Reviewerly/exHarmony huggingface dataset:
Automatic rat brain segmentation from MRI using statistical shape models and random forest

In MRI neuroimaging, the shimming procedure is used before image acquisition to correct for inhomogeneity of the static magnetic field within the brain. To correctly adjust the field, the brain’s location and edges must first be identified from quickly-acquired low resolution data. This process is currently carried out manually by an operator, which can be time-consuming and not always accurate. In this work, we implement a quick and automatic technique for brain segmentation to be potentially used during the shimming. Our method is based on two main steps. First, a random forest classifier is used to get a preliminary segmentation from an input MRI image. Subsequently, a statistical shape model of the brain, which was previously generated from ground-truth segmentations, is fitted to the output of the classifier to obtain a model-based segmentation mask. In this way, a-priori knowledge on the brain’s shape is included in the segmentation pipeline. The proposed methodology was tested on low resolution images of rat brains and further validated on rabbit brain images of higher resolution. Our results suggest that the present method is promising for the desired purpose in terms of time efficiency, segmentation accuracy and repeatability. Moreover, the use of shape modeling was shown to be particularly useful when handling low-resolution data, which could lead to erroneous classifications when using only machine learning-based methods.
A System for Natural Language Interaction With the Heterogeneous Information Network

In this chapter, the authors present their system, which can use natural language query to interact with heterogeneous information networks (HIN). This chapter proposes a solution combining the GraphFrames, recurrent neural network (RNN) long short-term memory (LSTM), and dependency relation of question for generating, training, understanding the question-answer pairs and selecting the best match answer for this question. The RNN-LSTM is used to generate the answer from the facts of knowledge graph. The authors need to build a training data set of question-answer pairs from a very large knowledge graph by using GraphFrames for big graph processing. To improve the performance of GraphFrames, they repartition the GraphFrames. For complicated query, they use the Stanford dependency parser to analyze the question and build the motif pattern for searching GraphFrames. They also develop a chatbot that can interact with the knowledge graph by using the natural language query. They conduct their system with question-answer generated from DBLP to prove the performance of our proposed system.
Ontology-Based Semantic Search for Open Government Data

Open data are increasingly available in amount, but often with unprecise or incomplete description. It is time consuming and difficult to discover relevant datasets. Current open data catalogues provide mostly keyword-based search without the ability to understand the user's intent and the contextual meaning of the datasets. Ontology-based semantic search has been well explored in semantic web as an attempt to improve the quality of search for relevant documents and web pages. This paper applies semantic and machine learning technologies to open data. It presents an approach for search of open government datasets, a relatively underexplored domain, where the semantics of data relies on metadata that describes the data. The idea is to link the published datasets with concepts from a well-defined ontology and allow searching based on hybrid indexing. A simplified ontology for the transport domain is constructed to demonstrate and test the idea. A prototype search engine has been implemented which supports both manual and automatic linking to concepts in the ontology and exploits hybrid indexing based on these linking methods. Natural language processing (NLP) techniques are applied to dataset linking and indexing and enable the independency of the natural language used for describing the datasets. The manual linking of datasets to ontology concepts is intended for domain experts and data publishers, while the automatic linking is based on the provided dataset descriptions. The automatic linking reduces the overhead of manual concepts linking and the dependency on domain experts. Preliminary results have indicated that semantic search based on ontologies is a promising approach to increase search quality and efficiency for open data search. The success of the automatic mechanism does however depend on the quality and comprehensiveness of the dataset descriptions.
Clarifying False Memories in Voice-based Search

Queries containing false memories (i.e., attributes the user misremembered about a searched item) represent a challenge for search systems. A query with a false memory will match inadequate results or even no result, and an automatic query correction is necessary to satisfy the user expectations. For voice-based search interfaces, which aim at a natural, dialog-based search experience, a sensible answer to this kind of unintentionally ill-posed queries is even more crucial. However, the usual solutions in display-based interfaces for queries without matches (e.g., suggesting to drop some query terms) cannot really be transferred to the voice-based setting. Based on the assumption that false memory queries could be identified---a research problem in its own right---, we present the first user study on how voice-based search systems may communicate the respective corrections to a user. Our study compares the user satisfaction in a voice-based search setting for three kinds of false memory clarifications and a baseline case where the system just answers "I don't know.'' Our findings suggest that (1)~users are more satisfied when they receive a clarification that and how the system corrected a false memory, (2)~users even prefer failed correction attempts over no such attempt, and (3)~the tone of the clarification has to be considered for the best possible user satisfaction as well.
Home Automation Platform Using Interaction-Based Sensing

