Reviewerly/exHarmony huggingface dataset:
A Cascade Rotated Anchor-Aided Detector for Ship Detection in Remote Sensing Images

Automatic ship detection in high-resolution remote sensing images has attracted increasing research interest due to its numerous practical applications. However, there still exist challenges when directly applying state-of-the-art object detection methods to real ship detection, which greatly limits the detection accuracy. In this article, we propose a novel cascade rotated anchor-aided detection network to achieve high-precision performance for detecting arbitrary-oriented ships. First, we develop a data preprocessing embedded cascade structure to reduce large amounts of false positives generated on blank areas in large-size remote sensing images. Second, to achieve accurate arbitrary-oriented ship detection, we design a rotated anchor-aided detection module. This detection module adopts a coarse-to-fine architecture with a cascade refinement module (CRM) to refine the rotated boxes progressively. Meanwhile, it utilizes an anchor-aided strategy similar to anchor-free, thus breaking through the bottlenecks of anchor-based methods and leading to a more flexible detection manner. Besides, a rotated align convolution layer is introduced in CRM to extract features from rotated regions accurately. Experimental results on the challenging DOTA and HRSC2016 data sets show that the proposed method achieves 84.12% and 90.79% AP, respectively, outperforming other state-of-the-art methods.
Private blockchain-based access control mechanism for unauthorized UAV detection and mitigation in Internet of Drones environment

Drones, which are also known as Unmanned Aerial Vehicles (UAVs), are very useful in delivering the packages, and real-time object detection and tracking with minimal human interference. However, there may be several security threats in such an environment, for instance, a malicious user can spy unauthorized drones, transfer malicious packages, or even damage the network reliability that can have direct impact on drones control. This may lead to a potential threat for people, governments, and business sectors. To deal with these issues, in this paper, we propose a novel access control scheme for unauthorized UAV detection and mitigation in an Internet of Drones (IoD) environment, called ACSUD-IoD. With the help of the blockchain-based solution incorporated in ACSUD-IoD, the transactional data having both the normal secure data from a drone (UAV) to the Ground Station Server (GSS) and the abnormal (suspected) data for detection of unauthorized UAVs by the GSS are stored in private blockchain, that are authentic and genuine. As a result, the Big data analytics can be performed on the authenticated transactional data stored in the blockchain. Through the detailed security analysis including formal security under the broadly-accepted Real-Or-Random (ROR) model, formal security verification using the widely-applied Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and non-mathematical security analysis show the robustness of the proposed scheme against a number of potential attacks needed in an IoD environment. The testbed experiments for various cryptographic primitives using the broadly-accepted Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL) have been performed under both server and Raspberry PI 3 configurations. Furthermore, a detailed comparative analysis among the proposed scheme and other existing competing schemes shows the efficacy and more robustness as compared to the existing schemes. Finally, the blockchain-based practical demonstration shows the effectiveness of the proposed scheme.
DCT &amp; DWT based watermarking scheme for medical information security

In order to secure the exchanged medical images in telemedicine, we propose in this work two blind watermarking approaches for the medical images protection. In the first scheme a combination of DCT and Schur decomposition is performed. In order to obtain a good compromise between robustness and imperceptibility, the integration is performed in the medium frequencies of the image. In the second scheme, the combination of DWT and Schur decomposition provide a more robust watermark distribution. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality watermarked images and are very robust against several conventional attacks. These schemes allow the protection of the patient's information and thus ensure the confidentiality of personal data.
Multi-Layer Blockchain-Based Security Architecture for Internet of Things

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.
Locality and Digital Quantum Simulation of Power-Law Interactions

The propagation of information in non-relativistic quantum systems obeys a speed limit known as a Lieb-Robinson bound. We derive a new Lieb-Robinson bound for systems with interactions that decay with distance $r$ as a power law, $1/r^\alpha$. The bound implies an effective light cone tighter than all previous bounds. Our approach is based on a technique for approximating the time evolution of a system, which was first introduced as part of a quantum simulation algorithm by Haah et al., FOCS'18. To bound the error of the approximation, we use a known Lieb-Robinson bound that is weaker than the bound we establish. This result brings the analysis full circle, suggesting a deep connection between Lieb-Robinson bounds and digital quantum simulation. In addition to the new Lieb-Robinson bound, our analysis also gives an error bound for the Haah et al. quantum simulation algorithm when used to simulate power-law decaying interactions. In particular, we show that the gate count of the algorithm scales with the system size better than existing algorithms when $\alpha>3D$ (where $D$ is the number of dimensions).
Design Guidelines for Blockchain-Assisted 5G-UAV Networks

