Reviewerly/exHarmony huggingface dataset:
An adaptive differential evolution framework based on population feature information

Differential Evolution (DE) is an effective global optimization algorithm, and many existing adaptive variants of it have been proposed to solve engineering problems. It is well known that population feature information that refers to some mathematical statistic feature information of all individuals in the dimension of decision space, and it can reflect the features of the problem to be solved. However, the population feature information has not been fully utilized by DE's adaptive variants. As a result, those adaptive variants do not obtain promising performance in optimizing nonlinear, non-differentiable and non-separable multi-modal problems. To make adequate extraction and effective use of population feature information, we propose an adaptive differential evolution framework based on population feature information in this paper, named PFI for short. In the PFI framework, the population feature information consists of the standard deviation of fitness value and the sum of standard deviation of each dimension of population. Besides, population feature information archive is designed to store the population feature information and success parameters, and the utilization mechanism of population feature information assigns historical success parameters with high population feature similarity to the current corresponding population. Four widely used mutation strategies of DE are incorporated into the PFI framework to evaluate its performance by optimizing CEC2005, CEC2015, CEC2020 benchmark functions and two real world applications to verify the performance of the PFI framework. Experiment results have demonstrated that PFI framework can significantly improve the performance of 4 popular mutation strategies of DE.
Unsupervised Learning of Optical Flow With Non-Occlusion From Geometry

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric principles of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric principles of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixel coordinate points is carefully considered and geometric constraints are conducted based on the geometric principles of optical flow. First, the optical flow vectors of neighboring pixel coordinate points in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixel coordinate points in the first frame moves to the second frame, no other pixel coordinate points will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric principles in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach. Our codes are open sourced at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IRMVLab/NccFlow</uri> .
CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking

3D multi-object tracking is a crucial component in the perception system of autonomous driving vehicles. Tracking all dynamic objects around the vehicle is essential for tasks such as obstacle avoidance and path planning. Autonomous vehicles are usually equipped with different sensor modalities to improve accuracy and reliability. While sensor fusion has been widely used in object detection networks in recent years, most existing multi-object tracking algorithms either rely on a single input modality, or do not fully exploit the information provided by multiple sensing modalities. In this work, we propose an end-to-end network for joint object detection and tracking based on radar and camera sensor fusion. Our proposed method uses a center-based radar-camera fusion algorithm for object detection and utilizes a greedy algorithm for object association. The proposed greedy algorithm uses the depth, velocity and 2D displacement of the detected objects to associate them through time. This makes our tracking algorithm very robust to occluded and overlapping objects, as the depth and velocity information can help the network in distinguishing them. We evaluate our method on the challenging nuScenes dataset, where it achieves 20.0 AMOTA and outperforms all vision-based 3D tracking methods in the benchmark, as well as the baseline LiDAR-based method. Our method is online with a runtime of 35ms per image, making it very suitable for autonomous driving applications.
ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention

Attacks exploiting the innate and the acquired vulnerabilities of human users have posed severe threats to cybersecurity. This work proposes ADVERT, a human-technical solution that generates adaptive visual aids in real-time to prevent users from inadvertence and reduce their susceptibility to phishing attacks. Based on the eye-tracking data, we extract visual states and attention states as system-level sufficient statistics to characterize the user's visual behaviors and attention status. By adopting a data-driven approach and two learning feedback of different time scales, this work lays out a theoretical foundation to analyze, evaluate, and particularly modify humans' attention processes while they vet and recognize phishing emails. We corroborate the effectiveness, efficiency, and robustness of ADVERT through a case study based on the data set collected from human subject experiments conducted at New York University. The results show that the visual aids can statistically increase the attention level and improve the accuracy of phishing recognition from 74.6% to a minimum of 86%. The meta-adaptation can further improve the accuracy to 91.5% (resp. 93.7%) in less than 3 (resp. 50) tuning stages.
Substation instrumentation target detection based on multi‐scale feature fusion

