Reviewerly/exHarmony huggingface dataset:
ResDNN: deep residual learning for natural image denoising

Image denoising is a thoroughly studied research problem in the areas of image processing and computer vision. In this work, a deep convolution neural network with added benefits of residual learning for image denoising is proposed. The network is composed of convolution layers and ResNet blocks along with rectified linear unit activation functions. The network is capable of learning end-to-end mappings from noise distorted images to restored cleaner versions. The deeper networks tend to be challenging to train and often are posed with the problem of vanishing gradients. The residual learning and orthogonal kernel initialisation keep the gradients in check. The skip connections in the ResNet blocks pass on the learned abstractions further down the network in the forward pass, thus achieving better results. With a single model, one can tackle different levels of Gaussian noise efficiently. The experiments conducted on the benchmark datasets prove that the proposed model obtains a significant improvement in structural similarity index than the previously existing state-of-the-art techniques.
COVID-19 and quarantine orders: A practical approach

Quarantine is a very effective method for containing the spread of highly infectious diseases in large populations during a pandemic, but it is only effective if properly implemented. The co-operation and compliance of people entering quarantine are critical to its success. However, owing to the isolating and social distancing nature of quarantine, it often leads to extreme economic hardship and shortages in basic needs such as food, medicine, water and communication – and to the curtailment of certain universal social norms such as attending a parent’s funeral. To escape these hardships, people often refuse to enter voluntary quarantine, or breach quarantine rules. In these circumstances, health authorities are obliged to act in the best interests of the public and obtain court orders to force some people into quarantine. In further extreme circumstances, when a national lockdown is ordered, non-compliance with quarantine measures may result in arrests and penalties. The scope of this article is limited to the period prior to and following such a lockdown, during which quarantine may still be vital for the containment of COVID-19. Because a quarantine order will deprive an individual of his or her freedom, this must be carefully balanced with the public interest. This article explains the legal and ethical considerations of this balancing exercise and provides practical guidance for obtaining quarantine orders.
Robust fusion for RGB-D tracking using CNN features

Recently, RGB-D sensors have become popular. Many computer vision problems can be better dealt with depth data. It is a challenging problem to integrate depth data into a visual object tracker to address the problems such as scale change and occlusion. In this paper, we propose a robust fusion based RGB-D tracking method. Specifically, hierarchical convolutional neural network (CNN) features are first adopted to encode RGB and depth images separately. Next, target is tracked based on correlation filter tracking framework. Then the results of each CNN feature are localized according to the tracking results in a short period of time. Finally, the target is localized by jointly fusing the results of RGB and depth images. Model updating is finally carried out according to the differences between RGB and depth images. Experiments on the University of Birmingham RGB-D Tracking Benchmark (BTB) and the Princeton RGB-D Tracking Benchmark (PTB) achieve comparable results to state-of-the-art methods.
HTN Planning as Heuristic Progression Search

The majority of search-based HTN planning systems can be divided into those searching a space of partial plans (a plan space) and those performing progression search, i.e., that build the solution in a forward manner. So far, all HTN planners that guide the search by using heuristic functions are based on plan space search. Those systems represent the set of search nodes more effectively by maintaining a partial ordering between tasks, but they have only limited information about the current state during search. In this article, we propose the use of progression search as basis for heuristic HTN planning systems. Such systems can calculate their heuristics incorporating the current state, because it is tracked during search. Our contribution is the following: We introduce two novel progression algorithms that avoid unnecessary branching when the problem at hand is partially ordered and show that both are sound and complete. We show that defining systematicity is problematic for search in HTN planning, propose a definition, and show that it is fulfilled by one of our algorithms. Then, we introduce a method to apply arbitrary classical planning heuristics to guide the search in HTN planning. It relaxes the HTN planning model to a classical model that is only used for calculating heuristics. It is updated during search and used to create heuristic values that are used to guide the HTN search. We show that it can be used to create HTN heuristics with interesting theoretical properties like safety, goal-awareness, and admissibility. Our empirical evaluation shows that the resulting system outperforms the state of the art in search-based HTN planning.
TDSD: A New Evolutionary Algorithm Based on Triple Distinct Search Dynamics

Spherical evolution is a recently proposed nature-inspired meta-heuristic algorithm which is proven to have nontrivial efficiency and effectiveness in solving complex optimization problems.However, it has some limitations caused by its inherent scale factor and dimension factor.Hypercube search and chaotic local search are two kinds of effective search mechanisms.To construct an algorithm which has better exploration and exploitation abilities, we propose a novel algorithm which contains triple distinct search dynamics (TDSD), i.e., spherical search, hypercube search and chaotic local search.Effective control among them enhances search performance of TDSD.It is verified on thirty CEC2017 benchmark functions and three real-world optimization problems.
Enhancing tree-seed algorithm via feed-back mechanism for optimizing continuous problems

