Reviewerly/exHarmony huggingface dataset:
Cycle: Sustainable Off-Chain Payment Channel Network with Asynchronous Rebalancing

Payment channel network (PCN) is a promising off-chain technology for blockchain scalability, but it suffers from poor sustainability in practice. In other words, due to the imbalanced transfer in channels, the balance in one direction of channels gradually becomes exhausted until the PCN is rebalanced via a consensus-based rebalancing protocol, during which the involved channels must be suspended. This paper presents Cycle, the first off-chain protocol for a sustainable PCN. It not only keeps the PCN at a balanced level consistently but also avoids the channel freeze incurred by the rebalancing protocol, leading to minimum failed payments and sustained PCN service, respectively. Cycle achieves these benefits based on a novel idea of asynchronous rebalancing. During the normal off-chain running, the participants share the information about their payments and asynchronously rebalance the PCN following the principle that payments along circular channels can cancel each other out. To guarantee security, the protocol resolves the disputes resulting from network latency or malicious participants by a message mechanism for synchronization and a smart contract for arbitration. Moreover, to address the privacy concern during the information sharing, a truncated Laplace mechanism is designed to achieve differential privacy. Finally, we provide a proof-of-concept implementation in Ethereum, over which a real data-based simulation shows that Cycle satisfies 31% more payments than the state-of-the-art technique.
${\mathsf{EZFusion}}$: A Close Look at the Integration of LiDAR, Millimeter-Wave Radar, and Camera for Accurate 3D Object Detection and Tracking

A recent trend is to combine multiple sensors ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , cameras, LiDARs and millimeter-wave Radars) to achieve robust multi-modal perception for autonomous systems such as self-driving vehicles. Although quite a few sensor fusion algorithms have been proposed, some of which are top-ranked on various leaderboards, a systematic study on how to integrate these three types of sensors to develop effective multi-modal 3D object detection and tracking is still missing. Towards this end, we first study the strengths and weaknesses of each data modality carefully, and then compare several different fusion strategies to maximize their utility. Finally, based upon the lessons learnt, we propose a simple yet effective multi-modal 3D object detection and tracking framework (namely EZFusion). As demonstrated by extensive experiments on the nuScenes dataset, without fancy network modules, our proposed EZFusion makes remarkable improvements over the LiDAR-only baseline, and achieves comparable performance with the state-of-the-art fusion-based methods.
Self-Supervised Scene-Debiasing for Video Representation Learning via Background Patching

Self-supervised learning has considerably improved video representation learning by discovering supervisory signals automatically from unlabeled videos. However, due to the scene-biased nature of existing video datasets, the current methods are biased to the dominant scene context during action inference. Hence, this paper proposes Background Patching (BP), a scene-debiasing augmentation strategy to alleviate the model reliance on the video background in a self-supervised contrastive manner. The BP reduces the negative influence of the video background by mixing a randomly patched frame to the video background. BP randomly crops four frames from four different videos and patches them to construct a new frame for each video separately. The patched frame is mixed with all frames of the target video to produce a spatially distorted video sample. Then, we use existing self-supervised contrastive frameworks to pull representations of the distorted and original videos closer together. Moreover, BP mixes the semantic labels of patches with the target video's label, resulting in the regularization of the contrastive model to soften the decision boundaries in the embedding space. Therefore, the model is explicitly constrained to suppress the background influence by emphasizing more on the motion changes. The extensive experimental results show that our BP significantly improved the performance of various video understanding downstream tasks including action recognition, action detection, and video retrieval.
What Are the Users’ Needs? Design of a User-Centered Explainable Artificial Intelligence Diagnostic System

The application of artificial intelligence (AI) systems has surged in the high-risk area of medicine, and these systems must explain their decisions to different users. However, existing explainable AI (XAI) design practices in the medical domain are mostly focused on domain experts, such as physicians, and there is a lack of XAI design practices for consumer users. Therefore, we developed a library of XAI user needs in the medical domain, which can be used as an auxiliary tool for the development of user-centered XAI design solutions in this domain. Moreover, through empirical research, based on our XAI user Needs Library, we designed an XAI-based electrocardiogram diagnostic system prototype for consumer users and conducted a user evaluation. The results provide the empirical experience of the design space of XAI and promote consumer user-centered XAI practices.
Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach

An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author’s knowledge, the current study is the first attempt to create global maps for siting onshore wind and solar power systems and formulate novel weights for decision criteria. A total of 13 conditioning factors (independent variables) defined through a comprehensive literature review and multicollinearity analysis were assessed. Real-world renewable energy experiences (more than 55,000 on-site wind and solar plants worldwide) are exploited to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Then, the output of ML models was explained using SHapley Additive exPlanations (SHAP). RF outperformed SVM and MLP in both wind and solar modeling with an overall accuracy of 90% and 89%, kappa coefficient of 0.79 and 0.78, and area under the curve of 0.96 and 0.95, respectively. The high and very high suitability categories accounted for 23.2% (~26.84 million km2) of the site suitability map for wind power plants. In addition, they covered more encouraging areas (24.0% and 19.4%, respectively, equivalent to ~50.31 million km2) on the global map for hosting solar energy farms. SHAP interpretations were consistent with the Gini index indicating the dominance of the weights of technical and economic factors over the spatial assessment under consideration. This study provides support to decision-makers toward sustainable power planning worldwide.
A kernel-free fuzzy reduced quadratic surface <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e433" altimg="si390.svg"><mml:mi>ν</mml:mi></mml:math>-support vector machine with applications

The kernel-free support vector machine (SVM) models are recently developed and studied to overcome some drawbacks induced by the kernel-based SVM models. To further improve the classification accuracy and computational efficiency of existing kernel-free quadratic surface support vector machine (QSSVM) models, a novel kernel-free ν-fuzzy reduced QSSVM model is proposed. The proposed model utilizes a reduced quadratic surface for nonlinear binary classification as well as reducing the effect of outliers in the data set. Some theoretical properties are rigorously studied, especially, the effects of the parameter ν on the dual feasibility and the number of support vectors. Computational experiments are conducted on some public benchmark data sets to indicate the superior performance of the proposed model over some well-known binary classification models. The numerical results also favors the higher training efficiency of the proposed model over those of other kernel-free SVM models. Moreover, the proposed model is successfully applied to the prodromal detection of Alzheimer's Disease with good performance, by using the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Scale-selective and noise-robust extended local binary pattern for texture classification

As one of the most successful local feature descriptors, the local binary pattern (LBP) estimates the texture distribution rule of an image based on the signs of differences between neighboring pixels to obtain intensity- and rotation- invariance. In this paper, we propose a novel image descriptor to address scale transformation and noise interference simultaneously. We name it scale-selective and noise-robust extended LBP (SNELBP). First, each image in training sets is transformed into different scale spaces by a Gaussian filter. Second, noise-robust pattern histograms are obtained from each scale space by using our previously proposed median robust extended LBP (MRELBP). Then, scale-invariant histograms are determined by selecting the maximum among all scale levels for a certain image. Finally, the most informative patterns are selected from the dictionary pretrained by the two-stage compact dominant feature selection method (CDFS), maintaining the descriptor more lightweight with sufficiently low time cost. Extensive experiments on five public databases (Outex_TC_00011, TC_00012, KTH-TIPS, UMD and NEU) and one fresh texture database (JoJo) under two kinds of interferences (Gaussian and salt pepper) indicate that our SNELBP yields more competitive results than thirty classical LPB variants as well as eight typical deep learning methods.
DJI Tello Quadrotor as a Platform for Research and Education in Mobile Robotics and Control Engineering

Multirotor unmanned aerial vehicles (UAVs) equipped with various sensors and cameras on board are commonly used as experimental flying platforms for research and education in mobile robotics and control engineering. In the academic community, especially low-cost, ready-to-use, compact-size flying robots attract a lot of researchers attention due to the high level of safety they offer during experiments conducted in GPS-denied indoor laboratory spaces. In recent years, the DJI Tello quadrotor has gained importance. This nano-class UAV may be found in laboratories around the world and is often the first drone used by students and young scientists in their projects. This paper presents the potential of the drone as a useful experimental platform. Its hardware and software architecture, mathematical model with parametrization, as well as proposed new state machine (node for Robot Operating System) are presented. Moreover, representative research results on UAV position control based on cascade fuzzy neural network are illustrated. Exemplary student projects conducted with the use of DJI Tello are also shown. These projects are as follows: (a) “TelloBird” (QR trajectory generator), (b) “Sentry drone” for detecting and tracking any humans entering a given area. The considered UAV is used there as a rapid prototyping platform supported by the tello ros package in ROS.SUPPLEMENTARY MATERIALhttps://youtu.be/AV0gq1E_Cx8
Surakav: Generating Realistic Traces for a Strong Website Fingerprinting Defense

