Reviewerly/exHarmony huggingface dataset:
BHGSO: Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

In machine learning and data mining, feature selection (FS) is a traditional and complicated optimization problem. Since the run time increases exponentially, FS is treated as an NP-hard problem. The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios. This paper presents two binary variants of a Hunger Games Search Optimization (HGSO) algorithm based on V- and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset. The proposed technique transforms the continuous HGSO into a binary variant using V- and S-shaped transfer functions (BHGSO-V and BHGSO-S). To validate the accuracy, 16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms. The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features, classification accuracy, run time, and fitness values than other state-of-the-art algorithms. The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems. The proposed BHGSO-V achieves 95% average classification accuracy for most of the datasets, and run time is less than 5 sec. for low and medium dimensional datasets and less than 10 sec for high dimensional datasets.
ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting

End-to-end text-spotting, which aims to integrate detection and recognition in a unified framework, has attracted increasing attention due to its simplicity of the two complimentary tasks. It remains an open problem especially when processing arbitrarily-shaped text instances. Previous methods can be roughly categorized into two groups: character-based and segmentation-based, which often require character-level annotations and/or complex post-processing due to the unstructured output. Here, we tackle end-to-end text spotting by presenting Adaptive Bezier Curve Network v2 (ABCNet v2). Our main contributions are four-fold: 1) For the first time, we adaptively fit arbitrarily-shaped text by a parameterized Bezier curve, which, compared with segmentation-based methods, can not only provide structured output but also controllable representation. 2) We design a novel BezierAlign layer for extracting accurate convolution features of a text instance of arbitrary shapes, significantly improving the precision of recognition over previous methods. 3) Different from previous methods, which often suffer from complex post-processing and sensitive hyper-parameters, our ABCNet v2 maintains a simple pipeline with the only post-processing non-maximum suppression (NMS). 4) As the performance of text recognition closely depends on feature alignment, ABCNet v2 further adopts a simple yet effective coordinate convolution to encode the position of the convolutional filters, which leads to a considerable improvement with negligible computation overhead. Comprehensive experiments conducted on various bilingual (English and Chinese) benchmark datasets demonstrate that ABCNet v2 can achieve state-of-the-art performance while maintaining very high efficiency. More importantly, as there is little work on quantization of text spotting models, we quantize our models to improve the inference time of the proposed ABCNet v2. This can be valuable for real-time applications. Code and model are available at: https://git.io/AdelaiDet.
Detecting Human-Object Interaction via Fabricated Compositional Learning

Human-Object Interaction (HOI) detection, inferring the relationships between human and objects from images/videos, is a fundamental task for high-level scene understanding. However, HOI detection usually suffers from the open long-tailed nature of interactions with objects, while human has extremely powerful compositional perception ability to cognize rare or unseen HOI samples. Inspired by this, we devise a novel HOI compositional learning framework, termed as Fabricated Compositional Learning (FCL), to address the problem of open long-tailed HOI detection. Specifically, we introduce an object fabricator to generate effective object representations, and then combine verbs and fabricated objects to compose new HOI samples. With the proposed object fabricator, we are able to generate large-scale HOI samples for rare and unseen categories to alleviate the open long-tailed issues in HOI detection. Extensive experiments on the most popular HOI detection dataset, HICO-DET, demonstrate the effectiveness of the proposed method for imbalanced HOI detection and significantly improve the state-of-the-art performance on rare and unseen HOI categories. Code is available at https://github.com/zhihou7/HOI-CL.
Knowledge, diffusion and interest in blockchain technology in SMEs

Purpose The paper aims to understand the possible determinants of knowledge of, and interest in using, blockchain, with a particular focus in the future intention to apply this technology. Blockchain technology is deemed to radically change business models and processes. Using this technology in small and medium enterprises (SMEs) is still a novel idea. Moreover, not much is known about the diffusion and level of interest towards blockchain in SMEs. This research adopts a knowledge management perspective, drawing on technology acceptance model to highlight the level of blockchain technology diffusion, and to explore which factors lead SMEs’ to adopt blockchain. Design/methodology/approach This study distributed a questionnaire to a sample of 300 SMEs in Italy. This study received 96 responses (32% response rate). This study calculated descriptive statistics and undertook a reliability analysis. Finally, this study performed a logistic regression to analyse the determinants of further intention to use blockchain technology. Findings Results show that blockchain technology is quite well known, but the level of knowledge is limited. Moreover, the research reveals that the rate of adoption is very low. Interest in the future adoption of blockchain is associated with knowledge, perception of usefulness and ease of use of blockchain. Originality/value This paper is one of the first explorative studies showing which factors lead SMEs to adopt blockchain technologies and shedding some light on the interaction between knowledge management and blockchain adoption and diffusion in SMEs. It highlights how blockchain knowledge could determine future interest in blockchain innovation. This paper is relevant for public and private institutions that aim to promote, through knowledge management, the adoption of blockchain in SMEs.
Multi-population improved whale optimization algorithm for high dimensional optimization

