Reviewerly/exHarmony huggingface dataset:
Principles of Image Reconstruction in Interferometry ebaut1

Image reconstruction from interferometric data is an inverse problem.Owing to the sparse spatial frequency coverage of the data and to missing Fourier phase information, one has to take into account not only the data but also prior constraints.Image reconstruction then amounts to minimizing a joint criterion which is the sum of a likelihood term to enforce fidelity to the data and a regularization term to impose the priors.To implement strict constraints such as normalization and non-negativity, the minimization is performed on a feasible set.When the complex visibilities are available, image reconstruction is relatively easy as the joint criterion is convex and finding the solution is similar to a deconvolution problem.In optical interferometry, only the powerspectrum and the bispectrum can be measured and the joint criterion is highly multi-modal.The success of an image reconstruction algorithm then depends on the choice of the priors and on the ability of the optimization strategy to find a good solution among all the local minima.
A class-based evaluation approach to assess multidimensional projections

Multidimensional projection techniques have been widely used to visually explore datasets due to their ability to generate representations that preserve similarity relations of data points into lower dimensional spaces. To evaluate if the embedded space reflects high-dimensional structures, measures are usually employed to return a quality score of the whole projection. In contrast to this idea, we evaluate the embedded layouts by assessing each class of the datasets at a time by using well-known quality measures. In addition, we propose assessing multidimensional projection techniques using ROC curves. Experimental results on two datasets show that our approach can be useful to discover how classes interact each other by using different visualization techniques and how close-related they are without thoroughly exploring the layouts. ROC curves proved to be a good measure for analyzing projection techniques and can give highly valuable feedback to users when exploring multidimensional data.
Privacy-Preserving Pandemic Monitoring

The recent COVID-19 pandemic has presented a significant challenge for health organisations around the world in providing treatment and ensuring public health safety. While this has highlighted the importance of data sharing amongst them, it has also highlighted the importance of ensuring patient data privacy in doing so. This chapter explores the different techniques which facilitate this, along with their overall implementations. It first provides an overview of pandemic monitoring and the privacy implications associated with it. It then explores the different privacy-preserving approaches that have been used in existing research. It also explores the strengths as well as their limitations, along with possible areas for future research.
Is Human Behavior the Real Challenge in Combating Phishing

Computers can enhance our work activities, e.g., through greater efficiency in document production and through ease of communication. Although reliance on e-mail has reduced with the introduction of instant messaging applications, it continues to hold its own as a preeminent Internet-based communication service. Along with this eminence, we have persistent issues arising from e-mail-borne malware, phishing, and embedded malicious Web links. Existing steps to protect e-mail users still fail to address a significant proportion of online threats every year. A widely held view that we endorse is that this continuing challenge of e-mail is not wholly technical in nature and thereby cannot be entirely resolved through technical measures. Rather, we have here a socio-technical problem whose resolution requires attention to both technical issues and the specific attitudes and behavioral characteristics of end users. In this study, a structured questionnaire was used to collect data from 181 respondents while an experiment was used to identify the significant relation between respondents and their behavior in the broad diversity of one Malaysian organization.
NLP for Sentiment Computation

Sentiment is natural to human beings. Sentiments are expressed in different forms ranging from written, spoken to exhibiting. Our ancient scriptures record classifications of sentiments and propose Rasa Theory as the earliest study on human mind and its expressions. At the advent of digital era, sentiments are being poured into social media. The study on computational linguistics has become encouraging in developing computational models for automatic detection of sentiments in a given text be it a document or a social media posting though both the varieties project different class of problems for finding sentiments with them. This article discusses on some linguistic approaches known in the paradigm of natural language processing and their uses in sentiment detection. The approaches discussed are lexical analysis, corpora based, aspect based, social semantics and the trends of research in this field. It also discusses on exploring sentiments in a text of multiple domains.
A Modified HOG Algorithm based on the Prewitt Operator

The histogram of oriented gradient(HOG) is a feature descriptor used for object detection in the computer vision and image processing, and it is widely used for pedestrian detection. The conspicuous image feature can improve the detective accuracy of the pedestrian detection. In order to improve the conspicuousness of extracted gradient, this paper modifies the gradient extraction operator based on the traditional HOG algorithm. By the tests of different operators, this paper chooses the Prewitt operator to extract the gradient information. Experimental results indicate that the mean and variance of extracted gradient are larger than the gradient of traditional HOG algorithm. The extracted gradient should be generated the conspicuous HOG feature that may improve the performance of pedestrian detection.
A Vision Based Optical Measurement Technique With Augmented Reality Concepts

