Reviewerly/exHarmony huggingface dataset:
Quantum computing for financial risk measurement

Abstract Quantum computing allows a significant speed-up over traditional CPU- and GPU-based algorithms when applied to particular mathematical challenges such as optimisation and simulation. Despite promising advances and extensive research in hard- and software developments, currently available quantum systems are still largely limited in their capability. In line with this, practical applications in quantitative finance are still in their infancy. This paper analyses requirements and concrete approaches for the application to risk management in a financial institution. On the examples of Value-at-Risk for market risk and Potential Future Exposure for counterparty credit risk, the main contribution lies in going beyond textbook illustrations and instead exploring must-have model features and their quantum implementations. While conceptual solutions and small-scale circuits are feasible at this stage, the leap needed for real-life applications is still significant. In order to build a usable risk measurement system, the hardware capacity—measured in number of qubits—would need to increase by several magnitudes from their current value of about $$10^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>2</mml:mn> </mml:msup> </mml:math> . Quantum noise poses an additional challenge, and research into its control and mitigation would need to advance in order to render risk measurement applications deployable in practice. Overall, given the maturity of established classical simulation-based approaches that allow risk computations in reasonable time and with sufficient accuracy, the business case for a move to quantum solutions is not very strong at this point.
Cross-modal information fusion for voice spoofing detection

In recent years, speaker verification systems have been used in many production scenarios. Unfortunately, they are still very vulnerable to different kinds of spoofing attacks, such as speech synthesis attacks, replay attacks, etc. Researchers have proposed many methods to defend against these attacks, but in the existing methods, researchers just focus on speech features. In recent studies, researchers have found that speech contains a large amount of face information. In fact, we can determine the speaker’s gender, age, mouth shape, and other information by voice. These information can help us distinguish spoofing attacks. Inspired by this phenomenon, we propose a generalized framework named GACMNet. To cope with different attack scenarios, we instantiated two different models. Our framework is mainly divided into data pre-processing phase, feature extraction phase, feature fusion phase, and classification phase. Specifically, our framework consists of two branches. On the one hand, we extract face features in speech by a convolutional neural network. On the other hand, we use a densely connected network to extract speech features. For the more, we designed a global attention-based information fusion mechanism to distinguish the importance of each part of the features. Our solution was proven to be effective in two large scenarios. Compared to the existing methods, our model improves the tandem decision cost function (t-DCF) and equal error rate (EER) scores by 9% and 11% in the logical access scenario, respectively, our model improves the EER score by 10% in the physical access scenario.
Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms

The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user's sport activity.
BFCN: A Novel Classification Method of Encrypted Traffic Based on BERT and CNN

With the speedy advancement of encryption technology and the exponential increase in applications, network traffic classification has become an increasingly important research topic. Existing methods for classifying encrypted traffic have certain limitations. For example, traditional approaches such as machine learning rely heavily on feature engineering, deep learning approaches are susceptible to the amount and distribution of labeled data, and pretrained models focus merely on the global traffic features while ignoring local features. To solve the above problem, we propose a BERT-based byte-level feature convolutional network (BFCN) model consisting of two novel modules. The first is a packet encoder module, in which we use the BERT pretrained encrypted traffic classification model to capture global traffic features through its attention mechanism; the second is a CNN module, which captures byte-level local features in the traffic through convolutional operations. The packet-level and byte-level features are concatenated as the traffic’s final representation, which can better represent encrypted traffic. Our approach achieves state-of-the-art performance on the publicly available ISCX-VPN dataset for the traffic service and application identification task, achieving F1 scores of 99.11% and 99.41%, respectively, on these two tasks. The experimental results demonstrate that our method further improves the performance of encrypted traffic classification.
Application of Pretrained Large Language Models in Embodied Artificial Intelligence

