Reviewerly/exHarmony huggingface dataset:
On-line intelligent visual identification algorithm of power equipment state under the complex environment based on SIFT

At present, the application of computer vision technology in power systems is increasing. The idea of using image processing and machine vision to monitor power equipment is not new. However, the research mainly focuses on the application of computer vision technology in the fields of transmission line environment and insulator detection. Combined the actual conditions of the intelligent grid substation and the need of construction, this paper proposed and studied a kind of identification algorithm based on intelligent computer vision technology, aiming at solving the problem of automatic identification of typical outdoor circuit breakers, disconnectors and indoor switchgear. First, using scale-invariant feature transform (scale invariant feature transform, SIFT) algorithm, the paper accurately positions the area to be detected; second, extracts isolating switch line information and switchgear circle information using randomized Hough transform, and through the k-NN (k-Nearest Neighbour) extracts and ferreting breaker character information; Finally, three kinds of electric power equipment are identified intelligently by threshold setting, and the identification effect and stability of the algorithm are validated in the disconnector and Qing He substation of a 500 kv substation in China.
WDC'22

The development of techniques for creating completely synthetic photographic images and videos, as well as the increasing prevalence of disinformation associated with such synthetic media, have sparked interest in the computer community. One such issue is the proliferation of deepfakes. However, much of the research is devoted to outwitting the SOTA in order to generate and detect fabricated images and video. Considering them from the perspective of computer security and human ethics is largely ignored. As such, this workshop aims to provide a forum for researchers to exchange ideas and methodologies, as well as to provide academia and industry with insights into the development and identification of fake media from a computer security perspective.
Message from the ICSA 2022 General Chairs and Program Chairs

The IEEE International Conference on Software Architecture is the premier venue for practitioners and researchers interested in software architecture, in component-based software engineering and in quality aspects of software and how these relate to the design of software. ICSA has a strong tradition as a working conference, where researchers meet practicing software architects who can explain the problems they face in their day-to-day duties, and who can influence the future of the field.
Fuzzy sets (in)equations with a complete codomain lattice

The paper applies some properties of the monotonous operators on the complete lattices to problems of the existence and the construction of the solutions to some fuzzy relational equations, inequations, and their systems, taking a complete lattice for the codomain lattice.The existing solutions are extremal -the least or the greatest, thus we prove some extremal problems related to fuzzy sets (in)equations.Also, some properties of upper-continuous lattices are proved and applied to systems of fuzzy sets (in)equations, in a special case of a meetcontinuous complete codomain lattice.
Intrusion signal discrimination method based on MFCC-energy entropy feature and FTO-SVM

Aiming at the problem that Distributed Optical Fiber Acoustic Sensing (DAS) system will misjudge external intrusion signals, an intrusion signal discrimination method based on MFCC-Energy entropy feature and FTO-SVM is proposed. Firstly, the former 13-dimensional Mel-Frequency Cepstral Coefficients (MFCC) is extracted from the collected sound signal, and Principal Component Analysis (PCA) is used to reduce the dimension of MFCC; Secondly, the energy entropy of the collected sound signal is calculated, which fused with the reduced MFCC as the feature parameter of the collected sound signal; Finally, the extracted feature parameters are discriminated by using Support Vector Machine (SVM) with the hyperparameters optimized by the Fibonacci Tree Optimization (FTO) algorithm. The results show that the proposed method can effectively improve the system discrimination accuracy for intrusion signals, and is of great significance to perimeter security and fault diagnosis and other related fields.
Architectural Refactoring for Functional Properties in Evolutionary Architecture

The evolutionary architecture supports software systems to make small functional changes frequently and reliably. With fitness functions defined, we can ensure that the architectural goals are met as the system evolves. However, some functionality changes cause architectural changes, which impact a large part of the software system. Therefore, we aim at ensuring that changes in the architectural design do not impact the existing functionalities, while new functionalities are incorporated into the new design. This paper proposes an approach to automate architectural design refactoring that supports changes in functionalities. Our approach applies formal modeling and verification to refactor and verify the evolution process of software systems. The proposed algorithms help to automatically refactor the design by referencing the given architecture design. With our appproach, the evolution process can be planned and formally verified to guarantee that the system can evolve safely to support functionality changes. We evaluated our approach with four real-world systems. The results show the effectiveness of our refactoring approach to support new functional properties.
Negative Adversarial Example Generation Against Naver's Celebrity Recognition API

