Reviewerly/exHarmony huggingface dataset:
A Text Mining based Method for Policy Recommendation

Governmental administrations frequently release various types of policies to enforce specific rules, laws, or economic stimulus plans. For a specific policy, there are many potential target individuals, companies and organizations. However, it is not always timely for a company to get to know the policies that are suitable to it. It is necessary to develop efficient policy recommendation methods to help companies catch up with useful policies right the first time. In this paper, we propose a policy recommendation method based on text mining. This method consists of three phases: 1) Policy structure division; 2) Attribute extraction; 3) Matching and recommendation. Since most of policy texts contain too many contents that might be suitable for multiple different types of companies, policy text is firstly divided into text fragments, and each text fragment is then divided into multiple elementary discourse units (EDUs) which are used as the basic recommendation unit with optimal granularity. Secondly, by combining a Named Entity Recognition (NER) based extractor with a rule-based extractor and a grammar-based extractor, we extract attribute entities and logical relations between these entities from each EDU. Thirdly, on the basis of the extracted attribute entities and logical relations, we calculate the matching score between each fragmented policy text and each company, and these scores are taken as the sorted criteria of policy recommendation. The policy recommendation results cover policy text fragments, related governmental administrations, attribute entities and logical relations, links to the entire policy text, and so on. On the basis of a data set collected from real world, the effectiveness of the proposed method is validated by the experiments of two different scenarios: 1) there is a list of policy texts, and it is needed to recommended suitable policies to a specific company; 2) there is a list of companies, and it is needed to help them to find suitable policy texts. This work can be considered as an intelligent government affairs service for accurately pushing useful policies to target users, and is of great significance to the modern governance system.
INFORM: A Tool for Classification and Semantic Annotation of IoT Datastreams

Internet of Things (IoT) is transforming every aspect of our life and driving innovation in nearly every application domain areas including Smart Cities. An important aspect of IoT is in its ability to generate fine grained data and, in particular, the ability to understand and contextualize such data in order to (re)use it effectively. However, the metadata (a description of data produced by IoT) required to make sense of IoT datastreams is mostly inaccurate, incomplete and, in many situations, only human interpretable. Lack of metadata diminishes the value and effective use of IoT datastreams. To address this challenge, we propose INFORM, a tool to automatically classify, annotate and semantically enrich IoT datastreams. INFORM incorporates a novel technique to 1) accurately classify IoT datastreams using both the numerical characteristics of the data and limited available metadata, and 2) enrich the IoT datastreams with domain specific metadata extracted from application domain ontologies such as air pollution and sensor ontologies such as IoT-Lite. This paper presents a proof of concept implementation of the proposed INFORM tool as well as empirical evaluations to validate the effectiveness and efficiency of the datastream annotation process with INFORM.
An Object-Based Detection of Splicing Forgery using Color Illumination Inconsistencies

This digital imaging era has become ingrained in people to share images and videos with their loved ones via social networking sites. On the other hand, photo editing software innovation allows anyone to easily and quickly modify multimedia content. As a result, people have become accustomed to alter digital content for the sake of amusement or to attract the attention of others. As a result, Digital Image Forensics emerges to provide trustworthiness to the content shared via the internet. This paper proposes efficient tampering detection and localization method for detecting splicing attacks using color illumination inconsistencies. First, the image's color illuminant features extract using the grayness index in rg-chromaticity space. The color illuminants of each color channel are then combined with a logistic regression model to reveal tampered objects in the image. The experimental results show that the proposed method outperforms the state-of-the-art.
A Malicious Traffic Detection Method Based on Attention Mechanism

At present, the malicious traffic detection method based on deep learning has become the mainstream method in this field. However, detection models using convolutional neural network often directly input the feature map formed after convolution operation to the next layer, without considering the different importance distribution of the feature map in channel dimension and spatial dimension, which leads to the failure of the model to learn more discriminative spatial feature representation of traffic data. This paper proposes a malicious traffic detection method based on attention mechanism. After convolution operation, attention modules are introduced from channel dimension and spatial dimension to emphasize important features and suppress secondary features, so as to enhance the feature learning ability of the model. Moreover, we use a traffic encoding model that can obtain context-dependent traffic encoding vectors. Comparative experiments show that our method has achieved better performance.
Learning Automata based Feature Selection for Duplex Output Software Effort Estimation Model