Fifth generation (5G) wireless networks are designed to meet various end-user quality of service (QoS) requirements through high data rates (typically of gigabits per second) and low latencies. Coupled with fog and mobile edge computing, 5G can achieve high data rates, enabling complex autonomous smart city services such as the large deployment of self-driving vehicles and large-scale artificial-intelligence-enabled industrial manufacturing. However, to meet the exponentially growing number of connected IoT devices and irregular data and service requests in both low- and high-density locations, the process of enacting traditional cells supported through fixed and costly base stations requires rethought to enable on-demand mobile access points in the form of unmanned aerial vehicles (UAV) for diversified smart city scenarios. This article envisions a 5G network environment that is supported by blockchain-enabled UAVs to meet dynamic user demands with network access supply. The solution enables decentralized service delivery (drones as a service) and routing to and from end users in a reliable and secure manner. Both public and private blockchains are deployed within the UAVs, supported by fog and cloud computing devices and data centers to provide a wide range of complex authenticated service and data availability. Particular attention is paid to comparing data delivery success rates and message exchange in the proposed solution against traditional UAV-supported cellular networks. Challenges and future research are also discussed with highlights on emerging technologies such as federated learning.
Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
Semantics of the Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable and Explainable?

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, and human-computer interactions. However, DL's black-box nature and over-reliance on massive amounts of data condensed into labels and dense representations pose challenges for interpretability and explainability. Furthermore, DLs have not proven their ability to effectively utilize relevant domain knowledge critical to human understanding. This aspect was missing in early data-focused approaches and necessitated knowledge-infused learning (K-iL) to incorporate computational knowledge. This article demonstrates how knowledge, provided as a knowledge graph, is incorporated into DL using K-iL. Through examples from natural language processing applications in healthcare and education, we discuss the utility of K-iL towards interpretability and explainability.
mustGAN: multi-stream Generative Adversarial Networks for MR Image Synthesis

Multi-contrast MRI protocols increase the level of morphological information available for diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors including scan time and patient motion. Synthesis of missing or corrupted contrasts from other high-quality ones can alleviate this limitation. When a single target contrast is of interest, common approaches for multi-contrast MRI involve either one-to-one or many-to-one synthesis methods depending on their input. One-to-one methods take as input a single source contrast, and they learn a latent representation sensitive to unique features of the source. Meanwhile, many-to-one methods receive multiple distinct sources, and they learn a shared latent representation more sensitive to common features across sources. For enhanced image synthesis, we propose a multi-stream approach that aggregates information across multiple source images via a mixture of multiple one-to-one streams and a joint many-to-one stream. The complementary feature maps generated in the one-to-one streams and the shared feature maps generated in the many-to-one stream are combined with a fusion block. The location of the fusion block is adaptively modified to maximize task-specific performance. Quantitative and radiological assessments on T1,- T2-, PD-weighted, and FLAIR images clearly demonstrate the superior performance of the proposed method compared to previous state-of-the-art one-to-one and many-to-one methods.
Linear and nonlinear optical properties in spherical quantum dots: Inversely quadratic Hellmann potential

The aims of the reported work are to provide new insights into the quantum dot optical properties confined in an inverse of a quadratic Hellmann potential. The Schr\"odinger equation is solved using the Nikiforov-Uvarov (NU) method, in order to obtain the analytical expressions of the eigenenergies and the eigenfunctions. The linear together with the third-order nonlinear changes in absorption coefficients and refractive index are investigated using the density matrix formalism. The absorption coefficients and the refractive index changes are strong related to the structure parameters and the optical intensity.
Detecting Deep-Fake Videos from Appearance and Behavior

Synthetically-generated audios and videos - so-called deep fakes - continue to capture the imagination of the computer-graphics and computer-vision communities. At the same time, the democratization of access to technology that can create a sophisticated manipulated video of anybody saying anything continues to be of concern because of its power to disrupt democratic elections, commit small to large-scale fraud, fuel disinformation campaigns, and create non-consensual pornography. We describe a biometric-based forensic technique for detecting face-swap deep fakes. This technique combines a static biometric based on facial recognition with a temporal, behavioral biometric based on facial expressions and head movements, where the behavioral embedding is learned using a CNN with a metric-learning objective function. We show the efficacy of this approach across several large-scale video datasets, as well as in-the-wild deep fakes.
The Potential of 360° Virtual Reality Videos and Real VR for Education—A Literature Review