Deep Neural Networks (DNNs) are very effective in image classification, detection and recognition due to a large number of available data. However, they can be easily fooled by adversarial examples and produce incorrect results, which can cause problems for many applications. In this work, we focus on generating adversarial images and exploring and assessing possible negative impacts caused by these examples. As a case study, we create adversarial images against Naver's celebrity recognition (NCR) API, as Naver is the leading machine learning APIs service provider in South Korea. We demonstrate that it is extremely easy to fool the online DNN-based APIs using adversarial examples and discuss possible negative impacts resulting from these adversarial examples.
Sentiment Classification for Social Media Posts using Machine Learning

With the advent of online technology and its growth, the web now contains a massive amount of data for internet users, as well as a large amount of data being generated. The internet has evolved into a platform for online learning, idea exchange, and opinion sharing. People use social networking sites like Twitter, Facebook, and Google+ to share and express their opinions on a variety of topics, participate in discussions with diverse communities, and send messages all over the world. The field of sentiment analysis of twitter data has seen a lot of progress. This study focuses on Twitter sentiment analysis, which is useful for analysing information in tweets where opinions are highly structured, varied, and either positive or negative. The proposed system is build using Support Vector Machine and Random Forest Techniques
Advanced Machine Learning Techniques to Detect Various Types of Deepfakes

Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Also, it is challenging to detect different types of deepfake images simultaneously. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capability in KD to enable a student network to learn discriminative features from low-quality images effectively. In addition, we also discuss the continual learning and domain adaptation methods to detect various types of deepfakes simultaneously.
Research on intelligent operation and maintenance scheme of railway signal system based on big data

With the continuous expansion of the scale of my country's railway electrical system and the increasingly complex equipment functions, the requirements for ensuring the safe, stable and long-term operation of the electrical system are becoming more and more urgent. The operation and maintenance system is mainly through manual maintenance, which is inefficient. With the continuous development and application of technologies such as information technology and intelligent terminals in my country, intelligent monitoring technology has been gradually applied to the maintenance of signal systems. In order to facilitate the electric service personnel to grasp the operating equipment status in real time, optimize the equipment maintenance process and improve the maintenance efficiency. This paper comprehensively considers the daily maintenance, operation, emergency response operation process and related precautions of the signal system, and combines key technologies such as data access, data fusion, data visualization, fault prediction and health management to realize the analysis and design of intelligent operation and maintenance application functions. Provide reference for the improvement of railway signal operation and maintenance level and the transformation of electric service operation and maintenance mode.
Research and Implementation of Host Behavior Anomaly Detection Technology Based on Deep Learning

In recent years, with the growth of network hosts and applications, Abnormal host behavior brings great challenges to the protection of user information privacy. Effective detection of abnormal host behavior is of great significance. Host anomaly detection technology can identify abnormal behaviors of the host by establishing the normal behavior benchmark, which is universal and capable of detecting new network attack behavior. However, the existing host behavior anomaly detection technology confronts with three problems(i.e., a large amount of data, difficulty in labeling, and strong manual dependence). To solve these problems, this paper proposes an unsupervised learning model based on BiGAN(Bidirectional Generative Adversarial Network) to detect abnormal host behavior. Our model introduces several loss functions and modifies them to apply to BiGAN. At the same time, we discuss which loss function is more suitable for host behavior anomaly detection. Experimental results show that the BiGAN model using the Hinge loss function is the most stable and has excellent anomaly detection performance.
Symbolic Synthesis of Indifferentiability Attacks

We propose automated methods for synthesising attacks against indifferentiability, a powerful simulation-based notion of security commonly used to reason about symmetric-key constructions. Our methods are inspired from symbolic cryptography which is popular to reason about, e.g., cryptographic protocols. For that, we introduce a core programming language for algebraic distinguishers and study the class of universal distinguishers, who win the indifferentiability game against every simulator; then, we show that the universality of algebraic distinguishers can be reduced to solving systems of algebraic, deducibility and static-equivalence constraints.
Research on fusion-based deep convolutional networks in 3D object detection

