nils-herrmann/clean_scirepeval huggingface dataset:
Text detection and recognition using enhanced MSER detection and a novel OCR technique

Detection and recognition of text from any natural scene image is challenging but essential extensively for extracting information from the image. In this paper, we propose an accurate and effective algorithm for detecting enhanced Maximally Stable Extremal Regions (MSERs) as main character candidates and these character candidates are filtered by stroke width variation for removing regions where the stroke width exhibits too much variation. For the detection of text regions, firstly some preprocessing is applied to the natural image and then after detecting MSERs, an intersection of canny edge and MSER region is produced to locate regions that are even more likely to belong to text. Finally, the selected text region is taken as an input of a novel Optical Character Recognition (OCR) technique to make the text editable and usable. The evaluation results substantiates 77.47% of the f-measure on the ICDAR 2011 dataset which is better than the previous performance 76.22%.
Total Data Quality Management and Total Information Quality Management Applied to Costumer Relationship Management

Data quality (DQ) is an important issue for modern organizations, mainly for decision-making based on information, using solutions such as CRM, Business Analytics, and Big Data. In order to obtain quality data, it is necessary to implement methods, processes, and specific techniques that handle information as a product, with well established, controlled, and managed production processes. The literature provides several types of quality data management methodologies that treat structured data, and few treating semi- and non-structured data. Choosing the methodology to be adopted is one the major issues faced by organizations, when challenged to treat the data quality in a systematic manner. This paper makes a comparative analysis between TDQM -- Total Data Quality Management and TIQM -- Total Information Quality Management approaches, focusing on data quality problems in the context of a CRM -- Costumer Relationship Management application. Such analysis identifies the strengths and weaknesses of each methodology and suggests the most suitable for the CRM scenario.
Learning to Classify Documents with Only a Small Positive Training Set

Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training examples be missing, the number of positive examples available for learning may also be fairly limited due to the impracticality of hand-labeling a large number of training examples. Current PU learning techniques have focused mostly on identifying reliable negative instances from the unlabeled set U. In this paper, we address the oft-overlooked PU learning problem when the number of training examples in the positive set P is small. We propose a novel technique LPLP (Learning from Probabilistically Labeled Positive examples) and apply the approach to classify product pages from commercial websites. The experimental results demonstrate that our approach outperforms existing methods significantly, even in the challenging cases where the positive examples in P and the hidden positive examples in U were not drawn from the same distribution.
An empirical performance comparison of machine learning methods for spam e-mail categorization

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable antispam filters. Using a classifier based on machine learning techniques to automatically filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning algorithms applied to different parts of e-mail. Experimental results reveal that the header of e-mail provides very useful information for all the machine learning algorithms considered to detect spam e-mail.
Interpolation and approximation in L2(gamma)

Assume a standard Brownian motion W = (Wt )t∈[0,1], a Borel function f : R → R such that f (W1) ∈ L2, and the standard Gaussian measure on the real line. We characterize that f belongs to the Besov space B 2,q ( ) := (L2( ), D1,2( )) ,q , obtained via the real interpolation method, by the behavior of aX(f (X1); ) := ‖f (W1)−P Xf (W1)‖L2 , where =(ti )ni=0 is a deterministic time net and P X : L2 → L2 the orthogonal projection onto a subspace of ‘discrete’stochastic integrals x0+ ∑n i=1 vi−1(Xti −Xti−1) with X being the Brownian motion or the geometric Brownian motion. By using Hermite polynomial expansions the problem is reduced to a deterministic one. The approximation numbers aX(f (X1); ) can be used to describe the L2-error in discrete time simulations of the martingale generated by f (W1) and (in stochastic finance) to describe the minimal quadratic hedging error of certain discretely adjusted portfolios.
Activities on Facebook Reveal the Depressive State of Users

As online social media have become prominent, much effort has been spent on identifying users with depressive symptoms in order to aim at early diagnosis, treatment, and even prevention by using various online social media. In this paper, we focused on Facebook to discern any correlations between the platform's features and users' depressive symptoms. This work may be helpful in trying to reach and detect large numbers of depressed individuals more easily.



