Reviewerly/exHarmony huggingface dataset:
An Attention Nested U-Structure Suitable for Salient Ship Detection in Complex Maritime Environment

Salient ship detection plays an important role in ensuring the safety of maritime transportation and navigation. However, due to the influence of waves, special weather, and illumination on the sea, existing saliency methods are still unable to achieve effective ship detection in a complex marine environment. To solve the problem, this paper proposed a novel saliency method based on an attention nested U-Structure (AU2Net). First, to make up for the shortcomings of the U-shaped structure, the pyramid pooling module (PPM) and global guidance paths (GGPs) are designed to guide the restoration of feature information. Then, the attention modules are added to the nested U-shaped structure to further refine the target characteristics. Ultimately, multi-level features and global context features are integrated through the feature aggregation module (FAM) to improve the ability to locate targets. Experiment results demonstrate that the proposed method could have at most 36.75% improvement in F-measure (Favg) compared to the other state-of-the-art methods.
Multi-objective optimization for scientific workflow scheduling based on Performance-to-Power Ratio in fog–cloud environments

The ever-expanding processing of large-scale scientific workflow applications has increased power consumption and reliability costs. However, considering the complexity of computational data processing in fog–cloud computing, existing workflow scheduling techniques conceived for traditional distributed systems is limited and powerless. Therefore, for profit-driven cloud service providers, minimizing power consumption and maximizing scheduling reliability of scientific workflows is a pressing concern. In this paper, we model the scientific workflow scheduling as a multi-objective optimization problem to compromise the conflicting energy consumption and scheduling reliability solution in two steps. The first step places the tasks with lower computational requirements on fog resources and computationally complex tasks on cloud resources, avoiding resources with high failure rates. In the second step, we adapt the Reliability-Aware stepwise Performance-to-Power Ratio procedure to further reduce energy consumption by sampling the machine's utilization levels with a distinguished Performance-to-Power Ratio (PPR). The PPR is defined as the calculation of the number of transactions performed at a specific time divided by the active power consumed. Simulation results under synthesized and real-world workflow applications demonstrated that dis-RMEE achieved considerably higher reliability values under the minimized energy constraint compared with some opponent workflow mapping algorithms.
Secure and reliable computation offloading in blockchain-assisted cyber-physical IoT systems

With the development of the Cyber-Physical Internet of Things System (CPIoTS), the number of Cyber-Physical System (CPS) applications accessed in networks has increased dramatically. Latency-sensitive resource orchestration in CPS applications is extraordinarily essential for maintaining the Quality of Experience (QoE) for users. Although edge-cloud computing performs effectively in achieving latency-aware resource allocation in CPIoTS, existing methods fail to jointly consider the security and reliability requirements, thereby increasing the process latency of tasks and degrading the QoE of users. This paper aims to minimize the system latency of edge-cloud computing coupled with CPS while simultaneously considering the security and reliability requirements. We first consider a time-varying channel model as a Finite-State Markov Channel (FSMC) and propose a distributed blockchain-assisted CPIoTS to realize secure consensus and reliable resource orchestration by offloading computation tasks in edge-cloud computing. Moreover, we propose an efficient resource allocation algorithm, PPO-SRRA, that optimizes computing offloading and multi-dimension resource (e.g., communication, computation, and consensus resource) allocation by using a policy-based Deep Reinforcement Learning (DRL) method. The experimental results show that the proposed resource allocation scheme can reduce the system latency and ensure consensus security.
HY-DBSCAN: A hybrid parallel DBSCAN clustering algorithm scalable on distributed-memory computers

Dbscan is a density-based clustering algorithm which is well known for its ability to discover clusters of arbitrary shape as well as to distinguish noise. As it is computationally expensive for large datasets, research studies on the parallelization of Dbscan have been received a considerable amount of attention. In this paper we present an exact, efficient and scalable parallel Dbscan algorithm which we call Hy-Dbscan. It employs three major techniques to enable scalable data clustering on distributed-memory computers i) a modified kd-tree for domain decomposition, ii) a spatial indexing approach based on grid and inference, and iii) a cluster merging scheme based on distributed Rem's Union-Find algorithm. Moreover, Hy-Dbscan exploits process level and thread level parallelization. In experiments, we have demonstrated performance and scalability using two scientific datasets on up to 2048 cores of a distributed-memory computer. Through extensive evaluation, we show that Hy-Dbscan significantly outperforms previous state-of-the-art Dbscan implementations.
Reversible data hiding in JPEG images based on coefficient-first selection

