Reviewerly/exHarmony huggingface dataset:
A Provocation on Privacy &amp;amp; Ethics in Blockchain-Based Systems: An Invitation

Privacy is a quality relating to human data that is typically governed by legal principles or guidelines for how private entities should conduct in relation to user data. The idea in blockchain systems is that privacy principles can be established and enforced data through software code, to provide stronger guarantees and accountability to people about how their data is managed.In late-2020, the ‘Privacy & Ethics’ sub-committee of the Cybersecurity Working Group for the National Blockchain Roadmap met multiple times to discuss the privacy and ethics concerns of blockchain systems. Areas of interest to the sub-committee included framing privacy and ethics in relation to blockchain, identifying relevant principles in Australia to these areas, the cybersecurity implications of different types of blockchain architectures, and links to case-study examples of best practice approaches to privacy at an individual and infrastructural level.The following briefing paper provides a synthesis of our findings and directions moving forward, as a contribution to the Cybersecurity Working Group Report, including approaching privacy and ethics in different blockchain-based architectures, and opportunities, limitations, and recommendations for enhancing privacy and ethics approaches in blockchains. This synthesis is designed to capture the privacy and ethics opportunities and concerns regarding distributed ledger technologies, offer further understanding on these topics, and invite further contribution.
Approximation of Impulse Responses of Filters Matched with Physically Possible Signals

To solve a task of optimal filtration of physically possible signals accompanied by background noise it has been suggested to use real filters with impulse responses that approximate responses of ideal filters. It has been demonstrated that such approach may result in significantly reduced number of components of the matched filter.
Framing photos in the digital dark age: towards a socio-technological 'ecology of images'

The ecology of images of which Susan Sontag spoke in 1978 is urgently needed now if we are to establish a framework within which the daily onslaught of uncertain imagery can be understood, and a reliable photographic record of our current and future real world can be preserved. Many technologies (ubiquitous digital photography, sophisticated camera sensors, image editing, ‘deep fakes,’ GAN imaging) have brought to fruition the potential for photographic images to expand into a wide range of ‘ecological’ niches. The contexts that apply to image distribution (digital platforms, social engineering, governmental regulation) are daily news.Drawing together a range of investigations into aspects of the human-image nexus using eye gaze signals, neural networks, online surveys, educational workshops and research into digital platforms, we outline the main technological and social features of a potential ‘ecology of images’ that may help shine light on the panoply of images of our times.
Edge Computing Architecture With an Extension

Cloud computing is highly being used for several years for various purpose s. From daily tasks, such as reading e-mails, watching videos to the factory automation and device control, it changed where the data is being processed and how it is accessed. However, increasing number of connected devices brings problems, such as low Quality of Service (QoS) due to infras-tructure resources and high latency because of the bandwidth limitations. The current tendency to solve the problems that the Cloud computing has is performing the computations as close as possible to the device. This paradigm is called Edge Computing. There are several proposed architectures for the Edge Computing, but there is no an accepted standard by the community or the industry. Besides, there is not a common agreement on how the Edge Computing architecture physically looks like. In this paper, we describe the Edge Computing, explain how its architecture looks like, its requirements, and enablers. We also define the major features that one Edge Server should support.
DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING

The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained model was re-trained on custom drone synthetic dataset by using transfer learning’s fine-tune technique. The results of detecting drone in our experiments were around 90.8%. The combination of drone detection, Dlib correlation tracking algorithm and centroid tracking algorithm effectively detects and tracks the small drone in various complex environments as well as is able to handle multiple target appearances.
Visual sources in rare science books: Importance of their digitization

The significance of digitalization of visual sources in rare science books for potential research and studies is emphasized. The consistent information growth in the modern world evokes the problem of selecting sources. Often, the significant fraction of these sources is outside the purview of researchers, which results in information entropy. This is particularly true for the rare scientific books and their visual components. Until recently, the visual sources, i. e. films, photos, posters, postcards, etc., remained outside the scope of scientific research. The author argues that visual information as presented in rare science books can be an important source of information for science owing to its accuracy, image clarity, reliability, and information value. Involvement of the RAS Library for Natural Sciences in the library collection digitalization programs is discussed. The examples are provided to demonstrate the scientific and information value of visual sources in rare science books. The conclusions are made on the importance of digitalization of visual sources in the DL of rare books. This approach enables to expand library information foundation for specialized scientific research in various fields of science as well as for educational programs, which would be of practical significance in perspective.
LESSON PLANNING AND TEACHING FINE ART WITH IMPLEMENTATION OF DIGITAL STYLUS

Creativity is closely linked to 21st century competencies. For students in secondary education, it is important to maintain and encourage creativity as part of fine arts classes. This can be achieved through meaningful integration of information and communication technology, as this allows students to express themselves in a more engaging and in-depth way. To help empower class teachers, this research presents a successful example of good practice. Courses are planned in several stages, starting from goals of the subject in relation to digital competencies. The app for the tablet and the digital pen is selected according to the criteria of quality, simplicity of use and relevance. Technology is included in the lessons with the goal to achieve a significant upgrade of the learning experience. Students evaluate their artistic expression as positive and successful. With this activity, students develop their creative and artistic expression abilities to the greatest extent. Their graphic products meet the art task and are aesthetically perfective for their age. The expressive elements within the computer graphics are originally rhythmically arranged. Keywords: digital pen, example of good teaching, fine arts, innovative pedagogy, lesson planning
Lightweight Algorithm for Preventing Malicious Fork in the Bitcoin Environment