SUMMARY With the promotion of smart grid construction work, the use of high‐precision and high‐efficiency substation inspection robot has become the development trend of substation inspection. A multi‐scale feature fusion meter target detection algorithm is proposed to address the problems of low efficiency and susceptibility to surrounding environmental factors by the traditional manual meter reading method. Kinecct is used to acquire color images of substation meters with different backgrounds, light intensities, and angles to build a substation meter dataset. Based on the complementarity and correlation of multi‐scale features, an SSD target detection model with multi‐scale feature fusion is established, and the performance of the algorithm is tested on the constructed dataset, and comparative experiments are conducted to verify the effectiveness of the algorithm for target detection accuracy improvement.
BCPIS: Blockchain-based Counterfeit Product Identification System

A key aspect of sustainable business development involves protecting a company's products from counterfeiting. Since this can impact brand's image and often, product counterfeits are of a minor quality which can be dangerous and even pose health hazards. Counterfeiters gain profits by manufacturing products using lesser quality materials and production methods. Many counterfeit products in the market are difficult to identify for a normal customer and require to be examined by a trained professional which is time-consuming and costly. In this paper, we propose a method for identifying counterfeit by a simple scan of the quick response code on the product. Since these codes are unique and are implemented by unique algorithms, it is almost impossible to forge them. Also, implementing an encrypted peer-to-peer system for the database makes it impossible for attackers to tamper with the database. The proposed method collectively not only helps laypersons to identify the authenticity of products but also an ownership tracking system where products are linked to the owners' account which will allow them to produce proof of purchase and authenticity when reselling products and when claiming the warranty of the product. This will help in reducing manufacturing and materials costs used from traditional methods against counterfeiting such as Radio-Frequency Identification and the Hologram technique.
Blockchain Stretching &amp; Squeezing: Manipulating Time for Your Best Interest

We present a novel way for cryptocurrency miners to manipulate the effective interest-rate on loans or deposits they make on decentralized finance (DeFi) platforms by manipulating difficulty-adjustment algorithms (DAAs) and changing the block-rate. This presents a new class of strategic manipulations available to miners.
An Effective and Secure Mechanism for Phishing Attacks Using a Machine Learning Approach

Phishing is one of the biggest crimes in the world and involves the theft of the user’s sensitive data. Usually, phishing websites target individuals’ websites, organizations, sites for cloud storage, and government websites. Most users, while surfing the internet, are unaware of phishing attacks. Many existing phishing approaches have failed in providing a useful way to the issues facing e-mails attacks. Currently, hardware-based phishing approaches are used to face software attacks. Due to the rise in these kinds of problems, the proposed work focused on a three-stage phishing series attack for precisely detecting the problems in a content-based manner as a phishing attack mechanism. There were three input values—uniform resource locators and traffic and web content based on features of a phishing attack and non-attack of phishing website technique features. To implement the proposed phishing attack mechanism, a dataset is collected from recent phishing cases. It was found that real phishing cases give a higher accuracy on both zero-day phishing attacks and in phishing attack detection. Three different classifiers were used to determine classification accuracy in detecting phishing, resulting in a classification accuracy of 95.18%, 85.45%, and 78.89%, for NN, SVM, and RF, respectively. The results suggest that a machine learning approach is best for detecting phishing.
EGODO Ontology

The electronic government is a new application field for the Semantic Web, and the ontologies play a key role in the development of the Semantic Web. Consequently, if we focus on the law and the public policy that base the governments and build a documentation ontology that encompasses the electronic governments' needs of services at the front-office and mainly at the back office, we will provide a starting point to enable a broad class of semantic electronic government application. In this chapter, the authors show the e-Government Documentation Ontology as a part of the EGO model in order to describe how to access and manage official and non-official documentation across the governments. They also show a couple of applications using the documentation ontology on a semantic information retrieval system and on a semantic peer-to-peer.
Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning

Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative for fast online execution is to train, offline, an information gathering policy, which indirectly reasons about the information value of new observations. However, these policies lack safety guarantees and do not account for the robot dynamics. To overcome these limitations we train an information-aware policy via deep reinforcement learning, that guides a receding-horizon trajectory optimization planner. In particular, the policy continuously recommends a reference viewpoint to the local planner, such that the resulting dynamically feasible and collision-free trajectories lead to observations that maximize the information gain and reduce the uncertainty about the environment. In simulation tests in previously unseen environments, our method consistently outperforms greedy next-best-view policies and achieves competitive performance compared to Monte Carlo Tree Search, in terms of information gains and coverage time, with a reduction in execution time by three orders of magnitude.
Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series

Anomalies are ubiquitous in real-world time-series data which call for effective and timely detection, especially in an unsupervised setting for labeling cost saving. In this paper, we develop an unsupervised density reconstruction model for multi-dimensional time-series anomaly detection. In particular, it directly handles an important realistic setting that the detection is achieved towards raw time-series contaminated with noise for training, in contrast to most existing anomaly detection works that assume the training data is in general clean i.e. not contaminated with anomaly. It extends recent advancements in deep generative models and state space models to achieve robust anomaly detection. Our approach comprises of a novel state space based generative model, a filtering based inference model, together with a carefully-designated emission model based on robust statistics theory. Extensive experimental results are conducted to show that our approach can adapt to complex patterns even given severely contaminated training data. We also develop visualization techniques to help better understand the behavior of the anomaly detection models. Empirical results show that our method outperforms state-of-the-arts on both synthetic and real-world datasets.
PCNN<sub>CEC</sub>: Efficient and Privacy-Preserving Convolutional Neural Network Inference Based on Cloud-Edge-Client Collaboration

Deploying convolutional neural network (CNN) inference on resource-constrained devices remains a remarkable challenge for Industrial Internet of Things (IIoT). Although the cloud computing shows great promise in machine learning training and prediction, outsourcing data to a remote cloud always incurs privacy risk and high latency. Therefore, we design a new framework for efficient and privacy-preserving CNN inference based on cloud-edge-client collaboration (named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{PCNN}_{\text{CEC}}$</tex-math></inline-formula> ). In <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{PCNN}_{\text{CEC}}$</tex-math></inline-formula> , the model of cloud and the data of client in IIoT are split into two secret shares and sent to two non-colluded edge servers. We proposed a new efficient private comparison protocol based on the additively secret sharing technique, which can be used to realize secure computation of ReLU function without approximation in semi-honest adversary model. By applying some secure two-party computation protocols, the two edge servers can jointly calculate the predicting results without learning anything about the model and data. Moreover, to speed up the pre-computation of offline phase but not sacrifice security, we delegate the task of triplets generation to the cloud, so that the edge servers do not require frequent interactions to generate triplets themselves or introducing additional trusted party. The experimental results show the proposed private comparison protocol achieves a better tradeoff between low latency and high throughput, when it is compared with garbled circuit based protocols and other secret sharing based protocols. Additionally, the benchmarks conducted on realistic MNIST and CIFAR-10 datasets demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{PCNN}_{\text{CEC}}$</tex-math></inline-formula> costs less communication and runtime than two recently related schemes under the same security level.
Why Did I Fail? A Causal-Based Method to Find Explanations for Robot Failures

Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust and transparency. To achieve this skill, the main challenges addressed in this paper are I) acquiring enough data to learn a cause-effect model of the environment and II) generating causal explanations based on that model. We address I) by learning a causal Bayesian network from simulation data. Concerning II), we propose a novel method that enables robots to generate contrastive explanations upon task failures. The explanation is based on setting the failure state in contrast with the closest state that would have allowed for a successful execution. This state is found through breadth-first search and is based on success predictions from the learned causal model. We assessed our method in two different scenarios I) stacking cubes and II) dropping spheres into a container. The obtained causal models reach a sim2real accuracy of 70% and 72%, respectively. We finally show that our novel method scales over multiple tasks and allows real robots to give failure explanations like 'the upper cube was stacked too high and too far to the right of the lower cube.'
Performance-Enhanced Federated Learning With Differential Privacy for Internet of Things

