Reviewerly/exHarmony huggingface dataset:
A new meta-heuristic optimization algorithm based on a paradigm from physics: string theory

The main goal of this paper is to outline a new optimization algorithm based on String Theory, which is a relative new area of physics. The String Theory Algorithm (STA) is a nature-inspired meta-heuristic, which is based on studies about a theory stating that all the elemental particles that exist in the universe are strings, and the vibrations of these strings create all particles existing today. The newly proposed algorithm uses equations based on the laws of physics that are stated in String Theory. The main contribution in this proposed method is the new techniques that are devised in order to generate potential solutions in optimization problems, and we are presenting a detailed explanation and the equations involved in the new algorithm in order to solve optimization problems. In this case, we evaluate this new proposed meta-heuristic with three cases. The first case is of 13 traditional benchmark mathematical functions and a comparison with three different meta-heuristics is presented. The three algorithms are: Flower Pollination Algorithm (FPA), Firefly Algorithm (FA) and Grey Wolf Optimizer (GWO). The second case is the optimization of benchmark functions of the CEC 2015 Competition and we are also presenting a statistical comparison of these results with respect to FA and GWO. In addition, we are presenting a third case, which is the optimization of a fuzzy inference system (FIS), specifically finding the optimal design of a fuzzy controller, where the main goal is to optimize the membership functions of the FIS. It is important to mention that we used these study cases in order to analyze the proposed meta-heuristic with: basic problems, complex problems and control problems. Finally, we present the performance, results and conclusions of the new proposed meta-heuristic.
Broad learning system with manifold regularized sparse features for semi-supervised classification

Broad learning system (BLS) is an efficient neural network, and is proven to be effective in fields like remote sensing, fault diagnosis, etc. As a critical branch of BLS, semi-supervised BLS has drawn increasing attention. Exploiting the information within additional unlabeled instances is key to semi-supervised learning. Studies have shown that incorporating this information into the feature nodes is a good way to implement semi-supervised BLS. However, the existing methods could not retain the sparsity of feature nodes. Besides that, these methods become computation consuming when dealing with the large scale datasets. To address these problems, a broad learning system with manifold regularized sparse features (BLS-MS) is proposed. We first propose a manifold regularized sparse autoencoder based on extreme learning machine (MS-ELM-AE) for feature mapping. Then, a subset training approach is introduced to alleviate the efficiency decline caused by large data size. Finally, the proposed BLS-MS is further modified to utilize the discriminant information of labeled data, namely discriminative BLS-MS (DBLS-MS). The proposed methods have been evaluated on 14 datasets. Experiment results have demonstrated both the effectiveness and the efficiency of proposed methods.
Multi-party bidirectional teleportation

In this contribution, a three-party bidirectional teleportation scheme is suggested, where the users initially share a maximally entangled three qubits state of the G H Z type. In this proposed scheme, it is assumed that each user has the unknown state to be teleported, a trigger state and two storage states. Any two members can teleport unknown information bidirectionally by the help of the third member who plays the role of a mediator. There are series of local operations and measurements are needed to perform the teleportation process. The maximization/minimization of the fidelity of the teleported state depends on the teleported information (classical/quantum), the trigger’s states of the receiver and the mediator. The phase angles of all states could be used as control parameters to improve the fidelity of the teleported state. The possibility of teleporting classical information is much better than teleporting quantum information. The possibility of generalizing this protocol to N users as members of quantum network which consists of n qubits of G H Z is discussed. • We suggest multi-party bidirectional teleportation protocol, to teleport quantum state bilaterally between three legitimate partners. Using a G H Z state and three auxiliary qubits. • We extend this protocol to a Network formed by “N” partners. • This protocol could be used in quantum communication and computing.
A Comparison of Learning Classifier Systems’ Rule Compaction Algorithms for Knowledge Visualization

Learning Classifier Systems (LCSs) are a paradigm of rule-based evolutionary computation (EC). LCSs excel in data-mining tasks regarding helping humans to understand the explored problem, often through visualizing the discovered patterns linking features to classes. Due to the stochastic nature of EC, LCSs unavoidably produce and keep redundant rules, which obscure the patterns. Thus, rule compaction methods are invoked to produce a better population by removing problematic rules. Previously, compaction methods have neither been tested on large-scale problems nor been assessed on the performance of capturing patterns. We review and test the most popular compaction algorithms, finding that across multiple LCSs’ populations for the same task, although the redundant rules can be different, the accurate rules are common. Furthermore, the patterns contained consistently refer to the nature of the explored domain, e.g., the data distribution or the importance of features for determining actions. This extends the [ O ] set hypothesis proposed by Butz et al. [1], in which an LCS is expected to evolve a minimal number of non-overlapped rules to represent an addressed domain. Two new compaction algorithms are introduced to search at the rule level and the population level by compacting multiple LCSs’ populations. Two visualization methods are employed for verifying the interpretability of these populations. Successful compaction is demonstrated on complex and real problems with clean datasets, e.g., the 11-bits Majority-On problem that requires 924 different interacting rules in the optimal solution to be uniquely identified to enable correct visualization. For the first time, the patterns contained in learned models for the large-scale 70-bits Multiplexer problem are visualized successfully.
Biological eagle eye-based method for change detection in water scenes

