Reviewerly/exHarmony huggingface dataset:
More classes of permutation quadrinomials from Niho exponents in characteristic two

In this paper, we characterize the coefficients of f(x)=x+a1xs1(2m−1)+1+a2xs2(2m−1)+1+a3xs3(2m−1)+1 over F22m that lead f(x) to be a permutation of F22m for the following three cases: 1) (s1,s2,s3)=(−12k−1,1,2k2k−1); 2) (s1,s2,s3)=(12k+1,1,2k2k+1), where m, k are two positive integers; 3) (s1,s2,s3)=(14,1,34). We transform the problems for the first two cases in the investigation of some (2k+1)-th degree equations with variable in the unit circle. The numerical results suggest that the sufficient conditions that we find in this paper on the coefficients are also necessary. It turned out that some well-known results can be recovered (or covered) by our results.
An ACP-Based Parallel Approach for Color Image Encryption Using Redundant Blocks

Public concerns on image encryption grow significantly as the development and application of edge computing and the Internet of Things intensified recently. However, most existing image cryptosystems are not sophisticated enough to resist the two major attack strategies available currently, that is: 1) differential attacks and 2) chosen-plaintext attacks, which are famous for their destructive power, especially their capability of exploiting cryptosystems' features to recover the secret key. In this article, we propose an artificial image, computational experiment, and parallel execution (ACP)-based color image encryption approach using redundant blocks. First, a redundant blocks strategy with redundant spaces is proposed to prevent differential attacks and accelerate operating speed while guaranteeing the security of image cryptosystems. Second, real-world chaotic data (e.g., stock data) are obtained to generate artificial images and conduct computational experiments. Furthermore, artificial images are encrypted via real-world chaos, while the original images are encrypted via simulated chaos (such as Chen's hyperchaos). Finally, we design the process of parallel execution for image encryption and use DNA XOR to merge two groups of encrypted subimages to fuse the effect of the chaotic characteristics in both the real world and the simulation. The final encrypted image is realized through the recovery of redundant blocks. The ACP mechanism of color image encryption achieves the goal of improving the sophistication of chaos-based cryptosystems and resists both the differential and chosen-plaintext attacks. Experimental results and security analysis show that our approach provides not only excellent encryption but also security sufficient to prevent known attacks.
Research on Text Information Mining Technology of Substation Inspection Based on Improved Jieba

With the development of smart grid, the power system has accumulated a lot of data. The inspection records of substations are mostly manual records. These data are recorded and stored in the power database in the form of text, which is difficult to use. Aiming at the problem that general word segmentation technologies have poor performance in power text recognition, this paper proposes to use TF-IDF algorithm to improve general Jieba word segmentation technology. The TF-IDF algorithm is used to identify and weight the power feature words, and update the data with higher weights to the keyword list, and more important words are retained. This article realizes the effective word segmentation of the text of the substation inspection record. Through comparative experiments with traditional techniques, segmentation technology that improves the accuracy and professionalism.
DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation

Segmentation is a key step in biomedical image analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly applied in the field of medical image processing; however, standard models still have some drawbacks. Due to the significant loss of spatial information at the coding stage, it is often difficult to restore the details of low-level visual features using simple deconvolution, and the generated feature maps are sparse, which results in performance degradation. This prompted us to study whether it is possible to better preserve the deep feature information of the image in order to solve the sparsity problem of image segmentation models.In this study, we (1) build a reliable deep learning network framework, named DCACNet, to improve the segmentation performance for medical images; (2) propose a multiscale cross-fusion encoding network to extract features; (3) build a dual context aggregation module to fuse the context features at different scales and capture more fine-grained deep features; and (4) propose an attention-guided cross deconvolution decoding network to generate dense feature maps. We demonstrate the effectiveness of the proposed method on two publicly available datasets.DCACNet was trained and tested on the prepared dataset, and the experimental results show that our proposed model has better segmentation performance than previous models. For 4-class classification (CHAOS dataset), the mean DSC coefficient reached 91.03%. For 2-class classification (Herlev dataset), the accuracy, precision, sensitivity, specificity, and Dice score reached 96.77%, 90.40%, 94.20%, 97.50%, and 97.69%, respectively. The experimental results show that DCACNet can improve the segmentation effect for medical images.DCACNet achieved promising results on the prepared dataset and improved segmentation performance. It can better retain the deep feature information of the image than other models and solve the sparsity problem of the medical image segmentation model.
A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.
Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead.
TrollMagnifier: Detecting State-Sponsored Troll Accounts on Reddit

