Reviewerly/exHarmony huggingface dataset:
BADet: Boundary-Aware 3D Object Detection from Point Clouds

Currently, existing state-of-the-art 3D object detectors are in two-stage paradigm. These methods typically comprise two steps: 1) Utilize a region proposal network to propose a handful of high-quality proposals in a bottom-up fashion. 2) Resize and pool the semantic features from the proposed regions to summarize RoI-wise representations for further refinement. Note that these RoI-wise representations in step 2) are considered individually as uncorrelated entries when fed to following detection headers. Nevertheless, we observe these proposals generated by step 1) offset from ground truth somehow, emerging in local neighborhood densely with an underlying probability. Challenges arise in the case where a proposal largely forsakes its boundary information due to coordinate offset while existing networks lack corresponding information compensation mechanism. In this paper, we propose $BADet$ for 3D object detection from point clouds. Specifically, instead of refining each proposal independently as previous works do, we represent each proposal as a node for graph construction within a given cut-off threshold, associating proposals in the form of local neighborhood graph, with boundary correlations of an object being explicitly exploited. Besides, we devise a lightweight Region Feature Aggregation Module to fully exploit voxel-wise, pixel-wise, and point-wise features with expanding receptive fields for more informative RoI-wise representations. We validate BADet both on widely used KITTI Dataset and highly challenging nuScenes Dataset. As of Apr. 17th, 2021, our BADet achieves on par performance on KITTI 3D detection leaderboard and ranks $1^{st}$ on $Moderate$ difficulty of $Car$ category on KITTI BEV detection leaderboard. The source code is available at https://github.com/rui-qian/BADet.
APNet: Adversarial Learning Assistance and Perceived Importance Fusion Network for All-Day RGB-T Salient Object Detection

To improve the performance of salient object detection (SOD) in scenes with low-light conditions (e.g., nighttime) and cluttered backgrounds, infrared thermal images are used to supplement RGB images to achieve good all-day imaging as infrared images are insensitive to light source changes. Therefore, we built an adversarial learning assistance and perceived importance fusion network (APNet) for all-day RGB-thermal (RGB-T) SOD. First, an iterative adversarial learning approach was used to establish a generator and three discriminators. The generator provides salient maps that are eventually accepted by the discriminators and are used to determine their similarities with the labels. Second, a progressively guided optimization structure with high-level features is used to refine low-level features across multiple scales gradually. To further improve the detection results, a perceived importance fusion module (PIFM) is used to weigh and fuse different modalities in cases where the presence of noise may degrade sensor fusion. Extensive experiments on existing RGB-T datasets demonstrate that the proposed APNet notably outperforms state-of-the-art models.
Cybersecurity Capabilities in Developing Nations and Its Impact on Global Security

Cybersecurity refers to the organizational practices followed by the different multinational companies to defend their computers, servers, mobile devices, and networks from malicious attacks. This data exploitation is usually done by accessing, changing, or destroying sensitive information or hacking the data for money extortion. It applies to systems and mechanisms aimed at stopping unauthorized entry, bugs, and cybercriminal threats to devices, networks, and records. It does not matter how many technologies are emerging to make our life easy; humans are the main vulnerability in every sector. In this chapter, the authors discussed social engineering techniques: how we are being attacked by unknown threats with simple manipulative actions.
Enterprise Data Sharing with Privacy-Preserved Based on Hyperledger Fabric Blockchain in IIOT’s Application