In Japan, care giving for elderly has received the support of families, relatives, and traditional communities. Recently, care giving service has started to be socialized by some nursing care insurance. However, quality is insufficient due to cost problems and understaffed issues. Therefore, understanding the supportive living environment utilized by cyber infrastructure which lightens the burden of family care and social care is important. This paper proposes a home automation platform using interaction based sensing technology for supportive living environment. In particular, an experimental home was developed with home automation platform integrated with a prototype system based on interaction based sensing.
Interactive Fabrication of CSG Models with Assisted Carving

We propose a method that helps an unskilled user to carve a physical replica of a 3D CAD model while only using manual cutting tools. The method starts by analyzing the input CAD model and generates a set of carving instructions. Then using a projector, we project the instructions sequentially one at a time to a block of material to guide the user in performing each of them. After each cutting step, we use the projector-camera setup to 3D scan the object after cutting. And automatically align the scanned point cloud to the CAD model, to prepare the position for the next instruction. We demonstrate a complete system to support this operation and show several examples manually carved while using the system.
Detecting outliers for complex nonlinear systems with dynamic ensemble learning

Process data has been used in most industrial systems to facilitate process control and process monitoring. Even if outliers have been proved to have negative influence on those data-driven techniques, dedicated detection methods are still rare or at a junior phase. Furthermore, due to the fact that most industrial systems are complex and nonlinear, many outlier detection methods developed in the field of data mining are inefficient or cannot be applied directly. In this paper thereby, we propose an outlier detection method dedicated to complex and nonlinear industrial systems. This method is on the basis of dynamic ensemble learning. It is observed that ensemble learning has made great achievement recently, and dynamic ensemble learning usually outperforms other ensemble techniques. Experimental results prove that our dynamic ensemble outlier detection method has better performance for complex nonlinear industrial systems.
Proof of Phone: A Low-cost Blockchain Platform

Blockchain which enables decentralization of data based on various consensus mechanisms is considered the leading technology of the next generation. Numerous applications are being proposed to benefit from its immunity against modification and manipulation. However, not many appears as a successful business up to now, except for crypto currencies. High operation cost which is essential for the conventional consensus, is acting as an obstacle to the wide adoption of blockchain applications. In this paper, we introduce a novel blockchain platform based on PoP (Proof of Phone), which lowers the operating cost by imposing high entry cost. We conduct theoretical analysis to show that our proposed platform reduces total cost by up to 98.2% compared to that of conventional blockchain. Our new platform will turn various blockchain applications from theory to practice.
Saliency detection by using blended membership maps of fast fuzzy-C-mean clustering

Extraction of salient object from blurred and similar background color image is very difficult task. Many image segmentation methods have been proposed to overcome this problem but their performance is unsatisfactory when the target object and background has similar color appearance. In this paper, we have proposed a technique to overcome this problem with fast fuzzy-c-mean membership maps. These maps are blended by using Porter-Duff compositing method. The composite process is accomplished under different blending modes where foreground element of one map blend on the dropback element of the second map. These blended maps contain some outliers, which are removed by applying morphological technique. Finally an image mask, which is the composite form of frequency prior, color prior and location prior of an image is used to extract the final salient map from the given blended maps. Experiments on four well-known datasets (MSRA, MSRA-1000, THUR15000 and SED) are conducted; The results indicate the efficiency of proposed method. Our approach produces more accurate image segmentation, where the background and foreground maps have similarity in color appearance.
How to Make the Most out of Very Little

Abstract I review the problem of referential ambiguity that arises when children learn the meanings of words, along with a number of models that have been proposed to solve it. I then provide a formal analysis of why a resource‐limited model that retains very few meaning hypotheses may be more effective than “big data” models that keep track of all word‐meaning associations.
Classification of Extremist Text on the Web using Sentiment Analysis Approach

