Reviewerly/exHarmony huggingface dataset:
CrowdSFL: A Secure Crowd Computing Framework Based on Blockchain and Federated Learning

Over the years, the flourish of crowd computing has enabled enterprises to accomplish computing tasks through crowdsourcing in a large-scale and high-quality manner, and therefore how to efficiently and securely implement crowd computing becomes a hotspot. Some recent work innovatively adopted a P2P (peer-to-peer) network as the communication environment of crowdsourcing. Based on its decentralized control, issues like single-point-of-failure or DDoS attack can be overcome to some extent, but the huge computing capacity and storage costs required by this scheme is always unbearable. Federated learning is a distributed machine learning that supports local storage of data, and clients implement training through interactive gradient values. In our work, we combine blockchain with federated learning and propose a crowdsourcing framework named CrowdSFL, that users can implement crowdsourcing with less overhead and higher security. In addition, to protect the privacy of participants, we design a new re-encryption algorithm based on Elgamal to ensure that interactive values and other information will not be exposed to other participants outside the workflow. Finally, we have proved through experiments that our framework is superior to some similar work in accuracy, efficiency, and overhead.
Defining Social Engineering in Cybersecurity

Social engineering has posed a serious security threat to infrastructure, user, data and operations of cyberspace.Nevertheless, there are many conceptual deficiencies (such as inconsistent conceptual intensions, a vague conceptual boundary, confusing instances, overgeneralization and abuse) of the term making serious negative impacts on the understanding, analysis and defense of social engineering attacks.In this paper, an in-depth literature survey is conducted, the original meaning of social engineering in cybersecurity is traced, the conceptual evolution and technical development are analysed systematically, and the conceptual problems are discussed.Based on above work, this paper attempts to address these conceptual deficiencies by proposing a more compatible and precise definition of social engineering in cybersecurity (SEiCS).This definition eliminates the conceptual inconsistencies, covers the mainstream conceptual connotations, clarifies the conceptual boundary, mitigates the overgeneralization and abuse, etc.Five analysis tables (i.e., the comparative analysis of the SEiCS definition vs. mainstream conceptual intensions in the conceptual evolution, the comparative analysis of the SEiCS definition vs. typical definitions in the literature, the analysis of confusing ''social engineering cases'', the analysis of popular social engineering attack scenarios, and the analysis of social-engineering-based attacks) are provided to illustrate the performance of the proposed definition.
A Novel Clustering Algorithm Based on DPC and PSO

Analyzing the fast search and find of density peaks clustering (DPC) algorithm, we find that the cluster centers cannot be determined automatically and that the selected cluster centers may fall into a local optimum and the random selection of the parameter cut-off distance d c value.To overcome these problems, a novel clustering algorithm based on DPC & PSO (PDPC) is proposed.Particle swarm optimization (PSO) is introduced because of its simple concept and strong global search ability, which can find the optimal solution in relatively few iterations.First, to solve the effect of the selection of the parameter d c on the calculation density and the clustering results, this paper proposes a method to calculate that parameter.Second, a new fitness criterion function is proposed that iteratively searches K global optimal solutions through the PSO algorithm, that is, the initial cluster centers.Third, each sample is assigned to K initial center points according to the minimum distance principle.Finally, we update the cluster centers and redistribute the remaining objects to the clusters closest to the cluster centers.Furthermore, the effectiveness of the proposed algorithm is verified on nine typical benchmark data sets.The experimental results show that the PDPC can effectively solve the problem of cluster center selection in the DPC algorithm, avoiding the subjectivity of the manual selection process and overcoming the influence of the parameter d c .Compared with the other six algorithms, the PDPC algorithm has a stronger global search ability, higher stability and a better clustering effect.
Improved Path Planning by Tightly Combining Lattice-Based Path Planning and Optimal Control

This paper presents a unified optimization-based path planning approach to efficiently compute locally optimal solutions to optimal path planning problems in unstructured environments. The approach is motivated by showing that a lattice-based planner can be cast and analyzed as a bilevel optimization problem. This insight is used to integrate a lattice-based planner and an optimal control-based method in a novel way. The lattice-based planner is applied to the problem in a first step using a discretized search space. In a second step, an optimal control-based method is applied using the lattice-based solution as an initial iterate. In contrast to prior work, the system dynamics and objective function used in the first step are chosen to coincide with those used in the second step. As an important consequence, the lattice planner provides a solution which is highly suitable as a warm-start to the optimal control step. This proposed combination makes, in a structured way, benefit of sampling-based methods ability to solve combinatorial parts of the problem and optimal control-based methods ability to obtain locally optimal solutions. Compared to previous work, the proposed approach is shown in simulations to provide significant improvements in terms of computation time, numerical reliability and objective function value.
A Survey on Privacy Protection of Blockchain: The Technology and Application