Tree-Seed Algorithm (TSA) is a novel population-based random search algorithm with its advantages in continuous optimization problems. However, there are some problems in its searching procedure. Problem (1): its balance mechanism of exploration and exploitation is implemented with a constant ST, and this fixed value is unreasonable in the random search procedure; Problem (2): the seed generation mechanism is achieved randomly without considering different searching phases based on function evaluations. To overcome these two problems, the feedback mechanism should be enhanced. Firstly, the st_TSA is proposed to solve the Problem (1); secondly, the ns_TSA is proposed to further solve the Problem (2); finally, in order to inherit these feedback mechanisms, a novel fb_TSA has been proposed and verified by standard 30 test benchmark functions from IEEE CEC 2014 with the basic TSA and its variants, such as STSA. In addition, GWO, ABC, SCA, DE, PSO and CLPSO are adopted for some comparative experiments with different dimensions. The computational results demonstrate that the enhanced feedback mechanism on ST and ns parameters can improve the optimization capability of the basic TSA significantly, especially in global optimum. The applicability of the proposed fb_TSA is proved by the 4 real engineering problems when compared with TSA, SCA, ABC and PSO.
EmgAuth: An EMG-based Smartphone Unlocking System Using Siamese Network

Screen lock is a critical security feature for smart-phones to prevent unauthorized access. Although various screen unlocking technologies including fingerprint and facial recognition have been widely adopted, they still have some limitations. For example, fingerprints can be stolen by special material stickers and facial recognition systems can be cheated by 3D-printed head models. In this paper, we propose EmgAuth, a novel electromyography(EMG)-based smartphone unlocking system based on the Siamese network. EmgAuth leverages the Myo armband to collect the EMG data of smartphone users and enables users to unlock their smartphones when picking up and watching their smartphones. In particular, when training the Siamese network, we design a special data augmentation technique to make the system resilient to the rotation of the armband. We conduct experiments including 40 participants and the evaluation results show that EmgAuth can effectively authenticate users with an average true acceptance rate of 91.81% while keeping the average false acceptance rate of 7.43%. In addition, we also demonstrate that EmgAuth can work well for smartphones with different sizes and at different locations, and is applicable for users with different postures. EmgAuth bears great promise to serve as a good supplement for existing screen unlocking systems to improve the safety of smartphones.
A Multiple-Format Steganography Algorithm for Color Images

This paper presents an image Steganography algorithm that can work for cover images of multiple formats.Having a single algorithm for multiple image types provides several advantages.For example, we can apply uniform security policies across all image formats, we can adaptively select the most suitable cover image based on data length, network bandwidth and allowable distortions, etc.We present our algorithm based on the abstract concept of image components that can be adapted for JPEG, Bitmap, TIFF and PNG cover images.To the best of our knowledge, the proposed algorithm is the first Steganography algorithm that can work for multiple cover image formats.In addition, we have utilized concepts like capacity pre-estimation, adaptive partition schemes and data spreading to embed secret data with enhanced security.The proposed method is tested for robustness against Steganalysis with favorable results.Moreover, comparative results for the proposed algorithm are very promising for three different cover image formats.
Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation

Several pedestrian navigation solutions have been proposed to date, and most of them are based on smartphones. Real-time recognition of pedestrian mode and smartphone posture is a key issue in navigation. Traditional ML (Machine Learning) classification methods have drawbacks, such as insufficient recognition accuracy and poor timing. This paper presents a real-time recognition scheme for comprehensive human activities, and this scheme combines deep learning algorithms and MEMS (Micro-Electro-Mechanical System) sensors’ measurements. In this study, we performed four main experiments, namely pedestrian motion mode recognition, smartphone posture recognition, real-time comprehensive pedestrian activity recognition, and pedestrian navigation. In the procedure of recognition, we designed and trained deep learning models using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) networks based on Tensorflow framework. The accuracy of traditional ML classification methods was also used for comparison. Test results show that the accuracy of motion mode recognition was improved from 89.9 % , which was the highest accuracy and obtained by SVM (Support Vector Machine), to 90.74 % (LSTM) and 91.92 % (CNN); the accuracy of smartphone posture recognition was improved from 81.60 % , which is the highest accuracy and obtained by NN (Neural Network), to 93.69 % (LSTM) and 95.55 % (CNN). We give a model transformation procedure based on the trained CNN network model, and then obtain the converted . t f l i t e model, which can be run in Android devices for real-time recognition. Real-time recognition experiments were performed in multiple scenes, a recognition model trained by the CNN network was deployed in a Huawei Mate20 smartphone, and the five most used pedestrian activities were designed and verified. The overall accuracy was up to 89.39 % . Overall, the improvement of recognition capability based on deep learning algorithms was significant. Therefore, the solution was helpful to recognize comprehensive pedestrian activities during navigation. On the basis of the trained model, a navigation test was performed; mean bias was reduced by more than 1.1 m. Accordingly, the positioning accuracy was improved obviously, which is meaningful to apply DL in the area of pedestrian navigation to make improvements.
Wind Turbine Anomaly Detection Based on SCADA Data Mining