The high volume of extremist materials online makes manual classification impractical. However, there is a need for automated classification techniques. One set of extremist web pages obtained by the TENE Web-crawler initially subjected to manual classification. A sentiment-based classification model was then developed to automate the classification of such extremist Websites. The classification model measures how well the pages could be automatically matched against their appropriate classes. The method also identifies particular data items that differ in manual classification from their automated classification. The results from our method showed that overall web pages were correctly matched against the manual classification with a 93% success rate. In addition, a feature selection algorithm was able to reduce the original 26-feature set by one feature to attain a better overall performance of 94% in classifying the Web data.
Ruta: Implementations of neural autoencoders in R

Autoencoders are neural networks which perform feature learning on data. Many variants can be found in the literature, but their implementations are scarce, in separate software pieces and utilizing different languages and frameworks. The ruta package implements a unified foundation for the construction and training of autoencoders on top of Keras and Tensorflow, and allows for easy access to the main functionalities as well as full customization of their diverse aspects.
A local geometrical metric-based model for polyp classification

Inspired by the co-occurrence matrix (CM) model for texture description, we introduce another important local metric, gradient direction, into polyp descriptor construction. Gradient direction and its two independent components, azimuth angle and polar angle, are used instead of the gray-level intensity to calculate the CMs of the Haralick model. Thus we obtain three new models: azimuth CM model (ACM), polar CM model (PCM) and gradient direction CM model (GDCM). These three new models share similar parameters with the traditional gray-level CM (GLCM) model which has 13 directions for volumetric data and 4 directions for image slices. To train and test the data, random forest method is employed. These three models are affected by angle quantization and, therefore, more than 10 experimental schemes are designed to get reasonable parameters for angle discretization. We compared our three models (ACM, PCM, GDCM) with the traditional GLCM model, a gradient magnitude CM (GMCM) model, and local anisotropic gradient orientations CM model (CoLIAge). Experimental results showed that our three models exceed the other three methods (GLCM, GMCM, CoLIAge) by their receiver operating characteristic (ROC) curves, AUC (area under the ROC curve) scores and accuracy values. Based on their AUC and accuracy, ACM should be the first choice for polyp classification.
Deep learning for identifying breast cancer malignancy and false recalls: a robustness study on training strategy

Identification of malignancy and false recalls (women who are recalled in screening for additional workup, but later proven benign) in screening mammography has significant clinical value for accurate diagnosis of breast cancer. Deep learning methods have recently shown success in the area of medical imaging classification. However, there are a multitude of different training strategies that can significantly impact the overall model performance for a specific classification task. In this study, we aimed to investigate the impact of training strategy on classification of digital mammograms by performing a robustness analysis of deep learning models to distinguish malignancy and false-recall from normal (benign) findings. Specifically, we employed several pre-training strategies including transfer learning with medical and non-medical datasets, layer freezing, and varied network structure on both binary and three-class classification tasks of digital mammography images. We found that, overall, deep learning models appear to be robust to some modifications of network structure and pre-training strategy that we tested for mammogram-specific classification tasks. However, for specific classification tasks, some training strategies offer performance gains. The most notable performance gains in our experiments involved residual network models.
Understanding Partitioning and Sequence in Data-Driven Storytelling

The comic strip narrative style is an effective method for data-driven storytelling. However, surely it is not enough to just add some speech bubbles and clipart to your PowerPoint slideshow to turn it into a data comic? In this paper, we investigate aspects of partitioning and sequence as fundamental mechanisms for comic strip narration: chunking complex visuals into manageable pieces, and organizing them into a meaningful order, respectively. We do this by presenting results from a qualitative study designed to elicit differences in participant behavior when solving questions using a complex infographic compared to when the same visuals are organized into a data comic.
Business dynamics, knowledge economy, and the economic performance of African countries

This paper develops a framework (a) to examine whether or not the African business environment hinders or promotes the knowledge economy (KE), (b) to determine how the KE affects economic performance, and (c) how economic performance relates to the inequality-adjusted human socioeconomic development (IHDI) of 53 African countries during the 1996-2010 time period. We estimate the linkages with three related equations. The results support a strong correlation between the dynamics of starting and doing business and variations in KE. The results also show that there exists a weak link between KE and economic performance. Nonetheless, KE-influenced performance plays a more important role in socioeconomic development than some of the conventional control variables like foreign direct investment (FDI), foreign aid, and even private investment.
Model of Fuzzy Regulator of Mobile Robot Motion Control System