Internet of Things (IoT) technology is now widely used in energy, healthcare, services, transportation, and other fields. With the increase in industrial equipment (e.g., smart mobile terminals, sensors, and other embedded devices) in the Internet of Things and the advent of Industry 4.0, there has been an explosion of data generated that is characterized by a high volume but small size. How to manage and protect sensitive private data in data sharing has become an urgent issue for enterprises. Traditional data sharing and storage relies on trusted third-party platforms or distributed cloud storage, but these approaches run the risk of single-node failure, and third parties and cloud storage providers can be vulnerable to attacks that can lead to data theft. To solve these problems, this paper proposes a Hyperledger Fabric blockchain-based secure data transfer scheme for enterprises in the Industrial Internet of Things (IIOT). We store raw data in the IIoT in the InterPlanetary File System (IPFS) network after encryption and store the Keyword-index table we designed in Hyperledger Fabric blockchain, and enterprises share the data by querying the Keyword-index table. We use Fabric's channel mechanism combined with our designed Chaincode to achieve privacy protection and efficient data transmission while using the Elliptic Curve Digital Signature Algorithm (ECDSA) to ensure data integrity. Finally, we performed security analysis and experiments on the proposed scheme, and the results show that overall the data transfer performance in the IPFS network is generally better than the traditional network, In the case of transferring 5 MB file size data, the transmission speed and latency of IPFS are 19.23 mb/s and 0.26 s, respectively, and the IPFS network is almost 4 times faster than the TCP/IP network while taking only a quarter of the time, which is more advantageous when transferring small files, such as data in the IIOT. In addition, our scheme outperforms the blockchain systems mainly used today in terms of both throughput, latency, and system overhead. The average throughput of our solution can reach 110 tps (transactions are executed per second), and the minimum throughput in experimental tests can reach 101 tps.
Ship Detection in SAR Images Based on Multi-Scale Feature Extraction and Adaptive Feature Fusion

Deep learning has attracted increasing attention across a number of disciplines in recent years. In the field of remote sensing, ship detection based on deep learning for synthetic aperture radar (SAR) imagery is replacing traditional methods as a mainstream research method. The multiple scales of ship objects make the detection of ship targets a challenging task in SAR images. This paper proposes a new methodology for better detection of multi-scale ship objects in SAR images, which is based on YOLOv5 with a small model size (YOLOv5s), namely the multi-scale ship detection network (MSSDNet). We construct two modules in MSSDNet: the CSPMRes2 (Cross Stage Partial network with Modified Res2Net) module for improving feature representation capability and the FC-FPN (Feature Pyramid Network with Fusion Coefficients) module for fusing feature maps adaptively. Firstly, the CSPMRes2 module introduces modified Res2Net (MRes2) with a coordinate attention module (CAM) for multi-scale features extraction in scale dimension, then the CSPMRes2 module will be used as a basic module in the depth dimension of the MSSDNet backbone. Thus, our backbone of MSSDNet has the capabilities of features extraction in both depth and scale dimensions. In the FC-FPN module, we set a learnable fusion coefficient for each feature map participating in fusion, which helps the FC-FPN module choose the best features to fuse for multi-scale objects detection tasks. After the feature fusion, we pass the output through the CSPMRes2 module for better feature representation. The performance evaluation for this study is conducted using an RTX2080Ti GPU, and two different datasets: SSDD and SARShip are used. These experiments on SSDD and SARShip datasets confirm that MSSDNet leads to superior multi-scale ship detection compared with the state-of-the-art methods. Moreover, in comparisons of network model size and inference time, our MSSDNet also has huge advantages with related methods.
Inclusive growth in Sub-Saharan Africa: Exploring the interaction between ICT diffusion, and financial development

Despite the momentous rise in ICT diffusion, and financial development in sub-Saharan Africa (SSA), their plausible joint effect on inclusive growth have not been explored, leaving a lacuna in the literature. This study, therefore, examines the direct and indirect effects of ICT diffusion on inclusive growth in 42 SSA countries over the period 1980–2019. We provide evidence robust to several specifications from the dynamic system GMM to show that: (i) ICT skills, access and usage induce inclusive growth in SSA, and (ii) the effects of ICT skills, access and usage are enhanced in the presence of financial development. These findings remain the same when we focussed on financial institution access. Policy recommendations are provided in line with the region's green growth agenda and striving efforts at improving socioeconomic development.
A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification

The ubiquitous usage of feature selection in search space optimization, information retrieval, data mining, signal processing, software fault prediction, and bioinformatics is paramount to expert and intelligent systems. Most of the conventional feature selection methods implemented are based on filter and wrapper approaches that suffer from poor classification accuracy, high computational cost, and selection of irrelevant and redundant features. This is due to the limitations of the employed objective functions leading to overestimation of the feature significance. On the contrary, hybrid feature selection methods formulated from information theory and nature-inspired metaheuristic algorithms are preferred because of their high computational efficiency, scalability in avoiding redundant and less informative features, and independence from the classifier. However, these methods have three common drawbacks: (1) poor trade-off between exploration and exploitation phase, (2) getting stuck into an optimal local solution, and (3) avoiding irrelevancy and redundancy of selected features. The first and the second drawback is related to metaheuristic algorithm implementation, while the third is concerned with applied information-theoretic paradigms. To address the aforementioned problems, we developed a new hybrid feature selection method, namely, the Iterative Feature Selection using Dynamic Butterfly Optimization Algorithm based Interaction Maximization (IFS-DBOIM) that combines Dynamic Butterfly Optimization Algorithm (DBOA) with a mutual information-based Feature Interaction Maximization (FIM) scheme for selecting the optimal feature subset. There is evidence that DBOA performs better in exploration, exploitation, and avoidance of local optima entrapment, and FIM comparatively scores the maximum relevancy with minimum redundancy of the new features with previously selected ones. The performance of the proposed method is compared using twenty publicly available datasets with ten baseline feature selection approaches. The results revealed that IFS-DBOIM outperforms other approaches on most datasets, maximizing the percent classification accuracy with the least number of features. The nonparametric Wilcoxon rank test confirms the statistical significance of these outcomes. Moreover, this method realizes the best trade-off between accuracy and stability.
A Novel Deep Learning Bi-GRU-I Model for Real-Time Human Activity Recognition Using Inertial Sensors

Wearable sensor based Human Activity Recognition (HAR) has been widely used these years. This paper proposed a novel deep learning model for HAR using inertial sensors. First, a wearable device platform was developed with 6 inertial sensor units to collect triaxial acceleration signals during human movements, and the dataset of Command Actions of Traffic Police (CATP) was acquired. Then, a deep learning model named Bidirectional-Gated Recurrent Unit-Inception (Bi-GRU-I) was designed to improve the accuracy and reduce the amount of parameters. It is consisting of 2 Bi-GRU layers, 3 Inception layers, 1 Global Average Pooling (GAP) layer and 1 softmax layer. Finally, the comparing experiments with other methods were taken on 3 datasets: the self-collected CATP dataset, widely used Wireless Sensor Data Mining (WISDM) and University of California, Irvine (UCI-HAR) dataset. And the proposed method shows better performance and robustness. Moreover, the sensor configuration optimization was analyzed, and it shows that this method can also apply to the task using less sensor units.
Blockchain application and collaborative innovation in the manufacturing industry: Based on the perspective of social trust

We use Chinese A-share listed firms from the manufacturing industry during 2016–2019 to examine the impact of social trust and blockchain application on firms’ collaborative innovation. The results reveal that social trust among acquaintances and social trust among strangers have heterogeneous impacts on corporate collaborative innovation: Trust in strangers promotes firms’ collaborative innovation more than trust in acquaintances. We then analyze the external governance effects of blockchain applications, including the direct impact of blockchain applications on firms’ collaborative innovation, and the indirect impact of blockchain applications on the relationship between social trust and corporate collaborative innovation. We find that blockchain application enhances collaborative innovation and strengthens the positive impact of social trust on collaborative innovation. These results indicate that the application of blockchain technology improves the performances of firms’ collaborative innovation. It is therefore advisable for firms and governments to construct an ecosystem to facilitate the application of blockchain technologies.
Task Allocation in Distributed Agile Software Development using Machine Learning Approach

In the 21st century, agile software development (ASD) has emerged as one of the prominent software development techniques. Every major global company has moved to ASD as a means of reducing costs. In pursuit of huge markets and cheap cost of labour, the industry has shifted to a Distributed Agile Software Development (DASD) environment. As a consequence of improper job allocation, clients may refuse to accept the project, team members may be demonized, and the project may collapse. Numerous scholars have spent the past decade researching different techniques for work allocation in Distributed Agile settings, and the results have been promising. Ontologies and Bayesian networks were among the techniques they employed. This is a list of brute force techniques that may be useful in certain situations. Additionally, these methods have not been used to distributed Agile software development job allocation. The purpose of this article is to design and implement a method for job allocation in distributed Agile software development that is based on machine learning. The findings indicate that the suggested model is more accurate in terms of task assignment.
Assessing the usability of blockchain for sustainability: Extending key themes to the construction industry

