Reviewerly/exHarmony huggingface dataset:
Toward Energy-Efficient Federated Learning Over 5G+ Mobile Devices

The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications. Despite the remarkable progress made in FL, huge energy consumption is one of the most significant obstacles restricting the development of FL over battery-constrained 5G+ mobile devices. To address this issue, in this article, we investigate how to develop energy- efficient FL over 5G+ mobile devices by making a trade-off between energy consumption for “working” (i.e., local computing) and that for “talking” (i.e., wireless communications) in order to boost the overall energy efficiency. Specifically, we first examine energy consumption models for GPU computation and wireless transmissions. Then, we overview the state of the art of integrating FL procedure with energy-efficient learning techniques (gradient sparsification, weight quantization, pruning, etc.). Finally, we present several potential future research directions for FL over 5G+ mobile devices from the perspective of energy efficiency.
Error self-correction method for phase jump in multi-frequency phase-shifting structured light

Among 3D measurement approaches, multi-frequency phase-shifting structured light has advantages such as high resolution and high sampling rate owing to its point-to-point calculation method. However, there is always phase jump in the measurement process, which greatly reduces measurement accuracy. This paper proposes an error self-correction method for phase jump based on the multi-frequency heterodyne approach. The method uses redundant measurement data to implement self-correction and does not require additional data acquisition steps. We perform both simulations and experiments using the proposed error self-correction method and the classical heterodyne approach to compare the results. The experiment results verify both the accuracy and suitability of the proposed method.
Private Information Retrieval for a Multi-Message Scenario With Private Side Information

We consider the problem of private information retrieval (PIR), where a single user with private side information (PSI) aims to retrieve multiple files from a library stored at some servers. We assume that the side information (SI) at the user includes a subset of files stored privately. Moreover, the identity of requests and side information at the user are not revealed to any of the servers. The problem involves finding the minimum load transmitted from the servers to the user such that the requested files can be decoded with the help of received data and side information. By providing matching lower and upper bounds for certain regimes, we characterize the minimum load imposed on all the servers. Our result shows that the capacity is the same as the capacity of a multi-message PIR problem without PSI, but with a library of reduced size, i.e., the library is equal to the original library size minus the size of SI. Finally, we extend our setup to the case where instead of storing complete files as SI, the user can store a fraction of files. For this scenario, we propose an achievability scheme based on which we discuss the best storing strategies.
Experimentally realizing efficient quantum control with reinforcement learning

We experimentally investigate deep reinforcement learning (DRL) as an artificial intelligence approach to control a quantum system. We verify that DRL explores fast and robust digital quantum controls with operation time analytically hinted by shortcuts to adiabaticity. In particular, the protocol’s robustness against both over-rotations and off-resonance errors can still be achieved simultaneously without any priori input. For the thorough comparison, we choose the task as single-qubit flipping, in which various analytical methods are well-developed as the benchmark, ensuring their feasibility in the quantum system as well. Consequently, a gate operation is demonstrated on a trapped 171Yb+ ion, significantly outperforming analytical pulses in the gate time and energy cost with hybrid robustness, as well as the fidelity after repetitive operations under time-varying stochastic errors. Our experiments reveal a framework of computer-inspired quantum control, which can be extended to other complicated tasks without loss of generality.
On Perfect Privacy

The problem of private data disclosure is studied from an information theoretic perspective. Considering a pair of dependent random variables (X, Y), where X and Y denote the private and useful data, respectively, the following problem is addressed: What is the maximum information that can be revealed about Y, measured by mutual information I(Y; U), in which U denotes the revealed data, while disclosing no information about X, captured by the condition of statistical independence, i.e., X ⊥ U, and henceforth called perfect privacy)? We analyze the supremization of utility, i.e., I(Y; U) under the condition of perfect privacy for two scenarios: output perturbation and full data observation models, which correspond to the cases where a Markov kernel, called privacy-preserving mapping, applies to Y and the pair (X, Y), respectively. When both X and Y have a finite alphabet, the linear algebraic analysis involved in the solution provides some interesting results, such as upper/lower bounds on the size of the released alphabet and the maximum utility. Afterwards, it is shown that for the jointly Gaussian (X, Y), perfect privacy is not possible in the output perturbation model in contrast to the full data observation model. Finally, an asymptotic analysis is provided to obtain the rate of released information when a sufficiently small leakage is allowed. In particular, in the context of output perturbation model, it is shown that this rate is always finite when perfect privacy is not feasible, and two lower bounds are provided for it; When perfect privacy is feasible, it is shown that under mild conditions, this rate becomes unbounded.
Deterrence and leadership factors: Which are important for information security policy compliance in the hotel industry

