Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Deep Learning Models for Re-identification of a Person in a Public Surveillance System

Nowadays, a huge network of cameras is deployed in public locations, generating enormous amounts of video data. These data are monitored manually and can only be accessed if the necessity arises to verify the facts. Automating the system can increase the quality of monitoring and can be beneficial for high-level surveillance activities such as person recognition, suspicious activity detection, and unwanted event prediction for timely notifications. Person re-identification (ReID) tries to identify and track the activities of persons across multiple cameras. The common challenges faced are non-overlapping cameras, occlusion, large changes of viewpoint, shadows, various scales, cluttered background, pose, and illumination across different fields of view. This chapter proposes an automatic real-world video surveillance system model that can track and re-identify multiple persons from a single-camera tracking environment. The proposed system compares the effect of different benchmarked deep neural networks on MARS and iLIDS-VID video re-identification dataset. DenseNet121, SEResNeXt50, Resnet50M, Resnet-50, and inceptionresnetV2 are the well-known neural networks used in the various image or video analytics domains. We train these models to analyze the effect in the re-identification domain. Further, we also analyzed the model based on the effect of hyper-parameters like learning rate, number of epochs, dropout rate, loss function, and pooling strategies. The results obtained by the training DenseNet121 model using the MARS dataset are 66.0% and the 24iLIDS-VID Dataset is 76.2%. The system’s benefit is that it does not require a pre-stored database of individuals in advance at the time of testing for person recognition; it will create the dataset in real time. It will be also beneficial for crime control and prevention. Based on the experimental results produced, we conclude that standard deep learning techniques did not perform well due to various challenges including computational requirements, adequate training, and environmental issues. Hence, there is a need to investigate a suitable solution for a real-world automatic surveillance system.
Computer says no to my upload? Article 17 on filtering and the GDPR prohibition of automated decision-making

This chapter discusses the so-called filtering obligation in Article 17 of the new EU copyright directive (2019/790), an obligation that has been heavily debated since it was first introduced. A copyright filter depends on the recognition of copyright-protected work through the automatic processing of data and blocks the uploading of this content. The filter establishes the boundaries between authorised and unauthorised communications and executes decisions regarding individual upload without the intervention of a human agent. It follows that the use of an automated process raises questions about the filtering obligation’s relationship with Article 22(1) of the GDPR (stating that the ‘data subject shall have the right not to be subject to a decision based solely on automated processing’), and as the European Commission has recognised, the filtering obligation itself raises questions about its compatibility with respect for fundamental rights, particularly the right to freedom of expression.
Intellectual property and data ownership in the European strategy for data

In the ongoing boom of digital technology and the Internet, data has become an invaluable asset to companies. Not only have access to data and the development of more and more sophisticated data interpretation techniques increased, but technological progress also allows for the collection, storage, and analysis of previously unimaginable amounts of information. This chapter analyses development of the data ownership issues at European level, from the attempt to provide protection through IPRs and the (failed) proposal of a property right to the recent European Strategy for data (2020). The change of paradigm in the European policies relinquishes ownership of data as a prerequisite for access to focus on creating common spaces where data can be shared. Welcome as such a change may be, in order to successfully increase data access and sharing, this new European strategy needs to be coordinated with the EU rules on data already in place.
‘You AIn't seen nothing yet’

Artificial intelligence (AI) has become a part of our daily life, and ‘algorithmic creativity’ has similarly been in the spotlight recently. From paintings to music, machines generate outputs that seem to comply with various prerequisites of copyright protection. The idea (and to some degree the need) to honour such achievements by legal protection has also emerged. This chapter demonstrates that, contrary to the view of the proponents of an AI-copyright regime, the time is not ripe for AI-copyright. The core elements of copyright law—namely, the concept of authorship, originality, and moral rights, as well as copyright’s history and incentives—are deeply rooted in an anthropocentric (although not only author-centric) world. Without a paradigm shift in copyright law, the lack of direct human element of an AI-generated output entails the unavailability of copyright protection for these outputs. The chapter similarly highlights why the proposed alternative forms of copyright-related rights or sui generis protection are doctrinally unfounded or practically unfit to provide for a sound solution that does not destroy the existing copyright regime and diminish the value of human creations.
Authorship versus influence