Software Effort Estimation (SEE) plays an instrumental role in estimating the effort required to complete a software development project. It involves the process of estimating the effort required in order to meet project delivery deadlines by appropriately scheduling and allocating resources during the Software Development Life Cycle (SDLC). In this paper, we have proposed a feature selection technique with regression models using learning automata to estimate software development effort. We have validated our approach in six data sets for software effort estimation. Further, we have discretized the predicted effort into three different classes: High (H), Moderate (M) and Low (L). We have used various regression models including Linear, LASSO, ElasticNet and Ridge regression to obtain the estimated effort and use quartiles for discretizing them. We have compared the result of our proposed approach with some existing models of feature selection and regression techniques and found that our approach is able to provide better prediction performance than some existing-models.
Recognition of Main Transformer Oil Conservator Oil Level Representation Number Based on FCM Segmentation

In the traction power supply system, the meter image collected by the inspection robot is difficult to read due to inaccurate positioning and insufficient light. To this, A low-light enhancement fusion algorithm is proposed, which combines adaptive fast bilateral filtering and a low-light image enhancement algorithm based on fractional-order improved Retinex, which effectively solves the problem of enhanced restoration of low-light images; at the same time, it combines perspective transformation and SIFT feature points The registration method corrects the geometric distortion image; finally, for the rectangular scale gauge of the main transformer oil conservator oil level gauge, a main transformer oil conservator oil level gauge recognition algorithm is designed that combines FCM and Otsu to segment. Experiments have proved that the readings obtained by this number recognition algorithm are basically the same as the manual readings, and the average recognition accuracy meets the requirements of practical applications. It can greatly promote the improvement of the recognition accuracy of outdoor instrument readings in traction substations under low-light distortion environments. effect.
The impact of the university environment on the development of student research potential: Implementing inbreeding in an open innovation environment

Abstract The purpose of the study is to assess the impact of the university environment on the development of the research potential of students, as well as build a model for identifying and retaining talented graduates in universities based on the concept of open innovation. In the study, sociological and statistical methods were used: a questionnaire survey of students and teachers of the Financial University, a correlation analysis of the results, as well as an analysis of the practices of working with gifted students in leading world universities. In the course of the study, it was found that students oriented towards further education showed a great subjective significance of research activity. In the conclusion, the authors present a model of ‘healthy’ academic inbreeding, developed in accordance with the concept of open innovation, which is designed to promote the ‘talented’ inbreeding, minimising the possible negative aspects of this phenomenon. The study results indicate the importance of the model since a significant part of students have a predisposition to inbreeding but some of the tools that motivate them to participate in research activities are insufficiently formed.
Efficient Deployment of Deep Learning Models on Autonomous Robots in the ROS Environment

Sarwar Murshed, M. G. Carroll, James J. Khan, Nazar Hussain, FarazAutonomous robots are often deployed in applications to continually monitor changing environments such as supermarket floors or inventory monitoring, patient monitoring, and autonomous driving. With the increasing use of deep learning techniques in robotics, a large number of robot manufacturing companies have started adopting deep learning techniques to improve the monitoring performance of autonomous robots. The Robot Operating System (ROS) is a widely used middleware platform for building autonomous robot applications. However, the deployment of deep learning models to autonomous robots using ROS remains an unexplored area of research. Most recent research has focused on using deep learning techniques to solve specific problems (e.g., shopping assistant robots, autopilot systems, automatic annotation of 3D maps for safe flight). However, integrating the data collection hardware (e.g., sensors) and deep learning models within ROS is difficult and expensive in terms of computational power, time, and energy (battery). To address these challenges, we have developed EasyDLROS, a novel framework for robust deployment of pre-trained deep learning models on robots. Our framework is open-source, independent of the underlying deep learning framework, and easy to deploy. To test the performance of EasyDLROS, we deployed seven pre-trained deep learning models for hazard detection on supermarkets floors in a simulated environment and evaluated their performances. Experimental results show that our framework successfully deploys the deep learning models on ROS environment.
Analysis of methods to optimize control systems for power supply of marine vessels using fuzzy logic and fractal analysis