Our goal was to develop a Web application and identify depressive symptom-related features from users of Facebook, a popular social networking platform.



55 Facebook users (male=40, female=15, mean age 24.43, SD 3.90) were recruited through advertisement fliers distributed to students in a large university in Korea. Using EmotionDiary, the Facebook application we developed, we evaluated depressive symptoms using the Center for Epidemiological Studies-Depression (CES-D) scale. We also provided tips and facts about depression to participants and measured their responses using EmotionDiary. To identify the Facebook features related to depression, correlation analyses were performed between CES-D and participants' responses to tips and facts or Facebook social features. Last, we interviewed depressed participants (CES-D≥25) to assess their depressive symptoms by a psychiatrist.



Facebook activities had predictive power in distinguishing depressed and nondepressed individuals. Participants' response to tips and facts, which can be explained by the number of app tips viewed and app points, had a positive correlation (P=.04 for both cases), whereas the number of friends and location tags had a negative correlation with the CES-D scale (P=.08 and P=.045 respectively). Furthermore, in finding group differences in Facebook social activities, app tips viewed and app points resulted in significant differences (P=.01 and P=.03 respectively) between probably depressed and nondepressed individuals.



Our results using EmotionDiary demonstrated that the more depressed one is, the more one will read tips and facts about depression. We also confirmed depressed individuals had significantly fewer interactions with others (eg, decreased number of friends and location tagging). Our app, EmotionDiary, can successfully evaluate depressive symptoms as well as provide useful tips and facts to users. These results open the door for examining Facebook activities to identify depressed individuals. We aim to conduct the experiment in multiple cultures as well.
Transfer learning for biomedical named entity recognition with neural networks

The explosive increase of biomedical literature has made information extraction an increasingly important tool for biomedical research. A fundamental task is the recognition of biomedical named entities in text (BNER) such as genes/proteins, diseases and species. Recently, a domain-independent method based on deep learning and statistical word embeddings, called long short-term memory network-conditional random field (LSTM-CRF), has been shown to outperform state-of-the-art entity-specific BNER tools. However, this method is dependent on gold-standard corpora (GSCs) consisting of hand-labeled entities, which tend to be small but highly reliable. An alternative to GSCs are silver-standard corpora (SSCs), which are generated by harmonizing the annotations made by several automatic annotation systems. SSCs typically contain more noise than GSCs but have the advantage of containing many more training examples. Ideally, these corpora could be combined to achieve the benefits of both, which is an opportunity for transfer learning. In this work, we analyze to what extent transfer learning improves upon state-of-the-art results for BNER.



We demonstrate that transferring a deep neural network (DNN) trained on a large, noisy SSC to a smaller, but more reliable GSC significantly improves upon state-of-the-art results for BNER. Compared to a state-of-the-art baseline evaluated on 23 GSCs covering four different entity classes, transfer learning results in an average reduction in error of approximately 11%. We found transfer learning to be especially beneficial for target datasets with a small number of labels (approximately 6000 or less).


Availability and implementation
Source code for the LSTM-CRF is available at https://github.com/Franck-Dernoncourt/NeuroNER/ and links to the corpora are available at https://github.com/BaderLab/Transfer-Learning-BNER-Bioinformatics-2018/.


Supplementary information
Supplementary data are available at Bioinformatics online.
Artificial immune systems - a new computational intelligence paradigm

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Group Atmosphere, Shared Understanding, and Perceived Conflict in Virtual Teams: Findings from an Experiment

In this paper, we present a theoretical model on the relationships among group atmosphere, shared understanding, and perceived task conflict in virtual teams. We validate the theoretical model by analyzing data that was collected in a laboratory experiment on virtual teams. We find that cultural diversity of virtual team adversely affects group atmosphere and group atmosphere has a positive influence on the development of shared understanding in these teams. We also find that the development of shared understanding weakens perceived task conflict in virtual teams. However, we do not find a strong support for the moderating effect of avoidance conflict management style on the relationship between shared understanding and perceived task conflict.
X-band 200W AlGaN/GaN HEMT for high power application

