Reviewerly/exHarmony huggingface dataset:
A reversible modified least significant bit (LSB) matching revisited method

Least Significant Bit (LSB) matching revisited method is a modification to the LSB matching, which is a data hiding method for embedding message bits into a cover image. The modified method achieves the same hiding payload as LSB matching, but with fewer changes to the cover image. According to the embedding algorithm, we found that the LSB matching revisited method can be viewed as a kind of Exploiting Modification Direction (EMD) method. By using the modified LSB matching method and EMD, a simple dual images Reversible Data Hiding (RDH) method is proposed. The experimental results show that the proposed method achieves better image quality and hiding capacity. The image quality is evaluated using Peak Signal Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Moreover, Regular and Singular (RS) analysis reveals that the proposed method is secure against Steganalysis.
An Edge based Federated Learning Framework for Person Re-identification in UAV Delivery Service

AI (Artificial Intelligence) technology has been widely used in smart systems which usually require computing services with high availability and fast response. However, the rapid growth of data and service requests generated by end devices brings critical challenges to the centralised cloud computing paradigm in terms of network bandwidth, reliability and response time. In addition, the problem of data privacy is arising due to a large amount of data being transferred to the cloud server. Recently, edge computing is becoming a popular platform for smart systems as its provisions computing services close to the end devices, and Federated Learning (FL) is emerging as a promising solution for AI applications to address the data privacy issue. Inspired by their success, in this paper, we propose an edge based FL framework named Fed-UAV to solve the person reidentification problem in the UAV delivery service which is a typical AI application in smart logistics. This framework enables the UAV to efficiently locate the target receivers, and effectively reduce the data transmission between the UAV and the cloud server to improve the response time and protect the data privacy. Comprehensive experiments are conducted on three real-world datasets, and the experimental result successfully demonstrates that Fed-UAV can achieve both high accuracy and efficiency in person re-identification while protecting data privacy.
Towards Mask-robust Face Recognition

In this paper, we focus on the problem of mask-robust face recognition. Facial mask usually covers a major part of face, causing a significant reduction in extracting effective features. Due to such restriction, even the most advanced face recognition models are confronted with significant challenges. In light of this, this paper attempts to provide a reliable solution. Specifically, we introduce a mask-to-face image blending approach based on UV texture mapping, and a self-learning based cleaning pipeline for processing noisy training datasets. Then, considering the impacts of the long-tail distribution and hard faces samples, a loss function named Balanced Curricular Loss is introduced. Together with a bag of tricks is briefly presented. Experimental results show that the proposed solution separately achieved 84.528% @ Mask and 88.355% @ MR-ALL in InsightFace ms1m Track, which ranks 3rd when the paper submitted.
Laconic Private Set Intersection and Applications

Consider a server with a large set S of strings \(\{x_1,x_2\ldots ,x_N\}\) that would like to publish a small hash h of its set S such that any client with a string y can send the server a short message allowing it to learn y if \(y \in S\) and nothing otherwise. In this work, we study this problem of two-round private set intersection (PSI) with low (asymptotically optimal) communication cost, or what we call laconic private set intersection (\(\ell \)PSI) and its extensions. This problem is inspired by the recent general frameworks for laconic cryptography [Cho et al. CRYPTO 2017, Quach et al. FOCS'18].
The smart building privacy challenge

Time-series data gathered from smart spaces hide user's personal information that may arise privacy concerns. However, these data are needed to enable desired services. In this paper, we propose a privacy preserving framework based on Generative Adversarial Networks (GAN) that supports sensor-based applications while preserving the user identity. Experiments with two datasets show that the proposed model can reduce the inference of the user's identity while inferring the occupancy with a high level of accuracy.
How Parallel Circuit Execution Can Be Useful for NISQ Computing?

Quantum computing is performed on Noisy Intermediate-Scale Quantum (NISQ) hardware in the short term.Only small circuits can be executed reliably on a quantum machine due to the unavoidable noisy quantum operations on NISQ devices, leading to the under-utilization of hardware resources.With the growing demand to access quantum hardware, how to utilize it more efficiently while maintaining output fidelity is becoming a timely issue.A parallel circuit execution technique has been proposed to address this problem by executing multiple programs on hardware simultaneously.It can improve the hardware throughput and reduce the overall runtime.However, accumulative noises such as crosstalk can decrease the output fidelity in parallel workload execution.In this paper, we first give an in-depth overview of stateof-the-art parallel circuit execution methods.Second, we propose a Quantum Crosstalk-aware Parallel workload execution method (QuCP) without the overhead of crosstalk characterization.Third, we investigate the trade-off between hardware throughput and fidelity loss to explore the hardware limitation with parallel circuit execution.Finally, we apply parallel circuit execution to VQE and zero-noise extrapolation error mitigation method to showcase its various applications on advancing NISQ computing.
A Novel Hybrid Approach for Multi-Dimensional Data Anonymization for Apache Spark

