Reviewerly/exHarmony huggingface dataset:
Privacy Preservation Using Anonymity in Social Networks

The COVID-19 pandemic has demanded voluminous data related to social network and health care be available for data analytics in the World Wide Web. Apart from security privacy preservation in data publishing has also become the need of the hour. The health information and personal information associated with an individual is supposed to be private data and not to be shared with others. Professional hackers even with very little background information would be able to identify individuals from the published data if data de-identification is not performed on the data set. Although data related to health condition is very important to perform accurate analysis on the spread of pandemic, it is equally important to ensure that the personal information of the user is not revealed. In this research, an initiative is taken to improve the usability of the data while still implementing data de-identification on health and social networking data sets. The proposed scheme in the paper prevents the adversary from learning about the target victim even when a small amount of background information about the victim is known. The work proposes to develop practical solutions to mitigate neighbourhood attack problems and to enhance privacy preservation using anonymity, diversity and closeness principles.
Semantic Enhanced Subspace Cross-Media Retrieval Based on Quadratic Discriminant Analysis

In the process of cross-media retrieval, the original features of semantic information in images and texts may be lost, which will reduce the accuracy of cross-media retrieval. In order to solve the problem, we optimize the traditional common semantic subspace cross-media retrieval model to enhance semantic information by raising the distinction between the original features of the image and the text. First of all, using the Quadratic Discriminant Analysis (QDA) classifies the original data features of images and texts, mapping the images and texts into two different semantic enhancement subspaces respectively, and improving the discrimination between data. Next different mapping models are learned according to different retrieval tasks. Then the image and text feature matrix and semantic matrix are mapped into a common semantic subspace by using the mapping model. Eventually the similarity between image and text is calculated through Euclidean distance. We used the above method to conduct experiments on two datasets, including Wikipedia and Pascal Sentence. The experimental results show that the proposed method achieves better performance.
Traffic Classification Using Cluster Analysis

The rapid development of new network application technologies has brought great challenges to the effective identification of network traffic. The application of dynamic ports and data encryption makes the traditional traffic classification methods invalid. With the discovery of the correspondence between traffic types and their statistical characteristics, the application of machine learning methods for network traffic identification has become a hot research topic. This paper proposes a semi-supervised traffic classification algorithm based on improved k-means clustering, and analyzes the principle of the algorithm and compares and verifies the algorithm with the test data set. The results show that the improved algorithm has higher network traffic identification accuracy.
“Blockchain May Automate Jobs Done by the Boss and AI Can Predict a Heart Attack”

This chapter explores how a shared understanding of the potential uses of new, abstract technologies is created in journalism. We analyse Finnish journalistic texts from 2015–2020 in which blockchain and artificial intelligence (AI) are introduced to the readers, conceptualized, and discussed. Drawing on the classic Technology Acceptance Model (TAM), we discuss how journalism frames and reframes the salient attributes of these technologies especially in terms of their perceived usefulness and ease of use. Furthermore, through regarding these speculative meanings as reflections of the technological or algorithmic imaginary we look into the discourses through which their potential or imaginary applications are explained. This research is important because technologies do not enter the lives of people as concrete devices or programs, but initially as conceptual, imaginary, and affective entities.
Developing the Concept for Managing Digitalization of the Education System in Russia

The article presents the results of developing the concept for managing digitalization of the education system in the Russian Federation. This concept consists of three stages, as a result of which strategies for the digitalization of education and their implementation in the context of the polycyclic environment are formed. The first stage involves the development of a system of distributed Smart Centers of educational organizations, which accumulates information about the digitalization of the educational process and transfers it to the next stage—to the system of regional situation centers. The latter, in turn, aggregate the information received from organizations, formulate regional strategies for the development of digital education, and project portfolios for their implementation, and then, transfer this information to a level higher—to the third stage, where the projected strategic management system is located. This system uses the information obtained in the formation of strategies for the development of digital education in the Russian Federation and options for their implementation. The output of the system “descends” to the level below (the second stage) to adjust regional strategies by situation centers, which, in turn, provide feedback to educational organizations (the first stage). Thus, a continuous and adaptive strategic cycle of information exchange among the stages is formed.
Off-Site Distortion and Color Compensation of Underwater Archaeological Images Photographed in the Very Turbid Yellow Sea