Aiming at the problem that the uniformly sampled point cloud is used for training in the three-dimensional object detection of RGB-D data, the accuracy of the point cloud in the actual scene is reduced, and the PointConv network is proposed to detect the loss of some key point cloud features. Firstly, by generating a 2D area proposal from the image and positioning it in the 3D point cloud data. Secondly, the 2D-driven 3D target detection is realized by the segmentation network based on PointConv and the 3D bounding box evaluation network. At last, evaluated on the KITTI and SUN RGB-D datasets, this method has real-time capabilities and surpasses the current detection levels of DoBEM, MV3D, Mono3D, Frustum PointNets ,and other methods. Experimental results show that compared with the previous state-of-the- art technology, our method improves mAP by 0.5% to 12.4%, and is 1-3 orders of magnitude faster.
Digital modernization of education: pros and cons

The article analyzes the pros and cons of the digital modernization of education and describes the experience of solving this issue in the Russian Federation and the People's Republic of China. Covid-19 has accelerated the transition of all important areas of society to digital field. Such areas as healthcare and education, the tax service, the law enforcement system, and trade have moved online, providing the necessary life needs for citizens of developed countries. In this regard, there have been changes at the legislative level; important bills were proposed for discussion in an accelerated mode. In the article, digitalization is considered as a process that has both positive and negative consequences.
Supplement to the Article “On Relations in Science: the Case of the Scientific Journal Editorial Board”

In this supplement it is shown that the dependences of the number of path lengths between vertices in the graph (between reviewers) and the number of connections of reviewers for the last few years that were discussed in Bolshakov’s article “On relations in science: the case of the scientific journal editorial board” are subordinate to the known statistical distributions. These data may allow us to draw a conclusion about the scientific journal editorial board’s functioning. See the article: Bolshakov D.Yu. On relations in science: the case of the scientific journal editorial board. Scholarly Research and Information. 2021;4(1–2):19–28. https://doi.org/10.24108/2658-3143-2021-4-1-2-19-28
Central Java regional geochemistry: influence of environmental geology and mineral occurrences

Abstract Stream sediments geochemical surveys were undertaken in Central Java, Indonesia as a part of the Geological Agency’s geochemical mapping project with support from several universities. A total of 1583 stream sediment samples were collected from Central Java and analyzed using X-Ray Fluoresces for 29 elements. Geochemical maps were processed by the GIS software. Based on the overlay between geological and geochemical maps, there are several elemental anomalies, for example, the ultramafic formation from Kebumen showing distinctive Chromium (Cr), Cobalt (Co), and Magnesium (Mg) anomalies. Meanwhile, the potassic igneous rocks from Muria Mountain have Cerium (Ce), Lanthanum (La), and Zirconium (Zr) anomalies. Elemental anomalies related to mineral resources and mining areas such as Arsenic (As), Copper (Cu), and Lead (Pb) are found in Trenggalek, Pacitan, and Kebumen; while Barium (Ba), Iron (Fe), and Manganese (Mn) are scattered along the southern coast of Central Java. Anthropogenic elements (Chlorine (Cl), Ce, and As) spread along the north coast of Central Java probably because of pollution by industrial, farming, and urban areas. Heavy metals pollutions that were identified previously in the Bengawan Solo River flow can be seen on this geochemical map which can convincingly delineate elements anomalies due to geological and environmental conditions.
Concept Drift Detection with Optimal Machine Learning Model for Data Classification

Recently, massive quantity of data streams have been produced rapidly due to the continual technological advancements and it poses concept drifting (CD) as a challenging problem. Since the performance of the classification model gets degraded owing to the presence of concept drifting, it is needed to design effective concept drift detection approaches. Classical classification models are not predicted to discover the pattern in a non-stationary data distribution. In real time scenarios, the classification models are required to properly identify the concept drift and adapt over time. With this motivation, this article develops a new concept of drift detection with chimp optimization algorithm based machine learning (CCD-COAML) model for data classification. The proposed CCD-COAML technique mainly aims to identify the concept drift and then classify the high dimensional data. Initially, the CCD-COAML technique undergoes pre-processing in three distinct stages namely format conversion, data transformation, and chunk generation. Besides, ADaptive WINdowing (ADWIN) approach was implemented to the detection of concept drift and then multilayer perceptron (MLP) model is employed for the data classification process. For improving the data classification efficiency of the MLP model, the weight values can be selected optimally by using COA and thereby improve classification accuracy. A wide-ranging experimental analyses is conducted on benchmark dataset and the outcome stated the remarkable performance of the CCD-COAML methodology on state-of- the-art approaches.
Operator Optimization Oriented Person Re-Identification

