Reviewerly/exHarmony huggingface dataset:
Using an Ensemble of Incrementally Fine-Tuned CNNs for Cross-Domain Object Category Recognition

When the training data is inadequate, it is difficult to train a deep Convolutional Neural Network (CNN) from scratch with randomized initial weights. Instead, it is common to train a source CNN model on a very large data set beforehand, and then use the learned source CNN model as an initialization to train a target CNN model. In deep learning realm, this procedure is called fine-tuning a CNN. This paper presents an experimental study on how to combine a collection of incrementally fine-tuned CNN models for cross-domain and multi-class object category recognition tasks. A group of fine-tuned CNN models is trained on the target data set by incrementally transferring parameters from a source CNN model trained on a large data set initially. The last two fully-connected (FC) layers of the source CNN model are eliminated, and two New FC layers are added to make the learned new CNN model suitable for the target task. Based on Caltech-101 and Office data sets, the experimental results demonstrate the effectiveness and good performance of the proposed methods. The proposed method is more suitable for the object recognition task when there is inadequate target training data.
Integrating Physical Activity Data with Electronic Health Record

Wearables allow individuals to track, analyze, and visualize their physical activities and associated data such as vitals, activity information, etc. across time. But, none of this activity data is anywhere to be found in an electronic health record - the primary source of patient medical data for the healthcare providers. This inability doesn’t allow experts to view the complete health summary of an individual and also, activity data can play a key role in healthcare decisions. This problem is due to the lack of standards that can capture activity data from disparate sources (e.g., wearables, smart watches, trackers, etc.) and integrate it with an EHR. This research article identifies and provides a detailed analysis of the key factors contributing to the problem. Based on the detailed analysis, we design an interoperable model by leveraging HL7 FHIR standard to capture activity data from wearables and develop it using FHIR HAPI - an implementation of HL7 FHIR. This initial prototype is tested by capturing Fitbit data and integrating it with OpenEMR - an open source EHR.
Arabic Question Answering: A Study on Challenges, Systems, and Techniques

The enormous increase of the amount of information available on the web creates the need for systems like Question Answering to bridge the gap between general end users and the web with its different data representations.A considerable portion of the available data on the web is written in Arabic for and by Arabic users.This paper provides a review of the Arabic Question Answering Systems building processes and the challenges met by the researchers in this topic due to the Arabic language special characteristics.A general architecture is represented for the Question Answering task on both structured and unstructured data.Then, an overview of the work done in Arabic Question Answering Systems is presented.Finally, a number of tools and linguistic resources are recommended for researchers to develop Arabic question answering systems.
A Comparative Study of Vehicle Detection Methods

Our project aims to tackle one of the most famous violation on the road; when a vehicle crosses to the wrong side of the road to overtake another vehicle traveling in the same direction. So, we aim to develop a computer vision system to detect robustly the prohibited overtaking observed from a static camera. Our approach is based on two main stages: Line Detection and Vehicle Detection. For each stage, several techniques can be used. In the presented work, we will focus on vehicle detection stage. Here, features are extracted from image and then classified using machine learning algorithms such as SVM (Support Vector Machines), kNN (K-nearest neighbor) and Decision Tree. We used two different kinds of features HOG (Histogram of Oriented Gradient) and SURF (Speeded up Robust Features) we constructed different combinations for vehicle detection. Finally, various parameters are calculated to evaluate the performance of each algorithm used in order to carry out a comparative study. The experiments reveal that the results vary according to each combination.
Scrum in a Strongly Hierarchical Organization

One of the duties of the Aeronautics Computing Center of São José dos Campos, Organization of the Brazilian Air Force, is to develop and operate Information Technology projects and applications assigned to it. As a Military Organization, this Center is constitutionally organized based on hierarchy and discipline. For the management of their projects, the software developers of this Military Organization decided to use Scrum and its good practices. However, the use of an agile framework implies in the horizontal interaction between the members of a team, without any hierarchy between them. At first, the use of Agile Methods in the military was opposed to the hierarchy, reflecting in the relationship between people and the quality of the product. This work aims to present the resources used for a healthy application of Agile Methods in an environment strongly based on the hierarchy between people, improving not only the relationship between them, but also the quality of the products they develop.
On the Performance of Exact Diffusion over Adaptive Networks

Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed {\em deterministic} optimization problems. These methods employ constant step-sizes and converge linearly to the {\em exact} solution under proper conditions. However, their performance under stochastic and adaptive settings is less explored. It is still unknown {\em whether}, {\em when} and {\em why} these bias-correction methods can outperform their traditional counterparts (such as consensus and diffusion) with noisy gradient and constant step-sizes. This work studies the performance of exact diffusion under the stochastic and adaptive setting, and provides conditions under which exact diffusion has superior steady-state mean-square deviation (MSD) performance than traditional algorithms without bias-correction. In particular, it is proven that this superiority is more evident over sparsely-connected network topologies such as lines, cycles, or grids. Conditions are also provided under which exact diffusion method match or may even degrade the performance of traditional methods. Simulations are provided to validate the theoretical findings.
Graph Learning Via Edge Constrained Sparse Representation for Image Analysis

The construction of a graph essentially determines the performance of the graph-based image analysis methods. Particularly, the sparse graph is vital in image analysis because of its sparse and adaptive properties for the enormous scale of data. However, most existing graph-based algorithms ignore some valuable natural information, such as edge information of image data. In this paper, we propose a novel graph learning method, called edge constrained sparse representation (ECSR), which makes full use of edge information to refine the similarity among image samples. We believe that it is beneficial to include edge constraints into the construction stage of a graph for image analyses. Compared with conventional graph construction methods, ECSR has not only automatic sparsity and adaptive neighborhood size but also more accurate similarity measurements among natural images. The experimental results on four natural image datasets demonstrate the validity and effectiveness of ECSR for semi-supervised classification and clustering tasks.
Comparison of Grayscale Image Colorization Methods in Different Color Spaces

With the aim of coloring old black and white photos, medical image illustrations, and classic movies, the gray-scale image colorization methods are used to assign color information to grayscale image. Color space, as the basis of quantitative color information, plays an important role in the gray-scale image colorization. In this paper, the different color spaces—Lab, Luv, YCrCb, YIQ used in the grayscale image colorization are analyzed. Two classical automatic colorization methods, Welsh approach and Gupta approach, are carried out in those color spaces. Different performances are observed in such color spaces when using the two colorization methods. In Welsh approach, the transfer result depends on the luminance information of reference image. Since the process of Gupta approach is on the purpose of propagating color information using the least-squares optimization method, the result shows limited relevance to the reference image luminance. The experimental results demonstrate that YCrCb and YIQ have better performance in texture similarity than Lab and LUV at both color transfer methods. LUV presents the worst performance for most of the images when applying color migration. The optimal results are obtained based on YCrCb and YIQ in Welsh approach. While, it is observed that Gupta method has limited effect on the colorization results.
Exploiting multi–core and many–core parallelism for subspace clustering

Abstract Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset, where a subspace is a subset of dimensions of the data. But the exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, which means that parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation shows linear speedup. Moreover, we develop an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.
Incorporating Personalization Features in a Hospital-Stay Summary Generation System

Most of the currently available health resources contain vast amount of information that are created by keeping the “general” population in mind, which in reality, might not be useful for anyone. One approach to providing comprehensible health information to patients is to generate summaries that are personalized to each individual. This paper details the design of a personalized hospital-stay summary generation system that tailors its content to the patient’s understanding of medical terminologies and their level of engagement in improving their own health. Our summaries were found to cover around 80% of the health concepts that were considered as important by a doctor or a nurse. An online survey conducted on 150 participants verified that our algorithm’s interpretation of the personalization parameters is representative of that of a larger population.
Deepwalk-assisted Graph PCA (DGPCA) for Language Networks

Language graph learning is an important task with many applications such as text classification, link prediction and community detection. One of the challenges in this domain is finding an efficient way to learn and encode graph into a low dimensional embedding. In this paper, a novel DeepWalk-assisted Graph PCA (DGPCA) method is proposed for processing language network data represented by graphs. This method can generate a precise text representation for nodes (or vertices) in language networks. Unlike other existing work, our learned low dimensional vector representations add flexibility in exploring vertices’ neighborhood information, while reducing noise contained in the original data. To demonstrate the effectiveness, we use DGPCA to classify vertices that contain text information in three language networks. Experimentally, DGPCA is shown to perform well on the language datasets in comparison to several state-of-the-art benchmarking methods.
Deep Residual Auto-Encoders for Expectation Maximization-based Dictionary Learning.