Abstract A feature of tasks in embodied artificial intelligence is that a query to an intelligent agent is formulated in natural language. As a result, natural language processing methods have to be used to transform the query into a format convenient for generating an appropriate action plan. There are two basic approaches to the solution of this problem. One is based on specialized models trained with particular instances of instructions translated into agent-executable format. The other approach relies on the ability of large language models trained with a large amount of unlabeled data to store common sense knowledge. As a result, such models can be used to generate an agent’s action plan in natural language without preliminary learning. This paper provides a detailed review of models based on the second approach as applied to embodied artificial intelligence tasks.
TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm

COVID-19, a novel virus from the coronavirus family, broke out in Wuhan city of China and spread all over the world, killing more than 5.5 million people. The speed of spreading is still critical as an infectious disease, and it causes more and more deaths each passing day. COVID-19 pandemic has resulted in many different psychological effects on people's mental states, such as anxiety, fear, and similar complex feelings. Millions of people worldwide have shared their opinions on COVID-19 on several social media websites, particularly on Twitter. Therefore, it is likely to minimize the negative psychological impact of the disease on society by obtaining individuals' views on COVID-19 from social media platforms, making deductions from their statements, and identifying negative statements about the disease. In this respect, Twitter sentiment analysis (TSA), a recently popular research topic, is used to perform data analysis on social media platforms such as Twitter and reach certain conclusions. The present study, too, proposes TSA using convolutional neural network optimized via arithmetic optimization algorithm (TSA-CNN-AOA) approach. Firstly, using a designed API, 173,638 tweets about COVID-19 were extracted from Twitter between July 25, 2020, and August 30, 2020 to create a database. Later, significant information was extracted from this database using FastText Skip-gram. The proposed approach benefits from a designed convolutional neural network (CNN) model as a feature extractor. Thanks to arithmetic optimization algorithm (AOA), a feature selection process was also applied to the features obtained from CNN. Later, K-nearest neighbors (KNN), support vector machine, and decision tree were used to classify tweets as positive, negative, and neutral. In order to measure the TSA performance of the proposed method, it was compared with different approaches. The results demonstrated that TSA-CNN-AOA (KNN) achieved the highest tweet classification performance with an accuracy rate of 95.098. It is evident from the experimental studies that the proposed approach displayed a much higher TSA performance compared to other similar approaches in the existing literature.
Non-Ellipsoidal Infrared Group/Extended Target Tracking Based on Poisson Multi-Bernoulli Mixture Filter and B-Spline

This study provides a solution for multiple group/extended target tracking with an arbitrary shape. Many tracking approaches for extended/group targets have been proposed. However, these approaches make assumptions about the target shape, which have limitations in practical applications. To address this problem, in this work, an extended/group target tracking algorithm based on B-spline is proposed. Specifically, the extension of an extended or a group target was modeled as a spatial probability distribution characterized by the control points of a B-spline function that was then jointly propagated with the measurement rate model and kinematic component model over time using the Poisson multi-Bernoulli mixture (PMBM) filter framework. In addition, an amplitude-aided measurement partitioning approach is proposed to improve the accuracy caused by distance-based approaches. The simulation results demonstrate that the extension, shape and orientation of targets can be estimated better by the proposed algorithm, even if the shape changes. The tracking performance is also improved by about 10% and 13% compared to the other two algorithms.
DevOps critical success factors — A systematic literature review

DevOps is a set of software development and operation practices and a recent addition to a large family of different kinds of software process models. The model emerged out of the observation that information systems operations and developments should be closely integrated activities to ensure the success of any organization. Thus, DevOps methods are an additive tool for companies to improve overall performance in their software development processes and operations. This paper aims to identify the various critical success factors (CSFs) of DevOps projects that have been discussed in prior research. In addition, this study proposes a comprehensive framework for depicting how these CSFs impact or drive DevOps success. This study consists of a systematic literature review to collect the primary articles for the analysis. After searches in four major publication databases and snowballing, we selected 38 primary studies for the analysis. Nearly 100 different CSFs were identified, which were then categorized into Technical, Organizational, and Social & Cultural dimensions. Based on the results of the literature analysis, a comprehensive framework is proposed that depicts how the CSFs impact or drive DevOps success. This paper presents a DevOps framework with various CSFs based on prior literature. The proposed framework will provide collective knowledge of DevOps success factors, which will allow researchers and practitioners to enhance their understanding of CSFs and learn how to handle DevOps issues in organizations. In particular, the paper highlights a number of future research directions related to CSFs.
An Unpaired Thermal Infrared Image Translation Method Using GMA-CycleGAN

