Reviewerly/exHarmony huggingface dataset:
Quality Feature Learning via Multi-Channel CNN and GRU for No-Reference Video Quality Assessment

Nowadays, video quality assessment (VQA) plays a vital role in video-related industries to predict human perceived video quality to maintain the quality of service. Although many deep neural network-based VQA methods have been proposed, the robustness and performance are limited by small scale of available human-label data. Recently, some transfer learning-based methods and pre-trained models in other domains have been adopted in VQA to compensate for the lack of enormous training samples. However, they result in a domain gap between the source and target domains, which provides sub-optimal feature representation for VQA tasks and deteriorates the accuracy. Therefore, in the paper, we propose quality feature learning via a multi-channel convolutional neural network (CNN) with a gated recurrent unit (GRU), taking into account both the motion-aware information and human visual perception (HVP) characteristics to solve the above issue for no-reference VQA. First, inspired by self-supervised learning (SSL), the multi-channel CNN is pre-trained on the image quality assessment (IQA) domain without using human annotation labels. Then, semi-supervised learning is applied on top of the pre-trained multi-channel CNN to fine-tune the model to transfer the domain from IQA to VQA while considering motion-aware information for better frame-level quality feature representation. After that, several HVP features are extracted with frame-level quality feature representation as the input of the GRU model to obtain the final precise predicted video quality. Finally, the experimental results demonstrate the robustness and validity of our model, which is superior to the state-of-the-art approaches and is closely related to human perception.
Intra-Native Accent Shared Features for Improving Neural Network-Based Accent Classification and Accent Similarity Evaluation

Accent similarity evaluation and accent identification are complex and challenging tasks for various applications due to the existence of variant types of native and non-native languages in the world. The absence of existing studies for the non-native and native English accent similarity evaluation and the limitation of individual feature extraction techniques for accent classifications have led us to propose a new model termed the intra-native accent feature sharing based native accent identification (NAI) framework using an English accent archive speech dataset. The NAI network was employed for non-native English accent classification, native English accent classification, and identification of native and non-native English accents. Finally, the accent similarity of native and non-native English accents was evaluated based on a delicate NAI pre-trained model. Moreover, the proposed approach has a high role in training data augmentation to overcome the challenge of a huge amount of training datasets demands of deep learning. The ordinary individual voice feature extraction with data augmentation and regularization techniques was the baseline for our work. The proposed approach boosted the accuracy of the baseline method with an average accuracy value of 3.7% -7.5% on different vigorous deep learning algorithms. The Quade test method for the performance comparison gave a 0.01 significant level (p-value) that proved that the proposed approach performed better than the baseline significantly. The model makes the rank for non-native English accents based on their similarity to native English accents and the proximity rank is Mandarin, Italian, German, French, Amharic, and Hindi.
Compactness score: a fast filter method for unsupervised feature selection

The rapid development of big data era incurs the generation of huge amount of data day by day in various fields. Due to the large-scale and high-dimensional characteristics of these data, it is often difficult to achieve better decision-making in practical applications. Therefore, an efficient big data analytical method is urgently necessary. For feature engineering, feature selection seems to be an important research topic which is anticipated to select “excellent” features from candidate ones. The implementation of feature selection can not only achieve the purpose of dimensionality reduction, but also improve the computational efficiency and result performance of the model. In many classification tasks, researchers found that data seem to be usually close to each other if they are from the same class; thus, local compactness is of great importance for the evaluation of a feature. Based on this discovery, we propose a fast unsupervised feature selection algorithm, named Compactness Score (CSUFS), to select desired features. To prove the superiority of the proposed algorithm, several public data sets are considered with extensive experiments being performed. The experiments are presented by applying feature subsets selected through several different algorithms to the clustering task. The performance of clustering tasks is indicated by two well-known evaluation metrics, while the efficiency is reflected by the corresponding running time. As demonstrated, our proposed algorithm is more accurate and efficient compared with existing ones.
Investigating on the robustness of flow-based intrusion detection system against adversarial samples using Generative Adversarial Networks

Recently, Software Defined Networking (SDN) has emerged as the key technology in programming and orchestrating security policy in the security operations centers (SOCs) for heterogeneous networks. Typically, machine learning-based intrusion detection systems (ML-IDS) have been deployed and associated with SDN to leverage the features of a programmable network to defend against sophisticated cyberattacks in anomaly detection. Unfortunately, such ML-based IDSs are easily vulnerable to adversarial attacks due to the lack of diverse forms of malicious records in the training dataset. The missing data sample in the training phase can lead to a lower detection rate in real-world scenarios with adversarial settings. In this paper, we explore the ability of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), WGAN-GP with two timescale update rule (WGAN-GP TTUR), and AdvGAN in generating perturbed attack samples to bypass attack detectors. Then, this approach is used to continuously evaluate the robustness of ML-based IDSs and then upgrade them as a service in SDN. The experimental results on CICIDS2018 and InSDN datasets demonstrate that generated adversarial samples can be used to fool targeted IDS. Later, those created samples can supplement the original ones in retraining IDS to improve the resilience of the attack detector.
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation

We study the problem of policy evaluation with linear function approximation and present efficient and practical algorithms that come with strong optimality guarantees. We begin by proving lower bounds that establish baselines on both the deterministic error and stochastic error in this problem. In particular, we prove an oracle complexity lower bound on the deterministic error in an instance-dependent norm associated with the stationary distribution of the transition kernel, and we use the local asymptotic minimax machinery to prove an instance-dependent lower bound on the stochastic error in the independent and identically distributed (i.i.d.) observation model. Existing algorithms fail to match at least one of these lower bounds: To illustrate, we analyze a variance-reduced variant of temporal difference learning, showing in particular that it fails to achieve the oracle complexity lower bound. To remedy this issue, we develop an accelerated, variance-reduced fast temporal difference (VRFTD) algorithm that simultaneously matches both lower bounds and attains a strong notion of instance-optimality. Finally, we extend the VRFTD algorithm to the setting with Markovian observations and provide instance-dependent convergence results. Our theoretical guarantees of optimality are corroborated by numerical experiments.
Quantum generative adversarial imitation learning

Abstract Investigating quantum advantage in the NISQ era is a challenging problem whereas quantum machine learning becomes the most promising application that can be resorted to. However, no proposal has been investigated for arguably challenging inverse reinforcement learning to demonstrate the potential advantage. In this work, we propose a hybrid quantum–classical inverse reinforcement learning algorithm based on the variational quantum circuit with the generative adversarial framework. We find an important connection between the quantum gradient anomaly and the performance degradation, which suggest a gradient clipping strategy to stabilize the training process. In light of the algorithm, we study three classic control problems and the Hamiltonian parameter estimation in quantum sensing with shallow quantum circuits. The numerical results showcase that the control-enhanced quantum sensor can saturate quantum Cramér-Rao bound only with a single variational layer, empirically demonstrating a parameter complexity advantage over the classical learning control. The proposed generative adversarial reinforcement learning algorithm achieves state-of-the-art performance in classical and quantum sensor control in terms of required number of parameters.
A black-box reversible adversarial example for authorizable recognition to shared images

Shared images on the Internet are easily collected, classified, and analyzed by unauthorized commercial companies through Deep Neural Networks (DNNs). The illegal use of these data damages the rights and interests of authorized companies and individuals. How to ensure that network-shared data is legally used by authorized users and not used by unauthorized DNNs has become an urgent problem. Reversible Adversarial Example (RAE) provides an effective solution, which can mislead the classification of unauthorized DNNs and does not affect the authorized users. The existing RAE schemes assumed that we could know the parameters of the target model and thus generate reversible adversarial examples. However, model parameters are often protected to avoid leakage, increasing the difficulty of generating accurate RAEs. In this paper, we first propose a Black-box Reversible Adversarial Example (B-RAE) scheme to generate robust reversible adversarial examples. We aim to protect image privacy while maintaining data usability in real scenarios. Experimental results and analysis have demonstrated that the proposed B-RAE is more effective and robust compared with the existing schemes.
Toward IoT device fingerprinting from proprietary protocol traffic via key-blocks aware approach

IoT Device fingerprinting of network traffic is valuable for many management and security solutions as it provides insights into the devices active on a network. Unfortunately, existing techniques focus on the public (de facto) standard protocol and suffer from low efficiency and high data packet dependence at flow-level granularity. Hence, accurately identifying the types of proprietary-protocol-oriented IoT devices is still a challenge that cannot be ignored. As a solution, we propose DeepFinger, a full-automated, byte-level payload fingerprint generation approach to fill this gap. DeepFinger aims to eliminate the manual intervention required to extract available payload-driven fingerprints, in the absence of priori protocol specification information. The key sight of DeepFinger is that it utilizes deep clustering to automatically cluster similar payloads and infer key-blocks as fingerprints. Through extensive evaluation, we demonstrate that DeepFinger achieves the average TPR with 98.81%, the average FTF with 98.74%, and the average FPR with 0.07% on the dataset containing multiple proprietary protocols. In addition, on three datasets containing public protocols such as MQTT, Modbus, HTTP, and XMPP, DeepFinger also achieved excellent performance by virtue of its extensibility. These results suggest that DeepFinger can be a promising tool for automatizing the payload fingerprint extraction process, based on proprietary protocol assumption.
Experimental observation of anomalous supralinear response of single-photon detectors