We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning problems, thus establishing a link between dictionary learning and neural networks. Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate Expectation-Maximization algorithm to develop autoencoders that enable the simultaneous training of the dictionary and regularization parameter (ReLU bias). The forward pass of the encoder approximates the sufficient statistics of the E-step as the solution to a sparse coding problem, using an iterative proximal gradient algorithm called FISTA. The encoder can be interpreted either as a recurrent neural network or as a deep residual network, with two-sided ReLU non-linearities in both cases. The M-step is implemented via a two-stage back-propagation. The first stage relies on a linear decoder applied to the encoder and a norm-squared loss. It parallels the dictionary update step in dictionary learning. The second stage updates the regularization parameter by applying a loss function to the encoder that includes a prior on the parameter motivated by Bayesian statistics. We demonstrate in an image-denoising task that CRsAE learns Gabor-like filters, and that the EM-inspired approach for learning biases is superior to the conventional approach. In an application to recordings of electrical activity from the brain, we demonstrate that CRsAE learns realistic spike templates and speeds up the process of identifying spike times by 900x compared to algorithms based on convex optimization.
Exploring the Conjunction Fallacy in Probability Judgment: Conversational Implicature or Nested Sets?

The Journal of European Psychology Students (JEPS) is an open-access, double-blind, peer-reviewed journal for psychology students worldwide. JEPS is run by highly motivated European psychology students and has been publishing since 2009. By ensuring that authors are always provided with extensive feedback, JEPS gives psychology students the chance to gain experience in publishing and to improve their scientific skills. Furthermore, JEPS provides students with the opportunity to share their research and to take a first step toward a scientific career.Submissions are welcomed at any time.Register to submit your work online.Visit our blog - JEPS Bulletin - for tips on writing and more.
Influence of wind speed on the performance of photovoltaic panel

The aim of this project is to investigate the performance of photovoltaic (PV) panel influence by wind speed in Kangar, Perlis, Malaysia. A low conversion energy efficiency of the PV panel is the major problem of a PV application system. The PV panel is absorbed solar irradiance minor converted into electrical energy, and the rest is converted into heat energy. Therefore, the heat energy generated by the PV panel is increased in its operating temperature. However, PV panel is necessary to operate them at the low operating temperatures to keep the PV panel electrical efficiency at an acceptable level. In this experiment, one unit of the PV panel was limited wind flow over its surface and the other one PV panel was operated in the normal condition. The operating temperature of the PV panel with wind speed is less than the PV panel without wind speed. This is due to wind flow over the surface of the PV panel can enhance heat extraction from the PV panel. Hence, PV panel with wind speed can generate a higher output power than that without wind speed. This improvement output performance of PV panel will have an important contribution to PV application systems.
The Combination of Logistic Regression and Gradient Boost Tree for Email Spam Detection

Convenience communicate in this era making trends throughout the world. Email is one of many tools that still used for communicating. In the activity of sending-receiving email, some irresponsible people often send spam messages to recipients only for profit. The process of spam filtering is still continue developed to reduce the number of spam emails. In this research, we propose Logistic Regression with Select by Weight and Gradient Boost Tree for developed spam filtering to make spam filters more advanced. The model has been built using Logistic Regression with Select by Weight and Gradient Boost Tree showing a good result. Accuracy generated from the mentioned models is 95.13%.
Cell-filtering-based multi-scale Shannon–Cosine wavelet denoising method for locust slice images