Growing evidence points to recurring influence campaigns on social media, often sponsored by state actors aiming to manipulate public opinion on sensitive political topics.Typically, campaigns are performed through instrumented accounts, known as troll accounts; despite their prominence, however, little work has been done to detect these accounts in the wild.In this paper, we present TROLLMAGNIFIER, a detection system for troll accounts.Our key observation, based on analysis of known Russian-sponsored troll accounts identified by Reddit, is that they show loose coordination, often interacting with each other to further specific narratives.Therefore, troll accounts controlled by the same actor often show similarities that can be leveraged for detection.TROLLMAGNIFIER learns the typical behavior of known troll accounts and identifies more that behave similarly.We train TROLLMAGNIFIER on a set of 335 known troll accounts and run it on a large dataset of Reddit accounts.Our system identifies 1,248 potential troll accounts; we then provide a multi-faceted analysis to corroborate the correctness of our classification.In particular, 66% of the detected accounts show signs of being instrumented by malicious actors (e.g., they were created on the same exact day as a known troll, they have since been suspended by Reddit, etc.).They also discuss similar topics as the known troll accounts and exhibit temporal synchronization in their activity.Overall, we show that using TROLLMAGNIFIER, one can grow the initial knowledge of potential trolls provided by Reddit by over 300%.
Value Proposition Assessment of Blockchain Technology for Luxury, Food, and Healthcare Supply Chains

Background: In relevant research, blockchain technology (BCT) is credited with great potential for supply chain management (SCM). However, even after more than 10 years of the technology’s existence, it is barely used for any self-sustaining applications. This raises the question of why BC cannot prevail against its alternatives. With this paper we want to identify criteria by which the added value of BCT can be measured. Furthermore, we want to evaluate how well the different supply chains (SC) exploit the added values of BCT. Methods: For this, we identified real-world examples and case studies for luxury, food, and healthcare SCs. These examples are described in detail and then analyzed for their added value compared to possible alternatives. Results: The results show that in the clusters of food and healthcare SCs, no general added value of BC over current best-practice solutions could be verified. Luxury SCs manage valuable products that are typically traded in small quantities. It is within this cluster that the implementation of BC can be justified best. Conclusions: In conclusion, this study shows that the application of BCT is especially beneficial for goods with a high value and low trade volume. In addition, the interface between reality and the digital twin should be as secure as the database or BC solution itself. Furthermore, the demand for transparency and immutability of data should be more important than the need to protect sensitive data. Finally, SC participants, especially the end customer, must also be able to appreciate the advantages of BCT.
An MCDM Approach for Cloud Computing Service Selection Based on Best-Only Method

Owing to the significant and continuous development of cloud computing technology and the increasing number of cloud service providers (CSPs) that have emerged, CSP selection has become a challenging decision for many organizations.To accurately evaluate the services provided by various CSPs, many independent criteria must be considered, resulting in a multi-criteria decision-making (MCDM) problem.In MCDM problems, several alternatives are assessed against several criteria to identify the optimal alternative (s).The challenges stem from complex computation as well as less consistency, resulting in less reliable results.In this paper, a new method, called the best-only method (BOM), is proposed for solving the CSP selection problem, which is viable, efficient, and fully consistent.The proposed method was evaluated and validated using a use-case scenario, demonstrating its efficiency and appropriateness.The proposed method is compared with two popular MCDM methods, analytical hierarchical process (AHP) and best-worst method (BWM), from three perspectives: efficiency, consistency ratio (CR), and total deviation (TD).Based on the obtained results, AHP and BWM require 75.3% and 46.2% more comparisons, respectively, than the proposed method.The consistency ratios for AHP, BWM, and the proposed method were 37.92%, 13.35%, and 0%, respectively.The total deviations are 21.26, 8.65, and 0 for AHP, BWM, and the proposed method, respectively.These results clearly indicate that the proposed method outperforms AHP and BWM in terms of computing complexity and consistency, making it more efficient and reliable.
Dual Gated Graph Attention Networks with Dynamic Iterative Training for Cross-Lingual Entity Alignment

Cross-lingual entity alignment has attracted considerable attention in recent years. Past studies using conventional approaches to match entities share the common problem of missing important structural information beyond entities in the modeling process. This allows graph neural network models to step in. Most existing graph neural network approaches model individual knowledge graphs (KGs) separately with a small amount of pre-aligned entities served as anchors to connect different KG embedding spaces. However, this characteristic can cause several major problems, including performance restraint due to the insufficiency of available seed alignments and ignorance of pre-aligned links that are useful in contextual information in-between nodes. In this article, we propose DuGa-DIT, a dual gated graph attention network with dynamic iterative training, to address these problems in a unified model. The DuGa-DIT model captures neighborhood and cross-KG alignment features by using intra-KG attention and cross-KG attention layers. With the dynamic iterative process, we can dynamically update the cross-KG attention score matrices, which enables our model to capture more cross-KG information. We conduct extensive experiments on two benchmark datasets and a case study in cross-lingual personalized search. Our experimental results demonstrate that DuGa-DIT outperforms state-of-the-art methods.
Photonic Reservoir Computing based on Optical Filters in a Loop as a High Performance and Low-Power Consumption Equalizer for 100 Gbaud Direct Detection Systems

We propose and numerically simulate a passive neuromorphic processor performing equalization in C-band IM-DD links, that employs a spatial reservoir computing scheme based on recurrent optical filters.Followed by a feed forward equalizer, the system achieves sub HD-FEC performance up to 60km in 224 Gbps/λ.
An efficient tree seed inspired algorithm for parameter estimation of Photovoltaic models

