nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of pencil-grip patterns in first graders with good and poor writing skills.

The purpose of this study was to compare nondysfunctional children with and without handwriting difficulties for an examination of grip and hand preference. Additionally, in the group of children with handwriting difficulties, the children with decreased proprioceptive-kinesthetic finger awareness were compared with the children without such a decrease. The results suggest that children with handwriting difficulties may demonstrate a lower grip score than children without handwriting problems. In addition, among children with poor handwriting, those with decreased proprioceptive-kinesthetic finger awareness may demonstrate a lower grip score than those with good proprioceptive-kinesthetic awareness. Children with poor hand-writing also show less hand preference than those with good handwriting.
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music

The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-term structure. To address this issue, we propose the use of a hierarchical decoder, which first outputs embeddings for subsequences of the input and then uses these embeddings to generate each subsequence independently. This structure encourages the model to utilize its latent code, thereby avoiding the “posterior collapse” problem which remains an issue for recurrent VAEs. We apply this architecture to modeling sequences of musical notes and find that it exhibits dramatically better sampling, interpolation, and reconstruction performance than a “flat” baseline model. An implementation of our “MusicVAE” is available
Gender and corporate finance : Are male executives overconfident relative to female executives ?

We examine corporate financial and investment decisions made by female executives compared with male executives. Male executives undertake more acquisitions and issue debt more often than female executives. Further, acquisitions made by firms with male executives have announcement returns approximately 2% lower than those made by female executive firms, and debt issues also have lower announcement returns for firms with male executives. Female executives place wider bounds on earnings estimates and are more likely to exercise stock options early. This evidence suggests men exhibit relative overconfidence in significant corporate decision making compared with women. & 2013 Elsevier B.V. All rights reserved.
Autoencoder in Time-Series Analysis for Unsupervised Tissues Characterisation in a Large Unlabelled Medical Image Dataset

The topic of deep-learning has recently received considerable attention in the machine learning research community, having great potential to liberate computer scientists from hand-engineering training datasets, because the method can learn the desired features automatically. This is particularly beneficial in medical research applications of machine learning, where getting good hand labelling of data is especially expensive. We propose application of a single-layer sparse-auto encoder to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for fully automatic classification of tissue types in a large unlabelled dataset with minimal human interference -- in a manner similar to data-mining. DCE-MRI analysis, looking at the change of the MR contrast-agent concentration over successively acquired images, is time-series analysis. We analyse the change of brightness (which is related to the contrast-agent concentration) of the DCE-MRI images over time to classify different tissue types in the images. Therefore our system is an application of an auto encoder to time-series analysis while the demonstrated result and further possible successive application areas are in computer vision. We discuss the important factors affecting performance of the system in applying the auto encoder to the time-series analysis of DCE-MRI medical image data.
Traffic accident segmentation by means of latent class clustering.

Traffic accident data are often heterogeneous, which can cause certain relationships to remain hidden. Therefore, traffic accident analysis is often performed on a small subset of traffic accidents or several models are built for various traffic accident types. In this paper, we examine the effectiveness of a clustering technique, i.e. latent class clustering, for identifying homogenous traffic accident types. Firstly, a heterogeneous traffic accident data set is segmented into seven clusters, which are translated into seven traffic accident types. Secondly, injury analysis is performed for each cluster. The results of these cluster-based analyses are compared with the results of a full-data analysis. This shows that applying latent class clustering as a preliminary analysis can reveal hidden relationships and can help the domain expert or traffic safety researcher to segment traffic accidents.
High-variance multivariate time series forecasting using machine learning

