Reviewerly/exHarmony huggingface dataset:
Automatic Resolution of Domain Name Disputes

We introduce the new task of domain name dispute resolution (DNDR), that predicts the outcome of a process for resolving disputes about legal entitlement to a domain name.The ICANN UDRP establishes a mandatory arbitration process for a dispute between a trademark owner and a domain name registrant pertaining to a generic Top-Level Domain (gTLD) name (one ending in .COM, .ORG, .NET, etc).The nature of the problem leads to a very skewed data set, which stems from being able to register a domain name with extreme ease, very little expense, and no need to prove an entitlement to it.In this paper, we describe the task and associated data set.We also present benchmarking results based on a range of models, which show that simple baselines are in general difficult to beat due to the skewed data distribution, but in the specific case of the respondent having submitted a response, a fine-tuned BERT model offers considerable improvements over a majority-class model.
Migration, mobility and digital technology in a post-COVID-19 world: initial reflections on transformations underway

This chapter provides initial analysis of the impacts of COVID19 on mobility and migration globally with reference to the uptake of digital technology in pandemic response as well as reflections on the key strategic implications for migration and mobility systems. Initial data indicate that COVID-19 has resulted in mass immobility, with countries enacting unprecedented restrictions on freedom of movement as well as a surge in population surveillance technologies for contact tracing measures, including through the use of AI systems. COVID-19 has further intensified the migration securitization rhetoric while enabling the uptake of digital technology for both pandemic and non-pandemic purposes, such as broader population surveillance. While technological innovations can help mitigate the impact of COVID-19 considerable concerns are being raised by scholars and civil society actors on the privacy and human rights implications of the data collection and usage. The full implications for migration and mobility, individual privacy and security are as yet unknown, however, alarming developments regarding the extent to which human rights principles have been so rapidly set aside during the pandemic are cause for major concern.
Asymmetric Encryption of Surveillance Videos for Adaptive Threshold based Moving Object Detection

The use of video surveillance (VS) has grown significantly using the internet as a platform. Thus security issues on such videos must be addressed. Video frames can have multiple objects and various features over video length. Moving object detection (MOD) and real-time tracking requires security strategies designed to protect videos. This paper is proposed to design an asymmetric encryption method (RSA). The paper has contributed in two stages. In the first phase the fast video segmentation method based on a global variable threshold is designed to facilitate MOD. Later in second pass the RSA-based encryption is used to maintain the efficiency of the object detection. The secure key generation method is demonstrated. The performances of two global thresholds are demonstrated and compared under the encrypted video data. It is found that that method is very effective in finding objects under the context of video surveillance in real time.
Picking out the bad apples: unsupervised biometric data filtering for refined age estimation

Introduction of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, facial image datasets with biometric trait labels are scarce and usually limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts of weakly labeled and noisy data. This work is focused on picking out the bad apples from web-scraped facial datasets by automatically removing erroneous samples that impair their usability. The unsupervised facial biometric data filtering method presented in this work greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web-scraped datasets demonstrate the effectiveness of the proposed method with respect to real and apparent age estimation based on five different age estimation methods. Furthermore, we apply the proposed method, together with a newly devised strategy for merging multiple datasets, to data collected from three major web-based data sources (i.e., IMDb, Wikipedia, Google) and derive the new Biometrically Filtered Famous Figure Dataset or B3FD. The proposed dataset, which is made publicly available, enables considerable performance gains for all tested age estimation methods and age estimation tasks. This work highlights the importance of training data quality compared to data quantity and selection of the estimation method.
DESIGN THINKING IN FUTURE TEACHERS TRAINING

Innovation in education entails not only the incorporation of technological tools and applications, but also the implementation of methodologies that could break the inertia and incorporate processes of Divergent Thinking. Future teachers must be creative professionals capable of developing professional activities to improve and not only to replicate the social experience in order to create new learning experiences. In this way, the main aim of our work was to explore how the use of Design Thinking Methodology improves learning processes in a group of 75 college students of the Primary Education Degree. This group of students, divided into 21 smaller working groups, created different educational videos and multimedia design projects through Design Thinking methodology. They were also encouraged to use Social Networks (specifically Instagram) to present and publicize the progress they were making on their work, in order to obtain feedback from their classmates. All the projects developed were related to three thematic areas: Art for everyone; Attention to diversity and Making great neighborhoods. At the end of the creation process, we analysed the students’ perception of how the design thinking methodology helps them through their learning process. Based on an ad hoc questionnaire, the impact of the use of Design Thinking in the learning process and its influence on variables such as motivation, communication, creativity, among others, was studied. The results show that the use of this methodology had a positive impact on practically all the variables, facilitating and optimizing the learning process of future teachers.
Formation of digital competencies in the public policy sphere: the EU and Russia experience

