Reviewerly/exHarmony huggingface dataset:
Scholar-friend recommendation in online academic communities: An approach based on heterogeneous network

As a particular type of social networking site, online academic communities have revolutionized the way researchers collaborate and communicate with each other. Accompanying the growth in the number of users registered on online academic communities, the information overload problem presents a great challenge for researchers trying to find relevant and reliable friends there. Different from friends in conventional social networking sites, friends in online academic communities are denoted as scholar-friends in this research. Scholar-friend recommendation in online academic communities involves different entities (e.g., researchers, research articles, affiliations, research interests, and status updates) and various relationships among entities (e.g., scholar-friend relationship of researchers, post relationship between researchers and status updates, and writing relationship between researchers and papers), which constitute a complex heterogeneous network. By leveraging the entity and relationship data in online academic communities, this research proposes a heterogeneous network-based approach to recommending scholar-friends where information gain is used to identify valuable meta paths and a regularization-based optimization is employed to make personalized recommendations for each individual researcher. Experimental results based on historical data and user experiments demonstrate that the proposed approach can achieve better performance compared to some baseline approaches. Additionally, we further discuss how the meta paths and corresponding learned weights can help to understand researchers' preferences and behaviors.
Characterizing sediment sources by non-negative matrix factorization of detrital geochronological data

This paper explores an inverse approach to the problem of characterizing sediment sources' (“source” samples) age distributions based on samples from a particular depocenter (“sink” samples) using non-negative matrix factorization (NMF). It also outlines a method to determine the optimal number of sources to factorize from a set of sink samples (i.e., the optimum factorization rank). We demonstrate the power of this method by generating sink samples as random mixtures of known sources, factorizing them, and recovering the number of known sources, their age distributions, and the weighting functions used to generate the sink samples. Sensitivity testing indicates that similarity between factorized and known sources is positively correlated to 1) the number of sink samples, 2) the dissimilarity among sink samples, and 3) sink sample size. Specifically, the algorithm yields consistent, close similarity between factorized and known sources when the number of sink samples is more than ∼3 times the number of source samples, sink data sets are internally dissimilar (cross-correlation coefficient range >0.3, Kuiper V value range >0.35), and sink samples are well-characterized (>150–225 data points). However, similarity between known and factorized sources can be maintained while decreasing some of these variables if other variables are increased. Factorization of three empirical detrital zircon U–Pb data sets from the Book Cliffs, the Grand Canyon, and the Gulf of Mexico yields plausible source age distributions and weights. Factorization of the Book Cliffs data set yields five sources very similar to those recently independently proposed as the primary sources for Book Cliffs strata; confirming the utility of the NMF approach. The Grand Canyon data set exemplifies two general considerations when applying the NMF algorithm. First, although the NMF algorithm is able to identify source age distribution, additional geological details are required to discriminate between primary or recycled sources. Second, the NMF algorithm will identify the most basic elements of the mixed sink samples and so may subdivide sources that are themselves heterogeneous mixtures of more basic elements into those basic elements. Finally, application to a large Gulf of Mexico data set highlights the increased contribution from Appalachian sources during Cretaceous and Holocene time, potentially attributable to drainage reorganization. Although the algorithm reproduces known sources and yields reasonable sources for empirical data sets, inversions are inherently non-unique. Consequently, the results of NMF and their interpretations should be evaluated in light of independent geological evidence. The NMF algorithm is provided both as MATLAB code and a stand-alone graphical user interface for Windows and macOS (.exe and .app) along with all data sets discussed in this contribution.
Development of an educational virtual reality training system for marine engineers

Abstract In this study, an educational virtual reality training system named “DMS‐VLCC3D” has been developed to help students or marine engineers learn the working principle of the marine engineering system and improve their ability of practical operation in a more efficient and vivid way. During the stage of establishing the geometric model of the virtual marine engine room, optimization methods including instantiation modeling, functional nodes optimization, level of detail, and texture mapping optimization are adopted to improve the fluency of the virtual marine engine room and the loading speed of geometric model resource. Reasonable human‐machine interactive mechanism is designed to promote the development efficiency and simulation efficiency. Several auxiliary learning and training tools are designed to make the DMS‐VLCC3D become a self‐learning and training platform for the trainees. Additionally, to fit different training purposes, the system provides three training mode: standalone and multi‐user collaborative training and evaluation. At last, 60 seniors majored in marine engineering from Dalian Maritime University are selected to carry out an assessment experiment to study the learning effectiveness of DMS‐VLCC3D, which shows a promising result. This training system is currently used to train thousands of students majored in marine engineering in about 20 maritime universities in China.
A pattern based algorithmic autotuner for graph processing on GPUs