The task of autonomous motion control of a mobile robot is considered. A mathematical model of a motion control system is constructed and analyzed. The methods of fuzzy logic allow building logical-linguistic models of motion control systems, reflecting the entire semantic formulation of the problem, using qualitative representations corresponding to "human" methods of reasoning and decision-making.A method for synthesizing a fuzzy algorithm of tracked mobile robot motion control is proposed. The input and output fuzzy variables are selected, the forms of their functions and linguistic description are proposed. The applicability of various fuzzy algorithms to solve the problem is analyzed. A knowledge base that establishes dependencies between input and output variables has been developed. The implementation of the mathematical model was carried out in the LabView graphical programming environment.The effectiveness of samples and implemented fuzzy control algorithms is confirmed by the results of computer experiments.
Fast SRC using quadratic optimisation in downsized coefficient solution subspace

Extended sparse representation-based classification (ESRC) has shown interesting results on the problem of under-sampled face recognition by generating an auxiliary intraclass variant dictionary for the representation of possible appearance variations. However, the method has high computational complexity due to the l1-minimization problem. To address this issue, this paper proposes two strategies to speed up SRC using quadratic optimisation in downsized coefficient solution subspace. The first one, namely Fast SRC using Quadratic Optimisation (FSRC-QO), applies PCA and LDA hybrid constrained optimisation method to achieve compressed linear representations of test samples. By design, more accurate and discriminative reconstruction of a test sample can be achieved for face classification, using the downsized coefficient space. Secondly, to explore the positive impact of our proposed method on deep-learning-based face classification, we enhance FSRC-QO using CNN-based features (FSRC-QO-CNN), in which we replace the original input image using robust CNN features in our FSRC-QO framework. Experimental results conducted on a set of well-known face datasets, including AR, FERET, LFW and FRGC, demonstrate the merits of the proposed methods, especially in computational efficiency.
Logical framework for smart discussion in learning process

Different views on a problem are things that often occur in a discussion. In order to find a good conclusion in the discussion, it is necessary to make logical framework that builds a healthy discussion climate. This article discusses the logical framework in a discussion, which frequently uses common sense and rationality as the main pillar in the conclusion making process. This study applied a qualitative analysis approach that originates from the observation and interview with the class X students at the International Standard Boarding School of Amanatul Ummah who attended learning process with discussion method. The number of respondents covers 24 students. Research finding shows that discussion method in a learning process will work well when it has problem identification mechanism, factual argument, cause-effect analysis, and good communication pattern. Moreover, this research recommends ethical behaviors in the process of discussion learning activity as an important part in the discussion.
A Rapport and Gait Monitoring System Using a Single Head-Worn IMU during Walk and Talk

Public health is promoted when physical activity and social interaction increase. One's physical activity can be measured by wearable pedometers based on step counting, which contributes to physical health. However, thus far, no wearable device supports monitoring social interactions. In this paper, a single head-worn inertial measurement unit (H-IMU) is proposed to evaluate interpersonal activities, which can measure three objective components of rapport: positivity, mutual attentiveness, and coordination. Using angular kinematics, the number of head nods is counted for positivity (recognition rate: 94.34%). In terms of mutual attentiveness, we computed angle differences between two interactants' head orientations. The H-IMU can also measure coordination of two interactants' gait patterns based on individual gait events. These results can be a reference to estimate the level of rapport during walk and talk. The H-IMU is applicable to head-worn devices, such as smart earphones and head mounted displays (HMD).
An investigation of aggregated transfer learning for classification in digital pathology

Complex 'Big Data' questions that involve machine learning require large datasets for training. This is particularly problematic for Deep Learning methods in the biomedical imaging domain and specifically Digital Pathology. Transfer Learning has been shown to be a promising method for training classifiers on smaller sized datasets. In this work we investigate the effectiveness of aggregated Transfer Learning using VGG19 trained on ImageNet and then fine-tuning parameters with tissue histopathological patches from breast cancer metastatic tissue patches to classify soft tissue sarcoma patches. We compare results with and without transfer learning, and fine tuning applied to different layers. From the results, it is apparent that fine-tuning earlier VGG19 convolutional blocks with breast cancer patches and applying bottleneck feature extraction to soft tissue sarcoma can have an adverse effect on accuracy and other performance measures. Nevertheless, the aggregated approach is a promising method for digital pathology and requires much more investigation.