Underwater photographing and image recording are essential for pre-excavation survey and during excavation in underwater archaeology. Unlike photographing on land, all underwater images suffer various quality degradations such as shape distortions, color shift, blur, low contrast, high noise levels and so on. Outcome is very often heavily photographing equipment and photographer dependent. Excavation schedule, weather conditions, and water conditions can put burdens on divers. Usable images are very limited compared to the efforts. In underwater archaeological study in very turbid water such as in the Yellow Sea (between mainland China and the Korean peninsula), underwater photographing is very challenging. In this study, off-site image distortion and color compensation techniques using an image processing/analysis software is investigated as an alternative image quality enhancement method. As sample images, photographs taken during the excavation of 800-year-old Taean Mado Shipwrecks in the Yellow Sea in 2008-2010 were mainly used. Significant enhancement in distortion and color compensation of archived images were obtained by simple post image processing using image processing/analysis software (PicMan) customized for given view ports, lenses and cameras with and without optical axis offsets. Post image processing is found to be very effective in distortion and color compensation of both recent and archived images from various photographing equipment models and configurations. Merits and demerit of in-situ, distortion and color compensated photographing with sophisticated equipment and conventional photographing equipment, which requires post image processing, are compared.
Facial Makeup Detection Using Multi-Scale Local Binary Patterns and Convolutional Neural Network Fusion

This study presents a novel facial makeup detection scheme using fusion of multi-scale Local Binary Patterns (ms-LBP) texture methods and convolutional neural network (CNN) deep learning extractor. Facial makeups affect the accuracy of face recognition systems due to appearance alteration of individuals. In order to fuse the facial features, the proposed scheme first considers concatenation of extracted global histogram of a whole image using three different LBP operators. The LBP scales are used to extract and then concatenate the local histogram of overlapping and nonoverlapping blocks of images. This way utilizes the advantages of microtexton information of local primitives besides the extracted global textures. Finally, the extracted features using CNN feature extractor are combined with global and local multi-scale textures. In general, CNN deep learning extractor learns high-level discriminative characteristics of images and therefore the proposed system improves the facial makeup detection rate by involving both microtexton and discriminative information of facial images. In addition, the proposed method attempts to select the optimized subset of facial features to increase the detection performance of system by applying Particle Swarm Optimization (PSO) technique after feature level fusion. The paper uses Support Vector Machine (SVM) classifier for classifying the facial vectors into makeup or no-makeup classes. The proposed scheme is then evaluated using YMU, VMU and MIW facial makeup databases with consideration of light, medium and heavy makeups on several datasets. Experimental results analysis clarifies the effectiveness of proposed facial makeup detection framework of this study.
Analysis of Multi-dimensional Image Fusion and Joint Information Mining in Landscape Design with Vision Information Mining

Analysis of multi-dimensional image fusion and the ioint information mining in landscape design with vision information mining is studied in the paper. In the question discussed, the images have different spatial resolutions. For example, before making a decision using a distributed detection structure, the targets detected between the sensors must undergo very complex correlation processing. Before starting to merge the images. It must be ensured that each pixel in the related image corresponds to each other, and image matching is the process of comparing these differences to find their similarities. Hence, the latest fusion model is considered to achieve this goal. Through the experimental analysis, the performance is validated.
Predict joint angle of body parts based on sequence pattern recognition

The way organs are positioned and moved in the workplace can cause pain and physical harm. Therefore, ergonomists use ergonomic risk assessments based on visual observation of the workplace, or review pictures and videos taken in the workplace. Sometimes the workers in the photos are not in perfect condition. Some parts of the workers' bodies may not be in the camera's field of view, could be obscured by objects, or by self-occlusion, this is the main problem in 2D human posture recognition. It is difficult to predict the position of body parts when they are not visible in the image, and geometric mathematical methods are not entirely suitable for this purpose. Therefore, we created a dataset with artificial images of a 3D human model, specifically for painful postures, and real human photos from different viewpoints. Each image we captured was based on a predefined joint angle for each 3D model or human model. We created various images, including images where some body parts are not visible. Nevertheless, the joint angle is estimated beforehand, so we could study the case by converting the input images into the sequence of joint connections between predefined body parts and extracting the desired joint angle with a convolutional neural network. In the end, we obtained root mean square error (RMSE) of 12.89 and mean absolute error (MAE) of 4.7 on the test dataset.
A Blockchain Based Land Registration and Ownership Management System for Bangladesh