There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is not always clear which model is appropriate for selection, as different models are suited for different types of data, and domain-specific transformations and considerations are usually required. This research aims to examine the issue by modeling four types of machineand deep learning algorithms support vector machine, random forest, feed-forward neural network, and a LSTM neural network on a high-variance, multivariate time series to forecast trend changes one time step in the future, accounting for lag. The models were trained on clinical trial data of patients in an alcohol addiction treatment plan provided by a Uppsala-based company. The results showed moderate performance differences, with a concern that the models were performing a random walk or naive forecast. Further analysis was able to prove that at least one model, the feed-forward neural network, was not undergoing this and was able to make meaningful forecasts one time step into the future. In addition, the research also examined the effect of optimization processes by comparing a grid search, a random search, and a Bayesian optimization process. In all cases, the grid search found the lowest minima, though its slow runtimes were consistently beaten by Bayesian optimization, which contained only slightly lower performances than the grid search.  Data science, alcohl abuse, time series, forecasting, machine learning, deep learning, neural networks, regression
From Strategy to Business Models and onto Tactics

Strategy scholars have used the notion of the Business Model to refer to the ‘logic of the firm’ e how it operates and creates value for its stakeholders. On the surface, this notion appears to be similar to that of strategy. We present a conceptual framework to separate and relate the concepts of strategy and business model: a business model, we argue, is a reflection of the firm’s realized strategy. We find that in simple competitive situations there is a one-to-one mapping between strategy and business model, which makes it difficult to separate the two notions. We show that the concepts of strategy and business model differ when there are important contingencies on which a well-designed strategy must be based. Our framework also delivers a clear distinction between strategy and tactics, made possible because strategy and business model are different constructs.
Efficient Dense Point Cloud Object Reconstruction Using Deformation Vector Fields

Some existing CNN-based methods for single-view 3D object reconstruction represent a 3D object as either a 3D voxel occupancy grid or multiple depth-mask image pairs. However, these representations are inefficient since empty voxels or background pixels are wasteful. We propose a novel approach that addresses this limitation by replacing masks with “deformation-fields”. Given a single image at an arbitrary viewpoint, a CNN predicts multiple surfaces, each in a canonical location relative to the object. Each surface comprises a depth-map and corresponding deformation-field that ensures every pixel-depth pair in the depth-map lies on the object surface. These surfaces are then fused to form the full 3D shape. During training we use a combination of perview loss and multi-view losses. The novel multi-view loss encourages the 3D points back-projected from a particular view to be consistent across views. Extensive experiments demonstrate the efficiency and efficacy of our method on single-view 3D object reconstruction.
Neonatal brain MRI segmentation: A review

This review paper focuses on the neonatal brain segmentation algorithms in the literature. It provides an overview of clinical magnetic resonance imaging (MRI) of the newborn brain and the challenges in automated tissue classification of neonatal brain MRI. It presents a complete survey of the existing segmentation methods and their salient features. The different approaches are categorized into intracranial and brain tissue segmentation algorithms based on their level of tissue classification. Further, the brain tissue segmentation techniques are grouped based on their atlas usage into atlas-based, augmented atlas-based and atlas-free methods. In addition, the research gaps and lacunae in literature are also identified.
The Pemberton Happiness Index

The Pemberton Happiness Index (PHI) is a recently developed integrative measure of well-being that includes components of hedonic, eudaimonic, social, and experienced well-being. The PHI has been validated in several languages, but not in Portuguese. Our aim was to cross-culturally adapt the Universal Portuguese version of the PHI and to assess its psychometric properties in a sample of the Brazilian population using online surveys.An expert committee evaluated 2 versions of the PHI previously translated into Portuguese by the original authors using a standardized form for assessment of semantic/idiomatic, cultural, and conceptual equivalence. A pretesting was conducted employing cognitive debriefing methods. In sequence, the expert committee evaluated all the documents and reached a final Universal Portuguese PHI version. For the evaluation of the psychometric properties, the data were collected using online surveys in a cross-sectional study. The study population included healthcare professionals and users of the social network site Facebook from several Brazilian geographic areas. In addition to the PHI, participants completed the Satisfaction with Life Scale (SWLS), Diener and Emmons' Positive and Negative Experience Scale (PNES), Psychological Well-being Scale (PWS), and the Subjective Happiness Scale (SHS). Internal consistency, convergent validity, known-group validity, and test-retest reliability were evaluated. Satisfaction with the previous day was correlated with the 10 items assessing experienced well-being using the Cramer V test. Additionally, a cut-off value of PHI to identify a "happy individual" was defined using receiver-operating characteristic (ROC) curve methodology.Data from 1035 Brazilian participants were analyzed (health professionals = 180; Facebook users = 855). Regarding reliability results, the internal consistency (Cronbach alpha = 0.890 and 0.914) and test-retest (intraclass correlation coefficient = 0.814) were both considered adequate. Most of the validity hypotheses formulated a priori (convergent and know-group) was further confirmed. The cut-off value of higher than 7 in remembered PHI was identified (AUC = 0.780, sensitivity = 69.2%, specificity = 78.2%) as the best one to identify a happy individual.We concluded that the Universal Portuguese version of the PHI is valid and reliable for use in the Brazilian population using online surveys.
The mucosal barrier at implant abutments of different materials.