Abstract The paper considers the features of promising methods to optimize the control systems for power supply of marine vessels using fuzzy logic and fractal analysis. In order to design an effective control contour for the power supply system (PSS), it is proposed to use synergistic mechantronic systems based on intelligent technologies with fundamentally new properties that allow for a more effective solution of control problems using fractal analysis of time series to increase the adequacy of forecasting through in-depth analysis of the causes of emergency situations. The synergistic effect SE of the control within such systems is a set of effects obtained as a result of their combination and synchronization in time and space. Practical aspects of fractal analysis are considered on the example of a two-cycle engine with supercharging and air cooling. In the study of fractal processes, a method is proposed for identifying and eliminating the short-term dependence of the value of the time series of the process S(t) , which is characteristic of autoregressive processes, using regression with respect to S(t-1) and conducting the R/S analysis of the remainder X(t) . Short-term dependence is eliminated provided that long-term dependence is maintained. Autoregressive AR(1) differences are analyzed at a certain time interval for various engine operating modes. The results of the R/S analysis of the engine operation and the determination of the Hurst exponent are used to increase the efficiency of forecasting and control of the PSS in the period of the detected chaotic behavior of the time series.
Optimizing Hamiltonian control using quantum machine learning method

Machine learning brings the promise of deriving meaning from different data, which is widely used for medicine and computer science. Quantum computing exploits the phenomena of quantum states to realize parallel computing, which believed to be significantly faster than classical computers. Quantum machine learning is the integration of quantum algorithms within machine learning programs, thereby improving classical machine learning techniques. Quantum control such as gate decomposition and Hamiltonian control is an essential problem to solve in quantum dynamics. Here we present a hybrid quantum machine learning model to conduct quantum control optimization. Firstly, according to Bloch theorem, any single-qubit unitary gate can be specified by three vector rotations. We find the optimal decomposition of the given gate, which is the optimal control sequence. Secondly, we build a hybrid quantum-classical neural networks to solve gate decomposition problem utilizing supervised learning. And we prepare the training data and train the hybrid supervised model. Finally, we demonstrate the quantum control optimization results in hybrid model on quantum gates.
Privacy Preserving Big Data mining using Pseudonymization and Homomorphic Encryption

Today’s data is so huge so it’s referred to as “Big data.” Such data now exceeds petabytes, and hence businesses have begun to store it in the cloud. Because the cloud is a third party, data must be secured before being uploaded to the cloud in such a way that cloud mining may be performed on protected data, as desired by the organization. Homomorphic encryption permits mining and analysis of encrypted data, hence it is used in the proposed work to encrypt original data on the data owner’s site. Since, homomorphic encryption is a complicated encryption, it takes a long time to encrypt, causing performance to suffer. So, in this paper, we used Hadoop to implement homomorphic encryption, which splits data across nodes in a Hadoop cluster to execute parallel algorithm and provides greater privacy and performance than previous approaches. It also enables for data mining in encrypted form, ensuring that the cloud never sees the original data during mining.
Research and Implementation of Face Recognition in Remote Intelligent Monitoring System

This paper studies the application and realization of face recognition in remote intelligent monitoring system. First, it introduces the development and importance of remote intelligent monitoring system, and then understands the steps and principles of face recognition. Subsequently, an embedded intelligent video surveillance system architecture is proposed, which combines face detection, face recognition, feature matching and other technologies to complete the recognition of the target face in the surveillance system. Finally, the Adaboost face detection algorithm is introduced, which greatly reduces the detection time and the amount of data that needs to be calculated. The feasibility of the algorithm is verified through experimental simulations.
Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition

Face images with partially-occluded areas create huge deteriorated problems for face recognition systems. Linear regression classification (LRC) is a simple and powerful approach for face recognition, of course, it cannot perform well under occlusion situations as well. By segmenting the face image into small subfaces, called modules, the LRC system could achieve some improvements by selecting the best non-occluded module for face classification. However, the recognition performance will be deteriorated due to the usage of the module, a small portion of the face image. We could further enhance the performance if we can properly identify the occluded modules and utilize all the non-occluded modules as many as possible. In this chapter, we first analyze the texture histogram (TH) of the module and then use the HT difference to measure its occlusion tendency. Thus, based on TH difference, we suggest a general concept of the weighted module face recognition to solve the occlusion problem. Thus, the weighted module linear regression classification method, called WMLRC-TH, is proposed for partially-occluded fact recognition. To evaluate the performances, the proposed WMLRC-TH method, which is tested on AR and FRGC2.0 face databases with several synthesized occlusions, is compared to the well-known face recognition methods and other robust face recognition methods. Experimental results show that the proposed method achieves the best performance for recognize occluded faces. Due to its simplicity in both training and testing phases, a face recognition system based on the WMLRC-TH method is realized on Android phones for fast recognition of occluded faces.
The Distance Learning Implications on High School Students