Automatically translating chromaticity-free thermal infrared (TIR) images into realistic color visible (CV) images is of great significance for autonomous vehicles, emergency rescue, robot navigation, nighttime video surveillance, and many other fields. Most recent designs use end-to-end neural networks to translate TIR directly to CV; however, compared to these networks, TIR has low contrast and an unclear texture for CV translation. Thus, directly translating the TIR temperature value of only one channel to the RGB color value of three channels without adding additional constraints or semantic information does not handle the one-to-three mapping problem between different domains in a good way, causing the translated CV images not only to have blurred edges but also color confusion. As for the methodology of the work, considering that in the translation from TIR to CV the most important process is to map information from the temperature domain into the color domain, an improved CycleGAN (GMA-CycleGAN) is proposed in this work in order to translate TIR images to grayscale visible (GV) images. Although the two domains have different properties, the numerical mapping is one-to-one, which reduces the color confusion caused by one-to-three mapping when translating TIR to CV. Then, a GV-CV translation network is applied to obtain CV images. Since the process of decomposing GV images into CV images is carried out in the same domain, edge blurring can be avoided. To enhance the boundary gradient between the object (pedestrian and vehicle) and the background, a mask attention module based on the TIR temperature mask and the CV semantic mask is designed without increasing the network parameters, and it is added to the feature encoding and decoding convolution layers of the CycleGAN generator. Moreover, a perceptual loss term is applied to the original CycleGAN loss function to bring the translated images closer to the real images regarding the space feature. In order to verify the effectiveness of the proposed method, the FLIR dataset is used for experiments, and the obtained results show that, compared to the state-of-the-art model, the subjective quality of the translated CV images obtained by the proposed method is better, as the objective evaluation metric FID (Fréchet inception distance) is reduced by 2.42 and the PSNR (peak signal-to-noise ratio) is improved by 1.43.
D-MFPN: A Doppler Feature Matrix Fused with a Multilayer Feature Pyramid Network for SAR Ship Detection

Ship detection from synthetic aperture radar (SAR) images has become a major research field in recent years. It plays a major role in monitoring the ocean, marine rescue activities, and marine safety warnings. However, there are still some factors that restrict further improvements in detecting performance, e.g., multi-scale ship transformation and unfocused images caused by motion. In order to resolve these issues, in this paper, a doppler feature matrix fused with a multi-layer feature pyramid network (D-MFPN) is proposed for SAR ship detection. The D-MFPN takes single-look complex image data as input and consists of two branches: the image branch designs a multi-layer feature pyramid network to enhance the positioning capacity for large ships combined with an attention module to refine the feature map’s expressiveness, and the doppler branch aims to build a feature matrix that characterizes the ship’s motion state by estimating the doppler center frequency and frequency modulation rate offset. To confirm the validity of each branch, individual ablation experiments are conducted. The experimental results on the Gaofen-3 satellite ship dataset illustrate the D-MFPN’s optimal performance in defocused ship detection tasks compared with six other competitive convolutional neural network (CNN)-based SAR ship detectors. Its satisfactory results demonstrate the application value of the deep-learning model fused with doppler features in the field of SAR ship detection.
Efficient hybrid centralized and blockchain-based authentication architecture for heterogeneous IoT systems