This paper proposes Gswitch, a pattern-based algorithmic auto-tuning system that dynamically switches between optimization variants with negligible overhead. Its novelty lies in a small set of algorithmic patterns that allow for the configurable assembly of variants of the algorithm. The fast transition of Gswitch is based on a machine learning model trained using 644 real graphs. Moreover, Gswitch provides a simple programming interface that conceals low-level tuning details from the user. We evaluate Gswitch on typical graph algorithms (BFS, CC, PR, SSSP, and BC) using Nvidia Kepler and Pascal GPUs. The results show that Gswitch runs up to 10× faster than the best configuration of the state-of-the-art programmable GPU-based graph processing libraries on 10 representative graphs. Gswitch outperforms Gunrock on 92.4% cases of 644 graphs which is the largest dataset evaluation reported to date.
Automatic Mining of Opinions Expressed About APIs in Stack Overflow

With the proliferation of online developer forums, developers share their opinions about the APIs they use. The plethora of such information can present challenges to the developers to get quick but informed insights about the APIs. To understand the potential benefits of such API reviews, we conducted a case study of opinions in Stack Overflow using a benchmark dataset of 4,522 sentences. We observed that opinions about diverse API aspects (e.g., usability) are prevalent and offer insights that can shape developers' perception and decisions related to software development. Motivated by the finding, we built a suite of techniques to automatically mine and categorize opinions about APIs from forum posts. First, we detect opinionated sentences in the forum posts. Second, we associate the opinionated sentences to the API mentions. Third, we detect API aspects (e.g., performance, usability) in the reviews. We developed and deployed a tool called Opiner, supporting the above techniques. Opiner is available online as a search engine, where developers can search for APIs by their names to see all the aggregated opinions about the APIs that are automatically mined and summarized from developer forums.
Embedded Deep Learning for Ship Detection and Recognition

Ship detection and recognition are important for smart monitoring of ships in order to manage port resources effectively. However, this is challenging due to complex ship profiles, ship background, object occlusion, variations of weather and light conditions, and other issues. It is also expensive to transmit monitoring video in a whole, especially if the port is not in a rural area. In this paper, we propose an on-site processing approach, which is called Embedded Ship Detection and Recognition using Deep Learning (ESDR-DL). In ESDR-DL, the video stream is processed using embedded devices, and we design a two-stage neural network named DCNet, which is composed of a DNet for ship detection and a CNet for ship recognition, running on embedded devices. We have extensively evaluated ESDR-DL, including performance of accuracy and efficiency. The ESDR-DL is deployed at the Dongying port of China, which has been running for over a year and demonstrates that it can work reliably for practical usage.
Graph Embedded Convolutional Neural Networks in Human Crowd Detection for Drone Flight Safety

In this paper, we propose a novel human crowd detection method that uses deep convolutional neural networks for drone flight safety purposes. The first contribution of this paper is to provide lightweight architectures, as restricted by the computational capacity of the specific application, capable of effectively distinguishing between crowded and non-crowded scenes from drone-captured images, and provide crowd heatmaps which can be used to semantically enrich the flight maps by defining no-fly zones. The second contribution of this paper is to propose a novel generic regularization technique, based on the graph embedding framework, applicable to different deep architectures for generic classification problems. The experimental validation is performed on a new dataset constructed for the task of human crowd detection from drone-captured images, and indicates the effectiveness of the proposed detector, as well as of the proposed regularizers in terms of classification accuracy. Finally, since the proposed regularization scheme is applicable in generic classification problems, we have also conducted experiments on two additional datasets, where the enhanced performance of the regularizers is also validated.
Video Summarization Via Actionness Ranking

To automatically produce a brief yet expressive summary of a long video, an automatic algorithm should start by resembling the human process of summary generation. Prior work proposed supervised and unsupervised algorithms to train models for learning the underlying behavior of humans by increasing modeling complexity or craft-designing better heuristics to simulate human summary generation process. In this work, we take a different approach by analyzing a major cue that humans exploit for summary generation; the nature and intensity of actions. We empirically observed that a frame is more likely to be included in human-generated summaries if it contains a substantial amount of deliberate motion performed by an agent, which is referred to as actionness. Therefore, we hypothesize that learning to automatically generate summaries involves an implicit knowledge of actionness estimation and ranking. We validate our hypothesis by running a user study that explores the correlation between human-generated summaries and actionness ranks. We also run a consensus and behavioral analysis between human subjects to ensure reliable and consistent results. The analysis exhibits a considerable degree of agreement among subjects within obtained data and verifying our initial hypothesis. Based on the study findings, we develop a method to incorporate actionness data to explicitly regulate a learning algorithm that is trained for summary generation. We assess the performance of our approach on 4 summarization benchmark datasets, and demonstrate an evident advantage compared to state-of-the-art summarization methods.
An intelligent adaptive fuzzy-based inference system for computer-assisted language learning