The metaheuristic algorithms do not depend on the functional form when solving the optimization problem. They have strong adaptability and are widely used in many fields. Whale Optimization Algorithm (WOA) is a metaheuristic algorithm based on the social behavior of humpback whales. Compared with other metaheuristic algorithms, WOA shows better performance. However, when solving high dimensional optimization problems, WOA tends to fall into local optima and has slow convergence speed and low accuracy of solution. Aiming at these problems, a multi-population improved WOA (MIWOA) is proposed to improve the performance of WOA when tackling high dimensional optimization. First of all, the multi-population exploitation and exploration processes are introduced. The population is divided into a better group and a worse group. The better individuals are used to improve exploitation performance, while the poorer individuals are used to improve exploration performance. Secondly, the current optimal individual and weighted center are taken to improve the learning process, which enhances the exploration ability and convergence speed. Moreover an interpolation method is introduced to enhance the search ability in the vicinity of the current optimum and further improve the exploitation performance. Finally, a control parameter is used to balance the exploitation and exploration processes. In the experimental part, MIWOA is compared with several state-of-the-art algorithms on 30 high dimensional benchmark functions with dimensions ranging from 100 to 2000. Simulation results show that MIWOA has good performance in both high dimensional single-mode and multi-mode optimization problems. MIWOA is superior to other algorithms in solution accuracy, convergence speed, and execution time.
Boosting Ship Detection in SAR Images With Complementary Pretraining Techniques

Deep learning methods have made significant progress in ship detection in synthetic aperture radar (SAR) images.The pretraining technique is usually adopted to support deep neural networks-based SAR ship detectors due to the scarce labeled SAR images.However, directly leveraging ImageNet pretraining is hard to obtain a good ship detector because of different imaging perspectives and geometry.In this paper, to resolve the problem of inconsistent imaging perspectives between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique to transfer the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset.On the other hand, to handle the problem of different imaging geometry between optical and SAR images, we propose an optical-SAR matching (OSM) pretraining technique, which transfers plentiful texture features from optical images to SAR images by common representation learning on the optical-SAR matching task.Finally, observing that the OSD pretraining-based SAR ship detector has a better recall on sea area while the OSM pretraining-based SAR ship detector can reduce false alarms on land area, we combine the predictions of the two detectors through weighted boxes fusion to further improve detection results.Extensive experiments on four SAR ship detection datasets and three representative CNN-based detection benchmarks are conducted to show the effectiveness and complementarity of the two proposed detectors, and the state-of-the-art performance of the combination of the two detectors.The proposed method won the sixth place of ship detection in SAR images in the 2020 Gaofen challenge.
Guidance for publishing qualitative research in informatics

Qualitative research, the analysis of nonquantitative and nonquantifiable data through methods such as interviews and observation, is integral to the field of biomedical and health informatics. To demonstrate the integrity and quality of their qualitative research, authors should report important elements of their work. This perspective article offers guidance about reporting components of the research, including theory, the research question, sampling, data collection methods, data analysis, results, and discussion. Addressing these points in the paper assists peer reviewers and readers in assessing the rigor of the work and its contribution to the literature. Clearer and more detailed reporting will ensure that qualitative research will continue to be published in informatics, helping researchers disseminate their understanding of people, organizations, context, and sociotechnical relationships as they relate to biomedical and health data.
DV-Net: Accurate liver vessel segmentation via dense connection model with D-BCE loss function

Recently, liver vessel segmentation has aroused widespread interest in medical image analysis. Accurately extracting blood vessels from livers is a difficult task due to their complex vessel structures and image noises. To make a neural network better adapt to this complexity, a deeper neural network is required to fit this nonlinear transformation. In particular, accurate segmentation of small blood vessels is always a challenge, since onefold down-sampling usually causes the loss of information. In this study, we introduce a dense block structure into the V-net to construct a new Dense V-Net (DV-Net) and use data augmentation to segment liver vessels from abdominal CT volumes with a few training samples. In addition, we propose a dual-branch dense connection down-sampling strategy (DCDS) to better capture vascular features and a D-BCE loss function to maximize the utilization of image resources. The proposed DV-Net structure is more powerful in the discrimination of vessel and non-vessel areas. We extensively evaluated the proposed method on the datasets of 3Dircadb and MICCAI 2018 Medical Segmentation Decalthon (MSD) Challenge. Experimental results show that the proposed DV-Net significantly improves the average segmentation Dice score. The average Dice score and sensitivity on 3Dircadb were (75.46%) and (76.93%), respectively, which are better than those of existing methods.
MAF-YOLO: Multi-modal attention fusion based YOLO for pedestrian detection