Change detection (CD) is an important vision task for autonomous landing of unmanned aerial vehicles (UAV) on water. High-density photoreceptors and lateral inhibition mechanisms have inspired a novel biologic computational method based on structure and properties in eagle eyes as proposed for change detection. We call this method "STabCD," which ensures spatiotemporal distribution consistency to achieve foreground acquisition, noise reduction, and background adaptability. Therefore, our proposed model responds strongly to object information and suppresses noise and wave textures. Then, we present a cloning method to simulate water scenes and collect a new synthetic dataset (called "Synthetic Boat Sequence") for UAV vision research. Besides, we utilize synthetic datasets and corresponding real datasets to conduct change detection experiments. The experimental results indicate that: 1) the STabCD model achieves the best results in real or synthetic water landing scenes; and 2) change detection models for UAV can be quantitatively analyzed and tested under challenging synthetic scenarios.
Deep learning based HEVC in-loop filter and noise reduction

The lossy compression techniques at low bit rate often create ringing and contouring effects on the output images and introduce various blurring and distortion at block bounders. To overcome those compression artifacts different neural network based post-processing techniques have been experimented with over the last few years. The traditional loop-filter methods in the HEVC frame-work support two post-processing operations namely a de-blocking filter followed by a sample adaptive offset (SAO) filter. These operations usually introduce extra signaling bits and become overhead to the network with high-resolution video processing. In this study, we came up with a new deep learning-based algorithm for SAO filtering operations and substantiated the merits of the proposed method. We introduced a variable filter size sub-layered dense CNN (SDCNN) to improve the denoising operation and incorporated large stride deconvolution layers for further computation improvement. We demonstrate that our deconvolution model can effectively be trained by leveraging the high-frequency edge features learned in a shallow network using residual learning and data augmentation techniques. Extensive experiments show that our approach outperformed other state-of-the-art approaches in terms of SSIM, Bjøntegaard delta bit-rate (BD-BR), BD-PSNR measurements on the standard video test set and achieves an average of 8.73 % bit rate saving compared to HEVC baseline.
Panoramic depth estimation via supervised and unsupervised learning in indoor scenes

Depth estimation, as a necessary clue to convert 2D images into the 3D space, has been applied in many machine vision areas. However, to achieve an entire surrounding 360-degree geometric sensing, traditional stereo matching algorithms for depth estimation are limited due to large noise, low accuracy, and strict requirements for multi-camera calibration. In this work, for a unified surrounding perception, we introduce panoramic images to obtain larger field of view. We extend PADENet first appeared in our previous conference work for outdoor scene understanding, to perform panoramic monocular depth estimation with a focus for indoor scenes. At the same time, we improve the training process of the neural network adapted to the characteristics of panoramic images. In addition, we fuse traditional stereo matching algorithm with deep learning methods and further improve the accuracy of depth predictions. With a comprehensive variety of experiments, this research demonstrates the effectiveness of our schemes aiming for indoor scene perception.
Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution

Recent research on single-image super-resolution (SISR) using deep convolutional neural networks has made a breakthrough and achieved tremendous performance. Despite their significant progress, numerous convolutional neural networks (CNN) are limited in practical applications, owing to the requirement of the heavy computational cost of the model. This paper proposes a multi-path network for SISR, known as multi-path deep CNN with residual inception network for single image super-resolution. In detail, a residual/ResNet block with an Inception block supports the main framework of the entire network architecture. In addition, remove the batch normalization layer from the residual network (ResNet) block and max-pooling layer from the Inception block to further reduce the number of parameters to preventing the over-fitting problem during the training. Moreover, a conventional rectified linear unit (ReLU) is replaced with Leaky ReLU activation function to speed up the training process. Specifically, we propose a novel two upscale module, which adopts three paths to upscale the features by jointly using deconvolution and upsampling layers, instead of using single deconvolution layer or upsampling layer alone. The extensive experimental results on image super-resolution (SR) using five publicly available test datasets, which show that the proposed model not only attains the higher score of peak signal-to-noise ratio/structural similarity index matrix (PSNR/SSIM) but also enables faster and more efficient calculations against the existing image SR methods. For instance, we improved our method in terms of overall PSNR on the SET5 dataset with challenging upscale factor 8× as 1.88 dB over the baseline bicubic method and reduced computational cost in terms of number of parameters 62% by deeply-recursive convolutional neural network (DRCN) method.
Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking

This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process. A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is often a critical variable to track in many chemical, petrochemical, metallurgical, and oil industries. This method utilizes less than 100 images for creating an environment, from which the agent generates its own data without the need for expert knowledge. Unlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requires far fewer parameters, which naturally reduces its maintenance cost. Besides its frugal nature, the agent is robust to environmental uncertainties such as occlusion, intensity changes, and excessive noise. From a closed-loop control context, an interface location-based deviation is chosen as the optimization goal during training. The methodology showcases RL for real-time object-tracking applications in the oil sands industry. Along with a presentation of the interface tracking problem, this paper provides a detailed review of one of the most effective RL methodologies: actor–critic policy.
An oscillator with position-dependent mass exposed to a thermal bosonic bath

In this paper, we present the differential master equation (or Liouville–Von​ Neumann equation) for a harmonic oscillator with position-dependent mass (PDM) exposed to a thermal bosonic bath. Also, we obtain the density matrix of the system after time evolution. Using the density matrix, we consider the Von-Neumann entropy and Fisher Information (FI) as a function of temperature, time, and position. As a witness, we compare our results with an approximate relation for the lower bound of quantum Fisher information (QFI) which recently has been issued. Our work may be useful in quantum metrology.
Physics-Informed Gaussian Process Regression for Optical Fiber Communication Systems

We present a framework for enhancing Gaussian process regression machine learning models with a priori knowledge derived from models of the transmission physics in optical networks. This is done by framing the regression problem as multi-task learning, in which both the measured data and targets derived from a physical model of the system are used to optimise the kernel hyperparameters. We discuss the theoretical assumptions made and the validity of the approach. It is demonstrated that physics-informed Gaussian processes facilitate Bayesian inference with fewer data points than standard Gaussian processes, opening up application areas in which measurements are expensive. The transparency, interpretability and explainability of the proposed technique and the subsequent increased likelihood of adoption by industry are discussed.
Exploring the Strengths of Neural Codes for Video Retrieval

Websites like YouTube, Facebook, Twitter, etc. encounter large amounts of videos every day, mostly uploaded from mobile devices, digital cameras, etc. These videos rarely have metadata (semantic tags) attached, without which it is very difficult to retrieve similar videos without using content-based search techniques. More recently, two-dimensional convolutional networks (2d-CNN) have shown breakthrough performance over hand-engineered methods on image-related tasks in all aspects of computer vision field. The video is also composed of 2D frames arranged along time dimension, which can also be processed by 2d-CNN. In this paper, we investigate the significance of activations of CNN layers for video representation and analyzed its performance on the basis of nearest the neighbor search task, i.e. video retrieval. Three well-known CNN networks (AlexNet, GoogleNet and ResNet18) are exploited for feature extraction, and UCF101 dataset is chosen to conduct the experiment. The results showed that feature fusion of multiple CNN layers can strengthen the video representation.
Combining Text and Visual Features to Improve the Identification of Cloned Webpages for Early Phishing Detection

Phishing attacks arrive in high numbers and often spread quickly, meaning that after-the-fact countermeasures such as domain blacklisting are limited in efficacy. Visual similarity-based approaches have the potential of detecting previously unseen phishing webpages. These approaches, however, require identifying the legitimate webpage(s) they reproduce. Existing approaches rely on textual feature analysis for target identification, with misclassification rates of approximately 1%; however, as most websites a user might visit are legitimate, additional research is needed to further reduce classification errors. In this work, we propose a novel method for target identification that relies on both visual features (extracted from a screenshot of the web page) and textual features (extracted from the DOM of the web page) to identify which website a phishing web page is replicating, and assess its effectiveness in detecting phishing websites using data from phishing aggregators such as OpenPhish, PhishTank and PhishStats. Compared to state-of-the-art text-based classifiers, our method reduces the phishing misclassification rate by 67% (from 1.02% to 0.34%), for an accuracy of 99.66%. This work provides a further step forwards toward semi-automated decision support systems for phishing detection.
Attention Based Network For No-Reference UGC Video Quality Assessment