We demonstrate a direct single-source measurement of absolute nonlinearity of single-photon detectors with unprecedented accuracy. We discover a surprising supralinear behavior of single-photon avalanche diodes and show that it cannot be explained using known theoretical models. We also fully characterize sub- and supra-linear operation regimes of superconducting nanowire single-photon detectors and uncover the supralinearity under faint continuous illumination. The results identify new detector anomalies that supersede existing knowledge of nonlinear effects at the single-photon level.
The Role of DLT for Beyond 5G Systems and Services: A Vision

Security, privacy, and trust are the key factors to unlock the full potential of future communication as beyond 5G and 6G systems enable new and more disruptive business models involved with multiple stakeholders, including mobile network operators, mobile virtual network operators, infrastructure providers, third party service providers, policy makers, end-users, and etc. The huge set of data, interaction process, and service management across these stakeholders require new technologies that can help the telecom industry to manage various data related to users, interaction process, and services in a way that is immutable, transparent, and secure with reduced OPEX to stay ahead of the market and to meet the evolving horizontal and vertical service and security requirements. This article presents an overview of the more promising key enablers, such as blockchain, permissioned distributed ledger technologies, and smart contract that can tackle the challenges of data security, privacy, and trust management in various potential beyond 5G and 6G application scenarios.
Incorporating theoretical and practical approaches to assess the amount of sunlight captured by a tilted surface in a tropical climate

Global solar radiation can theoretically be approximated in terms of tilt and azimuth of the surface regarding the impossibility of simultaneous measurement of solar radiation at various surface tilt and azimuth angles. Moreover, the random and anisotropic nature of diffuse radiation in a tropical climate makes it extremely difficult to estimate global solar radiation accurately as a function of surface tilt and azimuth angles. This study aims to develop a novel experimental and theoretical approach in the form of a computational network in order to determine a precise combined model integrated with global horizontal solar radiation to evaluate global tilted solar radiation in a tropical climate. Obtained results revealed that precisely estimation of the global tilted solar radiation was possible, by combining geometric factors for the tilted beam solar radiation, a combination of Gueymard and Louche models for the tilted diffuse solar radiation, and isotropic ground reflectance model for the ground reflected radiation, along with global horizontal solar radiation. It was observed that the accuracy of the model developed was higher for the partly sunny sky compared to the cloudy and rainy sky, estimates were more accurate on south-facing surfaces, and the model's accuracy declined with the increasing tilt angle of the surface. The statistical analysis exhibited excellent agreement between the measured data and simulation results, considering the value of normalized mean absolute error (nMAE %), normalized root mean squared error (nRMSE %), and mean absolute percentage error (MAPE %), which were in the ranges 0.22-0.94, 0.27-1.11, and 0.23-1.02, respectively for estimating global tilted solar radiation in various regions of Peninsular Malaysia, and they were respectively found in the range of 10.2-27.5%, 16.1-38.9%, and 6.0-17.8%, for evaluating the monthly optimum tilt angle towards the south, that leads to a loss of solar energy from 1.3 to 5.4 kWh/m2/year in Peninsular Malaysia. This search revealed that the experimental and theoretical approach employed in this study can be extended to more climatic regions.
2-Stage Convolutional Neural Network for Breast Cancer Detection from Ultrasound Images

Breast Cancer is one of the most commonly occurring cancers in women. Detecting cancer at an earlier stage increases the chance of survival. Thus the development of an automatic Computer-Aided Diagnosis (CAD) system is essential for cancer diagnosis. Various researchers have used different image modalities for detecting cancer. This manuscript proposes a 2-stage Convolutional Neural Network (CNN) for detecting breast cancer from Ultrasound images. The CNN in the first stage is a customized DenseNet architecture whose primary task is to extract features from the input Ultrasound Images. The initial layers of the DenseNet network are pre-trained on the ImageNet dataset, while the final five layers are fine-tuned on the Breast Ultra Sound Image dataset. In the second stage, we used a bi-stream CNN with depth-wise separable layers to classify the features into three classes, namely, Normal, Benign, and Malignant. Conducting five-fold cross-validation using the proposed 2-stage CNN an accuracy of 85.38±1.21 was obtained. We have proved that the proposed network effectively classifies the input Ultrasound Images into three categories. Statistical significance test and Grad-CAM visualization is provided to prove the same.
Quantum Network Coding and Distribution of Maximally Entangled States in Measurement-Based Quantum Computing