Hotels have developed information security policies to deal with information security incidents, and managers are interested in how to enhance employees' compliance with these policies. This study examines how deterrence perception and managers' leadership influence employees' intention to comply with information security policies. Two rounds of surveys were conducted to collect data from Chinese hotel employees. The results show that perceived deterrent certainty has a positive effect. On the other hand, ethical leadership has a significant indirect effect with the second-round compliance intention. Abusive supervision does not have positive effects, even when managers' intent is motivational. Our study is among the first to examine the effect of managers' leadership on information security compliance intentions in the hotel industry. Our study also provides important strategic guidelines for by informing hotel managers that ethical leadership should be practiced as a means of enhancing employees' information security policy compliance.
Compact learning for multi-label classification

Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. It confronts with the great challenge for the exploration of the latent label relationship and the intrinsic correlation between feature and label spaces. MLC gave rise to a framework named label compression (LC) to obtain a compact space for efficient learning. Nevertheless, most existing LC methods failed to consider the influence of the feature space or misguided by original problematic features, which may result in performance degradation instead. In this paper, we present a compact learning (CL) framework to embed the features and labels simultaneously and with mutual guidance. The proposal is a versatile concept that does not rigidly adhere to some specific embedding methods, and is independent of the subsequent learning process. Following its spirit, a simple yet effective implementation called compact multi-label learning (CMLL) is proposed to learn a compact low-dimensional representation for both spaces. CMLL maximizes the dependence between the embedded spaces of the labels and features, and minimizes the loss of label space recovery concurrently. Theoretically, we provide a general analysis for different embedding methods. Practically, we conduct extensive experiments to validate the effectiveness of the proposed method.
Two-step multi-view and multi-label learning with missing label via subspace learning

In multi-view and multi-label learning, each example can be represented by multiple data view features and annotated with a set of discrete non-exclusive labels. Missing label learning is an important branch of multi-label learning, which can handle incomplete labels with annotations. Previous work on multi-label learning with missing labels mainly considered data in a single view representation. Based on intuitive understanding, we propose a Two-step Multi-view and Multi-label Missing Label learning optimization solution(TM3L). The first step is to solve the multi-view learning problem by finding the data representation of the common low-dimensional space of all views through subspace learning. While fully considering the complementary information between multiple views, the different degrees of contribution combined with different views are weighted differently. The second step is to solve the multi-label missing label learning problem by using the label matrix completion method in combination with the kernel extreme learning machine classifier. The kernel extreme learning machine can effectively enhance the robustness of the algorithm to missing labels. The experimental results and analysis on multiple benchmark multi-view and multi-label data sets verify the effectiveness of TM3L compared with the state-of-the-art solutions.
LiteZKP: Lightening Zero-Knowledge Proof-Based Blockchains for IoT and Edge Platforms

This article presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> LiteZKP <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> a framework for supporting multiple anonymous payments using a smart contract-based zero-knowledge proof (ZKP) protocol on resource-limited devices. Specifically, to address challenges related to minimizing the computational overhead and offer a fully anonymous system, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> LiteZKP <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> includes novel schemes such a new merkle tree mechanism to reduce the burden of ZKP operations, and integrates smart contract-based ZKP with an off-chain payment channel to reduce the amount of ZKP operations when performing continuous data exchange. We present evaluation results of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> LiteZKP <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> from both PC-scale and resource-limited client devices and our results suggest that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> LiteZKP <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> reduces the latency and energy consumption by more than 55% on Internet of Things (IoT)/mobile edge computing platforms, while requiring only 8% of block processing fee compared to a naive ZKP-based scheme.
Co-Movement of COVID-19 and Bitcoin: Evidence from Wavelet Coherence Analysis