Achieving the rapid and precise detection of pedestrians in natural environments is crucial for many applications of artificial intelligence systems. However, accurate pedestrian detection at nighttime is challenging due to the low-luminous density and low-resolution of infrared images, and the detection speed is required. In this paper, a real-time pedestrian detection method using a novel multi-modal attention fusion YOLO (MAF-YOLO) was proposed. Firstly, a multi-modal feature extraction module based on the compressed Darknet53 framework was built to adapt the nighttime pedestrian detection and ensure efficiency. The features were extracted from both modalities and then fused by a modal weighted fusion module. Secondly, we defined a loss function and regenerated the size of the anchor box based on the K-means clustering algorithm to improve the detection speed and the robustness of small objects. Finally, a dual attention module was applied to acquire more semantic features from small objects with low-resolution. The experimental results on KAIST and OSU Color-Thermal datasets corroborated the effectiveness of the proposed MAF-YOLO. The proposed methods could also be utilized to facilitate the performance of other pedestrian detection algorithms.
Analysis of the tilt and azimuth angles of photovoltaic systems in non-ideal positions for urban applications

A professional point of view suggests that photovoltaic systems should be installed at the optimum tilt angle and orientation. However, in photovoltaic systems integrated in buildings the flexibility of installation is common. This paper is organized in two different parts. In the first one, the energy losses caused by deviations from the tilt angle (β) and the orientation (γ) of the installation in relation to the ideal position are evaluated. This work considers the cloudy-sky conditions in each locality and theoretically calculates by applying the Cavaleri’s principle, the energy losses. Ten cities around the world, in the northern hemisphere, have been studied with a MATLAB code and the findings demonstrate that non-ideal tilt and azimuth angles can also lead to acceptable levels of electric energy generation. A photovoltaic system installed in South orientation (γ=0°) and β deviations of up to 10 (°) in relation to the optimum tilt angle has a very small influence on the energy losses. The energy losses are: 5%, 10%, 15% and 20% when β deviations are respectively: 21–23 (°), 31–33 (°), 37–40 (°) and 43–47 (°). Then, in the second part, an important application of this previous outcome comes out: the best distribution of the photovoltaic modules on a flat roof of irregular shape of an urban building is achieved.The aim of this work is to maximize the amount of energy get by a photovoltaic system. This engineering problem is highly complex as it involves 10 variables: the available flat roof area, the shape and the orientation of the available flat roof area, the dimensions (length and width) of the commercial photovoltaic modules, the orientation and the position of the photovoltaic modules, the number of the photovoltaic modules, the minimum distances (maintenance operations, to avoid shadowing effects) between rows of photovoltaic modules, and the minimum distance to the terrace boundary. In this context, this work aims to present a study to assist the decision-making. This paper shows a packing algorithm (in Mathematica™) which maximizes the energy generation area of the solar photovoltaic system, considering shadings and distances required for maintenance. Eventually, using the initial study, it comes out the influence of β on the potential capacity of the solar photovoltaic system and it is demonstrated that a decrease in the optimal tilt angle results in an increase up to 24% in the amount of obtained energy keeping invariable the available area. For example, in Almeria, with an optimum tilt angle of 30.3 (°) the amount of obtained energy is 149.8 (MWh) while with a tilt angle of 14 (°) the amount of obtained energy is 186.2 (MWh). This analysis enables to find the optimal answer to the following practical questions: what number of photovoltaic modules is required?, which is the right position for the photovoltaic modules?, and what orientation of photovoltaic modules is the right one?. There are many installers of photovoltaic systems who would benefit from studies about this issue.
Breast cancer detection from histopathology images using modified residual neural networks

Breast cancer is one of the major causes of death among women worldwide. Efficient diagnosis of breast cancer in the early phases can reduce the associated morbidity and mortality and can provide a higher probability of full recovery. Computer-aided detection systems use computer technologies to detect abnormalities in clinical images which can assist medical professionals in a faster and more accurate diagnosis. In this paper, we propose a modified residual neural network-based method for breast cancer detection using histopathology images. The proposed approach provides good performance over varying magnification factors of 40X, 100X, 200X and 400X. The network obtains an average classification accuracy of 99.75%, precision of 99.18% and recall of 99.37% on BreakHis dataset with 40X magnification factor. The proposed work outperforms the existing methods and delivers state-of-the-art results on the benchmark breast cancer dataset.
Auxiliary Tasks and Exploration Enable ObjectGoal Navigation