From the times of Satoshi Nakamoto's Bitcoins invention in 2008 Blockchain has become one of the most popular technologies to keep records secure, immutable, transparent and in distributed ledger. Blockchain is the underlining technology of Bitcoin. Blockchain technology has been used in many domains such as IoT, healthcare, education, business, Land management and so on. Among them Land Registration and Ownership Management is a very tough process all over the world. Land is a state immovable object and many frauds claim false ownership. It creates disputes among people. Also, many intermediaries are involved in this system. These intermediaries take huge amount of money to solve problems in time. For this reason, many people fail to achieve their own land. Blockchain technology has the all characteristics to solve these problems. It keeps data transparent, immutable and secure, at the same time it does not need any intermediaries. For the powerful characteristics of blockchain technology, the authors of this paper proposed a permissioned blockchain-Hyperledger based solution model to solve the problems of land registration and ownership management system. Here the authors present a system overview of the proposed system, a flowchart of working flow and an architecture. And last of all, some basic algorithms are also built to conduct a proper research.
Computational Intelligent Systems in Oncology: A Way Toward Translational Healthcare

Cancer is a word and not a sentence! However, cancer is one such malady in humankind with a low median survival rate, a complex examination process, and an expensive treatment. As per the GLOBOCAN 2020 report, the global cancer pattern map showcases incidence cases of eight different types of cancers in men, while for women is of seven different types. The most common cancer in men is prostate cancer, followed by lung cancer, whereas, in the case of females, breast cancer followed by cervical and ovarian cancers are more lethal. With the completion of the Human Genome Project in 2003, there has been a rise in the deployment of Artificial Intelligence in Medicine (AIM), aiding the rapid growth of translational healthcare. Advents in computer science, statistics, mathematics, computer engineering, and biological sciences have allowed researchers to deploy computational methods such as multivariate statistical analysis to understand the prognosis of the disease. The accuracy score of such analyses is better when compared to empirical approaches. Moreover, the rise of Computational Intelligent Systems (CIS) that encapsulates the power of both—machine learning and deep learning—has allowed some successful applications in clinical cancer research with a quick and robust cancer prediction performance in patients at a pocket-friendly cost. This chapter highlights the CIS used to diagnose, prognosis, and provide better treatment approaches for translational healthcare in oncology.
Fuzzy Neural Network Classification Model for Multi Labeled Electricity Consumption Data Set

Multi-level Hierarchical Fuzzy classification technique is significant classification model based on fuzzy membership value. The results of classification rules are obtained using MHFRB classification algorithm which has been used in Neural Networks technique for multi labelled data Classification. The proposed MHFRB classification algorithm is constructed on the improvement of a simple semantic Fuzzy Model in a hierarchical structure. The Fuzzy neural network classification model has been used to analysis the regional wise electricity consumption data for different multi labelled dataset. The results of the model provide the need of the electricity energy resources and accuracy of the classification model is considerably improved. Fuzzy Neural Networks technique is used to get a compact and accurate model at multiple levels. In this paper, the proposed methods are used to validate the Classification Model with the multi-label datasets and demonstrated that there is a considerable enhancement in performance when compared with the existing methods in the literature.
IOT Data Storage Solution Based on Hybrid Blockchain Edge Architecture

In order to solve the problem of inefficient data storage and high latency in the case of a large number of IoT devices networked, for this purpose, this paper implements a data storage scheme for IoT devices based on blockchain and edge computing architecture, using an interstellar file system network formed by edge computing servers as nodes to achieve distributed localized storage of data and storing data hash addresses into the blockchain. Combined with smart contracts and an attribute-based access control framework, secure dynamic fine-grained access control of data and behavior tracking of visitors are achieved. Experiments show that the proposed scheme can effectively reduce data transmission latency, as well as lower latency request response and higher throughput, which also makes it more compatible with the current multi-user and multi-request access requirements in the IoT environment.
Accurate hand gestures recognition through plastic barrier based on Doppler frequency using latent Dirichlet allocation classifier