As a kind of point-to-point distributed public ledger technology, blockchain has been widely concerned in recent years.The privacy protection of blockchain technology has always been the core issue of people's attention.In this paper, some existing solutions to the current problems of user identity and transaction privacy protection are surveyed, including coin mixing mechanism, zero knowledge proof, ring signature and other technologies.Secondly, five typical applications of privacy protection technology based on blockchain are proposed and analyzed, which are mainly divided into technology applications based on coin mixing protocol, encryption protocol, secure channel protocol and so on.Finally, in view of the shortages of the existing blockchain privacy protection technology, we explore future research challenges that need to be studied in order to preserve privacy in blockchain system, and looks forward to the future development direction.
Big data, privacy and COVID-19 – learning from humanitarian expertise in data protection

Abstract The COVID-19 pandemic leads governments around the world to resort to tracking technology and other data-driven tools in order to monitor and curb the spread of SARS-CoV-2. Such large-scale incursion into privacy and data protection is unthinkable during times of normalcy. However, in times of a pandemic the use of location data provided by telecom operators and/or technology companies becomes a viable option. Importantly, legal regulations hardly protect people’s privacy against governmental and corporate misuse. Established privacy regimes are focused on individual consent, and most human rights treaties know derogations from privacy and data protection norms for states of emergency. This leaves little safeguards nor remedies to guarantee individual and collective autonomy. However, the challenge of responsible data use during a crisis is not novel. The humanitarian sector has more than a decade of experience to offer. International organisations and humanitarian actors have developed detailed guidelines on how to use data responsibly under extreme circumstances. This article briefly addresses the legal gap of data protection and privacy during this global crisis. Then it outlines the state of the art in humanitarian practice and academia on data protection and data responsibility during crisis.
Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds

We present Frozone, a novel algorithm to deal with the Freezing Robot Problem (FRP) that arises when a robot navigates through dense scenarios and crowds. Our method senses and explicitly predicts the trajectories of pedestrians and constructs a Potential Freezing Zone (PFZ); a spatial zone where the robot could freeze or be obtrusive to humans. Our formulation computes a deviation velocity to avoid the PFZ, which also accounts for social constraints. Furthermore, Frozone is designed for robots equipped with sensors with a limited sensing range and field of view. We ensure that the robot's deviation is bounded, thus avoiding sudden angular motion which could lead to the loss of perception data of the surrounding obstacles. We have combined Frozone with a Deep Reinforcement Learning-based (DRL) collision avoidance method and use our hybrid approach to handle crowds of varying densities. Our overall approach results in smooth and collision-free navigation in dense environments. We have evaluated our method's performance in simulation and on real differential drive robots in challenging indoor scenarios. We highlight the benefits of our approach over prior methods in terms of success rates (upto 50% increase), pedestrian-friendliness (100% increase) and the rate of freezing (>80% decrease) in challenging scenarios.
$\alpha $-variational inference with statistical guarantees

We provide statistical guarantees for a family of variational approximations to Bayesian posterior distributions, called α-VB, which has close connections with variational approximations of tempered posteriors in the literature.The standard variational approximation is a special case of α-VB with α = 1.When α ∈ (0, 1], a novel class of variational inequalities are developed for linking the Bayes risk under the variational approximation to the objective function in the variational optimization problem, implying that maximizing the evidence lower bound in variational inference has the effect of minimizing the Bayes risk within the variational density family.Operating in a frequentist setup, the variational inequalities imply that point estimates constructed from the α-VB procedure converge at an optimal rate to the true parameter in a wide range of problems.We illustrate our general theory with a number of examples, including the mean-field variational approximation to (low)-highdimensional Bayesian linear regression with spike and slab priors, Gaussian mixture models and latent Dirichlet allocation.
Are We Ready for the Challenge of Banks 4.0? Designing a Roadmap for Banking Systems in Industry 4.0

The purpose of the present paper is to provide an advanced overview of the practical applications of Banking 4.0 in Industry 4.0. This paper examines the technology trends in the Fourth Industrial Revolution and identifies the key indicators behind the creation of a strategic map for the fourth-generation banks and their readiness to enter Industry 4.0. This paper examines a systematic review of fully integrated Banking 4.0 and the application of the technologies of Industry 4.0 and illustrates a distinct pattern of integration of Banking 4.0 and Industry 4.0. One of the prominent features of this article is the performance of successful global banks in applying these technologies. The results showed that Banking 4.0 in Industry 4.0 is an integrative value creation system consisting of six design principles and 14 technology trends. The roadmap designed for banks to enter Industry 4.0 and how they work with industrial companies will be a key and important guide.
Thresholds Based Image Extraction Schemes in Big Data Environment in Intelligent Traffic Management