The locust slice images have all the features such as strong self-similarity, piecewise smoothness and nonlinear texture structure. Multi-scale interpolation operator is an effective tool to describe such structures, but it cannot overcome the influence of noise on images. Therefore, this research designed the Shannon–Cosine wavelet which possesses all the excellent properties such as interpolation, smoothness, compact support and normalization, then constructing multi-scale wavelet interpolative operator, the operator can be applied to decompose and reconstruct the images adaptively. Combining the operator with the local filter operator (mean and median), a multi-scale Shannon–Cosine wavelet denoising algorithm based on cell filtering is constructed in this research. The algorithm overcomes the disadvantages of multi-scale interpolation wavelet, which is only suitable for describing smooth signals, and realizes multi-scale noise reduction of locust slice images. The experimental results show that the proposed method can keep all kinds of texture structures in the slice image of locust. In the experiments, the locust slice images with mixture noise of Gaussian and salt–pepper are taken as examples to compare the performances of the proposed method and other typical denoising methods. The experimental results show that the Peak Signal-To-Noise Ratio (PSNR) of the denoised images obtained by the proposed method is greater 27.3%, 24.6%, 2.94%, 22.9% than Weiner filter, wavelet transform method, median and average filtering, respectively; and the Structural Similarity Index (SSIM) for measuring image quality is greater 31.1%, 31.3%, 15.5%, 10.2% than other four methods, respectively. As the variance of Gaussian white noise increases from 0.02 to 0.1, the values of PSNR and SSIM obtained by the proposed method only decrease by 11.94% and 13.33%, respectively, which are much less than other 4 methods. This shows that the proposed method possesses stronger adaptability.
The Status of Entrepreneurship in Libraries: Content Analysis and Assessment from the Conference for Entrepreneurial Librarians

To identify change in the understanding of entrepreneurship in libraries through content analysis of presentations that were a part of the Conference for Entrepreneurial Librarians from 2009 through 2016. This chapter will discuss key topics and findings related to libraries and entrepreneurship.Presentations delivered at the conference were categorized into topics and were tracked to uncover perceptions of what is entrepreneurial in libraries and how the importance of certain issues has changed over time. This chapter summarizes the results of that evaluation and of a survey of attendees after the conferences ended.Entrepreneurship in libraries in 2009 was more heavily linked to making money. Over time, however, the term “entrepreneurial” became more conceptually associated with finding value, reaching out to new constituencies, and taking risks. There is a definite distinction as to the definition of “entrepreneurial” between public libraries (who consider community outreach to be a part of their core mission) and academic libraries (who often see this as an entrepreneurial enterprise). The finding that librarians attended the conference to “find change agents” indicates a yearning to identify with others in the field who are likewise seeking ways to be entrepreneurial.The evaluation of the status of “entrepreneurship in libraries” has never before been undertaken by evaluating the presentations of the practitioners in a conference setting. Since the practitioners are determining what is most valuable to discuss with others in the field, this provides some insight into the status of entrepreneurship in the field.
Bi-directional Re-ranking for Person Re-identification

For person re-identification, previous re-ranking methods focus on the unidirectional query-find-gallery ranking list and target to improve the performance of person re-identification. However, the matched images with the same identity may get lower ranks in the query-find-gallery ranking list, which limits the improvement of these re-ranking methods. To solve this problem, we propose the Bi-directional re-ranking method. Different from existing methods, we consider the bi-directional matching including the query-find-gallery ranking list and the gallery-find-query ranking list. In addition, we construct the graph of image relationship based on feature distances and expand the qualified images other than the initial top-k nearest images. By combining the bi-directional re-ranking performance and the k-neighbor similarity score, we re-rank the initial ranking list and get higher improvements. Extensive experiments show that the Bi-directional re-ranking method can facilitate the state-of-the-art person re-identification methods.
An Empirical Study on the Architecture Instability of Software Projects

Software architecture is an artifact that expresses how the initial concept of a software system has actually been implemented. However, changes to the requirement imply continuous modification of the software system and may affect its architecture. It is expected that when a software system reaches the mature state, the requirements for evolution decrease and its architecture becomes more stable. The paper analyzes how the architecture of a software system evolves during its life cycle, with the aim of obtaining quantitative information on its possible instability after it has been declared mature. The goal is to verify if the architectural instability decreases with the increase of the software system maturity and to identify the software components that are more unstable among multiple releases. The paper proposes metrics that measure the instability of the architecture of a software system and its components through different releases. Open source software projects classified as mature and active and related historical data are analyzed. The results of the empirical study point out that the instability of software projects continues to evolve even after they are declared mature. The proposed metrics give a useful support for investigating the instability of a software project, even if further factors can be analyzed. Furthermore, the study can be replicated on other software systems belonging to different domains and developed using different programming languages.
Author Correction: When Twitter Fingers Turn to Trigger Fingers: a Qualitative Study of Social Media-Related Gang Violence

The original version of this article unfortunately contained a mistake. The name of the second author was spelled incorrectly. The correct name of the second author is “David Pyrooz.”