Website Fingerprinting (WF) attacks utilize size and timing information of encrypted network traffic to infer the user’s browsing activity, posing a great threat to privacy-enhancing technologies like Tor; nevertheless, Tor has not adopted any defense because existing defenses are not convincing enough to show their effectiveness. Some defenses have been overcome by newer attacks; other defenses are never implemented and tested in the real open-world scenario.In this paper, we propose Surakav, a tunable and practical defense that is effective against WF attacks with reasonable overhead. Surakav makes use of a Generative Adversarial Network (GAN) to generate realistic sending patterns and regulates buffered data according to the sampled patterns. We implement Surakav and evaluate it on the live Tor network. Experiments show that Surakav is able to reduce the attacker’s true positive rate by 57% with 55% data overhead and 16% time overhead, saving 42% data overhead compared to FRONT. In the heavyweight setting, Surakav outperforms the strongest known defense, Tamaraw, requiring 50% less overhead in data and time to lower the attacker’s true positive rate to only 8%. We also show that two existing defenses, Walkie-Talkie and TrafficSliver, can be fortified with our GAN-based trace generator.
SpecHammer: Combining Spectre and Rowhammer for New Speculative Attacks

The recent Spectre attacks have revealed how the performance gains from branch prediction come at the cost of weakened security. Spectre Variant 1 (v1) shows how an attacker-controlled variable passed to speculatively executed lines of code can leak secret information to an attacker. Numerous defenses have since been proposed to prevent Spectre attacks, each attempting to block all or some of the Spectre variants. In particular, defenses using taint-tracking are claimed to be the only way to protect against all forms of Spectre v1. However, we show that the defenses proposed thus far can be bypassed by combining Spectre with the well-known Rowhammer vulnerability. By using Rowhammer to modify victim values, we relax the requirement that the attacker needs to share a variable with the victim. Thus, defenses that rely on this requirement, such as taint-tracking, are no longer effective. Furthermore, without this crucial requirement, the number of gadgets that can potentially be used to launch a Spectre attack increases dramatically; those present in Linux kernel version 5.6 increases from about 100 to about 20,000 via Rowhammer bit-flips. Attackers can use these gadgets to steal sensitive information such as stack cookies or canaries, or use new triple gadgets to read any address in memory. We demonstrate two versions of the combined attack on example victims in both user and kernel spaces, showing the attack’s ability to leak sensitive data.
Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails

Using technology to prevent cyber-attacks has allowed organisations to somewhat automate cyber security. Despite solutions to aid organisations, many are susceptible to phishing and spam emails which can make an unwanted impact if not mitigated. Traits that make organisations susceptible to phishing and spam emails include a lack of awareness around the identification of malicious emails, explicit trust, and the lack of basic security controls. For any organisation, phishing and spam emails can be received and the consequences of an attack could result in disruption. This research investigated the threat of phishing and spam and developed a detection solution to address this challenge. Deep learning and natural language processing are two techniques that have been employed in related research, which has illustrated improvements in the detection of phishing. Therefore, this research contributes by developing Phish Responder, a solution that uses a hybrid machine learning approach combining natural language processing to detect phishing and spam emails. To ensure its efficiency, Phish Responder was subjected to an experiment in which it has achieved an average accuracy of 99% with the LSTM model for text-based datasets. Furthermore, Phish Responder has presented an average accuracy of 94% with the MLP model for numerical-based datasets. Phish Responder was evaluated by comparing it with other solutions and through an independent t-test which demonstrated that the numerical-based technique is statistically significantly better than existing approaches.
BVFLEMR: an integrated federated learning and blockchain technology for cloud-based medical records recommendation system

Abstract Blockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.
Measurement device–independent quantum secure direct communication with user authentication

