Reviewerly/exHarmony huggingface dataset:
Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets

This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles using an onboard standard RGB camera. The implementation requires two stages: an instance segmentation network able to produce fast and reliable preliminary segmentation results and a post-processing 3D fully connected Conditional Random Field, which significantly improves segmentation results by exploring temporal correlations between nearby frames in video sequences. Moreover, due to the absence of maritime datasets consisting of properly labeled video sequences, we create a new dataset comprising synthetic video sequences of maritime surveillance scenarios (MarSyn). The main advantages of this approach are the possibility of generating a vast set of images and videos, being able to represent real-world scenarios without the necessity of deploying the real vehicle, and automatic labels, which eliminate human labeling errors. We train the system with the MarSyn dataset and with aerial footage from publicly available annotated maritime datasets to validate the proposed approach. We present some experimental results and compare them to other approaches, and we also illustrate the temporal stability provided by the second stage in missing frames and wrong segmentation scenarios.
Quantum speed limits for observables

In the Schr{\"o}dinger picture, the state of a quantum system evolves in time and the quantum speed limit describes how fast the state of a quantum system evolves from an initial state to a final state. However, in the Heisenberg picture the observable evolves in time instead of the state vector. Therefore, it is natural to ask how fast an observable evolves in time. This can impose a fundamental bound on the evolution time of the expectation value of quantum mechanical observables. We obtain the quantum speed limit time-bound for observable for closed systems, open quantum systems and arbitrary dynamics. Furthermore, we discuss various applications of these bounds. Our results can have several applications ranging from setting the speed limit for operator growth, correlation growth, quantum thermal machines, quantum control and many body physics.
Comparison of encrypted control approaches and tutorial on dynamic systems using Learning With Errors-based homomorphic encryption

Encrypted control has been introduced to protect controller data by encryption at the stage of computation and communication, by performing the computation directly on encrypted data. In this article, we first review and categorize recent relevant studies on encrypted control. Approaches based on homomorphic encryption, multi-party computation, and secret sharing are introduced, compared, and then discussed with respect to computational complexity, communication load, enabled operations, security, and research directions. We proceed to discuss a current challenge in the application of homomorphic encryption to dynamic systems, where arithmetic operations other than integer addition and multiplication are limited. We also introduce a Learning With Errors based homomorphic cryptosystem called “Gentry-Sahai-Waters” scheme and discuss its benefits that allow for recursive multiplication of encrypted dynamic systems, without use of computationally expensive bootstrapping techniques.
Harvesting metadata in clinical care: a crosswalk between FHIR, OMOP, CDISC and openEHR metadata

Metadata describe information about data source, type of creation, structure, status and semantics and are prerequisite for preservation and reuse of medical data. To overcome the hurdle of disparate data sources and repositories with heterogeneous data formats a metadata crosswalk was initiated, based on existing standards. FAIR Principles were included, as well as data format specifications. The metadata crosswalk is the foundation of data provision between a Medical Data Integration Center (MeDIC) and researchers, providing a selection of metadata information for research design and requests. Based on the crosswalk, metadata items were prioritized and categorized to demonstrate that not one single predefined standard meets all requirements of a MeDIC and only a maximum data set of metadata is suitable for use. The development of a convergence format including the maximum data set is the anticipated solution for an automated transformation of metadata in a MeDIC.
Decoherence and quantum steering of accelerated qubit–qutrit system

The bidirectional steerability between different-size subsystems is discussed for a single parameter accelerated qubit–qutrit system. The decoherence due to the mixing and acceleration parameters is investigated, where for the total system and the qutrit, it increases as the mixing parameter increases, while it decreases for the qubit. The non-classical correlations are quantified by using the local quantum uncertainty, where the uncertainty increases at large values of the acceleration parameter. The possibility that each subsystem steers each other is studied, where the behavior of the steering inequality predicts that the qubit has a large ability to steer the qutrit. The degree of steerability decays gradually when the qubit is accelerated. However, it decays suddenly when the qutrit or both subsystems are accelerated. The degree of steerability is shown for the qutrit vanishes at small values of the acceleration, while the qubit at large acceleration. The difference between the degrees of steerability depends on the initial state settings and the size of the accelerated subsystem.
Multimodality information fusion for automated machine translation

