Reviewerly/exHarmony huggingface dataset:
Dynamic State Estimation of Cyber-Physical Power and Energy Systems Based on Bad Data Identification

Dynamic state estimation is an important approach to realize power network state perception in Cyber-Physical power and energy systems. However, more and more bad data are transferred into power grid energy management system because of data attack on transmission line or damage of measuring equipment. Meanwhile, as clean energy access to the power grid, the random nature of these sources leads to a large amount of fluctuating measurement data in the system, which is true data. The traditional robust Cubature Kalman Filter (CKF) method cannot distinguish bad data from fluctuating data. Fluctuating data are regarded as bad data, and the real characteristics of the data are lost. Based on the above deficiencies, a bad data pre-identification method based on big data is proposed, and improved robust volume CKF is conducted on each node of the Cyber-Physical power and energy systems. The innovation covariance based on the innovation sequence is calculated firstly and the measured variance with the total variance is compared to obtain the first-level identification; Secondly the correlation of the data according to the measurement sequence is calculated, and the secondary identification is conducted to distinguish the fluctuation data from the bad data. Simulation results show that the improved method can reduce the impact of bad data, while ensuring the authenticity of the measurement data, and improving the accuracy of state perception.
Security Verification Method of Embedded Operating System Semaphore Mechanism based on Coq

The semaphore mechanism is an important part of the embedded operating system. Therefore, it is very necessary to ensure its safety. Traditional software testing methods are difficult to ensure 100% coverage of the program. Therefore, it is necessary to adopt a formal verfication method which proves the correctness of the program theoretically. This paper proposes a proof framework based on the theorem proof tool Coq: modeling the semaphore mechanism, extracting important properties from the requirement documents, and finally verifying that the semaphore mechanism can meet these properties, which means the correctness of the semaphore mechanism is proved and also illustrates the feasibility of the verification framework proposed in this paper, which lays a foundation for the verification of other modules of operating systems.
Optimizing the Key-Pair Generation Phase of McEliece Cryptosystem

Quantum computers are no longer ideas but concrete devices. They could represent a threat for public key cryptography; therefore, in 2017 NIST initiated a process to evaluate and standardize a number of quantum-resistant cryptographic algorithms. Some of these algorithms handle large size keys that may cause a reduction of performances in specific contexts. In this paper, we investigate the possibility to speed up the key-pair generation phase of McEliece cryptosystem. Taking advantage of improved polynomial multiplication in field of characteristic 2, remodelling matrices and handling cache in a clever way, it is possible to speed up the generation phase of private and public keys, paying a negligible amount of memory.
Small sample vehicle target recognition method for unmanned aerial vehicle system based on deep learning

Abstract Vehicle target detection technology refers to the process of vehicle detection and recognition in the image data set of different backgrounds by means of feature extraction. The vehicle target detection technology based on deep learning shows obvious advantages in the accuracy and speed of target detection. With the development of science and technology, the detection and recognition of vehicles in UAV aerial images has become an important applied research direction. This paper studies the detection and recognition of UAV aerial vehicle based on deep learning, and proposes a new deep learning-based algorithm to solve the problem that incomplete vehicle targets in the UAV aerial vehicle based on YOLOV3 algorithm cannot be recognized, and vehicles close to the UAV aerial vehicle are missed. Experimental verification results show that, compared with the existing algorithms, the proposed algorithm can significantly improve the detection accuracy of UAV aerial vehicle based on deep learning while ensuring real-time performance.
Organizing Decentralized Energy Data Using Semantic Approach

With recent ICT technologies developed for smart energy systems, sensors are popularly used for metering energy consumption by devices in decentralized smart energy networks. Processing energy consumption data at decentralized level will help to facilitate the high-efficiency energy use. Many smart energy projects have published the data on the web for other scholars to perform reanalysis on the data. However, such data tend to vary in storage format (e.g., CSV, JSON, XML) and focus more on the energy aspects. Moreover, adding information from outside data sources into the system such as weather is challenging because data of different sources are usually heterogeneous and exist as fragments on the web. In this paper, we propose using semantic approach to manage the decentralized energy data. The main advantage of this work is that it builds a well-defined interoperable structure for organizing the energy data. Furthermore, a variety of cross-domain data sources can be integrated naturally by human words to enhance the reanalysis on the decentralized energy data.
Effectively Analyzing Evolving Software with Differential Facts

