian data from big data in sufficient scale. This model presents the new gateway for big data for statistical and mathematical analysis.