Multi-dimensional data anonymization approaches (e.g., Mondrian) ensure more fine-grained data privacy by providing a different anonymization strategy applied for each attribute. Many variations of multi-dimensional anonymization have been implemented on different distributed processing platforms (e.g., MapReduce, Spark) to take advantage of their scalability and parallelism supports. According to our critical analysis on overheads, either existing iteration-based or recursion-based approaches do not provide effective mechanisms for creating the optimal number of and relative size of resilient distributed datasets (RDDs), thus heavily suffer from performance overheads. To solve this issue, we propose a novel hybrid approach for effectively implementing a multi-dimensional data anonymization strategy (e.g., Mondrian) that is scalable and provides high-performance. Our hybrid approach provides a mechanism to create far fewer RDDs and smaller size partitions attached to each RDD than existing approaches. This optimal RDD creation and operations approach is critical for many multi-dimensional data anonymization applications that create tremendous execution complexity. The new mechanism in our proposed hybrid approach can dramatically reduce the critical overheads involved in re-computation cost, shuffle operations, message exchange, and cache management.
The permuton limit of strong-Baxter and semi-Baxter permutations is the skew Brownian permuton

The skew Brownian permuton is a new universal family of random permutons, depending on two parameters, which should describe the permuton limit of several models of pattern-avoiding permutations. For some specific choices of the parameters, the skew Brownian permuton coincides with some previously studied permutons: the biased Brownian separable permuton and the Baxter permuton. The latter two permutons are degenerate cases of the skew Brownian permuton. In the present paper we prove the first convergence result towards a non-degenerate skew Brownian permuton. Specifically, we prove that strong-Baxter permutations converge in the permuton sense to the skew Brownian permuton for a non-degenerate choice of the two parameters. In order to do that, we develop a robust technique to prove convergence towards the skew Brownian permuton for various families of random constrained permutations. This technique relies on generating trees for permutations, allowing an encoding of permutations with multi-dimensional walks in cones. We apply this technique also to semi-Baxter permutations.
Deep collaborative multi-task network: A human decision process inspired model for hierarchical image classification

Hierarchical classification is significant for big data, where the original task is divided into several sub-tasks to provide multi-granularity predictions based on a tree-shape label structure. Obviously, these sub-tasks are highly correlated: results of the coarser-grained sub-tasks can reduce the candidates for the fine-grained sub-tasks, while results of the fine-grained sub-tasks provide attributes describing the coarser-grained classes. A human can integrate feedbacks from all the related sub-tasks instead of considering each sub-task independently. Therefore, we propose a deep collaborative multi-task network for hierarchical image classification. Specifically, we first extract the relationship matrix between every two sub-tasks defined by the hierarchical label structure. Then, the information of each sub-task is broadcasted to all the related sub-tasks through the relationship matrix. Finally, to combine this information, a novel fusion function based on the task evaluation and the decision uncertainty is designed. Extensive experimental results demonstrate that our model can achieve state-of-the-art performance.
Utilising design principles to improve the perception and effectiveness of public health infographics

Abstract Infographics are becoming a common tool in the communication of public-health information. However, research-based resources in how to create effective infographics are rare. The application of design principles in the creation of infographics has been shown to more effectively communicate information. Here, the research explores the adherence of 84 research-based infographic design principles on 3 designs of varying levels of application. A multi-method approach, including eye-tracking, was used to record information location efficiency, memorability and user perception. Support was found in favour of utilising design principles in the creation of public health infographics; resulting in improved user opinion and information location.
Explainability for experts: A design framework for making algorithms supporting expert decisions more explainable

Algorithmic decision support systems are widely applied in domains ranging from healthcare to journalism. To ensure that these systems are fair and accountable, it is essential that humans can maintain meaningful agency, understand and oversee algorithmic processes. Explainability is often seen as a promising mechanism for enabling human-in-the-loop, however, current approaches are ineffective and can lead to various biases. We argue that explainability should be tailored to support naturalistic decision-making and sensemaking strategies employed by domain experts and novices. Based on cognitive psychology and human factors literature review we map potential decision-making strategies dependant on expertise, risk and time dynamics and propose the conceptual Expertise, Risk and Time Explainability framework, intended to be used as explainability design guidelines. Finally, we present a worked example in journalism to illustrate the applicability of our framework in practice.
GSK-RL: Adaptive Gaining-sharing Knowledge algorithm using Reinforcement Learning