Software systems evolve continuously during their lifecycle. Developers incrementally introduce new features and fix bugs during the process, leading to lots of changes and artifacts accumulated. Driven by those rich data recorded in version control systems or issue trackers, lots of work has been done to analyze the software histories. In this PhD work, we propose a universal representation to effectively store and query over knowledge extracted from the histories, with the hope of supporting software evolution research. We have created a toolset, named DIFFBASE, to extract both relations between program entities at the same version, as well as atomic changes between versions. Then users can compose queries using algebraic operators, Datalog or an SQL-like language to accomplish several different evolution management tasks. Based on the existing research outcome, possible future work includes utilizing the facts approach in a scalable solution to discovering compatibility issues involving changes of multiple components and improvement on the storage and query performance of DIFFBASE.
The Design of Reputation System for Blockchain-based Federated Learning

Federated learning (FL) is a new solution to fulfill machine learning (ML) in a decentralized manner. In FL, a group of participants train their local models using their private data, and then upload the locally trained models to a central server that completes the model aggregation process. Compared with the traditional ML that needs to upload private data of the participants to a server for a centralized training, FL protects the data privacy of participants. However, the existing FL has several drawbacks: FL relies to a large extent on a single central server that takes the risk of single point of failure; it lacks a fair and effective reward system to prevent malicious participants from damage the learning process. To overcome these drawbacks, this paper proposes a new FL system based on blockchain (BC). With BC, we introduce a voting-based reputation mechanism to guarantee that the locally trained models from effective FL participants (other than malicious FL participants) can be selected to improve the efficiency of model aggregation. Besides that, we combine the reputation system with a reward mechanism to dynamically adjust the reward shares that can be fairly allocated to the participants. This can attract more FL participants to participate in the learning process, so that the system can run stably for a long time. The experimental results show that our system can achieve more efficient FL learning and can also defend against malicious attacks to a certain extent.
Hybrid Resource Scaling for Dynamic Workload in Cloud Computing

In the Cloud, there are enough amount of various resources to be provided for the users according to their request. To provide services the cloud resources need to be scaled up and out. Cloud resource provisioning uses two approaches. They are horizontal and vertical scaling approaches. Horizontal scaling approaches take some minutes to configure additional machines and less utilization of resources. But this approach is available. In the vertical approach, requests are not handled by adding additional virtual machines like Horizontal, resources are added on the already executing devices within a second. The objective of this work is to develop a hybrid approach by hybridizing both scaling approaches to increase utilization of resources and provide flexible resources that can satisfy user’s requests by performing in such order Vertical, horizontal approaches. The results of the study demonstrate that the proposed approach is more efficient in comparison with the existing approach. CloudSim has been used for the implementation of the developed approach. Scaling is performed by following the capacity of the machine and resources threshold value.
A Hybrid Approach for Optimizing Arabic Semantic Query Expansion

Nowadays, information retrieval systems face significant challenges in providing users with accurate information due to the enormous growth of information. To better reformulate the query and narrow its results, semantic query expansion techniques add semantically related terms to the original query. However, semantic query expansion for Arabic queries is still a challenge due to the lack of rich semantic sources. Most of the existing solutions rely on using either English sources or specific-domain Arabic ontologies. Using English sources requires a translation phase which may lead to query drift, thus providing unrelated expansion terms. In this paper, we provide an overview of the query expansion approaches. Besides, we propose a hybrid comprehensive reference framework for Arabic semantic query expansion that overcomes the lack of Arabic semantic sources by using rich English ontologies to complement the limited Arabic sources (Arabic Wordnet) currently available. It ensures the Arabic-English translation process using a customized machine learning translation model to avoid query drifting. It also transforms natural language to SPARQL (an ontology query language) to easily query English sources (e.g., DBpedia). For enhanced accuracy, it provides an optimization module where meta-heuristics can be used for pertinent terms selection. This work represents a step forward in combining English sources and AI to design a practical Arabic semantic expansion.
Toward Secure K-means Clustering Based on Homomorphic Encryption in Cloud

In recent years, the cloud computation has provided great convenience for users to outsource data for storage and computation. But when data stored in cloud, it’s out of control from user, and there is a risk of private data leakage. In this paper, we propose a framework of secure K-means (CKKSKM) based on homomorphic encryption. The proposed scheme encrypts outsourcing data which can avoid to reveal the private information. We preprocess the data first and uses the Euclidean distance to calculate similarity, which can ensure the data accuracy and reduce the computing overhead. Based on CKKS homomorphic encryption, this scheme can solve the privacy security and reduce overhead of the user’s outsourcing data in cloud for storage and calculation.
MOTrack: Real-time Configuration Adaptation for Video Analytics through Movement Tracking