Adaptive e-learning employs algorithmic mechanisms in order to orchestrate the pace of instruction and provide tailored learning objects to support the unique educational experience of each learner. Taking this into consideration, this research work presents a fully operating and evaluated adaptive and intelligent e-learning system for second language acquisition. This system uses a hybrid model for misconception detection and identification (MDI) and an inference system for the dynamic delivery of the learning objects tailored to learners’ needs. More specifically, the MDI mechanism incorporates the Fuzzy String Searching and The String Interpreting Resemblance algorithms in order to reason between possible learners’ misconceptions. Furthermore, the inference system utilizes the knowledge inference relationship between the learning objects and creates a personalized learning environment for each student. The paper presents examples of operation and the system is evaluated using an evaluation model. The results are very encouraging and promising since they reveal that the hybrid model for misconception detection and identification and the inference system operate collaboratively and enhance the adaptivity of the students’ needs and preferences.
Task-Aware Attention Model for Clothing Attribute Prediction

Clothing attribute recognition, especially in unconstrained street images, is a challenging task for multimedia. Existing methods for multi-task clothing attribute prediction often ignore the relation between specific attributes and positions. However, the attribute response is always location-sensitive, i.e., different spatial locations have various contributions to attributes. Inspired by the locality of clothing attributes, in this paper, we introduce the attention mechanism to incorporate the impact of positions for clothing attribute prediction with only image-level annotations. However, the performance improvement is limited if we directly use the traditional spatial attention model for each task since it does not take the influence from other tasks into account. Instead, we propose a novel task-aware attention mechanism, which estimates the importance of each position across different tasks. We first evaluate a task attention network with an end-to-end multi-task clothing attribute learning architecture on the shop domain. And then, we employ curriculum learning strategy, which transfers the well-trained shop domain attribute knowledge to the street domain attribute prediction. Experiments are conducted on three clothing benchmarks, i.e., cross-domain clothing attribute dataset, woman clothing dataset, and man clothing dataset. The performance of attribute prediction demonstrates the superiority of the proposed task-aware attention mechanism over several state-of-the-art methods both in shop and street domains.
A novel adaptive LBP-based descriptor for color image retrieval

In this paper, we present two approaches to extract discriminative features for color image retrieval. The proposed local texture descriptors, based on Radial Mean Local Binary Pattern (RMLBP), are called Color RMCLBP (CRMCLBP) and Prototype Data Model (PDM). RMLBP is a robust to noise descriptor which has been proposed to extract texture features of gray scale images for texture classification. For the first descriptor, the Radial Mean Completed Local Binary Pattern is applied to channels of the color space, independently. Then, the final descriptor is achieved by concatenating the histogram of the CRMCLBP_S/M/C component of each channel. Moreover, to enhance the performance of the proposed method, the Particle Swarm Optimization (PSO) algorithm is used for feature weighting. The second proposed descriptor, PDM, uses the three outputs of CRMCLBP (CRMCLBP_S, CRMCLBP_M, CRMCLBP_C) as discriminative features for each pixel of a color image. Then, a set of representative feature vectors are selected from each image by applying k-means clustering algorithm. This set of selected prototypes are compared by means of a new similarity measure to find the most relevant images. Finally, the weighted versions of PDM is constructed using PSO algorithm. Our proposed methods are tested on Wang, Corel-5k, Corel-10k and Holidays datasets. The results show that our proposed methods makes an admissible tradeoff between speed and retrieval accuracy. The first descriptor enhances the state-of-the-art color texture descriptors in both aspects. The second one is a very fast retrieval algorithm which extracts discriminative features.
A Low Power Fall Sensing Technology Based on FD-CNN

The Bluetooth-based wearable fall detection technology faces problems such as short transmission distance, easily interfered by obstacles, and high-power consumption. To address these issues, we developed a sensor board integrated with low-power ZigBee and MPU6050, which can sample and cache three-axial acceleration and angular velocity data in the sleep mode; we also designed an interrupt-driven algorithm that can collect and transmit the data to the receiving end (namely server) with low-power consumption via ZigBee. Additionally, the received data are normalized according to the range specification and cached into a sliding window by the server. Meanwhile, the cached data are mapped into RGB bitmap, and a fall detection convolutional neural network (FD-CNN) is designed and trained using the open dataset to identify falls from the activities of daily livings according to bitmap. The experimental results show that the average accuracy of this method is 98.61%, while its average sensitivity and specificity are 98.62% and 99.80%, respectively. It takes advantage of the strong networking capacity of ZigBee, and the strong computing power of the server, which is very suitable for fall sensing in elderly community with low power and high accuracy.
Assessing the emotional impact of virtual reality-based teacher training