Martin Mueller has created a database called Shakespeare His Contemporaries, which consists of over 500 plays dated between 1552 and 1662. Shakespeare His Contemporaries lists play pairs that share large numbers of dislegomena (phrases that occur within only two plays in Mueller’s corpus) consisting of four words or more, and therefore provides empirical data that can help researchers to explore the intertextual relationships among early modern texts. This chapter investigates the number and nature of these parallels, drawing upon the idea of Shakespeare’s aural, or ‘actor’s’, memory, explored by scholars such as Geoffrey Bullough, John Tobin, Charles R. Forker, and Ian Lancashire. The chapter also elaborates on the theory that Shakespeare had acted in plays attributable to Kyd for Pembroke’s Men, as presented by J. O. Halliwell-Phillipps, Arthur Freeman, and Terence Schoone-Jongen. Having explored phrasal repetitions according to the dramatic contexts in which they appear, the chapter concludes that in order to distinguish between authorship and influence in contested texts such as Arden of Faversham, more work needs to be done to ascertain the patterns of influence in Shakespeare’s plays. The chapter also deals with claims that Shakespeare had a hand in that play, and establishes that, on the basis of phraseology, prosody, and versification habits, there is no evidence for Arden of Faversham being a co-authored play or for Shakespeare’s hand in the verbal fabric of the text. The stylistic unity of Arden of Faversham points to a single author, and that author is Kyd.
Detection of Suspicious Activities in Public Areas Using Staged Matching Technique

Abstract The authors have requested that this preprint be removed from Research Square.
Detecting correlated errors in twin-field quantum key distribution

We experimentally demonstrate that we can detect correlated errors in a twin-field quantum key distribution (TFQKD) system by using a technique that is related to self-consistent tomography. We implement a TFQKD system based on a fiber-Sagnac loop, in which Alice and Bob encode information in the phase of weak coherent states that propagate in opposite directions around the loop. These states interfere as they exit the loop and are detected by a third party, Charlie, who reports the results of their measurements to Alice and Bob. We find that it is possible for Alice and Bob to detect correlated state-preparation and measurement errors while trusting only their own individual states, and without trusting Charlie’s measurements.
Security Concerns and Forensic Aspects of IoHT

A rapid development in assisted living technologies is seen in recent times as the majority of the population across the globe is aging rapidly. The development of innovative ambient assisted living technologies for safe and independent aging is the need of the hour, especially in the pandemic times. Artificial Intelligence incorporated into such technologies is termed as Ambient Intelligence, which aims at ensuring health and happiness for the elderly. The abuse of these technologies to harm the beneficiaries by defeating the purpose of healthcare systems also has become a threat to society at large. Cyber security experts consider this as a security concern and has scope for cyber forensic investigations. Apart from cyber defence to safeguard the healthcare systems, this chapter also focuses on the forensic-readiness of the infrastructure.
Application of Machine Learning in IoHT

Machine learning is a powerful method for uncovering insights of any data. Similarly, Internet of Things may be characterized as the collection of the well-known ideas, tools, techniques, and concepts in any field. Machine learning along with internet of things create hybrid technologies which work smartly to enhance the efficiency in several sectors, including healthcare. Internet of Healthcare Things is the collection of IoT devices notably in the medical domains. In this chapter, various machine learning techniques and their combination with internet of things is discussed to build effective frameworks in healthcare sector.
Healthcare Solutions Using Wearable Devices

Innovative solutions to the healthcare technology are essential to ensure prevention of disease and maintaining of good health. The wearable devices ensure the same and help in decision making at the earliest. The quality of human life has been improved after the wearable devices came into the picture. The wearable devices can be in the form of wrist watches, shoes, eyeglasses, ear-rings, gloves and even in the form of textiles. The physical activities monitored by the wearable devices like fit bands, activity trackers, multi-angle video recorders are becoming a part of human life and adding solutions to the healthcare problems. Once the identification of the problem is done, the physiological data is transferred to the healthcare personal for remote monitoring. With this data, the healthcare providers can come to a decision, which is intimated to the end-user and helps in early diagnosis of the diseases. Some wearable devices like fitness band, smart shoe etc. helps in monitoring or tracking the activity of the sports person for better performance. Therefore, monitoring of data like mental conditions, physical activities and fall prevention through smart devices is main focus of this chapter. Security is one of the major concerns from users' point of view. Hence, patients' data should be kept confidential and it has to be secured from the third party. This in turn reduces the acceptance rate of the end-user. Furthermore, the inventory management has been a better option in recent days for healthcare problems but interoperability of the devices is a major concern.
Adaptive modulation attention-based face super-resolution reconstruction method