Abstract Using FPGA to accelerate the application of convolutional neural networks has attracted more and more attention. However, the design technology of the FPGA accelerator cannot follow up the latest achievements of the convolutional neural networks, nor can it consider optimizing the complex network structure combined with the characteristics of hardware to improve the accuracy of the network and simplify the implementation of hardware. To address these issues, in this paper, we adopt the development idea of algorithm-hardware co-design, compress the structure and optimize the operator for a state-of-the-art lightweight person re-identification network, and design a new network model SGCNet (Shift Gaussian Convolution Network). Specifically, SGCNet is obtained by structure optimization and operator replacement according to the optimization degree of the operator in the FPGA hardware environment. For convolution operation, only 1 × 1 kernel convolution operator is adopted in SGCNet, while spatial convolution is replaced by a more effective shift operation. SGCNet is more concise and clear in the operation types and complex structure of the network. Experimental evaluations demonstrate that SGCNet’s top-5 accuracy of 92.7% on ImageNet and R1 accuracy of 74.6% on CUHK03 of person re-identification data set, is higher than CMSNet network.
MODULAR RATING SYSTEM OF ASSESSMENT STUDENT KNOWLEDGE

In Permjakova Alexander Anatoljevicha and Zelenkovoj Natalia Ivanovny's clause theoretical questions of a modul-rating estimation of knowledge ofpupils of modem school are considered.
Expert System Diagnosis Disease of Oil Palm Plants Using Forward Chaining and Dempster Shafer

This research is motivated by the problem of inhibiting crop production from oil palm plants, namely disease. Diseases of oil palm plants can be caused by viruses, fungi and, the host plant or an unfavorable environment. The process of diagnosing oil palm plant diseases requires expertise, knowledge and experience. Therefore, this study aims to build an expert system that can diagnose 9 types of plant diseases in oil palm from 29 symptoms based on the knowledge of 1 expert with the forward chaining method of reasoning and the web-based Dempster Shafer calculation method. The testing technique used is black box testing, validation testing, testing and theoretical calculations. The results of the black box test state that the expert system has 100% conformity in terms of functionality. The results of the expert validation test state that the expert system has 100% conformity. The results of the theoretical calculation test state that the expert system calculations are in accordance with the results of manual calculations. The results of the test with a questionnaire based on 32 respondents said it went very well. The results of this study provide the information needed by farmers to be able to diagnose and increase knowledge about how to overcome the problems faced by their oil palm plantations even without direct expert assistance in order to improve quality and stabilize the amount of production according to farmers' expectations.
Contextualizer: Connecting the Dots of Context with Second-Order Attention

Composing the representation of a sentence from the tokens that it comprises is difficult, because such a representation needs to account for how the words present relate to each other. The Transformer architecture does this by iteratively changing token representations with respect to one another. This has the drawback of requiring computation that grows quadratically with respect to the number of tokens. Furthermore, the scalar attention mechanism used by Transformers requires multiple sets of parameters to operate over different features. The present paper proposes a lighter algorithm for sentence representation with complexity linear in sequence length. This algorithm begins with a presumably erroneous value of a context vector and adjusts this value with respect to the tokens at hand. In order to achieve this, representations of words are built combining their symbolic embedding with a positional encoding into single vectors. The algorithm then iteratively weighs and aggregates these vectors using a second-order attention mechanism, which allows different feature pairs to interact with each other separately. Our models report strong results in several well-known text classification tasks.
Knowledge-Driven Drug-Use NamedEntity Recognition with Distant Supervision

As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for human labor and train models incorporating domain-specific (e.g., drug use) external knowledge to recognize domain specific entities. We capture entities related the drug use and their trends in government epidemiology reports, with an improvement of 8% in F1-score.
Image Encryption using Chaotic Sorting Fortified with DNA Sequencing

Security is a profound term that is being vitally put into use especially when communication is made. Image encryption is one form of communication that requires handling of pixels in different way rather than textual data. This paper proposes the novel idea of using chaotic sorting to permutate the location of pixels in the original image and diffusion is introduced by the DNA sequencing. Use of chaotic map based sorting ensures the pixels are permuted well to break the neighbourhood relationship and DNA encoding ensures the pixels are diffused such that no information is revealed. The encrypted images are analysed for their strength under various security parameters like histogram, entropy, correlation coefficient, NPCR and UACI analyses. The test analyses verify the proposed idea has better security to the image.