Reviewerly/exHarmony huggingface dataset:
An Improved Firefly Algorithm Based Cluster Analysis Technique

Unsupervised machine learning approach like cluster analysis finds a large number of applications in different engineering domains. A variety of meta-heuristic algorithms have been proposed in the literature for clustering. Firefly is one of the most commonly used meta-heuristic algorithm as it has efficient capability of automatic subdivision of population and natural capability of dealing with multimodal optimization. But due to more dependency on local solution for movement, it generally leads to premature convergence. In this paper, an improved variant of firefly algorithm is proposed by introducing a new position updating equation for movement of firefly by using the idea of best solution for global search. A mutation operator is also incorporated in the basic firefly algorithm to enhance its convergence speed and exploration capability. The proposed firefly algorithm is simulated and compared with standard firefly algorithm on standard 13 benchmark functions. Moreover, the efficiency of the proposed firefly algorithm is also tested by adopting it as a clustering technique. The performance is tested on seven real-life datasets and also compared with various state-of-the-art meta-heuristic clustering techniques. The computation outcomes showed that the proposed algorithm is better in finding the optimal cluster center with minimum intra-cluster distance, along with fast convergence speed. Results are also verified quantitatively using Friedman, Wilcoxon, and post-hoc pairwise Nemenyi tests.
Vector-Degree: A General Similarity Measure for Co-location Patterns

In this chapter, we propose a general similarity measure for any two co-location patterns. Firstly, we study the characteristics of the co-location pattern and present a novel representation model based on maximal cliques. Then, two materializations of the maximal clique and the pattern relationship, 0-1 vector and key-value vector, are proposed and discussed. Moreover, based on the materialization methods, the similarity measure, Vector-Degree, is defined by applying the cosine similarity. Finally, similarity is used to group the patterns by a hierarchical clustering algorithm.
A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches: the decision-theoretic approach and the syntactic(structural) approach. In this paper, at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top – down tabular parser known as Earley’s algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problem and for this reason takes a lots of time, searching in array and grammar parsing, and wasting lots of memory. In order to solve these problems and most important, the cubic time complexity, in this article, a new algorithm has been introduced, which reduces wasting the memory to zero, with using linked list data structure. Also, with the changes in the implementation and performance of the algorithm, cubic time complexity has transformed into O (n*R) order. Key words: syntactic pattern recognition, tabular parser, context –free grammar, time complexity, linked list data structure.
An Efficient and Effective Approach of Copy–Move Image Forgery Detection for Small Predefined Block Size

Image forgery can be defined as a digital art in which manipulation of digital images is performed in order to hide some useful data of the image, and anyone tampers images for various reasons like to produce false evidence. The forger uses a one or a multiple series of image processing operations to temper any image. Copy–move tempering is one of the main image tampering methods used for image manipulation, where tempering is performed by covering an image part in order to delete or add information. In this paper, a predefined copy–move block is detected by the user with the help of proposed algorithm. This method involves selection of copied–moved region first. Later, the lexicographic sorting and block matching are performed, and finally, the morphological operation is applied in detected region.
A Learning Framework with Disposable Auxiliary Networks for Early Prediction of Product Success

Consider the scenario in which an investor seeks to identify potential products before they are unveiled to the public. For such a scenario, the investor poses questions such as “What characteristic better represents a product?” or “What features make a product popular?” Unlike traditional recommendation problems, in this case, there is no user feedback for the upcoming products, which greatly complicates prediction. To address this challenging yet common scenario, we present an unconventional multi-task learning framework that trains the prediction model on information for mature products that have user feedback, and then uses this model to predict the success of upcoming products for which there is no user feedback. To achieve this goal, we train a main task network to extract product features from their descriptions while at the same time training a disposable auxiliary network to learn domain-specific words and popular trends from user reviews.This disposable auxiliary network is beneficial during the training of the main task network but is unused at the inference stage. Empirical results on two real-world datasets in different languages demonstrate that the proposed framework not only improves the overall rating prediction for products but also identifies the top successful products without any user reviews.
Research on Identification Method of Sensitive Data in Power System

The power system equipment is currently facing more serious risk and threat of data leakage. The online monitoring of power transmission lines, the complexity of power consumption information collection methods and the uncontrollable operating environment make the business data of power system more prone to data leakage. On account of this, this paper firstly analyses the risk of sensitive data in power system, then studies the identification process of sensitive data in power system. The architecture and functional composition of sensitive data identification system for power system are proposed. Finally, the implementation process of sensitive data protection is given.
Research on Optimized and Fast UAV Image Horizon Extraction Method