In this paper, we present the implementation of an application of augmented reality which makes it possible to measure objects in a scene captured using cameras.
Feature Selection based Arabic Text Classification using Different Machine Learning Algorithms

Feature selection is a method of data pre-processing widely used when mining large data, such as textual classification. Several studies have been conducted to compare the different methods of feature selection applied to corpora in English. Unfortunately, a small number of works concern the Arabic language. This article aims to present a comparative study of different feature selection techniques including: Chi2, the ANOVA method and mutual information, applied on a corpus in Arabic language, while also diversifying the machine learning algorithms (Naive Bayes, SVM and KNN). This experimental study has shown in general that reducing dimensionality with feature selection techniques has slightly affected the performance of textual classification, reducing the size of the corpus by up to 1%.
Object Recognition with Fourier Descriptors

This paper is concerned with simple object recognition issue when it is desired to recognize the objects due to some significant features. Fourier Descriptors (FDs) have been chosen to come up with interesting results and analysis. FDs have been used as features when identifying the objects. While making experimentations, it has been kept in mind that the introduced technique stands for the cases when the objects are seen stand alone. Some of the preprocessing steps include outline capture and noise removal of the objects. Multiple number of similarity measures have been the used presenting various experiments. The aspect of occlusion is also partially part of the work. An object database of size 100 has been chosen for testing the proposed methodology. This includes objects of variant sizes, translations, rotations, and scalings. Finally, a different solution is proposed for future research which is based on the optimization of FDs.
QoE-Driven Cross-Layer Design for Video Communication over Software-Defined Radio

With the prevalence of wireless imaging applications, there is an emerging need to support the exchange of videos among different wireless communication entities. Software-defined radio (SDR) is a promising technology for handling the needs of video communication in dynamic wireless conditions. This paper presents a cross-layer video over SDR communication solution that aims to provide satisfactory Quality of Experience (QoE) to application users. First, a practical video communication over SDR platform is built upon GNU radio, Universal Software Radio Peripheral (USRP) B210 hardware, and GStreamer, a comprehensive library for video processing. Next, measurement results from the platform are summarized to understand how the parameters in the physical layer and the application layer contribute to the perceptual quality of videos presented to application users. Based on these measurement results, a cross-layer protocol is designed that jointly adjusts the physical layer parameters and the application settings to maximize the QoE for video streaming. Finally, the cross-layer video communication protocol is integrated in the testbed, and evaluation results show that it could effectively boost the QoE of received videos.
Hierarchical Image Object Search Based on Deep Reinforcement Learning

Abstract Object detection technology occupies a pivotal position in the field of modern computer vision research, its purpose is to accurately locate the object human beings are looking for in the image and classify the object. With the development of deep learning technology, convolutional neural networks are widely used because of their outstanding performance in feature extraction, which greatly improves the speed and accuracy of object detection. In recent years, reinforcement learning technology has emerged in the field of artificial intelligence, showing excellent decision-making ability to deal with problems. In order to combine the perception ability of deep learning technology with the decision-making ability of reinforcement learning technology, this paper incorporate reinforcement learning into the convolutional neural network, and propose a hierarchical deep reinforcement learning object detection model.
Application of WebCrawler in Information Search and Data Mining

Abstract With the development of network technology, the scope of information search and the ability of data mining are also increasing. On this basis, a WebCrawler technology is proposed. WebCrawler technology plays a very important role in improving the ability of data information search. This article mainly analyzes the working principle of WebCrawler technology and the principle of Web information search and data mining, and focuses on the application in information search.
Performance Analysis of a Distributed Steady-State Genetic Algorithm Using Low-Power Computers

In this chapter, we describe the implementation and evaluation of a distributed steady-state genetic algorithm on low-power computers. Specifically, we integrate the NVIDIA Jetson card and the ESP32 cards to form a master-slave model. NVIDIA Jetson card is the master, whereas the ESP32 cards are the slaves. We evaluated the distributed steady-state genetic algorithm on two challenging combinatorial problems: the n-Queens problem and the travelling salesman problem. To compare the performance of the distributed steady-state genetic algorithm on those well-known problems, we implemented a sequential steady-state genetic algorithm. The simulation results indicate that the distributed steady-state genetic algorithm can escape local minima in the travelling salesman problem; hence, the solutions have a better quality of fitness than the sequential genetic algorithm ones. In contrast, for the n-Queens problem, both genetic algorithms’ performance is remarkably similar.
A Validation Approach for Deep Reinforcement Learning of a Robotic Arm in a 3D Simulated Environment