Quantum secure direct communication (QSDC) and deterministic secure quantum communication (DSQC) are two important branches of quantum cryptography, where one can transmit a secret message securely without encrypting it by a prior key. In the practical scenario, an adversary can apply detector-side-channel attacks to get some non-negligible amount of information about the secret message. Measurement device–independent (MDI) quantum protocols can remove this kind of detector-side-channel attacks, by introducing an untrusted third party, who performs all the measurements during the protocol with imperfect measurement devices. In this paper, we put forward the first MDI-QSDC protocol with user identity authentication, where both the sender and the receiver first check the authenticity of the other party and then exchange the secret message. Then, we extend this to an MDI quantum dialogue protocol, where both the parties can send their respective secret messages after verifying the identity of the other party. Along with this, we also report the first MDI-DSQC protocol with user identity authentication. Theoretical analyses prove the security of our proposed protocols against common attacks.
Cyberattack Path Generation and Prioritisation for Securing Healthcare Systems

Cyberattacks in the healthcare sector are constantly increasing due to the increased usage of information technology in modern healthcare and the benefits of acquiring a patient healthcare record. Attack path discovery provides useful information to identify the possible paths that potential attackers might follow for a successful attack. By identifying the necessary paths, the mitigation of potential attacks becomes more effective in a proactive manner. Recently, there have been several works that focus on cyberattack path discovery in various sectors, mainly on critical infrastructure. However, there is a lack of focus on the vulnerability, exploitability and target user profile for the attack path generation. This is important for healthcare systems where users commonly have a lack of awareness and knowledge about the overall IT infrastructure. This paper presents a novel methodology for the cyberattack path discovery that is used to identify and analyse the possible attack paths and prioritise the ones that require immediate attention to ensure security within the healthcare ecosystem. The proposed methodology follows the existing published vulnerabilities from common vulnerabilities and exposures. It adopts the common vulnerability scoring system so that base metrics and exploitability features can be used to determine and prioritise the possible attack paths based on the threat actor capability, asset dependency and target user profile and evidence of indicator of compromise. The work includes a real example from the healthcare use case to demonstrate the methodology used for the attack path generation. The result from the studied context, which processes big data from healthcare applications, shows that the uses of various parameters such as CVSS metrics, threat actor profile, and Indicator of Compromise allow us to generate realistic attack paths. This certainly supports the healthcare practitioners in identifying the controls that are required to secure the overall healthcare ecosystem.
APSNet: Toward Adaptive Point Sampling for Efficient 3D Action Recognition

Observing that it is still a challenging task to deploy 3D action recognition methods in real-world scenarios, in this work, we investigate the accuracy-efficiency trade-off for 3D action recognition. We first introduce a simple and efficient backbone network structure for 3D action recognition, in which we directly extract the geometry and motion representations from the raw point cloud videos through a set of simple operations (i.e., coordinate offset generation and mini-PoinNet). Based on the backbone network, we propose an end-to-end optimized network called adaptive point sampling network (APSNet) to achieve the accuracy-efficiency trade-off, which mainly consists of three stages: the coarse feature extraction stage, the decision making stage, and the fine feature extraction stage. In APSNet, we adaptively decide the optimal resolutions (i.e., the optimal number of points) for each pair of frames based on any input point cloud video under the given computational complexity constraint. Comprehensive experiments on multiple benchmark datasets demonstrate the effectiveness and efficiency of our newly proposed APSNet for 3D action recognition.
ABRIS: Anonymous blockchain based revocable and integrity preservation scheme for vehicle to grid network

The upcoming development in vehicle to grid network (V2G) allows for the flow of energy from battery powered Electric Vehicle (EV) to grid as well as the exchange of information between them. However, during the information exchange, the EV’s confidential information should be transferred from one charging station to another in a secure manner. Furthermore, the anonymity of the EV and charging station should be preserved. Despite the fact that many works on anonymous authentication and privacy preservation exist, there is an increase in computational cost in existing surveys. In this work, the new charging station authenticates the EV using blockchain technology without the involvement of a trusted entity, resulting in a reduction in computational time. Moreover, an efficient revoking mechanism is suggested to block the misbehaving charging station from the V2G network. In addition, security analysis section proves the resistant of our work against several possible well known attacks. Finally, to evaluate the performance of the work, the simulation is performed using CYGWIN platform and the results are proved to be noteworthy.