A 200 Watts GaN high electron mobility transistor (HEMT) has been developed for X-band applications. The device consists of four dies of 0.35 um-gate GaN HEMT of 16 mm gate periphery together with input and output 2-stage impedance transformers assembled into a low thermal resistance package. The developed GaN HEMT provides 204W output power and 12dB small signal gain at 9.3 GHz with power added efficiency of 32% under pulse condition at a duty of 10% with a pulse width of 100 usec.
Design of 5.9 ghz dsrc-based vehicular safety communication

The automotive industry is moving aggressively in the direction of advanced active safety. Dedicated short-range communication (DSRC) is a key enabling technology for the next generation of communication-based safety applications. One aspect of vehicular safety communication is the routine broadcast of messages among all equipped vehicles. Therefore, channel congestion control and broadcast performance improvement are of particular concern and need to be addressed in the overall protocol design. Furthermore, the explicit multichannel nature of DSRC necessitates a concurrent multichannel operational scheme for safety and non-safety applications. This article provides an overview of DSRC based vehicular safety communications and proposes a coherent set of protocols to address these requirements
Cross-domain recommendations without overlapping data: myth or reality?

Cross-domain recommender systems adopt different techniques to transfer learning from source domain to target domain in order to alleviate the sparsity problem and improve accuracy of recommendations. Traditional techniques require the two domains to be linked by shared characteristics associated to either users or items. In collaborative filtering (CF) this happens when the two domains have overlapping users or item (at least partially). Recently, Li et al.  introduced codebook transfer (CBT), a cross-domain CF technique based on co-clustering, and presented experimental results showing that CBT is able to transfer knowledge between non-overlapping domains. In this paper, we disprove these results and show that CBT does not transfer knowledge when source and target domains do not overlap.
Approximate model for interactive-tendon driven mechanism of a multiple-DoFs myoelectric prosthetic hand

For practical use, a myoelectric prosthetic hand needs to (1) have a human-like structure, (2) be lightweight, (3) have multiple degrees of freedom (DoFs), and (4) have a high grip force. We have developed a myoelectric prosthetic hand with an interactive-tendon driven mechanism. This paper describes the control method by which the interactive-tendon driven mechanism produces fine and precise actions, as well as an approximate model for the control method. The approximate model was developed based on a geometry model and an equilibrium model for the joint torque. Experimental results show that the joint motions of the actual robotic hand are controlled with errors of between 9 and 15% using the approximate model.
Neural Network Implementation Using CUDA and OpenMP

Many algorithms for image processing and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation using GPU encounters two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job which needs much cooperation between CPU and GPU, which is usual in image processings and pattern recognitions contrary to the graphics area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results ineffectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text detection system using the proposed architecture, and the computational times showed about 15 times faster than implementation using CPU and about 4 times faster than implementation on only GPU without OpenMP.
Investigating Success of Open Source Software Projects: A Social Network Perspective

To date, numerous open source projects are hosted on many online repositories, reflecting the popularity of open source projects. While some of these projects are active and thriving, some of them are either languishing or showing no development activities at all. This phenomenon thus begs the important question of what are the influential factors that affect the success of open source projects. In a quest to deepen our understanding of the evolution of open source projects, this research aims to analyze the survival of open source projects by using the theoretical lens of social network analysis. Based on extensive analyses of empirical data collected from online open source repositories, we study the impact of the communication patterns of the development teams on the outcomes of these projects, while accounting for project-specific characteristics. Using panel data analysis, we find significant impacts of communication patterns on project outcomes over the long term.
Physics-Based Generative Adversarial Models for Image Restoration and Beyond

We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, image deraining, etc.). These problems are highly ill-posed, and the common assumptions for existing methods are usually based on heuristic image priors. In this paper, we find that these problems can be solved by generative models with adversarial learning. However, the basic formulation of generative adversarial networks (GANs) does not generate realistic images, and some structures of the estimated images are usually not preserved well. Motivated by an interesting observation that the estimated results should be consistent with the observed inputs under the physics models, we propose a physics model constrained learning algorithm so that it can guide the estimation of the specific task in the conventional GAN framework. The proposed algorithm is trained in an end-to-end fashion and can be applied to a variety of image restoration and related low-level vision problems. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art algorithms.
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
A review of optical Braille recognition

The process to take a Braille document image and convert its content into its equivalent natural language characters, is called Optical Braille Recognition (OBR). It involves two main consecutive steps: Braille cell recognition and Braille cell transcription. Braille cell recognition contains few steps including: Image acquisition, Image De-skewing, Image pre-processing, dot recognition, cell recognition and segmentation. Image transcription aims to convert the segmented Braille cell, into its equivalent natural language characters. In this survey we aim to study the earlier works done by other researchers on both Braille cell recognition and transcription.
Evidence-based strategies in weight-loss mobile apps.

