Reviewerly/exHarmony huggingface dataset:
KfreqGAN: Unsupervised detection of sequence anomaly with adversarial learning and frequency domain information

Sequence anomaly detection in time series is of critical importance to wide applications ranging from finance, healthcare to IT system monitoring. Most current researches use the reconstruction-based deep learning algorithms to solve the problem. In this article, we aim to use a prediction-based method to detect sequence anomalies in univariate time series, because the latter methods can detect anomalies using historical information revealing normal patterns in time series whereas the former methods simply consider current sequences. However, it is challenging because there exists both uncertainty in the future and performance deterioration under long detection horizon. To tackle the challenges, we propose an unsupervised algorithm called KfreqGAN, which is based on adversarially trained sequence predictor. The adversarial learning architecture helps the model make accurate predictions for future sequences. In addition, auxiliary information from frequency domain is used to help the model capture the characteristics of time series for achieving satisfactory predictions. We conduct extensive experiments on two public-available datasets, with results demonstrating the effectiveness of the proposed algorithm and its superiority to baseline algorithms.
A Fine-Grained Differentially Private Federated Learning Against Leakage From Gradients

Federated learning (FL) enables data owners to train a global model with shared gradients while keeping private training data locally. However, recent research demonstrated that the adversary may infer private training data of clients from the exchanged local gradients, e.g., having deep leakage from gradients (DLGs). Many existing privacy-preserving approaches take usage of differential privacy (DP) to guarantee privacy. Nevertheless, the widely used privacy budget of DP (e.g., evenly distribution) leads to a sharp decline of model accuracy. To improve the model accuracy, some schemes only consider allocating the privacy budget to the fully connected layers. However, we reveal that the adversary may still reconstruct the private training data by adopting the DLG attack with the gradients of convolutional layers. In this article, we propose a fine-grained DP federated learning (DPFL) scheme, which guarantees privacy and remains high model performance simultaneously. Specifically, inspired by the methods that measure the importance of layers in deep learning, we propose a fine-grained method to allocate noise according to the importance value of layers in order to remain high model performance. Besides, we combine an active client selection strategy with DPFL and perform fine-tuning with a public data set on the server to further ensure the model performance. We evaluate DPFL under both independent and identically distributed (i.i.d) and non-i.i.d data settings to show that our method can achieve similar accuracy as the plain FL (e.g., FedAvg). We also demonstrate that our DPFL can resist the DLG attack to verify its privacy guarantee.
Evidential data integration to produce porphyry Cu prospectivity map, using a combination of knowledge and data‐driven methods

ABSTRACT Producing an accurate and valid mineral prospectivity map is one of the most significant parts of mineral exploration studies. For this purpose, it is needed to obtain valid evidential layers and integrate them with an accurate methodology. Knowledge and data‐driven methods are two primary techniques applied to combine various evidential layers for mineral prospectivity mapping, of which each of them includes a variety of analytical techniques. In this study, in the first step, satellite data, aeromagnetic and airborne radiometric data, stream sediment geochemical data and geological data were applied to create valid remote sensing, geophysical, geochemical, lineaments and lithological evidential layers of the study area that are an essential factor in recognition porphyry copper mineralization, then in the second step, based on the known mineralization occurrences data, the evidential layers were weighted. Finally, these layers were integrated using fuzzy logic and index overlay methods in a combination of knowledge and data‐driven way. Validation of each layer was done using available data in the second step. The final mineral prospectivity map was evaluated, and the confirmation of this layer detected that the final mineral prospectivity map obtained from data‐driven multi‐index overlay method has a higher ore prediction rate of 76%, which identifies 24% of the area as potential zones for further exploration.
A conditional opposition-based particle swarm optimisation for feature selection

Because of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. As a popular metaheuristic algorithm, particle swarm optimisation has successfully applied to various feature selection approaches. Nevertheless, particle swarm optimisation tends to suffer from immature convergence and low convergence rate. Besides, the imbalance between exploration and exploitation is another key issue that can significantly affect the performance of particle swarm optimisation. In this paper, a conditional opposition-based particle swarm optimisation is proposed and used to develop a wrapper feature selection. Two schemes, namely opposition-based learning and conditional strategy are introduced to enhance the performance of the particle swarm optimisation. Twenty-four benchmark datasets are used to validate the performance of the proposed approach. Furthermore, nine metaheuristics are chosen for performance verification. The findings show the supremacy of the proposed approach not only in obtaining high prediction accuracy but also in small feature sizes.
Evaluation of visual-induced motion sickness from head-mounted display using heartbeat evoked potential: a cognitive load-focused approach