In view of the large deviation of the pixel value of the generated image caused by the abandonment of the BN layer by the previous deep face super-resolution module, and the inaccuracy of the face prior alignment module, an adaptive modulation super-resolution neural network combining the attention mechanism and face alignment is proposed. Firstly, in order to solve the problem of inaccurate face alignment, a specific attention module is used to extract features with low resolution, and the output feature map is aligned with the key point feature map to increase the accuracy of positioning landmarks. Secondly, aiming at the problem that local pixels have maximum values, an adaptive modulation super-division module is proposed to make the reconstructed image more suitable for visual senses. The experimental results show that compared with face super-resolution algorithms such as end-to-end learning facial prior network (FSRNET), facial landmark attention network (PFSR) and deep iterative collaboration network (DIC), better visual effects and performance indicators are achieved.
Intelligent information retrieval based on Ontology

Traditional search engines can no longer meet the increasing amount of information in today's world, and often face problems such as blurred results and long search times for some queries. And the addition of ontology makes information retrieval more intelligent, so that information data between various fields can be shared, to achieve the purpose of getting the answers users want quickly and accurately. This paper firstly introduces the definition of ontology and analyzes the traditional information retrieval system, and finally summarizes some experimental results in recent years in ontology-based intelligent information retrieval and reviews them to get a more efficient and accurate information retrieval system to achieve the effect of improving the accuracy of search engines.
On the implementation of educational and career guidance projects of the Diplomatic Academy of the Ministry of Foreign Aff airs of Russia for students of educational organizations of general education

The article analyzes the practical experience of the Department for the Development of Innovative Youth programs and Career Guidance of the Diplomatic Academy of the Ministry of Foreign Aff airs of Russia on the implementation of educational and career guidance projects for students of educational organizations of general education. Special attention is paid to the successful implementation of decisions on education and career guidance for students in the framework of continuing education projects, to promote the development of diploma cadet classes in general education organizations of Moscow as an element of the profile training system in the context of integration of general and supplementary education through the introduction of innovative formats of thematic events into the educational process.
Електронно дистанционно обучение на студенти по специална педагогика по дисциплини с практическа насоченост (брайл и ориентиране и мобилност)

The present study presents the results of a survey among undergraduate students in special education in full-time and part-time education, who have completed their distance- and e-learning in two highly practical disciplines – Braille, and Orientation and Mobility, studied as compulsory in 5th and in 7th semester. The study shows an insufficient level of mastered practical skills in both disciplines, encountered various difficulties in the course of training, as well as partial desire of students for in-person training and attendance.
Development of the Professional Skills of the Future Pedagogues through Activities in the Student Club “Mission: Teacher”

Research by a number of leading scholars shows that extracurricular employment of students is the strongest aspect of modern higher education. This is confirmed by the results of the activities of the Student Club “Mission Teacher” at the Burgas Free University. The new club unites the efforts of the students and the academic community of the Center of Humanities on the values, mission and vision of the Bulgarian education. The methods of non-formal education stimulate the initiative and creative energy of students who are future innovators in education. The article presents the concept of the club with its practical focus and the idea of continuity, as well as the results of a survey among the student community of BFU for their motivation and expectations regarding the practical training of future teachers.
Preservation of Digital Archives

Background of the study: Digital archives in institutions or organizations has increased which is marked by the number of institutions that have established archive units. In the process of preservation or preservation of digital archives, it has its own difficulties compared to other media. Preservation is the most strategic activity, because the availability of records is closely related to the acquisition results that are maintained properly so that they can be processed and accessed for users. The implementation of archive preservation is not easy, there are several obstacles that cause the preservation to be less than optimal. Purpose: This study aims to provide knowledge about the preservation of digital archives as an effort to save important information and data in archives, it requires preservation or preserving archives in digital format. So that further research is needed, so this research is to facilitate researchers in conducting further research. Method: This study uses a systematic literature review method by combining bibliometric analysis of the literature from the Scopus database for the period 2018 to 2022. Findings: The results of the study show that preservation is an effort to maintain and store archives, both content or media so that they are durable and longer. Conclusion: Research topics in the search results regarding the preservation of digital archives are still found that have not been widely studied, namely, data preservation, long-term preservation, digital archives, and web archives, which can provide opportunities for further researchers to examine digital archive preservation
The Role of Village Library to Improve Literacy Program for Community Welfare