Video traffic monitoring is an inexpensive and convenient source of traffic data. Traffic images processing are widely used to check traffic conditions and they can determine traffic control strategies in intelligent transportation systems (ITS). However, these traffic images always contain privacy-related data, such as vehicles registration numbers, human faces. Misuse of such data is a threat to the privacy of vehicles divers, passengers, pedestrians, etc. This paper proposes a thresholds-based images extraction solution for ITS. At first, a Faster Region Convolutional Neural Networks (RCNN) model is used to segment a traffic image into multi-regions with different importance levels; then, multi-threshold image extraction schemes are designed based on progressive secret image sharing schemes to extract images contain key traffic information, such as reg number, human faces, in which the region with higher importance level requires higher threshold for extraction. For different roles in ITS, they can extract images with different details, which can protect privacy and anonymity. The proposed methods provide a safe and intelligent way to extract images that can be used for further analysis in ITS.
IoT supported smart home for the elderly

Continuously progressing technology helps improve human living standards and make human life easier. A large portion of these developments is targeting the healthcare sector and especially in the last decade has been focused on helping elderly patients extend their independent existence in their preferred environments. In this article, we present a smart home IoT based system for elderly people and people with partial memory loss (MCI patients, dementia patients etc) which ensures their safety and provides early warnings to help them overcome small everyday problems typical for such disorders. Data is continuously collected from various ambient sensors and is used for the contextual detection of their activities. Unobtrusively monitoring the patient's behaviour in his/her home is assured as well as providing necessary data to relatives and/or caregivers. An emergency wristband is added to deal with extraordinary and dangerous situations like unexpected fall, gas/water left open in the house, etc. The developed prototype is a low-cost solution, under 100 USD, in which low energy consumption and ease of integration help greatly increase its applicability.
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching

In this paper, we propose a unified method to jointly learn optical flow and stereo matching. Our first intuition is stereo matching can be modeled as a special case of optical flow, and we can leverage 3D geometry behind stereoscopic videos to guide the learning of these two forms of correspondences. We then enroll this knowledge into the state-of-the-art self-supervised learning framework, and train one single network to estimate both flow and stereo. Second, we unveil the bottlenecks in prior self-supervised learning approaches, and propose to create a new set of challenging proxy tasks to boost performance. These two insights yield a single model that achieves the highest accuracy among all existing unsupervised flow and stereo methods on KITTI 2012 and 2015 benchmarks. More remarkably, our self-supervised method even outperforms several state-of-the-art fully supervised methods, including PWC-Net and FlowNet2 on KITTI 2012.
Flow Contrastive Estimation of Energy-Based Models

This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training method has the following traits. (1) The update of the energy-based model is based on noise contrastive estimation, with the flow model serving as a strong noise distribution. (2) The update of the flow model approximately minimizes the Jensen-Shannon divergence between the flow model and the data distribution. (3) Unlike generative adversarial networks (GAN) which estimates an implicit probability distribution defined by a generator model, our method estimates two explicit probabilistic distributions on the data. Using the proposed method we demonstrate a significant improvement on the synthesis quality of the flow model, and show the effectiveness of unsupervised feature learning by the learned energy-based model. Furthermore, the proposed training method can be easily adapted to semi-supervised learning. We achieve competitive results to the state-of-the-art semi-supervised learning methods.
Explainable AI (xAI) for Anatomic Pathology

Pathologists are adopting whole slide images (WSIs) for diagnosis, thanks to recent FDA approval of WSI systems as class II medical devices. In response to new market forces and recent technology advances outside of pathology, a new field of computational pathology has emerged that applies artificial intelligence (AI) and machine learning algorithms to WSIs. Computational pathology has great potential for augmenting pathologists’ accuracy and efficiency, but there are important concerns regarding trust of AI due to the opaque, black-box nature of most AI algorithms. In addition, there is a lack of consensus on how pathologists should incorporate computational pathology systems into their workflow. To address these concerns, building computational pathology systems with explainable AI (xAI) mechanisms is a powerful and transparent alternative to black-box AI models. xAI can reveal underlying causes for its decisions; this is intended to promote safety and reliability of AI for critical tasks such as pathology diagnosis. This article outlines xAI enabled applications in anatomic pathology workflow that improves efficiency and accuracy of the practice. In addition, we describe HistoMapr-Breast, an initial xAI enabled software application for breast core biopsies. HistoMapr-Breast automatically previews breast core WSIs and recognizes the regions of interest to rapidly present the key diagnostic areas in an interactive and explainable manner. We anticipate xAI will ultimately serve pathologists as an interactive computational guide for computer-assisted primary diagnosis.
Physical-Layer Authentication for Internet of Things via WFRFT-Based Gaussian Tag Embedding