Federated learning (FL), which enables multiple distributed devices (clients) to collaboratively train a global model without transmitting their private data, has attracted much attention in the Internet of Things (IoT) domain. Compared with centralized learning, FL has obvious privacy advantages because it can protect the clients’ raw data from direct access by adversaries. Furthermore, to prevent the adversaries from inferring private information from the transmitted parameters, several FL algorithms based on differential privacy (DP) have been proposed, where the clients add artificial noise to their local parameters for privacy protection. Nevertheless, the added noise would disrupt the learning process and degrade the performance of the trained model. Considering this, in this article, we develop a performance-enhanced DP-based FL (PEDPFL) algorithm, where a classifier-perturbation regularization method is proposed to improve the robustness of the trained model against DP-injected noise. We derive the theoretical privacy and convergence analysis of the proposed algorithm, and also demonstrate the influence of some hyperparameters on the convergence performance. Simulation results on real-world data sets show that the proposed algorithm has better classification performance than the existing DP-based FL algorithms at the same level of privacy protection, and thus, it is more applicable to IoT applications.
Active Deception Jamming Recognition in the Presence of Extended Target

Accurate sensing of the main-lobe active deception jamming is critical for radar anti-jamming and extended target detection in complex electromagnetic environment. This letter therefore deals with the problem of multiple active deception jamming recognition in extended target settings. A residual convolutional neural network with attention mechanism-based radar active deception jamming recognition algorithm is proposed leveraging a hybrid model to capture much rich features through multi-domain feature fusion. The proposed method can outperform state-of-the-art methods in terms of recognition accuracy, model size, and convergence speed. Experimental results demonstrate its effectiveness and robustness.
Semantic Communications With Artificial Intelligence Tasks: Reducing Bandwidth Requirements and Improving Artificial Intelligence Task Performance

A radical paradigm shift of wireless networks from “connected things” to “connected intelligence” is occurring, which coincides with Shannon and Weaver’s vision that communications will transform from the technical level to the semantic level. This article proposes a semantic communication (SC) method with artificial intelligence tasks (AITs). First, the architecture of SC-AIT is elaborated. Then, based on the proposed architecture, we implement SC-AIT for an image classifications task. A prototype of SC-AIT is also established for surface defect detection. Experimental results show that SC-AIT has much lower bandwidth requirements and can achieve up to 40% classification accuracy gains compared with communications at the technical level. Future trends and key challenges for SCs are also identified.
Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators

With the recent maturity of Cloud computing technology and the flexibility of the Cloud services, the offering of Cloud services has spread exponentially.Different Cloud service providers offer their services highlighting different features of their services.Because of the diversity of Cloud services and their highlighted features, the choice of the most suitable Cloud service is a complex problem for the Cloud users.Many Cloud users are unable to identify the Cloud services that best suit their needs and thus choose an unsuitable Cloud service, which results in financial loses as well as time delays.To this end, in this study, we propose an efficient three layered framework for evaluating and ranking IaaS, PaaS and SaaS Cloud services.We identified the functional and non-functional key performance indicators (KPIs) for Cloud services from 6 KPI classes.We classified these KPIs with respect to their types and criticality so that the Cloud user can easily choose according to his needs.The relative importance of the KPIs was determined using CRITIC method.We combined the KPIs values and their relative importance for an overall evaluation and ranking of the Cloud services using Vikor method.A case study is also presented for a step by step demonstration of the proposed method. INDEX TERMS Cloud services, Cloud service KPIs
SNARGs for P from Sub-exponential DDH and QR

We obtain publicly verifiable Succinct Non-Interactive Arguments (SNARGs) for arbitrary deterministic computations and bounded space non-deterministic computation from standard group-based assumptions, without relying on pairings. In particular, assuming the sub-exponential hardness of both the Decisional Diffie-Hellman (DDH) and Quadratic Residuosity (QR) assumptions, we obtain the following results, where n denotes the length of the instance:
Adaptive Kernel Kalman Filter Based Belief Propagation Algorithm for Maneuvering Multi-Target Tracking

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based multi-target tracking (MTT) BP algorithm.