In view of the poor real-time problem of UAV image horizon extraction based on the traditional Canny edge detection algorithm, a horizon extraction method based on an improved Canny algorithm was proposed. The main aspects of the improved Canny algorithm are for the gradient operator and edge connector, using Prewitt operator to detect the maximum gradient direction and four boundary tracking to connect edges, and conjunction with the DM642 optimization to optimize the algorithm to improve the processing speed. Then the processing on the horizon video captured was performed, the results show that the horizon can still be detected in case of the interference, the horizon linear parameters are also in line with the actual situation, and the processing speed reaches the real-time requirements of the aircraft. The experimental results demonstrate the feasibility and real-time of the algorithm.
InterCriteria Analysis Approach for Decision-Making in Virtual Screening: Comparative Study of Various Scoring Functions

The goal of this study is to evaluate the ability of the recently developed InterCriteria Analysis (ICrA) approach to support decision making in computer-aided drug design projects. ICrA is a method for multicriteria analysis based on two fundamental mathematical concepts – index matrices and intuitionistic fuzzy sets. The approach is designed to distinguish possible relations in the behavior of pairs of criteria when multiple objects are considered, meanwhile allowing to account for the uncertainty in information processing. In this study, ICrA has been applied to examine diverse scoring functions, implemented in AMMOS, FRED, and X-Score software platforms for docking performance. Two protein targets have been considered in this study – estrogen receptor and neuraminidase, selected for dissimilar physicochemical properties and topology of their binding sites. The ability of the scoring functions to evaluate the interactions between protein targets and receptor-based focused libraries, based on the ChemBridge diversity set of drug-like molecules, has been examined, and the relationships between the scoring functions have been analyzed, applying ICrA.
Visual Gesture-Based Character Recognition Systems for Design of Assistive Technologies for People With Special Necessities

In the present scenario, around 15% of the world's population experience some form of disability. So, there has been an enormous increase in the demand for assistive techniques for overcoming the restraints faced by people with physical impairments. More recently, gesture-based character recognition (GBCR) has emerged as an assistive tool of immense importance, especially for facilitating the needs of persons with special necessities. Such GBCR systems serve as a powerful mediator for communication among people having hearing and speech impairments. They can also serve as a rehabilitative aid for people with motor disabilities who cannot write with pen on paper, or face difficulty in using common human-machine interactive (HMI) devices. This chapter provides a glimpse of disability prevalence around the globe and particularly in India, emphasizes the importance of learning-based GBCR systems in practical education of differently-abled children, and highlights the novel research contributions made in this field.
Software Defect Prediction via Multi-Channel Convolutional Neural Network

With the growing complexity of modern software, the expense of improving software reliability becomes significant. To reduce costs, Software Defect Prediction (SDP) was proposed to detect hidden bugs a few decades ago. Recent studies demon-strate the superiority of the deep learning-based approach in SDP, such as Convolutional Neural Network (CNN). However, the noise in defect data can still deteriorate their performance. To enhance the performance of the state-of-the-art CNN-based method, we propose an improved SDP framework named Defect Prediction via Multi-Channel Convolutional Neural Network (DP-MC-CNN). The novelty is that DP-MC-CNN extracts sub-trees from the Abstract Syntax Tree, generates multiple paths, and feeds them to a multi-channel CNN, which weakens the noise effect. In evaluation, we assess DP-MC-CNN on seven projects by average F1 score, and it outperforms the state-of-the-art CNN-based method by 3.93%. Furthermore, we validate the effectiveness of the multi-channel approach and reveal the influences of each sub-tree component.
Mentoring Effectiveness for Primary School Teachers' Competencies Through Continuous Professional Development (CPD)

The aim of the current study was to identify different competencies developed through Continuous Professional Development (CPD) and to evaluate the effectiveness of mentoring in developing different competencies among primary teachers. It was a descriptive and quantitative study in nature. Primary school teachers (PSTs)from the district of Lahore were the population of the study. The sample was selected randomly from 10Percent of the total population (N = 786). For the data collection, a 5-point Likert-type questionnaire was developed by the searcher. To answer the research questions, data were analyzed using SPSS version 21 with mean SD, t-test, and ANOVA. The identified professional competencies through the CPD program were classroom management, micro-teaching, reinforcement, planning and strategies, learning environment,human growth and development, assessment, communication skills, asking questions, and lesson planning. Mentoring is recommended to be done at the elementary and secondary levels to develop competencies
Semantic-Based Techniques for Efficient and Secure Data Management