ObjectGoal Navigation (ObjectNav) is an embodied task wherein agents are to navigate to an object instance in an unseen environment. Prior works have shown that end-to-end ObjectNav agents that use vanilla visual and recurrent modules, e.g. a CNN+RNN, perform poorly due to overfitting and sample inefficiency. This has motivated current state-of-the-art methods to mix analytic and learned components and operate on explicit spatial maps of the environment. We instead re-enable a generic learned agent by adding auxiliary learning tasks and an exploration reward. Our agents achieve 24.5% success and 8.1% SPL, a 37% and 8% relative improvement over prior state-of-the-art, respectively, on the Habitat ObjectNav Challenge [35]. From our analysis, we propose that agents will act to simplify their visual inputs so as to smooth their RNN dynamics, and that auxiliary tasks reduce overfitting by minimizing effective RNN dimensionality; i.e. a performant ObjectNav agent that must maintain coherent plans over long horizons does so by learning smooth, low-dimensional recurrent dynamics. Site: joel99.github.io/objectnav/
Toward energy-efficient online Complete Coverage Path Planning of a ship hull maintenance robot based on Glasius Bio-inspired Neural Network

Regular Ship hull maintenance is an essential for sustainability. The maintenance work of ship hulls that involve human labor suffers from many shortcomings. Maintenance robots have been introduced for drydocks to eliminate these shortcomings. An energy-efficient Complete Coverage Path Planning (CCPP) is a crucial requirement from a ship hull maintenance robot. This paper proposes a novel energy-efficient CCPP method based on Glasius Bioinspired Neural Network (GBNN) for a ship hull inspection robot. The proposed method accounts for a comprehensive energy model for path planning. This energy model reflects the energy usage of a ship hull maintenance robot due to changes in direction, distance, and vertical position. Furthermore, the proposed method is effective for dynamic workspaces since it performs online path planning. These are the major contributions made to state of the art by the work proposed in this paper. The behavior and the performance of the proposed method have been compared against state of the art through simulations considering Hornbill, a multipurpose ship hull maintenance robot. The validation confirms the ability of the proposed in realizing a complete coverage of a given dynamic workspace. According to the statistical outcomes of the comparison, the performance of the proposed method significantly surpasses that of the state-of-the-art methods in terms of energy usage. Therefore, the proposed method contributes to the development of energy-efficient CCPP methods for a ship hull maintenance robot.
H2O: Two Hands Manipulating Objects for First Person Interaction Recognition

We present a comprehensive framework for egocentric interaction recognition using markerless 3D annotations of two hands manipulating objects. To this end, we propose a method to create a unified dataset for egocentric 3D interaction recognition. Our method produces annotations of the 3D pose of two hands and the 6D pose of the manipulated objects, along with their interaction labels for each frame. Our dataset, called H2O (2 Hands and Objects), provides synchronized multi-view RGB-D images, interaction labels, object classes, ground-truth 3D poses for left & right hands, 6D object poses, ground-truth camera poses, object meshes and scene point clouds. To the best of our knowledge, this is the first benchmark that enables the study of first-person actions with the use of the pose of both left and right hands manipulating objects and presents an unprecedented level of detail for egocentric 3D interaction recognition. We further propose the method to predict interaction classes by estimating the 3D pose of two hands and the 6D pose of the manipulated objects, jointly from RGB images. Our method models both inter- and intra-dependencies between both hands and objects by learning the topology of a graph convolutional network that predicts interactions. We show that our method facilitated by this dataset establishes a strong baseline for joint hand-object pose estimation and achieves state-of-the-art accuracy for first person interaction recognition.
AdvDrop: Adversarial Attack to DNNs by Dropping Information

Human can easily recognize visual objects with lost information: even losing most details with only contour reserved, e.g. cartoon. However, in terms of visual perception of Deep Neural Networks (DNNs), the ability for recognizing abstract objects (visual objects with lost information) is still a challenge. In this work, we investigate this issue from an adversarial viewpoint: will the performance of DNNs decrease even for the images only losing a little information? Towards this end, we propose a novel adversarial attack, named AdvDrop, which crafts adversarial examples by dropping existing information of images. Previously, most adversarial attacks add extra disturbing information on clean images explicitly. Opposite to previous works, our proposed work explores the adversarial robustness of DNN models in a novel perspective by dropping imperceptible de-tails to craft adversarial examples. We demonstrate the effectiveness of AdvDrop by extensive experiments, and show that this new type of adversarial examples is more difficult to be defended by current defense systems.