The quality assessment of user-generated content (UGC) videos is a challenging problem due to the absence of reference videos and their complex distortions. Traditional no-reference video quality assessment (NR-VQA) algorithms mainly target specific synthetic distortions. Less attention has been paid to authentic distortions in UGC videos, which are not distributed evenly in both the spatial and temporal domains. In this paper, we propose an end-to-end neural network model for UGC video quality assessment based on the attention mechanism. The key step in our approach is to embed the attention modules in the feature extraction network, which effectively extracts local distortion information. In addition, to exploit the temporal perception mechanism of the human visual system (HVS), the gated recurrent unit (GRU) and temporal pooling layer are integrated into the proposed model. We validate the proposed model on three public in-the-wild VQA databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm. Experimental results demonstrate that the proposed method outperforms state-of-the-art NR-VQA models. The implementation of our method is released at https://github.com/qingshangithub/AB-VQA.
An Interconnected Feature Pyramid Networks for object detection

Although deep learning makes major breakthroughs in object detection, object detection still faces several limitations listed as follows: (1) Many works underplay the feature selection, leading to the resulting key features are not prominent enough and prone to noise; (2) Many works pass back features in a layer-by-layer manner to achieve multi-scale features. However, as the distance of layers from each other increases, the semantics are diluted, and the transfer of information between layers becomes difficult. To overcome these problems, we propose a new Interconnected Feature Pyramid Networks (IFPN) for feature enhancement. It can simultaneously select attentive features through the attention mechanism and realize the free flow of information. On the basis of the improvements, we design a new IFPN Detector. Experiments on COCO dataset and Smart UVM dataset show that our method can bring a significant improvement.
Is News Recommendation a Sequential Recommendation Task?

News recommendation is often modeled as a sequential recommendation task, assuming there are rich short-term dependencies over historical clicked news. However, users usually have strong preferences on the temporal diversity of news information and may not tend to click similar news successively, which is very different from many sequential recommendation scenarios such as e-commerce recommendation. In this paper, we study whether news recommendation can be regarded as a standard sequential recommendation problem. Through extensive experiments on two real-world datasets, we find it suboptimal to model news recommendation as a conventional sequential recommendation problem. To handle this issue, we further propose a temporal diversity-aware sequential news recommendation method that can promote candidate news that are diverse from recently clicked news to help predict future clicks more accurately. Experiments show that our method can empower various news recommendation methods.
Machine learning based malicious payload identification in software-defined networking

Deep packet inspection (DPI) has been extensively investigated in software-defined networking (SDN) as complicated attacks may intractably inject malicious payloads in the packets. Existing proprietary pattern-based or port-based third-party DPI tools can suffer from limitations in efficiently processing a large volume of data traffic. In this paper, a novel OpenFlow-enabled deep packet inspection (OFDPI) approach is proposed based on the SDN paradigm to provide adaptive and efficient packet inspection. First, OFDPI prescribes an early detection at the flow-level granularity by checking the IP addresses of each new flow via OpenFlow protocols. Then, OFDPI allows for deep packet inspection at the packet-level granularity: (i) for unencrypted packets, OFDPI extracts the features of accessible payloads, including tri-gram frequency based on Term Frequency and Inverted Document Frequency (TF–IDF) and linguistic features. These features are concatenated into a sparse matrix representation and are then applied to train a binary classifier with logistic regression rather than matching with specific pattern combinations. In order to balance the detection accuracy and performance bottleneck of the SDN controller, OFDPI introduces an adaptive packet sampling window based on the linear prediction; and (ii) for encrypted packets, OFDPI extracts notable features of packets and then trains a binary classifier with a decision tree, instead of decrypting the encrypted traffic to weaken user privacy. A prototype of OFDPI is implemented on the Ryu SDN controller and the Mininet platform. The performance and the overhead of the proposed solution are assessed using the real-world datasets through experiments. The numerical results indicate that OFDPI can provide a significant improvement in detection accuracy with acceptable overheads.
360° Single Image Super Resolution via Distortion-Aware Network and Distorted Perspective Images

Effective 360° imaging requires a very high resolution because the field of view is extraordinarily high. Single-image super-resolution (SISR) applied to 360° imaging has the potential to solve the resolution/quality problem in this modality. In this paper, we exploit existing perspective SISR networks to address this problem by (1) introducing a distortion map as an additional input with the $360^{\circ}-$ distortion-aware loss function, and (2) augmenting the training 360° images by distorting the perspective images. We also present a new 360° image dataset from YouTube for training. Our extensive experiments show that how each component contributes to the better transfer from the perspective domain to the 360° domain and merging all the ideas leads to the best performance in quantitative and qualitative ways for the 360° SISR task.