Meta-heuristics and nature inspired algorithms have been prominent solvers for highly complex, nonlinear and hard optimization problems. The Gaining-Sharing Knowledge algorithm (GSK) is a recently proposed nature-inspired algorithm, inspired by human and their tendency towards growth and gaining and sharing knowledge with others. The GSK algorithm have been applied to different optimization problems and proved robustness compared to other nature-inspired algorithms. The GSK algorithm has two main control parameters k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</inf> and k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</inf> which controls how much individuals gain and share knowledge with their common society and circles or what they inherit from their parents. GSK has no control parameter adaptation scheme, the k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</inf> and k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</inf> are fixed for all individuals. In this paper we introduce an adaptation technique for the GSK algorithm by learning those parameters during search procedure of the algorithm. The new algorithm is dubbed as GSK-RL. The parameter controller in GSK-RL is a neural network trained using actor critic methods for reinforcement learning. The GSK-RL is compared against original GSK algorithm with its default parameters on CEC 2017 test suite functions for dimensions 10 and 30. The GSK-RL performed well on 10 dimensional problems but the performance started to degrade on 30 dimensional problems and it showed unstable behaviour on some functions that the controller has never been trained on before. This paper concludes that neither the state of the search algorithm as described for the RL model nor the reward function has a critical role in designing an RL-based controller for parameters of the GSK, however the training functions and the collected trajectories are the most important factor in designing such a controller.
Human-Computer Interaction Using Manual Hand Gestures in Real Time

This paper describes the construction of an electronic system that can recognise twelve manual motions made by an interlocutor with one of their hands in a situation with regulated lighting and background in real time. Hand rotations, translations, and scale changes in the camera plane are all supported by the implemented system. The system requires an Analog Devices ADSP BF-533 Ez-Kit Lite evaluation card. As a last stage in the development process, displaying a letter associated with a recognized gesture is advised. However, a visual representation of the suggested algorithm may be found in the visual toolbox of a personal computer. Individuals who are deaf or hard of hearing will communicate with the general population thanks to new technology that connects them to computers. This technology is being used to create new applications.
An Exploration of the Psychological Impact of Hacking Victimization

Cybercrime has rapidly grown in prevalence and potential for harm and disruption for victims. Studies have examined the adverse psychological impact of cybercrime for victims; however, the specific effects for victims of hacking are unexplored. The present study aimed to investigate the psychological impacts of hacking victimization through exploration of the experience of victims of hacking. The study employed an in-depth phenomenological approach to explore the experiences of 11 victims of hacking. Semi-structured interviews were used as a tool for data collection, and thematic analysis of the data revealed four main themes: emotional impact; an increased sense of vulnerability; a sense of violation; and coping strategies. The findings highlight that hacking may have significant consequences for victims, and further, that hacking may represent an intrusion into a victim’s “digital space.” Recommendations are discussed for providing support to victims through measures aimed at increasing victim’s self-efficacy, sense of control over their digital environment, and increasing community awareness about the potential adverse impacts for victims of hacking.
Dissected 3D CNNs: Temporal skip connections for efficient online video processing

Convolutional Neural Networks with 3D kernels (3D CNNs) currently achieve state-of-the-art results in video recognition tasks due to their supremacy in extracting spatiotemporal features within video frames. There have been many successful 3D CNN architectures surpassing state-of-the-art results successively. However, nearly all of them are designed to operate offline, creating several serious handicaps during online operation. Firstly, conventional 3D CNNs are not dynamic since their output features represent the complete input clip instead of the most recent frame in the clip. Secondly, they are not temporal resolution-preserving due to their inherent temporal downsampling. Lastly, 3D CNNs are constrained to be used with fixed temporal input size limiting their flexibility. In order to address these drawbacks, we propose dissected 3D CNNs, where the intermediate volumes of the network are dissected and propagated over depth (time) dimension for future calculations, substantially reducing the number of computations at online operation. For action classification, the dissected version of ResNet models performs 77%–90% fewer computations at online operation while achieving ∼5% better classification accuracy on the Kinetics-600 dataset than conventional 3D-ResNet models. Moreover, the advantages of dissected 3D CNNs are demonstrated by deploying our approach onto several vision tasks, which consistently improved the performance.
MAPLE: Microprocessor A Priori for Latency Estimation