Machine translation is a popular automation approach for translating texts between different languages. Although traditionally it has a strong focus on natural language, images can potentially provide an additional source of information in machine translation. However, there are presently two challenges: (i) the lack of an effective fusion method to handle the triangular-mapping function between image, text, and semantic knowledge; and (ii) the accessibility of large-scale parallel corpus to train a model for generating accurate machine translations. To address these challenges, this work proposes an effective multimodality information fusion method for automated machine translation based on semi-supervised learning. The method fuses multimodality information, texts and images to deliver automated machine translation. Specifically, our objective fuses multimodalities with alignment in a multimodal attention network, which advances the method through the power of mapping text and image features to their semantic information with accuracy. Moreover, a semi-supervised learning method is utilized for its capability in using a small number of parallel corpus for supervised training on the basis of unsupervised training. Conducted on the Multi30k dataset, the experimental results shows the promising performance of our proposed fusion method compared with state-of-the-art approaches.
MLstruct: principal type inference in a Boolean algebra of structural types

Intersection and union types are becoming more popular by the day, entering the mainstream in programming languages like TypeScript and Scala 3. Yet, no language so far has managed to combine these powerful types with principal polymorphic type inference. We present a solution to this problem in MLstruct, a language with subtyped records, equirecursive types, first-class unions and intersections, class-based instance matching, and ML-style principal type inference. While MLstruct is mostly structurally typed, it contains a healthy sprinkle of nominality for classes, which gives it desirable semantics, enabling the expression of a powerful form of extensible variants that does not need row variables. Technically, we define the constructs of our language using conjunction, disjunction, and negation connectives, making sure they form a Boolean algebra, and we show that the addition of a few nonstandard but sound subtyping rules gives us enough structure to derive a sound and complete type inference algorithm. With this work, we hope to foster the development of better type inference for present and future programming languages with expressive subtyping systems.
There's No place like home: Understanding users' intentions toward securing internet-of-things (IoT) smart home networks

Modern households are increasingly becoming digitized as they contain numerous Internet-connected networked devices throughout the home. However, this growth in the adoption of smart devices in households comes with the risk of Internet cyber-attacks that seem to be increasing every year. With many individuals working from home due to the COVID-19 pandemic, smart home networks are becoming small extensions of the organizational IT infrastructure. Amidst this backdrop, this behavioral study aims to understand the factors that drive an individual's intention toward securing their home network from cyber-attacks. We draw upon rational choice theory (RCT) and theory of planned behavior (TPB) to derive a model consisting of cognitive and psychological components to explain an individual's intention to secure their smart home network. From a survey of 503 working professionals, our data analysis shows strong support for our research model, and, thus, the hypothesized relationships between the cognitive and psychological factors and individual security intentions. Practical implications of these results for home users, organizations, and researchers are discussed, which will be helpful for organizational IT security managers in planning for organizational security as the line between home and workplace is becoming increasingly blurred. • Cognitive and psychological factors explain users' intention to secure smart homes. • Theory of planned behavior and rational choice theory applied to smart home context. • Individual's normative beliefs do not have effect in smart home context. • Cost of securing does not impact one's attitude towards securing home network. • Higher intention and awareness generate greater emphasis on security intentions.
Consumers and service robots: Power relationships amid COVID-19 pandemic

Robotics significantly influence retail and consumer services. The COVID-19 pandemic further amplified the rise of service robots (SRs) through social distancing measures. While robots are embraced widely by retailers and service providers, consumers’ interaction with SRs remains an intriguing avenue of research across contexts. By taking a relative social power perspective, we report on a series of pre- and intra-COVID-19 studies. Our findings suggest that Gen-Z consumers hold more positive attitudes towards SRs perceived as lower in power vis-à-vis the human user. The longitudinal nature of our study also reveals that while attitudes towards such low-power services turned more negative during the COVID-19 pandemic, attitudes towards SRs that are high in power vis-à-vis the human user remained stable. In practical terms, while Gen-Z consumers hold more positive attitudes towards low-power robots, such service providers also face the challenge of relatively changeable attitudes towards them, especially during crisis times.
An Efficient Hybrid QHCP-ABE Model to Improve Cloud Data Integrity and Confidentiality