Research background: Today information technologies occupy many spheres of human activity, including political government systems. The new digital reality requires a revision of the political and economic science methodology and a transition to a new paradigm that meets the challenges of information globalization. Purpose of the article: The article is devoted to the study of the European and Russian experience in teaching digital skills to citizens and civil servants. The need to improve existing digital competencies in the context of widespread digitalization is determined. Methods: The main research method is a comparative analysis of the European and Russian approaches to the digital competencies formation in the sphere of public administration and politics, emerging and transforming under the influence of widespread globalization and digitalization. Particular attention is paid to state strategies, programs and projects aimed at digital development and modernization of all areas of society. Findings &amp; Value added: On the basis of the study, the authors formulated the European and Russian models of teaching digital competencies, focusing on the sphere of public administration and politics, highlighted the disadvantages of the Russian model and the advantages of the European approach, and also offered recommendations on the prospects for the spread of European practice in Russian conditions.
Graph-Based Decoding for Task Oriented Semantic Parsing

The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders.In this work, we explore an alternative paradigm.We formulate semantic parsing as a dependency parsing task, applying graphbased decoding techniques developed for syntactic parsing.We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples.We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partiallyannotated data is available.
EveOut: an event-centric news dataset to analyze an outlet's event selection patterns

Automation of computational models to study the structure of events and their value to news outlets is an effective way to understand event-outlet relationships. However, the scarcity of publicly available, comprehensive event-centric news datasets restricts the implementation of such models. To overcome this bottleneck, we collected seventeen months of event data using Event Registry to generate EveOut, a publicly available event-centric news dataset. To conduct statistical analysis, we first select five English-language and three in Slovenian-language news outlets. We then retrieved all the events covered by them and used it to document the prevalence of geographical, temporal, categorical, and several other aspects of the event selection bias by these outlets. We illustrate the significance of our dataset in the field of digital humanities by identifying a motivating use case. The dataset is publicly available from the dedicated website http://cleopatra.ijs.si/EveOut/ , which provides a detailed description of the fields, usage information, and a link to the GitHub repository.
Object Detection and Image Segmentation for Autonomous Vehicles

With the rapid development of edge computing and the demand for green, safe and efficient transportation system, edge intelligence has been widely used in various traffic scenarios. By collecting images and videos, vehicles can obtain basic data and traffic flow information, which can be used to predict future movement trends. In addition, different traffic participants and their surroundings can be distinguished by image segmentation technology. In this paper, considering the resource limitation and latency constraint on edge vehicles, we proposed an improved vehicle detection algorithm based on tailored YOLOv4(You Only Look Once). To further increase the detection accuracy and speed, we introduce the Efficient Channel Attention (ECA) mechanism and High-Resolution Network (HRNet) into improved YOLOv4. After that, based on collected and detected objects, we proposed an image segmentation algorithm based on the DeepLabv3+ network, in which the MobileNetv2 is taken as the backbone network and the Softpool pooling algorithm is adopted as the pooling method. Experimental results show that compared with other classic methods, our proposed model has a higher mean Average Precision (mAP) for object detection and can improve the accuracy of original YOLOv4 from 83.34% to 87.64%. For image segmentation, our model also outperform other models with the Mean Intersection over Union (mIOU) improved from 72.18% to 74.99%.
Understanding Dilated Mathematical Relationship between Image Features and the Convolutional Neural Network’s Learnt Parameters