Video analytics has many applications in traffic control, security monitoring, action/event analysis, etc. With the adoption of deep neural networks, the accuracy of video analytics in video streams has been greatly improved. However, deep neural networks for performing video analytics are compute-intensive. In order to reduce processing time, many systems switch to the lower frame rate or resolution. State-of-the-art switching approaches adjust configurations by profiling video clips on a large configuration space. Multiple configurations are tested periodically and the cheapest one with a desired accuracy is adopted. In this paper, we propose a method that adapts the configuration by analyzing past video analytics results instead of profiling candidate configurations. Our method adopts a lower/higher resolution or frame rate when objects move slow/fast. We train a model that automatically selects the best configuration. We evaluate our method with two real-world video analytics applications: traffic tracking and pose estimation. Compared to the periodic profiling method, our method achieves 3%-12% higher accuracy with the same resource cost and 8-17x faster with comparable accuracy.
Scenes Segmentation in Self-driving Car Perception System Based U-Net and FCN Models

Visual perception for mobile robots and self-driving cars performs the task of semantic segmentation in order to comprehend the road scenes. Most of researchers in computer vision focuses to improve the accuracy of their image segmentation models. In autonomous driving application, the accuracy is not the only important metric but also it requires a model that can achieve a good Intersection Over Union (IoU). We propose a U-Net architecture in this work that allows and maintain a decent balance in terms of accuracy and (IoU) compared with the FCN architecture evaluated on CamVid dataset [4].
Clustering Ensemble via Cluster-wise Optimization Graph Learning

Clustering is an important research direction of unsupervised learning. With the development of research, clustering algorithms emerge in endlessly, and they all exert significant clustering effects in specific scenarios.However, in the face of complex practical problems, single clustering algorithm is often limited to its inherent clustering scene, and the emergence of clustering ensemble algorithm greatly alleviates this problem. At present, there are a large number of clustering ensemble methods, but the clustering results can not guarantee to meet the block diagonal property, resulting in incorrect clustering. In this paper, we propose a clustering ensemble method of cluster level fusion.First, because the reliability of different clustering results is inconsistent, we use different weights to measure the reliability of the similarity matrix formed by the base clustering results. Second, we use the block diagonal property as a priori to ensure that the similarity matrix is partitioned.Finally, we use symmetric non-negative matrix factorization on the final consistent similarity matrix to obtain the final low-dimensional non-negative clustering label matrix. Experiments show that our method is better than several most state-of-the-art baseline methods on different data sets.
Emotion Recognition from Facial Expression Using Machine Vision Approach

In this study emotion-based face expression recognition framework has been proposed using a machine vision (MV) approach. The face emotion dataset has been collected local survey in Bahawalpur city dataset divide into 3 classes happy, sad, and angry. A total of 600 images of size (256 x 256) were transformed into a gray level format and employed a median filter for noise removal. Three non-overlapping regions of interest (ROIs) of size (50 x 50) have been taken and analyze 1800 (600 x 3) ROIs on the overall dataset. Total 45 Statistical features named as texture, histogram, and binary features were extracted. Select optimize features using the correlation-based feature section technique. The optimized dataset employed of MV classifiers namely random forest (RF), logistic (Lg), and J48 are obtained very promising accuracy 96.33%, 95.67%, and 95.33% respectively.
Frequency-domain blind quality assessment of blurred and blocking-artefact images using Gaussian Process Regression model

Most of the standard image and video codecs are block-based and depending upon compression ratio the compressed images/videos suffer from different distortions. At low compression ratios, blurriness is observed and as compression increases blocking artefacts occur. Generally, in order to reduce blockiness, images are low-pass filtered which leads to more blurriness. Also, in bokeh mode images they are commonly observed: blurriness as a result of intentional blurred background while blocking artefact and global blurriness arising due to compression. Therefore, such visual media suffer from both blockiness and blurriness distortions. Along with this, noise is also a commonly encountered distortion. Most of the existing works on quality assessment quantify these distortions individually. This paper proposes a methodology to blindly measure overall quality of an image suffering from these distortions, individually as well as jointly. This is achieved by considering the sum of absolute values of low and high-frequency Discrete Frequency Transform (DFT) coefficients defined as sum magnitudes. The number of blocks lying in specific ranges of sum magnitudes including zero-valued AC coefficients and mean of 100 maximum and 100 minimum values of these sum magnitudes are used as feature vectors. These features are fed to the Machine Learning (ML) based Gaussian Process Regression (GPR) model, which quantifies the image quality. The simulation results show that the proposed method can estimate the quality of images distorted with the blockiness, blurriness, noise and their combinations. It is relatively fast compared to many state-of-art methods, and therefore is suitable for real-time quality monitoring applications.
Which mental model for inclusive cities that are learning