Modern deep neural networks must demonstrate state-of-the-art accuracy while exhibiting low latency and energy consumption. As such, neural architecture search (NAS) algorithms take these two constraints into account when generating a new architecture. However, efficiency metrics such as latency are typically hardware dependent requiring the NAS algorithm to either measure or predict the architecture latency. Measuring the latency of every evaluated architecture adds a significant amount of time to the NAS process. Here we propose Microprocessor A Priori for Latency Estimation (MAPLE) that leverages hardware characteristics to predict deep neural network latency on previously unseen hardware devices. MAPLE takes advantage of a novel quantitative strategy to characterize the underlying microprocessor by measuring relevant hardware performance metrics, yielding a fine-grained and expressive hardware descriptor. The CPU-specific performance metrics are also able to characterize GPUs, resulting in a versatile descriptor that does not rely on the availability of hardware counters on GPUs or other deep learning accelerators. We provide experimental insight into this novel strategy. Through this hardware descriptor, MAPLE can generalize to new hardware via a few shot adaptation strategy, requiring as few as 3 samples from the target hardware to yield 6% improvement over state-of-the-art methods requiring as much as 10 samples. Experimental results showed that, increasing the few shot adaptation samples to 10 improves the accuracy significantly over the state-of-the-art methods by 12%. We also demonstrate MAPLE identification of Pareto-optimal DNN architectures exhibit superlative accuracy and efficiency. The proposed technique provides a versatile and practical latency prediction methodology for DNN run-time inference on multiple hardware devices while not imposing any significant overhead for sample collection.
LCANet: Lightweight Context-Aware Attention Networks for Earthquake Detection and Phase-Picking on IoT Edge Devices

Aftershocks monitoring system is a large-scale Internet of Things (IoT) system. Earthquake signal detection and phase-picking, as the core of aftershocks monitoring system, is time-sensitive. Generally, the data generated by seismic stations are transmitted to the cloud for integration, storage, and processing through the network, contributing to network congestion and affecting the real time of earthquake warning as well as the supported applications. Edge computing, a new paradigm for real-time IoT tasks, has emerged as a trend to address concerns of response time, requirement, saving bandwidth costs, as well as data safety and privacy. Therefore, a neglected problem was tackled in this article. First, to meet the real-time requirement, we proposed a novel lightweight deep learning model called lightweight context-aware attention networks for earthquake signal detection and phase-picking. We optimized the deep learning model and reduced the computation requirements to deal with edge devices that have lower computation power than cloud servers. Second, we deployed the model to the Jetson Nano, a small edge device, to offload computation tasks from cloud servers to edge devices. In this way, seismic stations do not need to send raw data to a centralized server. Third, experiments show that our method is robust and easily generalized to other databases. The entire model is only 3.7 MB and achieves the accuracy-latency trade-off.
From learning loss to learning opportunity

All that is gold does not glitter,Not all those who wander are lost;The old that is strong does not wither,Deep roots are not reached by the frost.J. R. R. Tolkien, The Fellowship of the Ring (2020...
A Practical Approach for Estimating the Optimum Tilt Angle of a Photovoltaic Panel for a Long Period—Experimental Recorded Data

In this study, recorded empirical data were applied with a practical approach to investigate the optimal tilt angle of the flat plate collectors facing south for a long period in Tehran, Iran. The data included 20 years of recorded average total radiation on the horizontal plane in Tehran’s meteorological station. Based on the previous studies, the annual optimum tilt angle for Tehran was estimated at 33 degrees annually; however, this estimation does not focus on the energy absorption and effectiveness of changing the tilt angle monthly, seasonally, and bi-annually via measured data. This paper aims to explain this distinction between various radiation receptions with different tilt angle adjustments. This study shows that annual solar cumulative radiation energy gained via a monthly tilt angle can be approximately 7% higher than that achieved with an annual tilt angle setup. Additionally, the seasonal and bi-annual tilt angles have about 6% more annual cumulative radiation absorption than the annual tilt angle setup. Moreover, with consideration of similar monthly received radiation, the results illustrate that the radiation gained with a monthly tilt angle set up was 20% greater in the summer months than an annual tilt angle adjustment.
Design of Decision Based Recursive Weighted Median Filter With Exponential Weights

Abstract The prescribed algorithm for removing impulse noise effectively even under high noise densities without causing any loss of image details. Hence a cascaded section of median filters that, involves an Decision-based Median Filter followed by a Recursive Weighted Median (RWM) Filter employing exponential weights are used. The median controlled algorithm is employed to calculate the exponential weights. In the algorithms that where proposed in earlier which involves a cascaded section of the median with the RWM filters provided lesser Peak Signal/Noise Ratio (PSNR) and greater Mean Square Error(MSE) values. Hence the output appeared to be distorted for higher noise levels. These drawbacks have been eliminated in this proposed algorithm.