Classical network coding uses encoding of information over networks to achieve secure and high throughput communication with low latency. Its quantum analog, namely Quantum Network Coding (QNC) is a promising protocol to achieve quantum communication over quantum networks. In this paper, we propose a method for establishing QNC in Measurement-Based Quantum Computing (MBQC), which is a universal quantum computational model. We demonstrate that the problem of multiple unicast or multiple multicast of distinct computational basis states over a quantum network can be solved by simulating existing classical networks, with the CNOT implementation in MBQC as a basis for transmitting and encoding of states. We also show how it can be further extended to simultaneously distribute the Bell pairs and Greenberger–Horne–Zeilinger (GHZ) states required for quantum communication between distant transmitter and corresponding receiver nodes of a network.
Improved Epoch Based Prosody Modification by Zero Frequency Filtering of Gabor Filtered Telephonic Speech

Prosodic features are supra-segmental features which span over longer segments in speech signals. Modification of prosodic parameters is essential for some of the applications such as text to speech synthesis, emotion conversion, speech enhancement and so on. Prosody modification is achieved by the manipulation of pitch contours and duration of the given speech. Estimating the analysis pitch marks is one of the crucial steps in prosody modification of speech due to vocaltract system interaction during the phonation. The presence of noises, channel effects and other degradations make the epoch estimation even harder. Therefore, the context of the work presented in this paper is targeted towards epoch estimation and subsequent prosody modification from speech signals in telephone quality. In the case of telephonic speech signals, due to channel bandwidth (300 Hz-3.4 kHz), fundamental frequency components are significantly attenuated and therefore estimating epochs in such cases are challenging. In the present work, fast and efficient zero frequency filtering (ZFF) is used as the method for hypothesizing the epochs in telephonic speech. Due to attenuation of the low frequency components (less than 300 Hz), ZFF provides poor estimation of epochs in the case of telephonic speech signals. The strength of impulse-like discontinuities has to be enhanced for improved epoch estimation from telephonic speech using ZFF method. Further, a multiscale product of the Gabor filtered speech with given speech is proposed as a preprocessing step for improved epoch estimation using ZFF method. The improved epoch estimation performance of the proposed preprocessing method is confirmed by evaluation on the simulated telephonic version of the CMU-Arctic database where the ground truth reference epochs are available in simultaneous electro-glottogram recordings. Based on subjective evaluations, the proposed epoch estimation provided perceptually better prosody modified telephonic speech.
A Lightweight Deep Residual Attention Network for Single Image Super Resolution

In this paper, sparse coding has been used in learning-based single image super-resolution (SR) for text images to improve the accuracy of optical character recognition (OCR). For single image SR, we create a data-driven model with deep residual attention. The deep residual attention algorithm is built on a new deep architecture that has a high representational capability. In the proposed method the architecture consists of a residual network and a dual attention network. The feature recalibration is achieved by using Channel Attention and Spatial attention technique. The proposed model uses a residual map to recover lost high-frequency features and aids in overcoming the lower spatial resolution issue. Extensive testing on the Set5, Set14, BSD100, and Urban100 datasets shows that DRA is more effective and efficient for single image SR than other reported techniques. The experiments show encouraging results for optical character recognition (OCR) application.
Learning with limited memory: Bayesianism vs heuristics

Bayesian analysis is considered the optimal way of processing information. However, it often leads to problems for decision-makers with constrained cognitive capacity. Modeling such constrained capacity by finite automata, we answer two questions in the context of Wald's (1947) sequential analysis, namely in what environments is optimal Bayesian analysis possible even with constraints; also, when it is not possible what simplifications in the analysis enable us to obtain a satisfactory outcome. We identify two features of the simplified analysis: information stickiness (ignoring information) and rule stickiness (ignoring small differences in the environment).
An overview of gesture recognition

With the development of artificial intelligence and human-computer interaction technology, gesture has been widely used in intelligent vehicles, human-computer interaction, virtual reality, and other fields as an important way of communication in people’s daily life. However, there are few review studies that comprehensively evaluate and summarize gesture recognition in recent years. To address this situation, this paper classifies and summarizes the currently used gesture recognition methods. Firstly, the current status of research on static gestures is investigated, followed by an analysis of the progress of research on dynamic gesture recognition, then the commonly used gesture datasets are introduced and their respective characteristics are described, finally, the current challenges and future development trends of gesture recognition are analyzed.