Abstract the complex dynamics engendered by distributive asymmetry, climate change, and the effects of mass movement of people and cultures, in the context of transformations informed by processes of shifting and permanency, pose a question of a political and social but also cultural and epistemological nature. The promotion of learning processes focusing on these problems and on pertinent solutions emerges as a central aspect for the construction of a plural and inclusive city and it implies the cognizance of what can be referred to as citizenship. The article discusses a different approach to the crucial concepts of learning, intersubjectivity, and the relationship between our body and the environment of which it is part. The objective is the formulation of a definition of the concept of mind different from those still dominant, which can support new mindfulness of the range of problems affecting urban territories and the type of solution that needs to be adopted.
Amazigh Handwriting Recognition System—Multiple DCNN Strategies Applied to New Wide and Challenging Database

In this research work we proposed multiple strategy based on the Convolutional Neural Network (CNN) algorithm for Amazigh handwritten character recognition. The Amazigh language consists of many confusing characters that provide a similar feature map. Thus, classifying these characters is a challenging task. The most existing works do not lead to satisfactory results for practical applications, and have much lower recognition performance. To improve the performance of Amazigh handwritten recognition process, we investigate in Deep Learning (DL) techniques by implementing three learning methods based on Deep Convolutional Neural Network (CNN): training the CNN from scratch, fine tuning CNN and combined CNN-SVM. CNN algorithm revolutionized the field of handwritten recognition and shown excellent performance, but has not been attempted for Amazigh handwritten characters. We build our Model based on CNN and Support Vector Machine (SVM) classifiers and we took into consideration the effectiveness and the complexity of our architecture also the speed of the training process. A new dataset consisting of about 50000 characters and 33 classes is collected and used for training and testing. We were able to achieve an excellent recognition rate of 98.23%.
Stream-data-clustering based adaptive alarm threshold setting approaches for industrial processes with multiple operating conditions

The setting of alarm thresholds is a critical concern of alarm management systems in industrial processes. Conventional alarm thresholds less consider changes of operating conditions in production processes, which degrades the effectiveness of alarm management systems. In response to this problem, this paper proposes an adaptive alarm threshold setting approach based on stream data clustering (SDC). Firstly, we develop a stream data clustering algorithm termed as a-DenStream algorithm which realizes industrial flow data clustering through online micro-clustering and offline integration. Subsequently, we develop the C-BOUND algorithm to extract the edges of the clustering results. In response to alarms associated with multiple operating conditions, segmentations are conducted to set alarm threshold groups and build a multi-condition alarm threshold model. Consequently, an adaptive alarm threshold setting method based on model matching is created. The effectiveness of the proposed method is demonstrated by experiments on a coal gasification chemical process. The proposed method provides a potential application for industrial processes with multiple operating conditions alarm managements.
New local region-based model for inhomogeneous images with the simplex constraints of landmarks

Image segmentation is widely used in different fields, like the medical field, security, and defense, etc. There are different techniques used for image segmentation some of these are the Mumford-Shah model, Chan-Vese (CV) model and the Local Gaussian Distribution Fitting (LGDF) model. But in the case of the images having weak edges and severe noise segmentation is still a difficult task. In this study, we will work on the segmentation of images having severe noise and weak boundaries. This is done by introducing landmark constraints in local Gaussian distribution fitting energy. Landmarks highlight the prominent features of an image. Finally, comparison is given in both qualitative and quantitative ways.
MEREOLOGICAL BIMODAL LOGICS

Abstract In this paper, using a propositional modal language extended with the window modality, we capture the first-order properties of various mereological theories. In this setting, $\Box \varphi $ reads all the parts ( of the current object ) are $\varphi $ , interpreted on the models with a whole - part binary relation under various constraints. We show that all the usual mereological theories can be captured by modal formulas in our language via frame correspondence. We also correct a mistake in the existing completeness proof for a basic system of mereology by providing a new construction of the canonical model.