Reviewerly/exHarmony huggingface dataset:
3D dense convolutional neural network for fast and accurate single MR image super-resolution

Super-resolution (SR) MR image reconstruction has shown to be a very promising direction to improve the spatial resolution of low-resolution (LR) MR images. In this paper, we presented a novel MR image SR method based on a dense convolutional neural network (DDSR), and its enhanced version called EDDSR. There are three major innovations: first, we re-designed dense modules to extract hierarchical features directly from LR images and propagate the extracted feature maps through dense connections. Therefore, unlike other CNN-based SR MR techniques that upsample LR patches in the initial phase, our methods take the original LR images or patches as input. This effectively reduces computational complexity and speeds up SR reconstruction. Second, a final deconvolution filter in our model automatically learns filters to fuse and upscale all hierarchical feature maps to generate HR MR images. Using this, EDDSR can perform SR reconstructions at different upscale factors using a single model with one stride fixed deconvolution operation. Third, to further improve SR reconstruction accuracy, we exploited a geometric self-ensemble strategy. Experimental results on three benchmark datasets demonstrate that our methods, DDSR and EDDSR, achieved superior performance compared to state-of-the-art MR image SR methods with less computational load and memory usage.
Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision

In this paper, we present a conceptually simple, strong, and efficient framework for fully- and weakly-supervised panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground things and background stuff in a unified fully convolutional pipeline, which can be optimized with point-based fully or weak supervision. In particular, Panoptic FCN encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly. With this approach, instance-aware and semantically consistent properties for things and stuff can be respectively satisfied in a simple generate-kernel-then-segment workflow. Without extra boxes for localization or instance separation, the proposed approach outperforms the previous box-based and -free models with high efficiency. Furthermore, we propose a new form of point-based annotation for weakly-supervised panoptic segmentation. It only needs several random points for both things and stuff, which dramatically reduces the annotation cost of human. The proposed Panoptic FCN is also proved to have much superior performance in this weakly-supervised setting, which achieves 82% of the fully-supervised performance with only 20 randomly annotated points per instance. Extensive experiments demonstrate the effectiveness and efficiency of Panoptic FCN on COCO, VOC 2012, Cityscapes, and Mapillary Vistas datasets. And it sets up a new leading benchmark for both fully- and weakly-supervised panoptic segmentation.
Connecting Humans and Robots Using Physiological Signals – Closing-the-Loop in HRI

Technological advancements in creating and commercializing novel unobtrusive and wearable physiological sensors generate new opportunities to develop adaptive human-robot interaction (HRI) scenarios. Detecting complex human states such as engagement and stress when interacting with social agents could bring numerous advantages to create meaningful interactive experiences. Despite being widely used to explain human behaviors in post-interaction analysis with social agents, using bodily signals to create more adaptive and responsive systems remains an open challenge. This paper presents the development of an open-source, integrative, and modular library created to facilitate the design of physiologically adaptive HRI scenarios. The HRI Physio Lib streamlines the acquisition, analysis, and translation of human body signals to additional dimensions of perception in HRI applications using social robots. The software framework has four main components: signal acquisition, processing and analysis, social robot and communication, and scenario and adaptation. Information gathered from the sensors is synchronized and processed to allow designers to create adaptive systems that can respond to detected human states. This paper describes the library and presents a use case that uses a humanoid robot as a cardio-aware exercise coach that uses heartbeats to adapt the exercise intensity to maximize cardiovascular performance. The main challenges, lessons learned, scalability of the library, and implications of the physio-adaptive coach are discussed.
Identifying online risk markers of hard-to-observe crimes through semi-inductive triangulation: The case of human trafficking in the United States

Abstract Many types of crime are difficult to study because they are hard to operationalize, hidden from the public, or both. With communication increasingly moving to online domains, recent work has begun to examine whether the online domain contains traces of such hard-to-observe crimes. This study explores the online linguistic contours of hard-to-observe crimes through a rigorous mixed-methods approach that combines interviews and computational text analysis. Using human trafficking in illicit massage businesses as a proof-of-concept, we show how this approach, which we call semi-inductive triangulation, meets the empirical contextuality and relationality of crime traces in the online domain. The findings contribute to an emerging field of computational criminology and call for an integration of linguistic approaches in criminology.
Untouchable Once Revoking: A Practical and Secure Dynamic EHR Sharing System via Cloud