Abstract Based on sensory conflict theory, motion sickness is strongly related to the information processing capacity or resources of the brain to cope with the multi-sensory stimuli experienced by watching virtual reality (VR) content. The purpose of this research was to develop a method of measuring motion sickness using the heart-evoked potential (HEP) phenomenon and propose new indicators for evaluating motion sickness. Twenty-eight undergraduate volunteers of both genders (14 females) participated in this study by watching VR content on both 2D and head-mounted devices (HMD) for 15 min. The responses of HEP measures such as alpha power, latency, and amplitude of first and second HEP components were compared using paired t-tests and ANCOVA. This study confirmed that motion sickness leads to a decline in cognitive processing, as demonstrated by increasing in alpha power of HEP. Also, the proposed indicators such as latency and amplitude of the HEP waveform showed significant differences during the experience of motion sickness and exhibited high correlations with alpha power measures. Latencies of the first HEP component, in particular, are recommended as better quantitative evaluators of motion sickness than other measures, following the multitrait-multimethod matrix. The proposed model for motion sickness was implemented in a support vector machine with a radial basis function kernel, and validated on twenty new participants. The accuracy, F1 score, precision, recall, and area under the curve (AUC) of the motion-sickness classification results were 0.875, 0.865, 0.941, 0.8, and 0.962, respectively.
Super-resolution of near-surface temperature utilizing physical quantities for real-time prediction of urban micrometeorology

The present paper proposes a super-resolution (SR) model based on a convolutional neural network and applies it to the near-surface temperature in urban areas. The SR model incorporates a skip connection, a channel attention mechanism, and separated feature extractors for the inputs of temperature, building height, downward shortwave radiation, and horizontal velocity. We train the SR model with sets of low-resolution (LR) and high-resolution (HR) images from building-resolving large-eddy simulations (LESs) in a city, where the horizontal resolutions of LR and HR are 20 and 5 m, respectively The generalization capability of the SR model is confirmed with LESs in another city. The estimated HR temperature fields are more accurate than those of the bicubic interpolation and image SR model that takes only the temperature as its input. Except for the temperature input, the building height is the most important to reconstruct the HR temperature and enables the SR model to reduce errors in temperature near building boundaries. The SR model considers the appropriate boundary for each building from its height information. The analysis of attention weights indicates that the importance of the building height increases as the downward shortwave radiation becomes larger. The contrast between sun and shade is strengthened with the increase in solar radiation, which may affect the temperature distribution. The short inference time suggests the potential of the proposed SR model to facilitate a real-time HR prediction in metropolitan areas by combining it with an LR building-resolving LES model.
Single-Stage Detector With Dual Feature Alignment for Remote Sensing Object Detection

As a fundamental vision-based task in the remote sensing filed, object detection has achieved significant progress. However, remote sensing object detection is still an urgent challenge owing to dense distribution, large aspect ratio, and arbitrary orientations. To address this issue, we develop an end-to-end dual align single-stage rotation detector (DA-Net) consisting of two main components: a Rotation Feature Selection (RFS) module and a Rotation Feature Align (RFA) module. Specifically, RFS module can empower neurons with the capability to adjust receptive fields, which achieves the first stage of feature alignment on the image level. Furthermore, RFA module is employed to adaptively align the feature based on the size, shapes, orientations of its corresponding anchors, realizing the second stage of instance-level feature alignment. Extensive experiments have shown that our DA-Net can significantly improve remote sensing detection performance against several start-of-the-art algorithms on two benchmark datasets.
LEO Satellites Constellation-to-Ground QKD Links: Greek Quantum Communication Infrastructure Paradigm

Quantum key distribution (QKD) has gained a lot of attention over the past few years, but the implementation of quantum security applications is still challenging to accomplish with the current technology. Towards a global-scale quantum-secured network, satellite communications seem to be a promising candidate to successfully support the quantum communication infrastructure (QCI) by delivering quantum keys to optical ground terminals. In this research, we examined the feasibility of satellite-to-ground QKD under daylight and nighttime conditions using the decoy-state BB84 QKD protocol. We evaluated its performance on a hypothetical constellation with 10 satellites in sun-synchronous Low Earth Orbit (LEO) that are assumed to communicate over a period of one year with three optical ground stations (OGSs) located in Greece. By taking into account the atmospheric effects of turbulence as well as the background solar radiance, we showed that positive normalized secure key rates (SKRs) up to 3.9×10−4 (bps/pulse) can be obtained, which implies that satellite-to-ground QKD can be feasible for various conditions, under realistic assumptions in an existing infrastructure.
Trace and minor elements in sulfides from the Lengshuikeng Ag–Pb–Zn deposit, South China: A LA–ICP–MS study