We apply wavelet methods to daily data of COVID-19 world deaths and daily Bitcoin prices from 31th December 2019 to 29th April 2020.We find, especially for the period post April 5, that levels of COVID-19 caused a rise in Bitcoin prices.We contribute to the fast-growing body of work on the financial impacts of COVID-19, as well as to ongoing consideration of whether Bitcoin is a safe haven investment.Our results should be of great interest to both scholars and policy makers, as well as investment professionals interested in the financial implications of both COVID-19 and cryptocurrencies.
Structured discriminant analysis dictionary learning for pattern classification

Dictionary learning has been widely used in the field of pattern recognition. Although the existing synthesis dictionary learning methods have achieved impressive results, they need compute sparse codes using a time-consuming sparse coding procedure. As a dual viewpoint of synthesis dictionary learning, analysis dictionary learning (ADL) has attracted much attention due to its high efficiency and intuitive meaning in recent years. However, how to associate analysis dictionary atoms with class labels and learn a structured discriminant analysis dictionary, is still a challenging problem. In this paper, we propose a structured discriminant analysis dictionary learning (SDADL) method to learn a structured discriminant analysis dictionary that consists of the class-specific analysis sub-dictionaries associated with the corresponding classes. Specifically, we introduce a classification error term into SDADL model to learn an optimal linear classifier for classification. To obtain the discriminant analysis sparse codes, we introduce a discriminant analysis sparse code error term into SDADL model, which forces the samples from the same class to have similar analysis sparse codes. Moreover, we also introduce a structured discriminant term into SDADL model to improve the discrimination capability of both each class-specific analysis sub-dictionary and analysis sparse codes. An efficient iterative algorithm is also developed to solve the optimization problem of SDADL. In addition, we design a novel scheme for classification. Extensive experiments on five image datasets verify the effectiveness of SDADL for pattern classification.
Emotional reactions and coping responses of employees to a cyber-attack: A case study

Cybersecurity is a serious threat to information technology (IT) systems, with most organisations now relying on their IT systems to function day-to-day. Therefore, it is crucial that organisations ensure they are protected. This research investigates the emotional and coping responses of employees towards a cyber-attack. Data was collected through an in-depth case study approach (24 semi-structured interviews) conducted at a global manufacturing company, along with primary observations and a corpus of secondary materials. Our data analysis applied a grounded approach which was then refined using an emotion theoretic lens with the application of Technology Threat Avoidance Theory (TTAT). Drawing on structural appraisal theory, our findings indicate the IT security team in the case study oscillated between positive problem-focused coping and negative emotion-focused coping. A key explanatory feature or turnaround mechanism that mediated this oscillation was senior management empathy and resource mobilization. Building on the analyses, a conceptual model is developed called the Transformation of Coping through Empathic Leadership (T-CEL). The research suggests the crucial role that senior management play in transforming the emotional and coping capabilities of employees through their intervention and support. The study extends the IS Security theoretical background through a TTAT-lens for the analysis of cyber-attack incidents.
Artificial bee colony algorithm with adaptive covariance matrix for hearing loss detection

Artificial bee colony algorithm (ABC) is an efficient and popular evolutionary algorithm (EAs), which has been attracted wide attention by researchers, and improved ABC with various characteristics (ABCs) have been proposed. It is widely acknowledged that the search operator is the core element in the performance of ABC. However, the generally designed search operators of ABCs are rotation-variable processes and are dependent mainly on the natural coordinates and, as a result, the performance of those ABC is limited. In this paper, the mathematical characteristic of the search operator is deeply analyzed, and on this basis, ABC with adaptive covariance matrix (ACoM-ABC) is proposed, the adaptive covariance matrix (ACoM) is used to establish a proper coordinates by making use of the population distribution information, which can relieve the dependence of ABC on the coordinates to a certain extent and improve the exploitation capability. To balance the exploitation and exploration abilities of ABC, the search operators of ABC are implemented on eigen coordinates and natural coordinates Then, to estimate the performance of ACoM-ABC, which compares with six ABCs and other six EAs, and tests on CEC2014. The excellent experimental result shows that ACoM-ABC is an efficient and outstanding algorithm. Moreover, the proposed algorithm is applied to hearing loss detection, and the experiment result shows that the overall accuracy is 96.67%, which higher than the other five state-of-the-art approaches about 1%. Therefore, the ACoM-ABC has the practicability for realistic problems.
A Modified Sine Cosine Algorithm With Teacher Supervision Learning for Global Optimization