Healthcare Internet-of-Things (IoT) enables lightweight devices to observe patients’ vital signals and outsource them to a remote cloud to enjoy flexible data sharing. However, it faces many security threats as the outsourced data is no longer physically controlled by data owners, and the cloud that hosts the outsourced data is not fully trusted. Many privacy protection technologies have been adopted to solve this problem, among which cryptographic mechanisms have become one of the most promising tools. Unfortunately, current cryptographic mechanisms in healthcare IoT mainly suffer from the following challenges: 1) dynamic user groups for managing users’ accessibility; 2) efficient revocation mechanism to mitigate the burden during user revocation; 3) forward and backward secrecy to ensure session independence in the presence of session key leakage; 4) revocable storage to prevent data users from learning any unauthorized data even the data is authorized before; and 5) information manipulation during data transmission. In this article, we introduce a practical and secure system to address the above problems. Our system provides fine-grained access control with dynamic user groups for optimizing scalability and functionality. We prove that our system is secure against numerous real-world threats. Extensive comparison and experimental analysis demonstrate that our system enjoys superior performance than the state-of-the-art solutions.
A Learnable Joint Spatial and Spectral Transformation for High Resolution Remote Sensing Image Retrieval

Geometric and spectral distortions of remote sensing images are key obstacles for deep learning-based supervised classification and retrieval, which are worsened by cross-dataset applications. A learnable geometric transformation model imbedded in a deep learning model has been used as a tool for handling geometric distortions to process close-range images with different view angles. However, a learnable spectral transformation model, which is more noteworthy in remote image processing, has not yet been designed and explored up to now. In this paper, we propose a learnable joint spatial and spectral transformation (JSST) model for remote sensing image retrieval (RSIR), which is composed of three modules: a parameter generation network (PGN); a spatial conversion module; and a spectral conversion module. The PGN adaptively learns the geometric and spectral transformation parameters simultaneously from the different input image content, and these parameters then guide the spatial and spectral conversions to produce a new modified image with geometric and spectral correction. Our learnable JSST is imbedded in the front-end of the deep-learning-based retrieval network. The spatial and spectral-modified inputs provided by the JSST endow the retrieval network with better generalization and adaptation ability for cross-dataset RSIR. Our experiments on four open-source RSIR datasets confirmed that our proposed JSST embedded retrieval network outperformed state-of-the-art approaches comprehensively.
Short-term solar radiation forecasting using a new seasonal clustering technique and artificial neural network

Solar radiation represents the most important parameter for sizing and planning solar power systems. However, solar radiation depends significantly on meteorological conditions which are variable and uncontrollable. Therefore, forecasting global solar radiation can play a key role to integrate solar energy resources into the electric grid. This paper presents a new hybrid approach based on seasonal clustering technique and artificial neural network (ANN) for forecasting 1 h-ahead of global solar radiation. For this purpose, the fuzzy c-means algorithm (FCM) was used to cluster 3 years of monthly average experimental data into different seasons according to solar and meteorological parameters of Évora city. Subsequently, based on the seasonal clustering results, the meteorological dataset was divided into dfferent training subsets. Furthermore, for each subset, an ANN model has been designed to forecast hourly global solar radiation. In this study, hourly meteorological data from January 2012 to December 2016 have been used for forecasting. The hourly data were collected from Évora-city’s meteorological station in Portugal (38°34 N, 07°54 W). The results show the superiority of the hybrid approach compared to the individual ANN model according to statistical indicators.
Developing user-centered collections at a research library: An Evidence-Based Acquisition (EBA) pilot in STEM

The increasing cost of science and engineering books in both print and electronic format continues to challenge academic libraries' ability to support teaching, learning and research. Libraries are under pressure to meet the growing need of electronic resources with a flat or shrinking budget. To seek a solution to this phenomenon, the Stony Brook University Libraries works with publishers to implement an Evidence-Based Acquisition (EBA) model in engineering and science. Using this EBA model, the Stony Brook University Libraries empowers users to participate in purchase decisions on ebooks at the end of the agreed period with the publisher. Before the final purchase of the science and engineering ebooks, users have full access to over 3000 ebooks in the collection. This study is to understand the use pattern of ebooks acquired through EBA model versus subscriptions by analyzing use reports, percent used, and use intensity. The use comparison of two acquisition models provides insights on the ebook collection strategy and EBA impact. The implementation process on the Alma/Primo VE platform is also shared.
Blind Image Quality Assessment by Visual Neuron Matrix