In this paper, new approaches for secure data management will be described. Presented methods will be a semantic-based procedures, which for data handling use a semantic content and meaning. Such methods are designed for efficient data protection in cloud or distributed systems. Application of such procedures allow to significantly increase the security strength of existing solutions.
Improving Video Quality by Predicting Inter-Frame Residuals Based on an Additive 3d-Cnn Model

Video compression is essential for uploading videos to online platforms which usually have bandwidth limitations. However, the compression reduces the visual quality. To overcome this problem, the visual quality of the low bit-rate compressed videos for various standards, including H.264 and HEVC in decoders, needs to be improved. Accordingly, this paper proposes a novel method for improving video quality based on 3D convolutional neural networks (CNNs). In particular, the proposed neural network model receives five frames of the low bit-rate compressed video as input and subsequently predicts the compression error of frames using the first and fifth frames. Finally, it reconstructs an improved version of the frame with high quality. The CNN is an Additive (3D) model that can predict the eliminated inter-frame redundancies resulting from compression. Additive 3D-CNN achieves an average of 12.4%, 9.9% and 5% BD-rate increases for LP, LB and RA for the Y component.
English Teaching Mode under Computer Network Environment

With the gradual advancement of globalization and the rapid development of computer network technology, traditional English teaching is facing endless influences and challenges. This has also caused a huge change in the current teaching model. In the final analysis, innovating teaching models and constructing effective teaching models are one of the key ways to improve teaching quality. This article aims to explore how to teach English in a computer network environment. Combine computer network technology with traditional language teaching resources, make full use of the advantages of the network age, and make computer network an integral part of language courses. This article adopts a new teaching model, changes traditional concepts, improves teaching and learning methods, and improves the effectiveness of teaching and learning. The integration of educational resources is the main goal of computer network and language courses. Experimental studies have shown that by comparing the average scores of the mid-term and final English exams of the two classes, it can be seen that more than 80% of the students have improved their listening and reading test scores in the mid-term and final exams of the class, which shows that the new English teaching mode helps students learn English.
Latent Class Cluster Analysis

Latent class cluster analysis, which deals with latent classes parallelly, is discussed. The ML estimation procedure for the model parameters is constructed via the EM algorithm. By using practical data sets, the ML estimation procedure is demonstrated. In order to measure the goodness-of-fit of latent class models, the entropy coefficient of determination is applied to latent class models. For comparing latent classes, two approaches are given, and the methods are illustrated by using numerical examples. The latent profile model is also discussed, and the ML estimation procedure for the model is made via the EM algorithm. By using an artificial data set, the estimation procedure is demonstrated. Finally, a perspective of the latent class analysis leading to further studies in the future is also provided.
ALLNet: A Hybrid Convolutional Neural Network to Improve Diagnosis of Acute Lymphocytic Leukemia (ALL) in White Blood Cells

Due to morphological similarity at the microscopic level, making an accurate and time-sensitive distinction between blood cells affected by Acute Lymphocytic Leukemia (ALL) and their healthy counterparts calls for the usage of machine learning architectures. However, three of the most common models, VGG (see Fig. 3), ResNet (see Fig. 4), and Inception (see Fig. 5), each come with their own set of flaws with room for improvement which demands the need for a superior model. ALLNet, the proposed hybrid convolutional neural network architecture, consists of a combination of the VGG, ResNet, and Inception models. The ALL Challenge dataset of ISBI 2019 (available here) contains 10,691 images of white blood cells which were used to train and test the models. 7,272 of the images in the dataset are of cells with ALL and 3,419 of them are of healthy cells. Of the images, 60% were used to train the model, 20% were used for the cross-validation set, and 20% were used for the test set. ALLNet outperformed the VGG, ResNet, and the Inception models across the board, achieving an accuracy of 92.6567%, a sensitivity of 95.5304%, a specificity of 85.9155%, an AUC score of 0.966347, and an F1 score of 0.94803 in the cross-validation set. In the test set, ALLNet achieved an accuracy of 92.0991%, a sensitivity of 96.5446%, a specificity of 82.8035%, an AUC score of 0.959972, and an F1 score of 0.942963. The utilization of ALLNet in the clinical workspace can better treat the thousands of people suffering from ALL across the world, many of whom are children [6].
CESMP: Chinese-English Segment-aligned Multi-field Patent Data