Sphalerite, pyrite and galena are common sulfide minerals in Zn–Pb–(Ag) deposits, containing minor and trace elements that can be used to constrain the ore-forming processes. The Lengshuikeng Ag–Pb–Zn deposit (LSKD) is one of the largest silver deposits in China. The mode of silver occurrence in sulfides remains unclear except for individual silver minerals. In this study, we analyzed the minor and trace element contents of sulfide minerals from massive, vein, and veinlet-disseminated ores by in-situ laser ablation–inductively coupled plasma–mass spectrometry (LA–ICP–MS). The elemental compositions of sulfide minerals are variable. Combining petrographic observations and previous studies, we suggest that the sulfide minerals from two types of mineralization (porphyry and strata-bound types) were formed in a same hydrothermal system, and their content of minor and trace elements mainly is influenced by temperature, alteration of wall rocks and composition change of hydrothermal fluid. The Ag content in galena ranges n*103 ∼ n*104 ppm, whereas that in both sphalerite and pyrite ranges from n*10 ∼ n*103 ppm. Except for galena, a higher content of gold (mean = 0.76 ppm) and silver (mean = 151 ppm) occurs in sphalerite compared with the content of gold (mean = 0.26 ppm) and silver (mean = 69.9 ppm) in pyrite. Generally, altered samples display higher Ag and Au contents in pyrite and sphalerite. Silver occurs in the form of a solid solution in sulfides from samples with massive textures, and as micro-inclusions in sulfides from altered samples. The contents of minor and trace elements (e.g. Cd and In) and their ratios (Ga/In, Zn/Cd and Cd/Fe) are not always sensitive to the temperature and processes of deposit formation. We suggest that sulfides from samples with massive texture and limited alteration are suitable for determining the genetic type of the deposit. Our results show that the hydrothermal fluid is a magmatic fluid related to porphyry granite in the LSKD.
Design and Development of a Blockchain-Based System for Private Data Management

The concept of blockchain was introduced as the Bitcoin cryptocurrency in a 2008 whitepaper by the mysterious Satoshi Nakamoto. Blockchain has applications in many domains, such as healthcare, the Internet of Things (IoT), and data management. Data management is defined as obtaining, processing, safeguarding, and storing information about an organization to aid with making better business decisions for the firm. The collected information is often shared across organizations without the consent of the individuals who provided the information. As a result, the information must be protected from unauthorized access or exploitation. Therefore, organizations must ensure that their systems are transparent to build user confidence. This paper introduces the architectural design and development of a blockchain-based system for private data management, discusses the proof-of-concept prototype using Hyperledger Fabric, and presents evaluation results of the proposed system using Hyperledger Caliper. The proposed solution can be used in any application domain where managing the privacy of user data is important, such as in health care systems.
Affective Virtual Reality: How to Design Artificial Experiences Impacting Human Emotions

Computer graphics is—in many cases—about visualizing what you cannot see. However, virtual reality (VR), from its beginnings, aimed at stimulating all human senses: not just the visual channel. Moreover, this set of multisensory stimuli allows users to feel present and able to interact with the virtual environment. In this way, VR aims to deliver experiences that are comparable to real-life ones in their level of detail and stimulation, intensity, and impact. Hence, VR is not only a means to see, but also to feel differently. With the spreading of VR technologies, there is a growing interest in using VR to evoke emotions, including positive and negative ones. This article discusses the current possibilities and the authors’ experience collected in the field in trying to elicit emotions through VR. It explores how different design aspects and features can be used, describing their contributions and benefits in the development of affective VR experiences. This work aims at raising awareness of the necessity to consider and explore the full design space that VR technology provides in comparison to traditional media. Additionally, it provides possible tracks of VR affective applications, illustrating how they could impact our emotions and improve our life, and providing guidelines for their development.
CEModule: A Computation Efficient Module for Lightweight Convolutional Neural Networks