Joint photographic experts group (JPEG) is the most commonly used image format on the Internet, and using reversible data hiding (RDH) technology to verify its authenticity and integrity is of great significance. So far, many RDH algorithms for JPEG image have been proposed, which improve the performance of the algorithm via block sorting, frequency selection and other adaptive strategies. In this paper, a new JPEG RDH method based on coefficient-first selection strategy is proposed. In our new method, the distribution models of alternating current (AC) coefficients belonging to different discrete cosine transform (DCT) coefficient blocks are constructed first, and then the distortion value of each AC coefficient can be calculated through the estimated probability density function of the constructed coefficient model. In the embedding process, the coefficients with low distortion values will be preferentially selected for data hiding. Various experimental results demonstrate that compared with the state-of-the-art JPEG RDH methods, our proposed may have better visual quality and less increase of file storage size.
Identification of MRI-Based Adenocarcinoma Tumours with 3-D Convolutionary System

Computer-assisted adenocarcinoma diagnostics can help physicians achieve better diagnostic accuracy and efficient Ness that is not dependent on the subjective judgement and experience of the physician. Classification techniques of pancreatic tumours are subject to a partial automated test problem and disregards temporal and spatial character traits. For adenocarcinoma magnetic resonance imaging (MRT) classifications, we employed 3-dimensional variants of ResNet18, ResNet52, ResNet34, and Inception-ResNet throughout this study. We have suggested a normalised gradient descent to ameliorate the impact of imbalanced data. In order to match the impacts of the proposed gradient descent, there have been two different sets of experimental evaluation. With calibrated gradient descent, experimental findings demonstrate the effectiveness of the four methods is mainly enhanced with the exception of ResNet52's accuracy. In addition, the falsified imperfections are being diminished. ResNet18 is amongst them most efficient and attains 91% precision.
Omni-NeRF: Neural Radiance Field from 360° Image Captures

This paper tackles the problem of novel view synthesis (NVS) from 360° images with imperfect camera poses or intrinsic parameters. We propose a novel end-to-end framework for training Neural Radiance Field (NeRF) models given only 360° RGB images and their rough poses, which we refer to as Omni-NeRF. We extend the pinhole camera model of NeRF to a more general camera model that better fits omni-directional fish-eye lenses. The approach jointly learns the scene geometry and optimizes the camera parameters without knowing the fisheye projection.
Implementation of an E-voting Prototype using Ethereum Blockchain in Ganache Network

Digitization of a secure electoral system capable of ensuring fairness and privacy has been a long-standing challenge for a variety of reasons. One is the system's reliance on a third-party organization to manage and verify election results, rendering it insecure. There is a possibility that data will be altered during the election process. As a result, a decentralized e-voting system application capable of verification will be developed in this work using blockchain technology. The Ethereum network and the Truffle framework will be used to implement smart contracts as self-executing electoral agreements. The Ganache (local blockchain) network design demonstrated that the prototype is capable of recording every voting transaction on the network without the use of a central database. The voter can verify the election result by inspecting each voting transaction on the local blockchain. The proposed design will ensure that all voting transactions are stored in a decentralized database, ensuring that no administrative control over the ballot is exercised.
Markov clustering ensemble

Clustering ensemble is an unsupervised ensemble learning method that is very important in machine learning, since it integrates multiple weak base clustering results to produce a strong consistency result. This paper proposes the Markov clustering ensemble (MCE) model to solve the weak stability and robustness of soft clustering ensemble. First, the base clustering algorithms are regarded as new features of the original datasets. Then, the results of the base clustering algorithms are the values of these features, which can break through the framework of consensus cluster ensemble. Second, as the base clustering results are discrete data, the maximum information coefficient is applied to measure their similarity. Accordingly, a graph-based cluster ensemble model can be constructed with row vectors as vertices and the similarity between row vectors as edges. Then, the Markov process can be applied to infer the graph-based cluster ensemble model; therefore, the MCE algorithm is designed according to the inference. To test the performance of the MCE algorithm, clustering algorithms and clustering ensemble algorithms are used to conduct comparative experiments on ten datasets. The experimental results show that the MCE algorithm outperforms the other algorithms in terms of accuracy and purity.
PI2: End-to-end Interactive Visualization Interface Generation from Queries