Modeling of the human visual system (HVS) is considered to be the most appropriate method to evaluate the image quality. The natural image statistics (NIS) can not only reflect certain response characteristics of the visual system, but also provide accurate quantitative descriptions of the HVS. Inspired by this fact, a visual model, which is named as visual neuron matrix (VNM), is built by a statistical learning process on natural image samples. On the basis of the VNM, a blind image quality assessment (BIQA) model is proposed. First, the VNM is obtained by independent component analysis, which can simulate the visual neurons in the cerebral cortex as well as extract the visual features from test images. Then, a regression model (neural network or support vector machine) is used to associate the visual features with the quality score of a test image. The experimental results show that the proposed method is more competitive and efficient than the state-of-the-art BIQA models.
Topology- and convexity-preserving image segmentation based on image registration

Image segmentation aims to extract the target objects or to identify the corresponding boundaries. For corrupted images due to occlusions, obscurities or noises, to get an accurate segmentation result is very difficult. To overcome this issue, the prior information is often introduced and particularly, the convex prior attracts more and more attentions recently. In this paper, we propose a topology- and convexity-preserving registration-based segmentation model, which can be suitable for both 2D and 3D cases. By incorporating the level set representation and imposing the constraints on the suitable regions, we can explicitly force the fully convex segmentation results or the partially convex segmentation results. To solve the proposed model, we employ the alternating direction method of multipliers and numerical experiments on 2/3D synthetic and real images demonstrate that the proposed model can indeed lead to the accurately topology- and convexity-preserving segmentation.
Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm

In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behavior of honey bees, and GA, based on the mechanics of nature selection, are among the most efficient biologically inspired population-based algorithms. The performance of the proposed ABC-GA hybrid algorithm is examined, including classic benchmark test functions. To demonstrate the effectiveness of ABC-GA for a real-world problem, parameter identification of an Escherichia coli MC4110 fed-batch cultivation process model is considered. The computational results of the designed algorithm are compared to the results of different hybridized biologically inspired techniques (ant colony optimization (ACO) and firefly algorithm (FA))—hybrid algorithms as ACO-GA, GA-ACO and ACO-FA. The algorithms are applied to the same problems—a set of benchmark test functions and the real nonlinear optimization problem. Taking into account the overall searchability and computational efficiency, the results clearly show that the proposed ABC–GA algorithm outperforms the considered hybrid algorithms.
Stereo Refinement Dehazing Network

The performance of stereo vision tasks degrades when haze exists in the input stereo image pair. Independently applying single image dehazing algorithm on left and right images is not optimal. To overcome the problem, we propose an effective framework, called SRDNet, for simultaneously dehazing stereo images. The main idea of SRDNet is to make full use of the stereo information from cross views improving dehazing performance. It does not explicitly employ the disparity estimation and the correlation matrix. SRDNet comprises two parts: a weight-sharing coarse dehazing network (WSCDN) and a guided separated refinement network (GSRN). The WSCDN is utilized to predict a coarse dehazed image pair. Then the GSRN is introduced to predict the residues for different views by extracting the fused information of cross views and separating the features of different views with a guided channel and spatial refinement module. The residues are added to the coarse dehazed pair so as to make refinement and remove the remained haze. Experimental results demonstrate that our proposed SRDNet surpasses previous image dehazing methods by a significant margin both quantitatively and qualitatively. Moreover, our SRDNet could be a preprocessing step of the stereo-based 3D object detection and boosts the 3D detection accuracy in hazy scenes.
A CNN-Based Self-Supervised Synthetic Aperture Radar Image Denoising Approach