Background of study: The village library aims to improve the welfare of the community in the village. This library concept is called social inclusion-based library. Lentera Village Library also participates in efforts to improve the welfare of the people in the Tepi Laut Village through various efforts and activities in the development of the village library and community empowerment. Purpose: to look at the role of the village library in improving welfare literacy for the people in Tepi Laut Air Napal. Method: The research method in this research is using a qualitative approach. The informants in this study consisted of 5 informants, namely the village head, the person in charge of the library, the community, and students. The research was conducted using interviews and observation. Findings: The library in connectivity already has library infrastructure, in content the library has had welfare empowerment collections but it has not been said to be optimal because of the small number. Indicator Human where human resources in library management have not been said to be optimal because they still do not have the right manager. Conclusion: The library has empowered the community in improving literacy in community welfare and several forms of activities. However, it cannot be said to be optimal due to the unavailability of adequate collections and people who do not have the determination to use library services.
Physics Edutainment Learning based on Engklek and Ontang-Anting Games: Creating Fun Physics for Students

Abstract This study aims to describe students' responses to edutainment-based physics learning. The study was conducted with a qualitative approach involving 33 pre-service physics teachers. The data were analyzed using Miles and Huberman's model, including data reduction, data display, and conclusion drawing. The results showed that 52% of pre-serviceteachers did not know the existence of edutainment-based physics learning as a learning instrument to create exciting and fun physics learning. However, most pre-service physics teachers choose numbers from 7 to 10 to indicate their level of agreement for edutainment-based physics learning. This means that most pre-service physics teachers agree that traditional Engklek games and Ontang-anting are applied to learn physics based on edutainment physics.
COORDINATE: A model to analyse the benefits and costs of coordinating cybercrime

Recent leaks (such as Conti) have provided greater insights on the working of cybercriminal organisations. Just like any other business, these malicious actors strategically manage their processes in order to maximise their revenues. Coordinating different types of cybercrimes as part of a single attack campaign provides another opportunity to these criminal groups to improve the efficiency of their attacks. To investigate the promise of this “coordination” between cybercrimes in improving the financial gains realised by cybercriminals, we take a two-step approach. First, we perform a bibliometric analysis of past scientific literature discussing the concept of “coordination” w.r.t to cybercrime. Second, as a case study, analysing the attack chains of DDoS, phishing and ransomware attacks, we identify vantage points for potential coordination from an attacker’s perspective. Based on our findings, we propose a model (COORDINATE) to identify the types of potential cybercrime “coordinations”. COORDINATE considers three relevant types of coordination: direct collaborated coordination, indirect collaborated coordination, and opportunistic coordination. Given the advantages of coordinated attacks, our results suggest that one crime may provide opportunities for the next one. Coordinated attacks will become more prevalent, and that we may witness the development of a dynamic that leads to more online crime.
Bayesian networks

This chapter introduces the basics of Bayesian network (BN) classifiers that are used to address classification problems. Naive Bayes classifier is first presented, where a simplified (but unrealistic) assumption that the features are conditionally independent and are of equal importance is made. To weaken the assumption so as to improve the classification accuracy, semi-naive Bayes classifiers are then presented, where part of the dependencies between the features is considered. Finally, BN in more general form is introduced.
Rethinking Mobile Inverted Bottleneck Convolution for EfficientNet

EfficientNet is an efficient architecture that is as small as possible while still having acceptable performance. On the ImageNet challenge, with a much fewer parameter calculation load, it could have its position among the state-of-the-arts. The base module of the original EfficientNet is Mobile Inverted Bottleneck Convolution which has an expansion convolution to have a much bigger number of channels for depthwise convolution. That’s why it is called inverted bottleneck. In this paper, we introduce a new module called Mobile Equal channel Convolution blocks which has the same number of channels for all convolution layers inside to make the module more balance. Moreover, we also adjust a little bit the EfficientNet architecture to make the new variant can have a similar number of parameters to the original. The experiments on ImageNet dataset show that the new variant can have higher accuracy with a lower number of parameters.
A Comparison of Feature Construction Methods in the Context of Supervised Feature Selection for Classification

In supervised machine learning applications, feature construction may be used to create additional, informative features with the aim to support the prediction of the target output. This study investigates the impact of feature construction, specifically the use of quadratic and interaction terms, on the predictive performance of a classifier. Moreover, the Yager intersection operator is applied as a feature construction method to form additional interaction features. Since feature construction may also create irrelevant features, it can be combined with feature selection to maintain or even reduce the dimensionality of the feature set for model training. In this study, the supervised feature selection method ReliefF is used to rank all features and the k-nearest neighbor classifier is used for predicting the target classes. On the seven real-world data sets contained in this study, the features generated using feature construction are often among the most important features and provide competitive predictive performance.