Lightweight convolutional neural networks (CNNs) rely heavily on the design of lightweight convolutional modules (LCMs). For an LCM, lightweight design based on repetitive feature maps (LoR) is currently one of the most effective approaches. An LoR mainly involves an extraction of feature maps from convolutional layers (CE) and feature map regeneration through cheap operations (RO). However, existing LoR approaches carry out lightweight improvements only from the aspect of RO but ignore the problems of poor generalization, low stability, and high computation workload incurred in the CE part. To alleviate these problems, this article introduces the concept of key features from a CNN model interpretation perspective. Subsequently, it presents a novel LCM, namely CEModule, focusing on the CE part. CEModule increases the number of key features to maintain a high level of accuracy in classification. In the meantime, CEModule employs a group convolution strategy to reduce floating-point operations (FLOPs) incurred in the training process. Finally, this article brings forth a dynamic adaptation algorithm ( α -DAM) to enhance the generalization of CEModule-enabled lightweight CNN models, including the developed CENet in dealing with datasets of different scales. Compared with the state-of-the-art results, CEModule reduces FLOPs by up to 54% on CIFAR-10 while maintaining a similar level of accuracy in classification. On ImageNet, CENet increases accuracy by 1.2% following the same FLOPs and training strategies.
An effective linguistic steganalysis framework based on hierarchical mutual learning

In recent years, the study of linguistic steganalysis has been focused on altering network structure, such as replacing the basic neural units or increasing the model depth, which inevitably increases computational overhead and restricts further improvement in resource-constrained scenarios. In this paper, instead of relying on complex neural networks, we propose an alternative linguistic steganalysis framework based on hierarchical mutual learning to achieve higher detection accuracy with less inference time and model size. In the proposed method, networks with either identical or different structures are trained cooperatively to learn distinct text features from each other. To this end, in addition to the supervised learning loss function, we construct three mimicry loss functions at different feature extraction stages, which can integrate steganalytic features from various levels. Finally, we illustrate how the steganalysis framework can be extended from two networks to multiple networks. Four representative steganalysis networks with different structures are employed to verify the effectiveness of our framework. The experimental results show that the proposed framework can effectively assist networks with fewer resources to perform better in model size, inference time, and detection accuracy than state-of-the-art steganalysis algorithms.
Machine Learning Algorithm in Network Traffic Classification

Network traffic classification plays an important role in various network functions such as network security issues and network management. In addition to port-based and payload-based approaches, the classical machine learning approaches have been studied for past decades, but there are some limitations, namely time-consuming, frequent features updates, and the accuracy has decreased due to the rise of internet traffic, especially encrypted traffic. Deep learning comes with the ability of automatic feature learning, some studies try to apply it and reported better accuracy. This survey paper introduces the emerging research and general framework for deep learning-based methods for traffic classification. We present commonly used deep learning methods and their application in traffic classification tasks.
Design of a Blockchain Enabled Secure Vehicle-to-Vehicle Communication System

The automobile sector is experiencing tremendous changes with the proliferation of vehicular communications, intelligent transport systems, and self-contained motor vehicles during the last decade. Recent improvements in software, hardware, and communication system to create different applications and standards make these technologies conceivable. Today, new technologies are attached to vehicles that recognize and enhance the driving experience of possible drive concerns. The integration of vehicles involves three main components: sensors, information systems, and communication systems to develop a connected vehicle network. Vehicle-to-Vehicle (V2V) communication is employed within a connected network to ease traffic congestion and enhance passenger protection. There exist many issues like security of vehicles, communication interfaces and stored data. Therefore, a Blockchain-enabled Secure Vehicle-to-Vehicle Communication System (BSVCS) has been proposed to provide security to the communication interfaces for connected vehicles. Vehicles can communicate directly (for short-range) or via a cellular connection (for long-range) using C-V2X technology. It can also contribute to authoritative management to store the key securely and the process of its generation. A 2-step authentication process (2-SAP) has been introduced to identify a genuine user and to detect any unauthorized user. The proposed system can resolve the security issues such as no-authentication, broadcasting, the securities arbitration rule, and cybersecurity in the V2V communication system. It also secures vehicle security and user privacy in the V2V communication system.
An Adaptive Learning Path Builder based on a Context Aware Recommender System

The world of distance education is constantly expanding, enriching itself with tools and services to increase the ability to provide training content. Due to the new technologies, the training paths take on new appealing features; however, it remains complex to suggest the appropriate training path to the right student. In this scenario, the use of Recommender Systems (RSs) could be helpful. RSs could allow recommending personalized learning paths to students in order to improve their abilities and their knowledge. In particular, among Recommender Systems, some of them consider contextual information. This paper aims to describe a new approach that suggests learning paths to users taking advantage of recommendation techniques and introducing them through multimedia content. Moreover, the proposed approach aims to provide recommendations when ratings are unknown through the knowledge of profiles of users and items. The proposed approach has been tested through students of two courses with diverse characteristics.