Internet of Things (IoT) is regarded as the fundamental platform for many emerging services, such as smart city, smart home, and intelligent transportation systems. With ever-increasing penetration of IoT, it becomes of great importance to ensure the IoT security, as the security threats are extended from the cyber world to the physical world. In this article, we investigate physical-layer authentication to help verify the identity of IoT entities for preventing unauthorized access to information or service. Specifically, we propose a Gaussian-tag-embedded physical-layer authentication (GTEA) scheme by using a weighted fractional Fourier transform (WFRFT). Through the superimposition of a low-power Gaussian WFRFT tag onto the message signal, the legitimate receiver can verify the authenticity of the received signal at the physical layer, without being detected by adversaries. Moreover, security analysis shows that with the deliberately designed Gaussian tag, the GTEA scheme is robust against spoofing and replaying attacks. In addition, tradeoff analysis and simulation results are provided to demonstrate the capability of the GTEA scheme in achieving reliability of the message delivery, stealth of the embedded tag signal, and balancing the tradeoff among the robustness of user authentication. Moreover, a prototype is further developed using FPGA and experiments are conducted to demonstrate the effectiveness and performance improvement of the proposed GTEA scheme.
Self-Learning Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence

In this paper, we address the problem of rain streaks removal in video by developing a self-learned rain streak removal method, which does not require any clean groundtruth images in the training process. The method is inspired by fact that the adjacent frames are highly correlated and can be regarded as different versions of identical scene, and rain streaks are randomly distributed along the temporal dimension. With this in mind, we construct a two-stage Self-Learned Deraining Network (SLDNet) to remove rain streaks based on both temporal correlation and consistency. In the first stage, SLDNet utilizes the temporal correlations and learns to predict the clean version of the current frame based on its adjacent rain video frames. In the second stage, SLDNet enforces the temporal consistency among different frames. It takes both the current rain frame and adjacent rain video frames to recover structural details. The first stage is responsible for reconstructing main structures, and the second stage is responsible for extracting structural details. We build our network architecture with two sub-tasks, i.e. motion estimation, and rain region detection, and optimize them jointly. Our extensive experiments demonstrate the effectiveness of our method, offering better results both quantitatively and qualitatively.
Disentangled Image Generation Through Structured Noise Injection

We explore different design choices for injecting noise into generative adversarial networks (GANs) with the goal of disentangling the latent space. Instead of traditional approaches, we propose feeding multiple noise codes through separate fully-connected layers respectively. The aim is restricting the influence of each noise code to specific parts of the generated image. We show that disentanglement in the first layer of the generator network leads to disentanglement in the generated image. Through a grid-based structure, we achieve several aspects of disentanglement without complicating the network architecture and without requiring labels. We achieve spatial disentanglement, scale-space disentanglement, and disentanglement of the foreground object from the background style allowing fine-grained control over the generated images. Examples include changing facial expressions in face images, changing beak length in bird images, and changing car dimensions in car images. This empirically leads to better disentanglement scores than state-of-the-art methods on the FFHQ dataset.
Constrained R-Cnn: A General Image Manipulation Detection Model

Recently, deep learning-based models have exhibited remarkable performance for image manipulation detection. However, most of them suffer from poor universality of handcrafted or predetermined features. Meanwhile, they only focus on manipulation localization and overlook manipulation classification. To address these issues, we propose a coarse-to-fine architecture named Constrained R-CNN for complete and accurate image forensics. First, the learnable manipulation feature extractor learns a unified feature representation directly from data. Second, the attention region proposal network effectively discriminates manipulated regions for the next manipulation classification and coarse localization. Then, the skip structure fuses low-level and high-level information to refine the global manipulation features. Finally, the coarse localization information guides the model to further learn the finer local features and segment out the tampered region. Experimental results show that our model achieves state-of-the-art performance. Especially, the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score is increased by 28.4%, 73.2%, 13.3% on the NIST16, COVERAGE, and Columbia dataset.