The necessary changes in the educational processes because of COVID-19 pandemic has many immediate and long-time implications. It is now clear that the stress of distance learning and lock-down have changed young persons in many aspects. In our research, we have focused on the teens - high school students from secondary schools in Slovenia. We wanted to see how they cope with the distance learning and the implication on their performance. Anonymous survey was answered from 325 high school students from all four years of secondary education. The questionnaire consisted of 38 questions grouped in two categories: (1) application of learning styles (2) assessment of the students' performance. The stress of change affected the teachers and students alike. Some teachers used distance-learning methods more effectively than others did. We wanted to know how teachers' skills influence the students' performance. The obtained data and their analysis have shown that visual, reading and auditory kinesthetic learning styles have (significant) impact to the grade of high school students depending on study subjects. On the other hand, only kinesthetic distance-learning methods influence the performance of high school students in arts.
Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch

With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning classical models are highly dependent on manual selection features. To solve this problem, a neural network sketch recognition method based on DSCN structure is proposed in this paper. Firstly, the stroke sequence of the sketch is drawn; then, the feature is extracted according to the stroke sequence combined with neural network, and the extracted image features are used as the input of the model to construct the time relationship between different image features. Through the control experiment on TU-Berlin dataset, the results show that, compared with the traditional nonlearning methods, HOG-SVM, SIFT-Fisher Vector, MKL-SVM, and FV-SP, the recognition accuracy of DSCN network is improved by 15.8%, 10.3%, 6.0%, and 2.9%, respectively. Compared with the classical deep learning model, Alex-Net, the recognition accuracy is improved by 5.6%. The above results show that the DSCN network proposed in this paper has strong ability of feature extraction and nonlinear expression and can effectively improve the recognition accuracy of hand-painted sketches after introducing the stroke order.
Multimodal Optimization by Evolution Strategies with Repelling Subpopulations

This work presents a niching method based on the concept of repelling subpopulations for multimodal optimization. It utilizes several existing concepts and techniques in order to develop a new multimodal optimization algorithm that does not make any of specific assumptions on the shape, size, and distribution of minima. In the proposed method, several subpopulations explore the search space in parallel. Offspring of weaker subpopulations are forced to maintain a distance from the fitter subpopulations and the previously identified niches. This defines taboo regions to hinder the exploration of the same regions of the search space and previously identified niches. The size of each taboo region is adapted independently so that the method can handle challenges of minima with dissimilar basin sizes and irregular distribution. The local shape of a basin is approximated by the distribution of the subpopulation members converging to that basin. The proposed niching strategy is incorporated into the state-of-the-art evolution strategies, and the resulting method is compared with some of the most successful multimodal optimization methods on composite test problems. A comparison of numerical results demonstrates the superiority of our proposed method.
Generalized Fuzzy Logic with twofold fuzzy set: Learning through Neural Net and Application to Business Intelligence

The fuzzy logic deals with incomplete, inconsistent, uncertain, vagueand undecided information with belief. Fuzzy logic with twofold fuzzy set will give more accuracy then single membership function. In this paper, fuzzy logic with twofold fuzzy sets are studied. Fuzzy neural net used learn fuzzy inference. Fuzz certainity factor (FCF) is studied to eliminate conflict between two membership functions. Sometimes decision has to be taken under risk. Fuzzy Decision set is defined with fuzzy certainty factor (FCF). Different fuzzy resoning methods are discussed. Fuzzy inference anr reasoning mrthods are given for business intelligence.
Trends in web data extraction using machine learning

Web data extraction has seen significant development in the last decade since its inception in the early nineties. It has evolved from a simple manual way of extracting data from web page and documents to automated extraction to an intelligent extraction using machine learning algorithms, tools and techniques. Data extraction is one of the key components of end-to-end life cycle in web data extraction process that includes navigation, extraction, data enrichment and visualization. This paper presents the journey of web data extraction over the last many years highlighting evolution of tools, techniques, frameworks and algorithms for building intelligent web data extraction systems. The paper also throws light into challenges, opportunities for future research and emerging trends over the years in web data extraction with specific focus on machine learning techniques. Both traditional and machine learning approaches to manual and automated web data extraction are experimented and results published with few use cases demonstrating the challenges in web data extraction in the event of changes in the website layout. This paper introduces novel ideas such as self-healing capability in web data extraction and proactive error detection in the event of changes in website layout as an area of future research. This unique perspective will help readers to get deeper insights in to the present and future of web data extraction.