In recent years, deep reinforcement learning has increasingly contributed to the development of robotic applications and boosted research in robotics. Deep learning and model-free, off-policy, value-based reinforcement learning algorithms enabled agents to successfully learn complex robotic skills through trial and error process and visual inputs. The aim of this paper concerns the training of a robot in a simulation environment by designing a Deep Q-Network (DQN) that elaborates images acquired by an RGB vision sensor inside a 3D simulated environment and outputs a value for each action the robotic arm can execute given the current state. In particular, the robot has to push a ball into a soccer net without any knowledge of the environment and its own location. In addition, our further goal was to perform agent validation during training and assess its generalization level. Despite the many advances in reinforcement learning, it is still a challenge. Therefore, we devised a validation strategy similar to the method applied in supervised learning and tested the agent both on known and unknown experiences, achieving interesting and promising results.
A Multi-cloud Parallel Selection Approach for Unlinked Microservice Mapped to Budget’s Quota: The $$PUM^2Q$$

The world is facing a complicated moment in which social isolation is necessary. Therefore, to minimize the problems of companies, remote work is being widely adopted, which is only possible because of existing technologies, including cloud computing. Choosing the providers to host the business applications is a complex task, as there are many providers and most of them offer various services with the same functionality and different capabilities. Thus, in this paper, we propose an approach, called $$PUM^2Q$$ , for selecting providers to host a distributed application based on microservices that have little communication between them. $$PUM^2Q$$ is a provider selection approach based on multi-criteria, and it copes with the needs of microservices individually and in parallel. The proposed approach extends our previous one, $$UM^2Q$$ , and should be incorporated by PacificClouds. Besides, we carry out a performance evaluation by varying the number of requirements, microservices, and providers. We also compare $$PUM^2Q$$ and $$UM^2Q$$ . The results presented by $$PUM^2Q$$ are better than those given by $$UM^2Q$$ , showing not only its viability but also expanding the number of approaches adopted by PacificClouds. As a result, $$PUM^2Q$$ making the tasks of the software architect, who is the user of PacificClouds, more flexible.
Partially Reversible Gray Image Data Hiding based on Adjacent Pixel Difference

Data Hiding (DH) based on digital image is a technique with which data can be undetectably embedded into the images. To recover part of the processed image to the original one, a new partially reversible data hiding algorithm in spatial domain is now presented in this paper. The algorithm is based on local difference adjustment with the aid of the location table which is embedded in the image using least significant bit data hiding technique. Our technique belongs to fragile watermarking, which can be used for tamper detection and additional information transmission. The experiment has testified that the algorithm achieves blind data extraction and partially image recovery, and although the algorithm is based on the gray level images, it can be also extended to RGB images and video frames.
Research Data Infrastructures and Engineering Metadata

Abstract This chapter introduces metadata models as a semantic technology for knowledge representation to describe selected aspects of a research asset. The process of building a hierarchical metadata model is reenacted in this chapter and highlighted by the example of EngMeta. Moreover, an overview on data infrastructures is given, the general architecture and functions are disscussed, and multiple examples of data infrastructures in materials modelling are given.
Defining ‘Hate Speech’ under the Hate Speech Suppression Proclamation in Ethiopia: A Sisyphean exercise?

This article seeks to interrogate how ‘hate speech’ is defined under the Proclamation and its compatibility with the country’s legal reform measures. In February 2020, the Ethiopian parliament passed a Proclamation aimed at countering hate speech and defines ‘hate speech’ as any speech that deliberately promotes hatred, discrimination or attack against a person or group — based on protected status. However, this definition is still vague and susceptible to subjectivity because it fails to define the term ‘hatred’, which is one of the major ingredients of hate speech. The article questions how its vagueness could in practice enable officials at the federal and regional level to exercise unrestrained discretion to determine whom to investigate and indict for hate speech. This again casts doubt on the legality requirement under Ethiopian laws and international human rights law. In practice, there is a deep social fissure, absence of historical collective memory and disagreement on state building. All these are becoming a debacle for the hate speech legislative intervention. As such, defining hate speech without working on national reconciliation, and dialogue would be a meaningless legislative exercise. The article argues that in Ethiopia — where judicial review is not clearly provided in the Constitution — and courts are invariably oblivious to international human rights standards, legislating a Proclamation and defining hate speech in this way would shackle freedom of expression and result in a chilling effect on journalistic activities. The article suggests that defining hate speech remains to be an elusive exercise since any definition of hate speech should use specific examples and contexts.
On the Impact of Single and Multiple Effects on Quality of Experience for Multisensorial TV in Smart Home