Physicians have limited time for weight-loss counseling, and there is a lack of resources to which they can refer patients for assistance with weight loss. Weight-loss mobile applications (apps) have the potential to be a helpful tool, but the extent to which they include the behavioral strategies included in evidence-based interventions is unknown.



The primary aims of the study were to determine the degree to which commercial weight-loss mobile apps include the behavioral strategies included in evidence-based weight-loss interventions, and to identify features that enhance behavioral strategies via technology.



Thirty weight-loss mobile apps, available on iPhone and/or Android platforms, were coded for whether they included any of 20 behavioral strategies derived from an evidence-based weight-loss program (i.e., Diabetes Prevention Program). Data on available apps were collected in January 2012; data were analyzed in June 2012.



The apps included on average 18.83% (SD=13.24; range=0%-65%) of the 20 strategies. Seven of the strategies were not found in any app. The most common technology-enhanced features were barcode scanners (56.7%) and a social network (46.7%).



Weight-loss mobile apps typically included only a minority of the behavioral strategies found in evidence-based weight-loss interventions. Behavioral strategies that help improve motivation, reduce stress, and assist with problem solving were missing across apps. Inclusion of additional strategies could make apps more helpful to users who have motivational challenges.
Recent Progress in Synthesis and Functionalization of Multimodal Fluorescent-Magnetic Nanoparticles for Biological Applications

There is a great interest in the development of new nanomaterials for multimodal imaging applications in biology and medicine. Multimodal fluorescent-magnetic based nanomaterials deserve particular attention as they can be used as diagnostic and drug delivery tools, which could facilitate the diagnosis and treatment of cancer and many other diseases. This review focuses on the recent developments of magnetic-fluorescent nanocomposites and their biomedical applications. The recent advances in synthetic strategies and approaches for the preparation of fluorescent-magnetic nanocomposites are presented. The main biomedical uses of multimodal fluorescent-magnetic nanomaterials, including biological imaging, cancer therapy and drug delivery, are discussed, and prospects of this field are outlined.
Análisis de la Simplificación de Expresiones Numéricas en Español mediante un Estudio Empírico

In this paper we present the results of an empirical study carried out on a parallel corpus of original and manually simplified texts in Spanish and a subsequent survey, with the aim of targeting simplification operations concerning numerical expressions. For the purpose of the study, a “numerical expression” is understood as any phrase expressing quantity possibly modified with a numerical hedge, such as almost a quarter. Data is analyzed both in context and in isolation, and attention is paid to the difference the target reader makes to simplification. Our future work aims at computational implementation of the transformation rules extracted so far.
Fast and Accurate Calibration of a Kinect Sensor

The article describes a new algorithm for calibrating a Kinect sensor that achieves high accuracy using only 6 to 10 image-disparity pairs of a planar checkerboard pattern. The method estimates the projection parameters for both color and depth cameras, the relative pose between them, and the function that converts kinect disparity units (kdu) into metric depth. We build on the recent work of Herrera et. al  that uses a large number of input frames and multiple iterative minimization steps for obtaining very accurate calibration results. We propose several modifications to this estimation pipeline that dramatically improve stability, usability, and runtime. The modifications consist in: (i) initializing the relative pose using a new minimal, optimal solution for registering 3D planes across different reference frames, (ii) including a metric constraint during the iterative refinement to avoid a drift in the disparity to depth conversion, and (iii) estimating the parameters of the depth distortion model in an open-loop post-processing step. Comparative experiments show that our pipeline can achieve a calibration accuracy similar to  while using less than 1/6 of the input frames and running in 1/30 of the time.