Virtual reality (VR) has a wide variety of potentials for education. Especially 360$^{\circ }$∘ videos can provide immersive educational experiences of otherwise not accessible real-world environments. But what potential do 360$^{\circ }$∘ videos in VR and real VR settings have for teaching? Research into the use of real VR, providing live-learning experience in the classroom is still scarce, which is why this article investigates this issue in the context of a systematic review. We discuss use cases, advantages, and limitations as well as interaction characteristics of the potential of 360$^{\circ }$∘ videos, and also the promises of real VR scenarios. By analyzing 64 articles in-depth, our results suggest that 360$^{\circ }$∘ videos can be used for a wide variety of topics. While only a few articles report technological benefits, there are indicators that 360$^{\circ }$∘ videos can benefit learning processes in terms of performance, motivation, and knowledge retention. Most papers report positive effects on other human factors such as presence, perception, engagement, emotions, and empathy. Furthermore, an open research gap has been identified in use cases for real VR.
The improved AdaBoost algorithms for imbalanced data classification

Class imbalance is one of the most popular and important issues in the domain of classification. The AdaBoost algorithm is an effective solution for classification, but it still needs improvement in the imbalanced data problem. This paper proposes a method to improve the AdaBoost algorithm using the new weighted vote parameters for the weak classifiers. Our proposed weighted vote parameters are determined not only by the global error rate but also by the classification accuracy rate of the positive class, which is our primary interest. The imbalanced index of the data is also a factor in constructing our algorithms. Our proposed algorithms outperform the traditional ones, especially regarding the evaluation criterion of F-1Measure. Theoretic proofs of the advantages of our proposed algorithms are presented. Two kinds of simulated datasets and four real datasets are applied in the experiment as the specific support to our proposed algorithms.
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism

Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching view of graphs, neglecting finer substructures’ semantics, and suffering from interpretation enigmas. This paper presents a novel hierarchical subgraph-level selection and embedding-based graph neural network for graph classification, namely SUGAR, to learn more discriminative subgraph representations and respond in an explanatory way. SUGAR reconstructs a sketched graph by extracting striking subgraphs as the representative part of the original graph to reveal subgraph-level patterns. To adaptively select striking subgraphs without prior knowledge, we develop a reinforcement pooling mechanism, which improves the generalization ability of the model. To differentiate subgraph representations among graphs, we present a self-supervised mutual information mechanism to encourage subgraph embedding to be mindful of the global graph structural properties by maximizing their mutual information. Extensive experiments on six typical bioinformatics datasets demonstrate a significant and consistent improvement in model quality with competitive performance and interpretability.
Cost-Effective Federated Learning Design

Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. Theoretically, we analytically establish the relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different metric preferences. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for various datasets, different machine learning models, and heterogeneous system settings.
Toward More Rigorous Blockchain Research: Recommendations for Writing Blockchain Case Studies

About a decade ago the fundamental operating principle of the Blockchain was introduced. It took several years before the technology gained widespread recognition in industry and academic communities outside of the computer science sphere. Since then many academic communities have taken up the topic, but so far no well-defined research agenda has emerged: research topics are scattered and rigorous approaches are scarce. More often than not, use cases implemented by industry apply a trial and error approach and there exists a dearth of theory-based academic papers on the topic following robust methodologies. Being a nascent research topic, case studies on Blockchain applications are a suitable approach to systematically transfer industry experience into research agendas which benefit both theory development and testing as well as design science research. In this paper I offer guidelines and suggestions on how to design and structure Blockchain case studies to create value for academia and the industry. More specifically, I describe Blockchain characteristics and challenges, present existing Blockchain case studies, and discuss various types of case study research and how they can be useful for industry and academic research. I conclude with a framework and a checklist for Blockchain case study research.
Consumer engagement via interactive artificial intelligence and mixed reality

The use of immersive technologies has changed the consumption environment in which retailers provide services. We present findings from a study designed to investigate consumer responses toward a $17 million AI-embedded mixed reality (MR) exhibit in a retail/entertainment complex which combines advanced technology entertainment with retail shopping. Findings from our study demonstrate that the quality of AI (i.e., speech recognition and synthesis via machine learning) associated with an augmented object increases MR immersion associated with spatial immersion, MR enjoyment, and consumers’ perceptions of novel experiences. Collectively, these increase consumer engagement, and positively influence behavioral responses—specifically, purchase intentions and intentions to share experiences with social groups. Overall, findings from this study show that interactive AI and MR technology open new avenues to promote consumer engagement.