Recent research efforts try to provide media with multiple sensory effects to the users to trigger human five senses, as to enhance the Quality of Experience (QoE), since the traditional multimedia contents only stimulate sight and hearing senses. Customary devices in home environment (e.g., air conditioning, lights, etc.), provided with opportune smart features can be employed to deliver the sensory effects and the Internet of Things (IoT) paradigm can be utilized to connect these devices in the smart home scenario. This paper investigates the influence of single and multiple sensory effects on user annoyance, enjoyment, sense of reality, through conducting subjective test measurement to TV users in real smart home scenario. Five audiovisual sequences, were presented to the participants, enriched with three sensory effects (i.e., airflow, light and vibration) in several different configurations, to evaluate the impact of single and multiple effects on user experience.
Formation of information competencies when learning by pupils of desktop publishing systems in project activities in informatics

The article discusses the feasibility of the formation of information competencies of schoolchildren and the significance of the subject area of informatics for their formation. The necessity of project activities in informatics for the purposeful formation of information competencies is shown. The possibilities and advantages of the MS Publisher desktop publishing system as an information technology that allows the formation of information competencies of students in project activities in informatics are disclosed. The types of students' activities when using the desktop publishing system MS Publisher and information competencies formed in each type of activity are highlighted. The process of organizing project activities is described on the example of creating a school newspaper. In this process, students learn basic publishing concepts, master basic desktop publishing skills, plan project activities, create an information product, document project results, present and analyze work performed. Methodological recommendations are given, aimed at the purposeful formation of information competencies of schoolchildren.
VizObj2Vec: Contextual Representation Learning for Visual Objects in Video-frames

While the use of the distributional hypothesis has become popular in creating embedding for text corpus, it is rarely used for generating the contextual (distributed) representation of visual objects in video data. In this paper, we present a distributed representation model, vizObj2Vec, that leverages the contexts of visual objects learned from spatiotemporal placements of the objects in the video-frames to construct object-embeddings. The model enables computation of contextual similarity - rather than solely relying on the visual resemblance in similarity computation - between a pair of visual objects. As a result, objects that are contextually connected - because they are in close proximity in a frame or are in nearby frames - appear in a neighborhood in the constructed embedding space. Through a series of extensive experiments, the paper demonstrates (1) the context selection process for visual objects in video data and (2) the potential of the proposed model in distinguishing neighborhoods of contextual objects in the video embedding space.
Design of Computer Basic Course Examination System Based on Virtual Simulation Platform

Digital campus construction is the key construction project of computer basic colleges, and online examination system is an indispensable part of it. The overall structure of the examination system adopts a browser-based three-tier network application system, which divides the functions of the system into three blocks: representation, function and data. A perfect Web-oriented examination system for computer basic courses with multiple questions is designed and implemented. The modules and database of the examination system are designed in detail, and the development of the computer basic examination system is realized. The network examination system designed in this paper is based on virtual simulation platform, which is a test of teachers' teaching level and students' learning situation. The purpose is to make teachers and students get the most timely and effective feedback, so as to give teachers and students the greatest help in future teaching activities.
Management Information Systems and Geographic Information System for Managing Durian Resources

The objective of this research is to develop the data for durian resources with a data collection system via the web-based system and the development of geographic information systems for durian resource management, and the local wisdom of Thai durian gardeners by designing the implement in the same database system, then presenting the durian data in southern Thailand by linking the geographic information system visualization to the map. In this research, the system method is designed using the System Development Life Cycle (SDLC), which includes six steps: requirement gathering and analysis, system design, implementation, integration and testing, development of a system, and maintenance. The results will show the visualization from both systems form and provide the report data with durian gardeners' needs.