Patent data from various countries in the world implies the essence of scientific discovery and technological innovation of all human beings, but language differences have become a huge obstacle to patent data retrieval and communication. We hope to build a bridge from Chinese to English in the patent domain, so that English speakers can make better use of Chinese patent data. With the help of natural language processing technologies such as optical character recognition, Chinese text processing, machine translation and English text processing, we construct digital Chinese-English segment-aligned multi-field patent (CESMP) data from scanned Chinese patents. The current CESMP data consists of 610,310 patent documents in XML format. Each patent document contains six required fields (date, publication, ipc, title, abstract, and claim) and four optional fields (cpc, wipo, originalapplicant, and currentowner), among which the wipo, title, abstract, and claim fields are aligned with Chinese and English segments. Supported by well-structured bilingual patent data, on the one hand, the resource construction algorithms can efficiently build a bilingual patent dictionary and a parallel patent segment bank; on the other hand, the deep natural language processing algorithms can be effectively implemented into many practical intelligent applications such as cross-language patent retrieval, patent spam filtering, patent network analysis, patent machine translation, etc.
A Hybrid Frame Work on Real-Time Vehicle Detection and Speed Tracking Using Machine Learning

Vehicle detecting and speed tracking plays an important role in the community like control of roadway surveillance, control and also plays an important role in planning the urban traffic. Vehicle detection and speed tracking of the vehicle is a system which is used to track the vehicle, the count of vehicle, the speed of each and every vehicle, analyzing the traffic and vehicles and can be used in all variety of conditions. In this, we gave an overview of image processing techniques and various analytics tools which we have used in the development of the aforementioned applications which included improving road surveillance programs.
MultiSR: A SR-Based Framework for Multivariate Time Series Anomaly Detection in Complex Unmanned Scenarios

Nowadays, more and more unmanned systems are adopting time series anomaly detection algorithms as the primary tool for KPI monitoring. However, many anomalous data in real environments often have complex features such as large data fluctuations, irregular data fluctuations and large data drift. Traditional univariate or multivariate anomaly detection algorithms can hardly meet the demand for anomaly detection of time series with many features and large fluctuations. Therefore, we propose a multivariate anomaly detection framework adapted to complex environments, which can effectively reduce the complexity of time series data and improve the overall performance of anomaly detection algorithms in complex real systems. First, data preprocessing is performed on the time series before anomaly detection to reduce the impact of temporal bias of different data items in multivariate time series data. Second, to intuitively analyze the anomalous fluctuation characteristics of the time series, we use Spectral Residual to process the significance of the time series. Then, we combine the Spectral Residual processed data and Logistic Regression algorithm to construct anomaly classifiers for anomaly detection of real-time data in complex real systems. Finally, by experimenting with a prototype system on a private cloud platform, our approach obtains superior results under multiple anomaly patterns compared with traditional univariate and multivariate anomaly detection algorithms.
Homomorphic Encryption based Subgraph Isomorphism Protocol in Blockchain

Structural data have played a prominent role in various domain technologies, such as social networks, criminal tracking, epidemic spreading, and cheminformatics. Secure multi-party computation (MPC) is an indispensable research topic in the blockchain. Privacy within its concerns restricts the access to network data with sensitive information. Therefore, it is imperative to study the private computing of graph algorithms, in which the subgraph of private graphs is of great significance. The subgraph isomorphism has not yet been proved as an NP-complete problem. This paper gives an insight into solving the MPC protocol of subgraph matching. Besides, it designs a new encoding method to represent a graph and studies the MPC to determine whether one graph is sub-isomorphic to the other by both parties to collaborative compute through a homomorphic encryption method and a decryption technique. Also, this protocol is proposed for security preserving in the semi-honest model, and the computational complexity of the proposed protocol is shown to be decreased. Both the theoretical analysis and experimental results represent that the proposed protocol is effective and efficient. The protocol has been implemented in a real-time blockchain project.
TELECOMMUNICATIONS: Ethiopia

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 59, Issue 3 p. 23812C-23813B Communications and Transport TELECOMMUNICATIONS: Ethiopia First published: 28 April 2022 https://doi.org/10.1111/j.1467-6346.2022.10534.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume59, Issue3May 2022Pages 23812C-23813B RelatedInformation