Deep learning, in general, was built on input data transformation and presentation, model training with parameter tuning, and recognition of new observations using the trained model. However, this came with a high computation cost due to the extensive input database and the length of time required in training. Despite the model learning its parameters from the transformed input data, no direct research has been conducted to investigate the mathematical relationship between the transformed information (i.e., features, excitation) and the model’s learnt parameters (i.e., weights). This research aims to explore a mathematical relationship between the input excitations and the weights of a trained convolutional neural network. The objective is to investigate three aspects of this assumed feature-weight relationship: (1) the mathematical relationship between the training input images’ features and the model’s learnt parameters, (2) the mathematical relationship between the images’ features of a separate test dataset and a trained model’s learnt parameters, and (3) the mathematical relationship between the difference of training and testing images’ features and the model’s learnt parameters with a separate test dataset. The paper empirically demonstrated the existence of this mathematical relationship between the test image features and the model’s learnt weights by the ANOVA analysis.
Nurturing the African higher education teaching and learning ecology using data, information and knowledge from our physical, digital and biological learning spaces

This scholarly book focuses on the relationship between information knowledge and technology with regard to the Fourth Industrial Revolution.The latter is understood to be a set of highly disruptive technologies blurring the lines between the physical, digital and biological spheres, collectively referred to as cyber-physical systems.Technologies such as cognitive computing, cloud computing, the Internet of Things, big data, augmented or virtual reality, 3D systems, artificial intelligence and power supply are transforming social, economic and political systems.Ultimately, we need to transform, be more creative and innovative to develop and sustain existing and emerging capabilities in the 'new normal', which has been accidentally accelerated by COVID-19 requirements.Technologies for digital transformation create the opportunity for Africa to bypass traditional phases of industrial development as the continent has done successfully in the past.Challenges addressed in the book are inequality, social justice, ethics, access and success, policy, infrastructure and cybersecurity.The book contributes to the ongoing discourse among scholars in Africa.It consists of 10 chapters where conceptual and field research are combined.The first chapter, by Trywell Kalusopa from the University of Namibia, focuses on e-government, with an emphasis on digital government in the digital economy.The second chapter, by Kelvin J. Bwalya from the University of Johannesburg, focuses on e-government from an artificial intelligence perspective.The third chapter, by Tom Kwanya from the Technical University of Kenya, focuses on the perception of robots for information services.In the fourth chapter, Johannes Britz, from the University of Wisconsin, discusses information ethics in the digital economy.
Evaluating the Construction of Feature Descriptors in the Performance of the Image Data Stream Classification

The image data stream classification presents a number challenges, such as the evolution of previously known classes and the emergence of new classes. A large part of current studies for image data stream classification uses classifiers that are able to evolve. However, these do not take into consideration that the image descriptors also need to evolve, in order that these improve the representation of features on new images available in the stream. Nonetheless, studies that explore the evolution of image feature descriptors do not analyze a number of the scenarios that may occur in these applications. This work presents an experimental study on the construction of image feature descriptors for the image data stream classification, while considering different aspects, as in the fact that only part of the image instances can be made available or only part of the image classes are known at the moment of constructing the descriptors. Experiments were performed on 4 image datasets, considering the state-of-art descriptors, BoVW and CNN, as well as with an algorithm that considers the evolution of the image feature descriptor. The obtained results show that the performance of a classifier may degrade its performance when submitted to scenarios that were not explored in previous studies.
Hybrid and adaptive harmony search algorithm for optimizing energy efficiency in VMP problem in cloud environment

Data Center energy usage has risen dramatically because of the rapid growth and demand for cloud computing. This excessive energy usage is a challenge from an economic and environmental point. Virtual Machine Placement (VMP) along with virtualization technologies is widely used to manage power utilization in data centers. The assignment of virtual machines to physical machines affects energy consumption. VMP is a process of mapping VMs onto a set of PMs in a data center to minimize total power consumption and maximize resource utilization. The VMP is an NP-hard problem due to its constraints and huge combinations. In this paper, we formulated the problem as a single objective optimization problem in which the objective is to minimize the energy consumption in cloud data centers. The main contribution of this paper is hybrid and adaptive harmony search algorithm for optimal placements of VMs to PMs. HSA with adaptive PAR settings, simulated annealing and local search strategy aims at minimizing energy consumption in cloud data centers with satisfying given constraints. Experiments are conducted to validate the performance of these variations. Results show that these hybrid HSA variations produce better results than basic HSA and adaptive HSA. Hybrid HS with simulated annealing, and local search strategy gives better results than other variants for 80 percent datasets.
Analysis on the Dilemma Development Strategies of Higher Vocational Education in China