Synthetic aperture radar (SAR) plays an essential role in earth observation and projection due to its capability to penetrate clouds, which makes it possible to monitor terrestrial surfaces under all weather conditions. Multiplicative noise often occurs in the SAR signal, hampering the retrieval of information from SAR imagery. Convolutional neural networks (CNNs) have been used in many computer vision tasks and are helpful in image denoising. However, current CNN-based denoising approaches inevitably lead to a ``washed out'' effect that loses spatial details. Another limitation is that most typical CNN-based denoising models require a noise-free image for training. To address these issues, we propose a novel end-to-end self-supervised SAR denoising model: Enhanced Noise2Noise (EN2N), which can be trained without a noise-free image. To enhance the quality of the result images, the perceptual features from a pre-learned CNN are introduced to restore the spatial details by a hybrid loss function. Experiments show that our proposed method outperforms the typical denoising methods in terms of noise reduction and feature preservation based on image quality metrics. Also, the new hybrid loss could enhance the spatial details significantly. The good performance maintains the robustness throughout time, which reduces the uncertainty in time-series SAR caused by random noise. Benefiting from optimization of graphics processing unit (GPU) and multi-threading, the proposed method has higher computation efficiency than traditional methods. This study demonstrates the great potential of using our self-supervised deep learning approaches for SAR image denoising in the future.
Analysis of Subfeature for Classification in Data Mining

Feature selection is a crucial part of data mining applications in the diversity area of data analysis. Most of the feature selection methods issue features analysis for classification, which is not sufficient for classification by feature selection techniques. Classification is involved in traditional classes with redundant feature data, which is not helpful in the database for further analysis. There is a more negligible effect on feature analysis by taking an existing class. So, it proposed to obtain a new class or subclass using the analysis of feature's tiny data named subfeature (SF) data of corresponding instances from a traditional class. These data are involved in the limited number of significant instances where it generates the new class. It is a challenging task to find such instances with subfeatures from any database. Thus, this article proposed the optimization model based on the Lagrangian multiplier to find such data and analyze for a new class from a traditional class. Several algorithms have been taken to explain subfeature data. The theoretical approaches such as domain- and variance-based subfeatures and the convergence of subfeature are used to select subfeatures with the effectiveness of the proposed model. Furthermore, several classifiers with searching methods and statistical methods (i.e., local and global variances) analyze the classification through subfeature data. Result analysis in the experiment on different datasets shows that the proposed model is beneficial toward novel classes based on selected subfeature data.
Intelligent Narrative Summaries: From Indicative to Informative Summarization

Perceiving data can be challenging as the analysis goal is subjective. Storytelling approaches try to address this challenge by focusing on the research problem of understanding the data in general and, more particularly in curation, summarization, and presentation of the big data, i.e., a massive number of small data islands from personal, shared, and business data. In this context, summarization can facilitate understanding and surfacing insights that are embedded within the data. In this paper, we propose a general hierarchical personalized summarization framework, called Narrative Summaries (NARS), to improve the drawbacks of traditional summarization methods in various aspects. We proposed two variants of NARS: a semi-structured summarization approach (SNARS) and a fully-structured summarization approach (FNARS). The goal is to enable intelligent narrative construction of summaries based on the important features extracted from users' engagement. To achieve this goal, we propose a hierarchical structure to prevent users from being overwhelmed with less important information at first glance and to facilitate the selection process. Afterward, instead of providing a short static summary, we present an intelligent and interactive summarization approach to enable users to navigate through the hierarchy to gain more elaborated information upon request. Our approach aims to: (i) engage users in the summarization process, to guarantee the interactive speed even for extensive text collections, and (ii) eliminate the need for reference summaries, which is one of the most challenging issues in the summarization problem. The results demonstrate that our approach fabricates a summary that helps the users understand the topic better and faster compared to the existing state-of-the-art methods.
We live in cloud computing world, without using it in our libraries

Purpose The purpose of this study was to investigate the views of librarians in Iran academic libraries about the extent of their familiarity, application and knowledge of the cloud computing in libraries. Design/methodology/approach This was a survey research. The statistical population of research consisted of librarians working in five universities in northeastern Iran. The sample (83 librarian) selected by using random sampling method. Findings The findings showed that in 2019 librarians familiar with cloud computing was low, but their use of cloud computing services was moderate. Also the findings showed that the item of providing enough computers in the library had the lowest effect on promoting the use of cloud computing and the items. Cloud computing training for librarians and users and broadband Internet were best strategies offered by librarians to promote the use of cloud computing. Originality/value Cloud computing has received less attention from librarians and researchers. However, based on the results of the present study, the broader use of cloud computing in libraries requires specialized and trained staff. Librarians need to broaden their knowledge in this technology.