The sine cosine algorithm (SCA) is a recently developed meta-heuristic algorithm for solving global optimization problems. It has shown excellent performance in meta-heuristic algorithms. But this algorithm also has shortcomings such as low accuracy, easy to fall into a local solution, and slow convergence speed. Aiming at these deficiencies of the SCA, a modified sine cosine algorithm with teacher supervision learning (TSL-SCA) for global optimization is proposed. First, the teacher supervision strategy can guide the population convergence and accelerate the convergence speed. Second, individuals perform reflective learning after the standard SCA position is updated, which can effectively prevent individuals from stagnating in the evolutionary process and increase population diversity. In addition, a hybrid inverse learning method is proposed. It can not only enhance the ability of finding global optimal solution and increase population distributivity, but also balance the exploration and exploitation capabilities. Differential evolution algorithm (DE), particle swarm optimization (PSO), cuckoo search (CS) algorithm, moth-flame optimization (MFO), whale optimization algorithm (WOA), Teaching-Learning-Based Optimization (TLBO), SCA and TSL-SCA are selected for simulation experiment to solve 33 benchmark optimization problems. The experimental results show that the TSL-SCA can significantly enhance the optimization accuracy and convergence speed. Furthermore, the effectiveness of the proposed method is examined by solving analog circuit fault diagnosis of filter circuit examples.
A Novel Concentric Circular Coded Target, and Its Positioning and Identifying Method for Vision Measurement under Challenging Conditions

Coded targets have been demarcated as control points in various vision measurement tasks such as camera calibration, 3D reconstruction, pose estimation, etc. By employing coded targets, matching corresponding image points in multi images can be automatically realized which greatly improves the efficiency and accuracy of the measurement. Although the coded targets are well applied, particularly in the industrial vision system, the design of coded targets and its detection algorithms have encountered difficulties, especially under the conditions of poor illumination and flat viewing angle. This paper presents a novel concentric circular coded target (CCCT), and its positioning and identifying algorithms. The eccentricity error has been corrected based on a practical error-compensation model. Adaptive brightness adjustment has been employed to address the problems of poor illumination such as overexposure and underexposure. The robust recognition is realized by perspective correction based on four vertices of the background area in the CCCT local image. The simulation results indicate that the eccentricity errors of the larger and smaller circles at a large viewing angle of 70° are reduced by 95% and 77% after correction by the proposed method. The result of the wing deformation experiment demonstrates that the error of the vision method based on the corrected center is reduced by up to 18.54% compared with the vision method based on only the ellipse center when the wing is loaded with a weight of 6 kg. The proposed design is highly applicable, and its detection algorithms can achieve accurate positioning and robust identification even in challenging environments.
Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network

Colorectal imaging improves on diagnosis of colorectal diseases by providing colorectal images. Manual diagnosis of colorectal disease is labor-intensive and time-consuming. In this paper, we present a method for automatic colorectal disease classification and segmentation. Because of label unbalanced and difficult colorectal data, the classification based on self-paced transfer VGG network (STVGG) is proposed. ImageNet pretraining network parameters are transferred to VGG network with training colorectal data to acquire good initial network performance. And self-paced learning is used to optimize the network so that the classification performance of label unbalanced and difficult samples is improved. In order to assist the colonoscopist to accurately determine whether the polyp needs surgical resection, feature of trained STVGG model is shared to Unet segmentation network as the encoder part and to avoid repeat learning of polyp segmentation model. The experimental results on 3061 colorectal images illustrated that the proposed method obtained higher classification accuracy (96%) and segmentation performance compared with a few other methods. The polyp can be segmented accurately from around tissues by the proposed method. The segmentation results underpin the potential of deep learning methods for assisting colonoscopist in identifying polyps and enabling timely resection of these polyps at an early stage.