Distributed Ledger Technology (DLT) emerged as an innovative computer technology capable of ensuring information security through encryption algorithms. In recent years, this technology has been discussed in different industries, including the construction sector. Although the advantages of applying DLT in construction projects are numerous, several barriers and limitations are associated with its application. The difficulties are even more exacerbated when examining the uses of DLT for achieving more sustainable buildings. In this context, this article conducts a comprehensive literature review on blockchain, the most discussed DLT technology nowadays, for sustainability, focusing on extending key applications discussed in various fields to the construction industry and real estate. The novelty of this review paper is the presentation of an in-depth discussion of what the next steps in blockchain research should be in order to integrate its applications for achieving a sustainable construction environment with cleaner production and resource use efficiency. A conceptual framework is also proposed to showcase the integration of blockchain with other applications for facilitating the goal of achieving sustainable buildings, including Building Information Modelling (BIM) and Life Cycle Sustainability Assessment (LCSA).
Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh

Solar radiation is the energy or radiation we get from the sun, time-varying data. Solar radiation plays a vital role in various sectors. With better prediction, performances in these sectors can be enhanced. In this work, we proposed a system to forecast solar radiation using Neural Networks. Meteorological data from five different cities of Bangladesh were used. The system can forecast radiation values for any day using different meteorological data from the previous day. Three different networks were trained using the meteorological data, which are the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Also, predictions were made for all five cities separately. An elaborate evaluation of all three models has been done to produce a comparison using widely used performance metrics. The GRU model produced the best result among all three models, with a MAPE score of 19.28%.
A Lightweight Blockchain-Based Remote Mutual Authentication for AI-Empowered IoT Sustainable Computing Systems

Internet of Things (IoT) has led to significant advancements in communication technologies, specifically, concerning IoT-based sustainable information systems. Lately, industry-academic communities have made great strides for the development of security in IoT-based applications, such as traffic management, industrial automation systems, military surveillance systems, transportation, parking, etc. The sustainable IoT converges AI and blockchain technologies for enhancing quality of individual’s life. As a result, emerging IoT applications operate a distributed ledger technology to provide robust-level of encryption and execution for contractual agreement that resolves interoperability and security issues. Thus, this article proposes a blockchain-based remote mutual authentication (B-RMA) that considers smart devices and cloud networks to offer security and privacy. The proposed B-RMA can coexist with the IoT-based smart environment to decentralize the processing of user authentication requests. The prominence of the proposed strategies including security efficiency and privacy protection, is evaluated using informal security analysis. Moreover, a runtime platform “Node.js” was used to analyze the communication metrics, such as execution time, throughput, and overhead ratio, over the concurrent requests. The investigation results prove that the B-RMA achieves a scalable environment, accordingly.
Broaden Your Views for Self-Supervised Video Learning

Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by image techniques, where these two views are similarly extracted by cropping and augmenting the resulting crop. However, these methods miss a crucial element in the video domain: time. We introduce BraVe, a self-supervised learning framework for video. In BraVe, one of the views has access to a narrow temporal window of the video while the other view has a broad access to the video content. Our models learn to generalise from the narrow view to the general content of the video. Furthermore, BraVe processes the views with different backbones, enabling the use of alternative augmentations or modalities into the broad view such as optical flow, randomly convolved RGB frames, audio or their combinations. We demonstrate that BraVe achieves state-of-the-art results in self-supervised representation learning on standard video and audio classification benchmarks including UCF101, HMDB51, Kinetics, ESC-50 and AudioSet.
Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.
Space-time super-resolution for satellite video: A joint framework based on multi-scale spatial-temporal transformer

Satellite video is an emerging type of earth observation tool, which has attracted increasing attention because of its application in dynamic analysis. However, most studies only focus on improving the spatial resolution of satellite video imagery. In contrast, few works are committed to enhancing the temporal resolution, and the joint spatial-temporal improvement is even less. The joint spatial-temporal enhancement can not only produce high-resolution imagery for subsequent applications, but also provide the potentials of clear motion dynamics for extreme events observation. In this paper, we propose a joint framework to enhance the spatial and temporal resolution of satellite video simultaneously. Firstly, to alleviate the problem of scale variation and scarce motion in satellite video, we design a feature interpolation module that deeply couples optical flow and multi-scale deformable convolution to predict unknown frames. Deformable convolution can adaptively learn the multi-scale motion information and profoundly complement optical flow information. Secondly, a multi-scale spatial-temporal transformer is proposed to aggregate the contextual information in long-time series video frames effectively. Since multi-scale patches are embedded in multiple heads for spatial-temporal self-attention calculation, we can comprehensively exploit multi-scale details in all frames. Extensive experiments on the Jilin-1 satellite video demonstrate that our model is superior to the existing methods. The source code is available at https://github.com/XY-boy.