The state council meeting strongly supports the establishment of vocational schools and promotes the integration and mutual recognition of academic results between vocational education and general education. By analyzing the dilemmas and problems in the development of higher vocational schools in China, it is found that higher vocational education has long been in an awkward situation. On one hand is to provide the supply of talents for enterprises, on the other hand is to break the obstacles in the development of higher vocational schools. To put vocational schools play active role in educational field and job market is the key objective of this study.
Adaptation to change in times of pandemic the case of a Public University in Mexico

From an organizational learning perspective as a process of change to adapt to a dynamic and complexenvironment. The purpose of this article is to expose the adaptations to change at an organizational,technological, academic and operational management level, in teachers and students as a result of thechallenges that one of the Higher Education Institutions ies Pública de México has faced since the emergenceof the Coronavirus SARS-CoV2 (COVID-19) pandemic. The reaction capacity to give continuity to academicactivities framed in healthy distance protocols is shown and the vicissitudes to face and coexist successfullyin the face of crisis and uncertainty are raised. During the individual and collective experience, through thecontinuous monitoring strategy within the University, emerging areas of opportunity are observed that,when well channeled, can become an incentive to promote learning at an organizational level. Althoughthe changes were not planned in advance and occurred in a short period of time, they are part of the selflearningof Higher Education Institutions in unprecedented times of pandemic.
An Assistive and Assessment Smart Eye for Visually Impaired People using CAFFE Model

Those who are visionless or visually compromised and rely heavily on others, like their family or friends. Whereas in acquainted settings, they're going to face several obstacles whereas conducting their everyday activities. A bit like we tend to love democracy and knowledge it, so that they can expertise it too. They ought to not be set apart merely as a result of capable otherwise and particularly in today's world wherever we tend to are technologically thus advance. The planned work object discovery system identifies, calculates the gap to our camera from an illustrious entity in a picture and scans the important world objects from binary pictures or film, wherever the entity might belong to any category or cluster, like folks, cars, vehicles, etc. to finish this duty of detection an object in a picture or video, I used OpenCV packages, Caffe model, Python, and NumPy. This investigation work discovers however deep learning techniques are wont to notice a live object, find an item, reason an item, extract options and show information and lots of additional, in footage and videos use OpenCV and the way to use the Caffe model, and conjointly why select the restaurant prototype over alternative frames. To form our deep learning-based period factor detector with OpenCV, we did like to access webcams and apply factor discovery to every frame effectively.
Ensemble Learning based Credit Card Fraud Detection System

In this digital era cashless transactions are very much common. Simplicity and convenience of online and credit card transaction has raised the popularity and revenue of e-commerce site. Every coin has two side, as the credit card transaction growing day by day, so does the number of fraudulent transactions. Credit card fraud can be detected by evaluating client spending history from prior transaction data and detecting variation in their spending behavior. With the technological advancement rule based techniques are not so effective in detecting credit card fraud from huge datasets. Now banks and credit card firms are using various classification techniques like decision trees, logistic regression, Random forest for this purpose, but one of the biggest challenge for computational intelligence technologies in detecting credit card fraud are class imbalance and concept drift problem. To overcome these problem effectively hybrid approaches are gaining momentum. In this paper ensem-ble learning technique is employed by parallel applying Decision Tree, Logistic Regression, Naive base classifiers and then best output is selected through hard voting. The experimental results conclusively proven that accuracy of Ensemble learning with hard voting achieve better accuracy as compared to other classifiers in detecting credit card fraud.
A Research on Object Oriented Programming and Its Concepts

Object Oriented Software Engineering(OOSE) is popular and widely used in developing the real time complex projects and products due to innovations in the Software Engineering and the Development of Software Industry. Object Oriented Programming(OOP) is one of the key elements of Object Oriented Software Engineering(OOSE) besides Object Oriented Analysis(OOA) and Object Oriented Design(OOD).Object Oriented Programming(OOP) is one of the Programming Paradigms for the past few decades. The OOP Paradigm Has oven taken the Procedure Oriented Paradigm or POP, where real time complex projects an products can be developed by using OOP in an easy and reliable way. The main difference between the Object Oriented Programming(OOP) and Procedure Oriented Programming(POP) is “Data controlling access to Code” in OOP where as “Code acting on Data” in POP. In this Paper I am going to discuss about the Principles of Object Oriented Programming(OOP) , its concepts and some of the Popular and widely used Object Oriented Programming Languages. I also discussed the difference between Object Oriented Programming and Procedure Oriented Programming. Merits and Issues of Object Object Oriented Programming