In this paper, a wind turbine anomaly detection method based on a generalized feature extraction is proposed. Firstly, wind turbine (WT) attributes collected from the Supervisory Control And Data Acquisition (SCADA) system are clustered with k-means, and the Silhouette Coefficient (SC) is adopted to judge the effectiveness of clustering. Correlation between attributes within a class becomes larger, correlation between classes becomes smaller by clustering. Then, dimensions of attributes within classes are reduced based on t-Distributed-Stochastic Neighbor Embedding (t-SNE) so that the low-dimensional attributes can be more full and more concise in reflecting the WT attributes. Finally, the detection model is trained and the normal or abnormal state is detected by the classification result 0 or 1 respectively. Experiments consists of three cases with SCADA data demonstrate the effectiveness of the proposed method.
Utilizing Textual Information in Knowledge Graph Embedding: A Survey of Methods and Applications

Techniques that map the entities and relations of the knowledge graph (KG) into a low-dimensional continuous space are called KG embedding or knowledge representation learning.However, most existing techniques learn the embeddings based on the facts in KG alone, suffering from the issues of imperfection and spareness of KG.Recently, the research on textual information in KG embedding has attracted much attention due to the rich semantic information supplied by the texts.Thus, in this paper, a survey of techniques for textual information based KG embedding is proposed.Firstly, we introduce the techniques for encoding the textual information to represent the entities and relations from perspectives of encoding models and scoring functions, respectively.Secondly, methods for incorporating the textual information in the existing embedding techniques are summarized.Thirdly, we discuss the training procedure of textual information based KG embedding techniques.Finally, applications of KG embedding with textual information in the specific tasks such as KG completion in zero-shot scenario, multilingual entity alignment, relation extraction and recommender system are explored.We hope that this survey will give insights to researchers into textual information based KG embedding.
Cyber effect and security management aspects in critical energy infrastructures

The purpose of the paper is to compare various types of management models that regulate the response to cyber threats to Critical Infrastructures.The development of an effective management model that regulates the response to cyber-attack against Critical Infrastructure is an important issue in security management.Many frameworks attempt to regulate the response that has to be done to recover and eradicate possible threats, but still, there is not a universal appliable model for all Critical Infrastructures.The paper will offer a comparison of various frameworks in an attempt of evaluating the features that a hypothetical model for response to Cyber Incidents to Critical Infrastructures.The focus is on Critical Energy Infrastructure, as their damage directly means damage to other critical infrastructures, given their extreme interconnectivity.After the analysis of five frameworks of responses to Cyber Incidents, an evaluation will be provided, along with a recommendation.
MRD codes with maximum idealizers

Left and right idealizers are important invariants of linear rank-distance codes. In the case of maximum rank-distance (MRD for short) codes in $\mathbb{F}_q^{n\times n}$ the idealizers have been proved to be isomorphic to finite fields of size at most $q^n$. Up to now, the only known MRD codes with maximum left and right idealizers are generalized Gabidulin codes, which were first constructed in 1978 by Delsarte and later generalized by Kshevetskiy and Gabidulin in 2005. In this paper we classify MRD codes in $\mathbb{F}_q^{n\times n}$ for $n\leq 9$ with maximum left and right idealizers and connect them to Moore-type matrices. Apart from generalized Gabidulin codes, it turns out that there is a further family of rank-distance codes providing MRD ones with maximum idealizers for $n=7$, $q$ odd and for $n=8$, $q\equiv 1 \pmod 3$. These codes are not equivalent to any previously known MRD code. Moreover, we show that this family of rank-distance codes does not provide any further examples for $n\geq 9$.
Image edge detection based on singular value feature vector and gradient operator

&lt;abstract&gt; &lt;p&gt;This paper presents an edge detection algorithm based on singular value eigenvector and gradient operator. In the proposed algorithm, the singular values of image blocks are first calculated, and the Sobel gradient template is extended to eight other directions. Then the gradient values of image pixels are determined according to the stability of the singular values of image blocks. The determination of gradient threshold is considered from both global and local aspects. After calculating the global and local gradient thresholds of the original image, the gradient threshold of the whole image is determined by weighting function. Then the edge pixels of the image are filtered according to the gradient threshold, and the edge information image of the original image is obtained. The experimental data show that the proposed algorithm can resist a certain degree of noise interference, and the accuracy and efficiency of edge extraction are better than other similar algorithms.&lt;/p&gt; &lt;/abstract&gt;
BPS: A reliable and efficient pub/sub communication model with blockchain-enhanced paradigm in multi-tenant edge cloud