Interactive visual analysis interfaces are critical in nearly every data task. However, creating new interfaces is deeply challenging, as it requires the developer to understand the queries needed to express the desired analysis task, design the appropriate interface to express those queries for the task, and implement the interface using a combination of visualization, browser, server, and database technologies. Although prior work generates a set of interactive widgets that can express an input query log, this paper presents PI2, the first system to generate fully functional visual analysis interfaces from an example sequence of analysis queries. PI2 analyzes queries syntactically and represents a set of queries using a novel Difftree structure that encodes systematic variations between query abstract syntax trees. PI2 then maps each Difftree to a visualization that renders its results, the variations in each Difftree to interactions, and generates a good layout for the interface. We show that PI2 can express data-oriented interactions in existing visualization interaction taxonomies, reproduce or improve several real-world visual analysis interfaces, generate interfaces in 2-19s (median 6s), and scale linearly with the number of queries.
Healing Justice: A Framework for Collective Healing and Well-Being from Systemic Traumas

The 2020 Black Lives Matter protests in the United States demanded police reform and legislative action. Data-driven policing is just one technological intervention designed with the hope to tackle police brutality. However, these design interventions are often rooted in the continued racial profiling of poor and socially marginalized communities. Designers and researchers need a Healing Justice framework to circumvent this harm. Healing Justice addresses generational trauma and violence in marginalized communities and is not just a framework for policing but can address maternal mortality rates, COVID-19, medical malpractice, and other trauma issues. In this paper, we apply a Healing Justice framework to co-design activities focused on police brutality. We bridge Healing Justice and design by using an Afrofuturist Feminism framework, arguing that Healing Justice and Afrofuturist feminism frameworks lead to collective, grassroots, and pragmatic designs.
Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking

Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving. Approaches to monocular 3D perception including detection and tracking, however, often yield inferior performance when compared to LiDAR-based techniques. Through systematic analysis, we identified that per-object depth estimation accuracy is a major factor bounding the performance. Motivated by this observation, we propose a multi-level fusion method that combines different representations (RGB and pseudo-LiDAR) and temporal information across multiple frames for objects (tracklets) to enhance per-object depth estimation. Our proposed fusion method achieves the state-of-the-art performance of per-object depth estimation on the Waymo Open Dataset, the KITTI detection dataset, and the KITTI MOT dataset. We further demonstrate that by simply replacing estimated depth with fusion-enhanced depth, we can achieve significant improvements in monocular 3D perception tasks, including detection and tracking.
STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic

The outbreak of 2019 novel coronavirus (COVID-19) has triggered unprecedented challenges and put the whole world in a parlous condition. The impacts of COVID-19 is a matter of grave concern in terms of fatality rate, socio-economical condition, health infrastructure. It is obvious that only pharmaceutical solutions (vaccine) cannot eradicate this pandemic completely, and effective strategies regarding lockdown measures, restricted mobility, emergency services to users-in brief data-driven decision system is of utmost importance. This necessitates an efficient data analytics framework, data infrastructure to store, manage pandemic related information, and distributed computing platform to support such data-driven operations. In the past few decades, Internet of Things-based devices and applications have emerged significantly in various sectors including healthcare and time-critical applications. To be specific, health-sensors help to accumulate health-related parameters at different time-instances of a day, the movement sensors keep track of mobility traces of the user, and helps to assist them in varied conditions. The smartphones are equipped with several such sensors and the ability of low-cost connected sensors to cover large areas makes it the most useful component to combat pandemics such as COVID-19. However, analysing and managing the huge amount of data generated by these sensors is a big challenge. In this paper we have proposed a unified framework which has three major components: (i) Spatial Data Infrastructure to manage, store, analyse and share spatio-temporal information with stakeholders efficiently, (ii) Cloud-Fog-Edge-based hierarchical architecture to support preliminary diagnosis, monitoring patients' mobility, health parameters and activities while they are in quarantine or home-based treatment, and (iii) Assisting users in varied emergency situation leveraging efficient data-driven techniques at low-latency and energy consumption. The mobility data analytics along with SDI is required to interpret the movement dynamics of the region and correlate with COVID-19 hotspots. Further, Cloud-Fog-Edge-based system architecture is required to provision healthcare services efficiently and in timely manner. The proposed framework yields encouraging results in taking decisions based on the COVID-19 context and assisting users effectively by enhancing accuracy of detecting suspected infected people by ∼ 24% and reducing delay by ∼ 55% compared to cloud-only system.
A Combined Chaotic System for Speech Encryption