Cloud computational service is one of the renowned services utilized by employees, employers, and organizations collaboratively. It is accountable for data management and processing through virtual machines and is independent of end users’ system configurations. The usage of cloud systems is very simple and easy to organize. They can easily be integrated into various storages of the cloud and incorporated into almost all available software tools such as Hadoop, Informatica, DataStage, and OBIEE for the purpose of Extraction-Transform-Load (ETL), data processing, data reporting, and other related computations. Because of this low-cost-based cloud computational service model, cloud users can utilize the software and services, the implementation environment, storage, and other on-demand resources with a pay-per-use model. Cloud contributors across this world move all these cloud-based apps, software, and large volumes of data in the form of files and databases into enormous data centers. However, the main challenge is that cloud users cannot have direct control over the data stored at these data centers. They do not even know the integrity, confidentiality, level of security, and privacy of their sensitive data. This exceptional cloud property creates several different security disputes and challenges. To address these security challenges, we propose a novel Quantum Hash-centric Cipher Policy-Attribute-based Encipherment (QH-CPABE) framework to improve the security and privacy of the cloud user’s sensitive data. In our proposed model, we used both structured and unstructured big cloud clinical data as input so that the simulated experimental results conclude that the proposal has precise, resulting in approximately 92% correctness of bit hash change and approximately 96% correctness of chaotic dynamic key production, enciphered and deciphered time as compared with conventional standards from the literature.
Research on Pedestrian Detection Algorithm Based on MobileNet-YoLo

To address the problem that large pedestrian detection networks cannot be directly applied to small device scenarios due to the heavyweight and slow detection speed, this paper proposes a pedestrian detection and recognition model MobileNet-YoLo based on the YoLov4-tiny target detection framework. To address the problem of low accuracy of YoLov4-tiny, MobileNetv3 is used to optimize its backbone feature extraction network, and the MFF model is proposed to fuse the output of the first two layers to solve the information loss problem, and the attention mechanism CBAM is introduced after strengthening the feature extraction network to further improve the detection efficiency; then the 3 × 3 convolution is replaced by the depth separable convolution, which greatly reduces the number of parameters and thus improves the detection rate, then propose Ordinary data augmentation to efficiently augment the dataset and dynamically adjust the target detection anchor frame using the k-means++ clustering algorithm. Finally, the model weights trained by the VOC2007 + 2012 dataset were applied to the pedestrian dataset for retraining by the transfer learning method, which effectively solved the problem of scarce samples and greatly shortened the training time. The experimental results on the VOC2007 + 2012 dataset show that the average means accuracy of the MobileNet-YoLo model compared to YoLov4-tiny, MobileNet-YoLov4, MobileNet-YoLov3, and YoLov5s by 5.00%, 1.30%, 3.23%, and 0.74%, respectively and have reached the level to realize the landed application.
Is it the best for barista robots to serve like humans? A multidimensional anthropomorphism perspective

The COVID-19 pandemic has accelerated the use of contactless service robots in hospitality industries. However, the key drivers of consumer behaviors against service robots have been ill-understood. This study examines the interactive relationships between the physical (visual features) and psychological (service autonomy) dimensions of service-robot anthropomorphism and their impacts on consumer acceptance of service robots. Adopting an experimental vignette method (EVM) with 402 participants, the study reveals that the impacts of visual features on consumers’ intention are affected by the level of service robots’ autonomy; particularly, consumers showed the highest intention when the robots have medium visual features and high autonomy while their intention became lower for the same level of visual features with low autonomy. Interestingly, consumers showed the lowest intention with high level visual features, regardless of the levels of autonomy. Our results also show that human identity threats and consumer resistance play a significant counterproductive mechanism between service robot anthropomorphism and consumers’ intention. • A multidimensional perspective on the anthropomorphism of service robots. • Robot’s visual features as the physical dimension and autonomy as the psychological dimension. • Interactions effects between the visual features and autonomy on consumer’s intention to use. • Human identity threats and consumer resistance as another counterproductive social mechanism. • Use of an experimental vignette methodology to validate the theoretically proposed model. • A 3 × 2 between-subject experimental design (3 levels of visual features x 2 levels of autonomy). • Useful to the hospitality literature as more services become contactless using service robots.
Demystifying quantum blockchain for healthcare