In recent years, with the rapid development of smart city, prevalent pub/sub (publish/subscribe) streaming systems have been increasingly employed as upstream middleware layer in multi-tenant edge clouds, and feed large volume of data gathered from IoT devices of different tenants into downstream systems (e.g., data analytics and warehouse). A shared tenancy model where multiple untrusted applications or tenants utilize the same pub/sub system is generally exploited in edge cloud, which poses crucial challenges including privacy-sensitive data/metadata access threat and critical metadata modification by unauthorized tenants. A centralized monitoring node is invariably adopted in existing security strategies (such as ACL, TLS), which causes the pub/sub streaming model vulnerable to external malicious attacks and single point failure. In this paper, inspired by outstanding features of blockchain including tamper-resistance, decentralization, strong consistency, and traceability, we propose BPS, a general and decentralized Blockchain-enhanced Pub/Sub communication model for multi-tenant edge cloud, to redesign pub/sub system internal security mechanisms. Specifically, by exploiting blockchain technology, BPS can detect the illegal operations and behaviors from both malicious tenants and untrusted publishers or subscribers. BPS directly leverages Merkel Hash Tree (MHT) of blockchain to verify the integrity of critical and confidential metadata. Regarding authorization, BPS introduces smart-contract-enabled fine-grained control over partition topic-classified messages by storing access control list (ACL) into an append-only blockchain ledger. Additionally, an incentive mechanism is employed in BPS to reward honest publishers and subscribers. We implement BPS prototype based on Kafka and EoS blockchain. Our security analysis and extensive experiments demonstrate that BPS outperforms the state-of-the-art pub/sub streaming system Kafka in security with minimal performance overhead.
Bi-GRU Sentiment Classification for Chinese Based on Grammar Rules and BERT

Sentiment classification is a fundamental task in NLP, and its aim to predict the sentiment polarities of the given texts.Recent researches show great interest in modeling Chinese sentiment classification.However, the complexity of Chinese grammar makes the performance of the existing Chinese sentiment classification model not perform well.In order to address the above problem, we propose a sentiment classification method based on grammar rules and bidirectional encoder representation from transformers (BERT).We first preprocess data through BERT model.Then we combine the Chinese grammar rules with Bi-gated recurrent neural network (GRU) in the form of constraints, and simulate the linguistic functions at the sentence by standardizing the output of adjacent positions.Extensive experiments on two public datasets demonstrate the effectiveness of our proposed method, and our findings in the experiment provide new insights for the future development of Chinese sentiment classification.
An end-to-end exemplar association for unsupervised person Re-identification

Tracklet association methods learn the cross camera retrieval ability though associating underlying cross camera positive samples, which have proven to be successful in unsupervised person re-identification task. However, most of them use poor-efficiency association strategies which costs long training hours but gains the low performance. To solve this, we propose an effective end-to-end exemplar associations (EEA) framework in this work. EEA mainly adapts three strategies to improve efficiency: (1) end-to-end exemplar-based training, (2) exemplar association and (3) dynamic selection threshold. The first one is to accelerate the training process, while the others aim to improve the tracklet association precision. Compared with existing tracklet associating methods, EEA obviously reduces the training cost and achieves the higher performance. Extensive experiments and ablation studies on seven RE-ID datasets demonstrate the superiority of the proposed EEA over most state-of-the-art unsupervised and domain adaptation RE-ID methods.
A vehicle tracking algorithm combining detector and tracker

Abstract Real-time multichannel video analysis is significant for intelligent transportation. Considering that deep learning and correlation filter (CF) tracking are time-consuming, a vehicle tracking method for traffic scenes is presented based on a detection-based tracking (DBT) framework. To design the model of vehicle detection, the You Only Look Once (YOLO) model is used, and then, two constraints including object attribute information and intersection over union (IOU), are combined to modify the vehicle detection box. This approach improves vehicle detection precision. In the design of tracking model, a lightweight feature extraction network model for vehicle tracking is constructed. An inception module is used in this model to reduce the computational load and increase the adaptivity of the network scale. And a squeeze-and-excitation channel attention mechanism is adopted to enhance feature learning. Regarding the object tracking strategy, the method of combining a spatial constraint and filter template matching is adopted. The observation value and prediction value are matched and corrected to achieve stable tracking of the target. Based on the interference of occlusion in target tracking, the spatial position, moving direction, and historical feature correlation of the target are comprehensively employed to achieve continuous tracking of the target.