This paper presents a speech encryption scheme by performing a combination of modified chaotic maps inspired by classic logistic and cubic maps. The main idea was to enhance the performance of classical chaotic maps by extending the range of the chaotic parameter. The resulted combining map was applied to a speech encryption scheme by using the confusion and diffusion architecture. The evaluation results showed a good performance regarding the chaotic behaviors such as initial value, control parameter, Lyapunov exponent, and bifurcation diagram. Simulations and computer evaluations with security analysis showed that the proposed chaotic system exhibits excellent performance in speech encryption against various attacks. The results obtained demonstrated the efficiency of the proposed scheme compared to an existing valuable method for static and differential cryptographic attacks.
Responsible Knowledge Management in Energy Data Ecosystems

This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces.
Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data

With continued advancements in portable eye-tracker technology liberating experimenters from the restraints of artificial laboratory designs, research can now collect gaze data from real-world, natural navigation. However, the field lacks a robust method for achieving this, as past approaches relied upon the time-consuming manual annotation of eye-tracking data, while previous attempts at automation lack the necessary versatility for in-the-wild navigation trials consisting of complex and dynamic scenes. Here, we propose a system capable of informing researchers of where and what a user's gaze is focused upon at any one time. The system achieves this by first running footage recorded on a head-mounted camera through a deep-learning-based object detection algorithm called Masked Region-based Convolutional Neural Network (Mask R-CNN). The algorithm's output is combined with frame-by-frame gaze coordinates measured by an eye-tracking device synchronized with the head-mounted camera to detect and annotate, without any manual intervention, what a user looked at for each frame of the provided footage. The effectiveness of the presented methodology was legitimized by a comparison between the system output and that of manual coders. High levels of agreement between the two validated the system as a preferable data collection technique as it was capable of processing data at a significantly faster rate than its human counterpart. Support for the system's practicality was then further demonstrated via a case study exploring the mediatory effects of gaze behaviors on an environment-driven attentional bias.
Review of the Law Popularizing Education on Administrative Compulsion in Response to Major Epidemic Situations in China

The popularization of education on the administrative compulsion law in response to major epidemic situations in China refers to the educational activities carried out by an educational subject aimed at an educational object promoting understanding and mastery of knowledge of administrative compulsion. The goal is to improve the government’s legal ability to prevent and control behavior, enhance people’s awareness of the rule of law, and provide a strong legal basis for the rule of law in the context of major epidemics. Other aims are to cultivate citizens’ legal beliefs, restrain citizens’ conduct, and maintain a stable social order. However, problems arise in the subject, content, and mode of the educational activities. Therefore, we need to discuss compulsory education in the context of major epidemic situations, including the need for a favorable educational environment, the strengthening of information distribution, and the guidance of public opinion on administrative enforcement. We should also improve education subjects to enhance the legal quality of administrative organs, perfect the education content, encourage people to support administrative compulsion in epidemic situations, and deliver education in campus online classes.
Technology Behavior Model—Impact of Extended Reality on Patient Surgery

Smart surgery is a new way to utilize smart devices to change existing surgeries. Smart glasses can enhance the surgical procedure so that the patient can understand the procedure more intuitively. Surgery is for patients, and patient acceptance of extended reality surgery is the purpose of this study. This study uses the technology behavior model, which is more in line with the user’s assessment of the acceptance behavior of the new technology. A triangulated research approach was used, which applies to this study for a specific patient population. Primary data were collected from hospitals through questionnaires and were statistically analyzed by CB&amp;PLS-SEM multimodel using SmartPLS software. It was concluded that patients were influenced by operational emotional factors in undergoing extended reality surgery. The study provides a basis for future research related to the practical application of smart surgery from the patient’s perspective in viewing and accepting surgery.