Abstract The healthcare industry and the battle against the COVID‐19 pandemic are two areas where blockchain technology might be useful. In this study, blockchain's significance is examined. Blockchain technology and related procedures will be used in future healthcare systems for collecting sensor data, automated patient monitoring, and safe data storage. Because it can store a large amount of data in a dispersed and secure way and provide access whenever and wherever it is needed, this technology greatly simplifies the process of carrying out activities. The advantages of quantum computing, such as the speed with which patients can be found and monitored, may be fully used with the help of quantum blockchain. Quantum blockchain is an additional resource that may be used to safeguard the veracity, integrity, and availability of stored information. Combining quantum computing with blockchain technology may allow faster and more secure medical information processing. In this research, the authors examine the potential uses of blockchain and quantum technology in the healthcare industry. Quantum technologies, blockchain‐based technologies, and other cutting‐edge ICTs (such as ratification intelligence, machine learning, drones, and so on) were investigated and contrasted in this article.
Improving purity of blasting vibration signals using advanced Empirical Mode Decomposition and Wavelet packet technique

The study proposes a denoising method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet packet threshold denoising (WPTD) to reduce the noise of blasting vibration signals. CEEMDAN is first employed to decompose noisy blasting vibration signals into different intrinsic mode functions (IMFs), i.e., different frequencies of subcomponents. By using correlation coefficients, all of IMFs are automatically categorized as the dominant IMFs with higher portions of vibration components and the secondary IMFs with less vibration contents. Then, with further decomposing two groups of IMFs into different scales of Wavelet packet coefficient series, WPTD based on the fixed-threshold technique and the soft threshold function method is applied to minimize the level of noise in two groups of IMFs. Two groups of denoised IMFs are finally reconstructed as the pure blasting vibration waveform. Numerical waveforms with different types and intensities of noise and measured blasting vibration signals with different main frequency bands are used to verify the accuracy and capability of the proposed method. The numerical and field results show that the proposed method can not only effectively reduce noise components, but also well remain components of blasting vibrations.
AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder

More and more researches have focused on the use of knowledge graphs (KG) to solve the sparsity problem of traditional collaborative filtering recommendation systems. Most KG based recommendation algorithms focus on independent paths connecting users and items, or iteratively propagate user preferences in KG. However, the current approachs that focus on indedpent paths ignore the association between paths. Therefore, in this study, we propose a knowledge graph recommendation system algorithm for the multiple paths RNN encoder (AGRE), which fully considers the association between paths. Specifically, the paths between the user and the item are coded by a specified RNN (MRNN) to accurately learn the user’s preferences. Traditional RNNs can encode multiple paths without considering the association between paths, but our RNN can encode multiple paths with considering the association between paths. We have compared AGRE with other state-of-the-art algorithms on three real-world datasets, and achieved good results in terms of AUC and Precision@K. This indicates that AGRE could solve the problem of sparse interaction between users and items, and could make full use of the knowledge graph for recommendation.
Copy move forgery detection and segmentation using improved mask region-based convolution network (RCNN)

Copy-move forgery (CMF) is a common image manipulation approach that uses the information from the same sample to manipulate it with the intent of hiding the required content. Several approaches have been designed for the timely detection of CMF; however, accurate identification of manipulated samples is a complicated job due to the similar capturing conditions of the copied content as the patch is taken from the same image. Moreover, the occurrence of several post-processing attacks i.e., noise, blurring, brightness variations, etc. further enhances the difficulties of the detection approaches. In this work, we attempted to cover the limitations of existing methods by proposing a deep learning (DL)-based approach for the accurate detection of CMF. A custom Mask-RCNN model with the DenseNet-41 as the base network is presented which is capable of nominating a better set of image features and presents the complex image transformation effectively. More descriptively, the DenseNet-41 model is used as the base network for deep keypoints extraction which is then localized, segmented, and categorized by the Mask-RCNN model to locate the manipulated area. We have tested the proposed model on three standard databases namely the CoMoFoD, MICC-F2000, and CASIA-v2 databases, and attained a precision of 98.12%, 99.02%, and 83.41%, respectively. We have reported the results for numerous image post-processing attacks and confirmed that the presented work is robust to detect the CMF in the presence of translation, scale variations, rotation, color changes, noise, compression, and blurring in images. We have confirmed through extensive quantitative and qualitative evaluation that the DenseNet-41-based Mask-RCNN model is robust to CMF detection and can assist forensic analyzers to detect forensic manipulations accurately.