The aim of the present study was to analyze the soft tissue barrier formed to implant abutments made of different materials.



Six Labrador dogs, about 1 year old, were used. All mandibular premolars and the first, second and third maxillary premolars were extracted. Three months later four implants (OsseoSpeed, 4.5 x 9 mm, Astra Tech Dental, Mölndal, Sweden) were placed in the edentulous premolar region on one side of the mandible and healing abutments were connected. One month later, the healing abutments were disconnected and four new abutments were placed in a randomized order. Two of the abutments were made of titanium (Ti), while the remaining abutments were made of ZrO(2) or AuPt-alloy. A 5-months plaque control program was initiated. Three months after implant surgery, the implant installation procedure and the subsequent abutment shift were repeated in the contra-lateral mandibular region. Two months later, the dogs were euthanized and biopsies containing the implant and the surrounding soft and hard peri-implant tissues were collected and prepared for histological analysis.



It was demonstrated that the soft tissue dimensions at Ti- and ZrO(2) abutments remained stable between 2 and 5 months of healing. At Au/Pt-alloy abutment sites, however, an apical shift of the barrier epithelium and the marginal bone occurred between 2 and 5 months of healing. In addition, the 80-mum-wide connective tissue zone lateral to the Au/Pt-alloy abutments contained lower amounts of collagen and fibroblasts and larger fractions of leukocytes than the corresponding connective tissue zone of abutments made of Ti and ZrO(2).



It is suggested that the soft tissue healing to abutments made of titanium and ZrO(2) is different to that at abutments made of AuPt-alloy.
Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network

Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice.
A New Parallel Message-distribution Technique for Cost-based Steganography

This paper presents two novel approaches to increase performance bounds of image steganography under the criteria of minimizing distortion. First, in order to efficiently use the images’ capacities, we propose using parallel images in the embedding stage. The result is then used to prove sub-optimality of the message distribution technique used by all cost based algorithms including HUGO, S-UNIWARD, and HILL. Second, a new distribution approach is presented to further improve the security of these algorithms. Experiments show that this distribution method avoids embedding in smooth regions and thus achieves a better performance, measured by state-of-the-art steganalysis, when compared with the current used distribution.
Human Action Laws in Electronic Virtual Worlds: An Empirical Study of Path Steering Performance in VR

This paper is concerned with simple human performance laws of action for three classes of taskspointing, crossing, and steering, as well as their applications in Virtual Reality research. In comparison to Fitts' law of pointing, the law of steering the quantitative relationship between human temporal performance and the movement path's spatial characteristicshas been notably under investigated. After a review of research on the law of steering in different domains and time periods, we examine the applicability of the law of steering in a VR locomotion task. Participants drove a virtual vehicle in a virtual environment on paths whose shape and width were systematically manipulated. Results showed that the law of steering indeed applies to locomotion in Virtual Environments. Participants' mean trial completion times linearly correlated (r2 between 0.985 and 0.999) with an index of difficulty quantified as path length to width ratio for the straight and circular paths used in this experiment. On average both the mean and the maximum speeds of the participants were linearly proportional to path width. Such human performance regularity provides a quantitative tool for 3D human-machine interface design and evaluation. We also propose to use the law-of-steering model in Virtual Reality manipulation tasks such as the ring and wire task in the future.
Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings

Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant attention. In this study, we focused on the Named Entity Recognition (NER) problem on informal text types for Turkish. We utilized a semi-supervised learning approach based on neural networks. We applied a fast unsupervised method for learning continuous representations of words in vector space. We made use of these obtained word embeddings, together with language independent features that are engineered to work better on informal text types, for generating a Turkish NER system on microblog texts. We evaluated our Turkish NER system on Twitter messages and achieved better F-score performances than the published results of previously proposed NER systems on Turkish tweets. Since we did not employ any language dependent features, we believe that our method can be easily adapted to microblog texts in other morphologically rich languages.
Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings

For the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem. Neural models provide a solution with distributed representations, which could encode the latent semantic information, and are suitable for recognizing semantic relations between argument pairs. However, conventional vector representations usually adopt embeddings at the word level and cannot well handle the rare word problem without carefully considering morphological information at character level. Moreover, embeddings are assigned to individual words independently, which lacks of the crucial contextual information. This paper proposes a neural model utilizing context-aware character-enhanced embeddings to alleviate the drawbacks of the current word level representation. Our experiments show that the enhanced embeddings work well and the proposed model obtains state-of-the-art results.
Can online reviews reveal a product's true quality?: empirical findings and analytical modeling of Online word-of-mouth communication

As a digital version of word-of-mouth, online review has become a major information source for consumers and has very important implications for a wide range of management activities. While some researchers focus their studies on the impact of online product review on sales, an important assumption remains unexamined, that is, can online product review reveal the true quality of the product? To test the validity of this key assumption, this paper first empirically tests the underlying distribution of online reviews with data from Amazon. The results show that 53% of the products have a bimodal and non-normal distribution. For these products, the average score does not necessarily reveal the product's true quality and may provide misleading recommendations. Then this paper derives an analytical model to explain when the mean can serve as a valid representation of a product's true quality, and discusses its implication on marketing practices.
TIMIT Acoustic-Phonetic Continuous Speech Corpus

In the information age, computer applications have become part of modern life and this has in turn encouraged the expectations of friendly interaction with them. Speech, as “the” communication mode, has seen the successful development of quite a number of applications using automatic speech recognition (ASR), including command and control, dictation, dialog systems for people with impairments, translation, etc. But the actual challenge goes beyond the use of speech in control applications or to access information. The goal is to use speech as an information source, competing, for example, with text online. Since the technology supporting computer applications is highly dependent on the performance of the ASR system, research into ASR is still an active topic, as is shown by the range of research directions suggested in (Baker et al., 2009a, 2009b). Automatic speech recognition – the recognition of the information embedded in a speech signal and its transcription in terms of a set of characters, (Junqua & Haton, 1996) – has been object of intensive research for more than four decades, achieving notable results. It is only to be expected that speech recognition advances make spoken language as convenient and accessible as online text when the recognizers reach error rates near zero. But while digit recognition has already reached a rate of 99.6%, (Li, 2008), the same cannot be said of phone recognition, for which the best rates are still under 80% 1,(Mohamed et al., 2011; Siniscalchi et al., 2007). Speech recognition based on phones is very attractive since it is inherently free from vocabulary limitations. Large Vocabulary ASR (LVASR) systems’ performance depends on the quality of the phone recognizer. That is why research teams continue developing phone recognizers, in order to enhance their performance as much as possible. Phone recognition is, in fact, a recurrent problem for the speech recognition community. Phone recognition can be found in a wide range of applications. In addition to typical LVASR systems like (Morris & Fosler-Lussier, 2008; Scanlon et al., 2007; Schwarz, 2008), it can be found in applications related to keyword detection, (Schwarz, 2008), language recognition, (Matejka, 2009; Schwarz, 2008), speaker identification, (Furui, 2005) and applications for music identification and translation, (Fujihara & Goto, 2008; Gruhne et al., 2007). The challenge of building robust acoustic models involves applying good training algorithms to a suitable set of data. The database defines the units that can be trained and