Purpose The purpose of this paper is to propose the use of a virtual reality (VR)-based approach to improve teacher education and life-long professional development. Through constant training in real-life based situations but within a safe three-dimensional virtual school environment, teachers are given the opportunity to experience and learn how to react to different types of incidents that may take place in a school environment. Design/methodology/approach The current paper presents the design cycle that was followed for the implementation of the VR teacher training system. The effectiveness of the proposed approach is demonstrated with a case study that aimed to promote teachers’ understanding of student’s problematic situations related to substance use. As part of the experimental investigation, the impact of the VR system on participants’ emotions and mood states is evaluated through Electroencephalogram (EEG) measurements, heart rate (HR) recordings and self-reported data. Findings Results indicate significant changes to participant’s negative emotional and mood states, suggesting that the scenario and the VR experience had a strong impact on them. Moreover, participants’ HR was increased during the experiment, while the analysis of the EEG signal indicated that the participants experienced a stressful situation that could justify the change in their negative emotions and mood states. Originality/value The proposed VR-based approach aims to provide an innovative framework to teacher education and the related training methodology. In the long-term, the proposed VR system aims to form a new paradigm of teacher training, an alternative safe method that will allow user-teachers to learn through trial and error techniques that reflect real-life situations within a three-dimensional school space and without the risk of harming real students. To the best of our knowledge this is one of the first systematic attempts to use a VR-based methodology to address real teachers’ needs. The development of the VR application is linked to both strong theoretical foundations in education derived from the literature but also from real teachers’ problems and requirements derived from an extensive literature analysis, survey and interviews with experts including teachers, school counselors and psychologists. The VR tool addresses specific teachers’ competences as outcome, after an extensive documentation of existing Teachers’ Competence Models and significant guidance by experts who pointed specific competencies of primary importance to teachers.
CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation

As an important task in medical imaging analysis, automatic lymph node segmentation from computed tomography (CT) scans has been studied well in recent years, but it is still very challenging due to the lack of adequately-labeled training data. Manually annotating a large number of lymph node segmentations is expensive and time-consuming. For this reason, data augmentation can be considered as a surrogate of enriching the data. However, most of the traditional augmentation methods use a combination of affine transformations to manipulate the data, which cannot increase the diversity of the data's contextual information. To mitigate this problem, this paper proposes a data augmentation approach based on generative adversarial network (GAN) to synthesize a large number of CT-realistic images from customized lymph node masks. In this work, the pix2pix GAN model is used due to its strength for image generation, which can learn the structural and contextual information of lymph nodes and their surrounding tissues from CT scans. With these additional augmented images, a robust U-Net model is learned for lymph node segmentation. Experimental results on NIH lymph node dataset demonstrate that the proposed data augmentation approach can produce realistic CT images and the lymph node segmentation performance is improved effectively using the additional augmented data, e.g. the Dice score increased about 2.2% (from 80.3% to 82.5%).
Breaking High-Resolution CNN Bandwidth Barriers With Enhanced Depth-First Execution

Convolutional neural networks (CNNs) now also start to reach impressive performance on non-classification image processing tasks, such as denoising, demosaicing, super-resolution, and super slow motion. Consequently, CNNs are increasingly deployed on very high-resolution images. However, the resulting high-resolution feature maps pose unseen requirements on the memory system of neural network processing systems, as on-chip memories are too small to store high-resolution feature maps, while off-chip memories are very costly in terms of I/O bandwidth and power. This paper first shows that the classical layer-by-layer inference approaches are bounded in their external I/O bandwidth versus on-chip memory tradeoff space, making it infeasible to scale up to very high resolutions at a reasonable cost. Next, we demonstrate how an alternative depth-first network computation can reduce I/O bandwidth requirements up to >200× for a fixed on-chip memory size or, alternatively, reduce on-chip memory requirements up to >10 000× for a fixed I/O bandwidth limitation. We further introduce an enhanced depth-first method, exploiting both line buffers and tiling, to further improve the external I/O bandwidth versus on-chip memory capacity tradeoff and quantify its improvements beyond the current state of the art.