Energy is one of the most important requirements for countries and serves as an indicator of the development level of a country. Developments in social, economic and industrial areas have increased the energy needs of countries. Solar energy is one of the most promising renewable energy sources to meet increased energy demands. Photovoltaic (PV) models used to generate electricity from solar energy are among the most popular renewable energy systems. To obtain maximum efficiency from PV models, it is very important to estimate parameters based on current–voltage data, simulate and control PV models for electricity generation. To achieve maximum efficiency for PV models, their parameters should be estimated in an optimum way. Therefore, tree seed algorithm (TSA) developed inspired by nature was proposed in this study. TSA is a meta-heuristic algorithm inspired by the connection between trees and seeds and proposed for solving continuous optimization problems. Parameters of three different PV models (single diode model, double diode model and PV module) were optimized with TSA. Various analyses were performed to prove the success of the proposed method in parameter optimization of PV models. The results were also compared with those of similar studies in the literature. The analysis results indicated that the proposed method yielded very successful, effective and reliable results, and it was concluded that it was a competitive method compared to other methods in the literature.
A robust framework combined saliency detection and image recognition for garbage classification

Using deep learning to solve garbage classification has become a hot topic in computer version. The most widely used garbage dataset Trashnet only has garbage images with a white board as background. Previous studies based on Trashnet focus on using different networks to achieve a higher classification accuracy without considering the complex backgrounds which might encounter in practical applications. To solve this problem, we propose a framework that combines saliency detection and image classification to improve the generalization performance and robustness. A saliency network Salinet is adopted to obtain the garbage target area. Then, a smallest rectangle containing this area is created and used to segment the garbage. A classification network Inception V3 is used to identify the segmented garbage image. Images of the original Trashnet are fused with complex backgrounds of the other saliency detection datasets. The fused and original Trashnet are used together for training to improve the robustness to noises and complex backgrounds. Compared with the image classification networks and classic target detection algorithms, the proposed framework improves the accuracy of 0.50% - 15.79% on the testing sets fused with complex backgrounds. In addition, the proposed framework achieves the best performance with a gain of 4.80% in accuracy on the collected actual dataset. The comparisons prove that our framework is more robust to garbage classification in complex backgrounds. This method can be applied to smart trash cans to achieve automatic garbage classification.
Underwater Optical 3-D Reconstruction of Photometric Stereo Considering Light Refraction and Attenuation

Photometric stereo is a widely used optical 3-D reconstruction method, which has advantages for high-resolution and well-detailed 3-D reconstruction even in weak texture regions. However, in underwater photometric stereo, the light refraction and attenuation using the camera and point light sources usually result in significant errors in shape recovery. In this article, we present a novel solution to address these challenges for improving the performance of underwater photometric stereo by combining it with underwater laser triangulation. First, we employ underwater laser triangulation as references to correct the global surface-shape distortion caused by nonuniform close-range illumination. Second, we propose to use an underwater camera refractive model to eliminate nonlinear refraction distortion. Third, we build a device implementing the proposed method for 3-D seabed reconstruction in on-site sea trials. Experimental results demonstrate that the proposed approach is able to produce accurate 3-D reconstruction results in the underwater environment.
Entering a new Dimension in Virtual Reality Research: An Overview of Existing Toolkits, their Features and Challenges

Virtual reality becomes a medium to be explored for itself, to study human factors and human behavior within these worlds, and to infer possible behavior in the real world. Among many advantages, building test routines in virtual environments remains a challenge due to the lack of established procedures and toolkits. To encourage research in this direction and lower the barrier to entry, it is necessary to simplify the process of setting up a research environment in virtual reality by providing appropriate toolkits. This paper discusses what challenges need to be overcome, what features might be relevant, and compares available toolkits.
FE-DaST: Fast and effective data-free substitute training for black-box adversarial attacks

Deep learning models have shown their advantages in computer vision, e.g., image classification, whereas they are well-known to be susceptible to imperceptible perturbations of input images which are called adversarial attacks. Recently proposed data-free substitute training (DaST), an adversarial framework based on a multi-branch generator where each branch generated images of the corresponding class to balance synthetic images, trained surrogate models without the requirement of any real image for transfer-based black-box adversarial attacks. However, this multi-branch framework was too redundant to converge quickly and was limited to datasets of a few categories. In this paper, we propose a simpler adversarial framework based on a single-branch generator to train substitute models fast and effectively, named FE-DaST. More specifically, we adopt a single-branch deep convolutional generator with an information entropy loss to stimulate the generation of balanced images, promote the similarity between substitute models and target models, and further enhance the strength of the transfer-based attack. Despite its simplicity, experimental results demonstrate the superiority of our proposed FE-DaST over DaST in terms of computational loads, similarities between surrogate models and target models, and attack success rates of transferable adversarial examples on MNIST and CIFAR-10 datasets. For CIFAR-100 and Tiny-ImageNet datasets where DaST is not available, our FE-DaST also achieves competitive attack success rates compared with pre-trained models which are trained with realistic training images. Furthermore, the attack performance of FE-DaST outperforms other state-of-the-art substitute training methods on the four datasets.