Hand gesture recognition is vital for medical, industrial, military, and educational applications. Many parameters must be known its effects, the cross-section area of the moving hand part, motion angle, and its velocity. Also, the sensor which is used for hand detection is essential to achieve this requirement. Most studies in the field of hand detection have only focused on the camera sensors, while the drawback of that sensor is not working in all environments. Recently, there has been renewed interest in using radar as a detection sensor representing a solution to this problem. In the present work, radar has been used as a detector sensor and the latent Dirichlet allocation (LDA) as a classifier. The current results highlight the recognition rate gained by the radar has on; 98.1% without a barrier, while 88% with it.
Question answering system of discipline inspection laws and regulations based on knowledge graph

Discipline inspection and supervision laws and regulations are an important basis for discipline inspection and supervision personnel. In this study, the data of laws and regulations in the discipline inspection and supervision field were firstly obtained from the latest "Regulations on Discipline Punishment of the Communist Party of China" (fourth edition), and the knowledge graph of laws and regulations in the discipline inspection and supervision field was constructed after pretreatment. Secondly, based on the knowledge graph of discipline inspection and supervision laws and regulations, the core semantic matching algorithm of the question answering system is tested and the TF-IDF model with the highest accuracy in TOP1 is selected to finally realize the question answering system of discipline inspection and supervision laws and regulations.
Gender Inequality in Computer Science Higher Education: A Case Study

Following the reduced number of female students at ISTEC Porto computer science and engineering courses, we tried to find out what are the factors that might be causing this fact. This gender inequality was found looking back at the data from the last 15 years, clearly showing the gap between male and female students managing to conclude the degree. To point out the reasons and probable explanation a literature review was made to figure out conceivable paths to change this status quo. The article concludes that gender inequality resides on cultural differences as well as in the lack of information about the real role played by women in technology companies, particularly IT companies.
Towards a High-Performance Foucault Pendulum

The Foucault pendulum has become one of the fundamental experiments of physics since Léon Foucault’s famous demonstration of a 67-metre pendulum with a 22 kg bob mass at the Panthéon in Paris in 1851. This paper attempts to show that Foucault’s fundamental experiment could perhaps be developed into a highly sensitive measurement system capable of resolving the tiny precessional motions of relativistic frame-dragging. The authors have shown that their mathematical model of a Foucault pendulum performs extremely well in terms of predicting the Newtonian rotation of the Earth. The model takes account of latitude and incorporates parametric excitation of the length as a harmonic modulating motion of ≤0.01 of the nominal pendulum length. The main aim of the ongoing work discussed in this paper is to try to resolve the tiny motions of Lense-Thirring frame-dragging precession, for which we confirm that a first approximation prediction at a chosen terrestrial latitude can be obtained through an analogy between Maxwellian electrodynamics and gravitoelectromagnetism. A new experimental measurement will require an increase in resolution of at least 2 × 109 over that required for measuring the Newtonian rotation of the Earth.
The fusion model of short text sentiment classification based on Attention mechanism

The task of short text sentiment classification is one of the research hotspots, and the current mainstream methods are RNN and CNN. This paper research on combining the strengths of both models to better handle the short text sentiment classification task. Based on the advantage of RNN in dealing with text sequences, as well as CNN in dealing with local problems, the fusion model is put forward. With BiLSTM to extract text data in both directions at the same time, add the Self-Attention mechanism to distribute weight to further improve the fitting ability of the model. Compared with previous models, the fusion model in this paper achieves a better performance.
One Shot Object Detection with Mutual Global Context

At present, DNN-based algorithms have achieved the best performance in some computer vision tasks, but these methods are usually based on large amounts of data. In this work, a new attention mechanism mutual Global Context(mGC) block is proposed for one-shot object detection, which enables RPN head to generate higher-quality region proposals. And abstract this mGC function to get a three-step general framework, which integrates Non-local block and SE block in a brand new way. In order to classify categories that the model has not seen before without training, this paper propose a fully connected classifier for one-shot scenarios. This classifier can classify unseen class without any fine-tuning. This type of classifier provides a new idea for the improvement of metric learning. The experimental results show that the two modules this paper proposed have greatly improved the results of one-shot object detection.
Information Fusion Center For Uncertain Environment

It is necessary to introduce the idea of information fusion to construct MIS in the uncertain environment. In order to overcome the deficiency of the traditional information fusion technology in the uncertain situation, this paper proposes a new MIS model — Information Fusion Center(IFC). Based on the definition of the role of the IFC in the process of information integration, the system framework which shows the characteristics of agility are elaborated in detail.