With the rapid increase in the number of Internet of Things (IoT) devices in recent years, massive amounts of sensitive IoT data are being generated and transmitted over the Internet. Despite its growing adoption in various fields, IoT security remains a major challenge requiring further research. IoT authentication is an essential security mechanism for building trust in IoT systems. However, conventional authentication approaches use expensive cryptographic primitives that do not align with the resource-constrained nature of IoT devices. Furthermore, centralized authentication schemes have proven to be inapplicable for cross-domain authentication and do not limit the scalability of IoT networks. Recently, blockchain technology has been applied to building decentralized authentication between IoT devices. Nevertheless, most existing blockchain-based authentication approaches incur high overhead in IoT computation, storage, and energy consumption. Authentication time is another critical issue in real-time IoT systems. When numerous IoT authentication requests are transferred to the blockchain, an additional time delay is imposed, in addition to the high computational cost of the blockchain caused by the consensus mechanism. This study proposes a hybrid centralized and blockchain-based authentication architecture for IoT systems. Edge servers are deployed to provide centralized authentication for associated IoT devices. A blockchain network of centralized edge servers is then established to ensure decentralized authentication and verification of IoT devices that belong to different and heterogeneous IoT systems. Lightweight cryptographic methods are implemented to achieve efficient authentication, in which limiting the consumption of IoT resources is required. The architecture is demonstrated using a local Ethereum blockchain network. The results indicate that the proposed method achieves significant improvements in terms of computation cost, execution time, and power consumption for IoT compared with centralized and blockchain-based authentication schemes. A security analysis proves the ability of our architecture to mitigate attacks and satisfy the IoT security requirements.
Augmenting energy time-series for data-efficient imputation of missing values

This study explores the applicability of data augmentation techniques for reconstructing missing energy time-series in limited data regimes. In particular, multiple synthetic copies of a relatively small training dataset are stacked together with pseudo-random noise. First, an existing convolutional denoising autoencoder is selected from a previous work, as the base imputation model of this study. Then, an optimal augmentation rate, which minimizes the training set of the model, is chosen based on the preliminary results obtained from one building. The results proved that, augmenting 80 times a nine days-long training set could reduce the initial average root mean squared error (RMSE) by 37% and 48%, for continuous and random missing scenarios. Additionally, the augmented model outperformed the benchmark methods with 23% and 12% lower average RMSE. No additional tuning or calibration costs were required for the existing base imputation model. Therefore, the presented data augmentation technique could significantly reduce the expensive computational costs associated with deep learning models.
BBAP-WSN: A New Blockchain-Based Authentication Protocol for Wireless Sensor Networks

Wireless Sensor Networks—WSNs, an important part of IoT—consist of sensor nodes with limited processing, memory capacities, and energy. Wireless Sensor Networks face many dangers as they are often distributed into untrusted regions. The accuracy of the data obtained in a WSN, where security threats cannot be prevented, is also questioned. In WSNs, the authentication of the resources and the data can be verified with the authentication mechanism. Authentication in WSNs allows the node to verify whether data have been sent from authorized sources and protects the original data from changes. However, there are some deficiencies in terms of security in existing authentication protocols such as ID spoofing attacks. In addition, blockchain, one of the emerging technologies, gives significant successful results in security applications. Cryptographically secured, immutable, non-repudiable, irrevocable, auditable, and verifiable can be given as security-related characteristics of the blockchain. This study aims to use these features of the blockchain in WSNs. In this study, a new blockchain-based authentication protocol was developed for WSNs. Based on the study’s system model, sensor nodes, cluster nodes, base station, and blockchain networks were created using a private blockchain, and users. A detailed security analysis was carried out for the study. At the same time, efficiency analysis was performed by implementing the proposed model on the WiSeN sensor node.
Deep Multimodal Subspace Interactive Mutual Network for Specific Emitter Identification

This article investigates the problem of specific emitter identification (SEI), i.e., radar emitter fingerprint or individual emitter identification, which first measures the emitter-specific differences caused by radar's nonlinearities, e.g., mixer, power amplifiers, transmitter, and then makes a decision. In this article, the SEI problem is considered in the single-modal, dual-modal, and multimodal scenarios, respectively. First, a multimodal subspace interactive mutual unit is proposed to perform information interaction between radar signal and its multiple transformations. Based on this, a data-driven multimodal subspace interactive mutual network is then built to solve the SEI problem. Extensive experiment results demonstrate that the proposed algorithm achieves superior identification performance on the airplane measured data.