Blockchain is considered the most secure technology in data storing, which stores data in blocks. Therefore. Blockchain is a linked list structure but may be changed to a fork structure; a usefulfork or a malicious fork. The usefulfork may occur when the rules of the block are updated. On the other side, the malicious fork occurs when a miner has supercomputer properties that generate a set of blocks as a branch and does not publish this branch to the blockchain until the length of that branch is greater than the length of the main branch, or when two blocks saved at the same time in the blockchain. TVhen theseforks occur in the systenb a set of blockchain transactions will be rollbacked, user waiting times increases, and miner rewards increases illegally. In this paper, a Lightweight algorithm is proposed to avoid the malicious fork issue, and therefore rollback issue, users waiting time is reduced in the transaction process. The proposed algorithm is to make a single publisher in the system and divides the block construction into two phases. In the first phase, a miner generates a block and sends it to the memory pool. In the second phase, the memory pool will publish the block to the blockchain. The results show that the proposed algorithm has a good ability to avoid the malicious fork issue in the blockchain and the rollback issue thus minimize user waiting times.
Serial image media for writing narrative text skills

Background: Using the media, the students are able to do proper sorting and treatment in learning activities.&#x0D; Purpose: This study aims to determine the effect of the application of serial image media on writing narrative text skills.&#x0D; Design and methods: This research uses a quantitative approach. The type used is a Quasi Experimental Design research, this design has a control group, but does not fully function to control the external variables that affect the implementation of the experiment. The research design used is the Non Equivalent Control Group Design.&#x0D; Results: The result showed that the experimental group obtained an average score of 82.86 and the control group obtained an average score of 71.18. the use of serial image media has a positive effect on writing narrative text skills of stu-dents in grade V Cipinang Besar Utara 7 Elementary school.
Scalable and Explainable User Role Detection in Social Media

While identifying specific user roles in social media -in particular bots or spammers- has seen significant progress, generic and all-encompassing user role classification remains elusive on the large data sets of today's social media. Yet, such broad classifications enable a deeper understanding of user interactions and pave the way for longitudinal studies capturing the evolution of users such as the rise of influencers. We build on the fundamental role definitions of previous empirical studies and provide a largely automated, scalable detection of fine-grained roles. Our approach clusters users hierarchically and explains the salient features. To associate clusters with roles, we use supervised classifiers: trained by experts on completely new media, but transferable on related data. Furthermore, we employ the combination of samples in order to improve scalability and allow probabilistic assignments of user roles. Our evaluation on Twitter indicates that a) stable and reliable detection of a wide range of roles is possible b) the labeling transfers well as long as the fundamental properties don't strongly change between datasets and c) the approaches scale well with little need for human intervention.
Neural Topic Modeling to Understand Breast Cancer Peer-to-peer Online Information Seeking at Diagnosis

Topic modeling is a valuable method that enhances researchers’ ability to interpret information. Our research is the first to investigate neural topic modeling (NTM) methods for healthcare content analysis and topic discovery. We evaluated two NTM models to understand online information-seeking right after being diagnosed with breast cancer. Based on the achieved topic coherence and qualitative analysis on the topics discovered by each method, the NTM models work better than the traditional Latent Dirichlet Allocation (LDA) method when evaluated using the topic coherence score. The qualitative analysis shows that NTM models can find specific content-relevant topics, whereas LDA finds the general topics. Based on the results, we discover the main topics of online peerto-peer information seeking after the diagnosis of breast cancer, which included treatment and medication for different breast cancer types, symptom management after radiation treatments, insurance coverage of pathology, and family decision-making and support.
Atom search optimization algorithm

This chapter introduces the atom search optimization (ASO) algorithm, including its inspiration, model, and framework. A set of benchmark functions with different characteristics are presented to demonstrate the validity of the algorithm.
HoloFight: An Augmented Reality Fighting Game

Augmented Reality (AR) provides opportunities to create exciting new kinds of digital entertainment, such as watching movies on a large virtual screen or playing games that interact with a real physical room. While a number of AR games have been built, many do not build on the control innovations found in modern console, PC, and mobile gaming [Von Itzstein et al. 2019]. To explore the space of immersive multiplayer experiences with support for control innovations found in common non-immersive video games, we present HoloFight, a multiplayer fighting game using two or more Microsoft HoloLens 2s, two or more Xbox controllers, and the various natural user interfaces supported by the Microsoft HoloLens 2.
Online Fault Detection Based on OCKELM and Sparse Dictionary

In order to improve the accuracy and to curb the matrix expansion of the kernel learning methods in the process of online fault detection, a new method based on one-class kernel extreme learning machine (OCKELM) and incremental learning is proposed. Firstly, the OCKELM fault detection target function is established only depended on the target samples. And the expressions of kernel function and kernel weight vector are derived. Sparse dictionary is introduced to prevent the expansion of the dictionary scale. During the process of forward and backward sparse, with the strategy of construction and pruning, a set of key nodes with a predetermined scale is selected based on minimizing the cumulative coherence of the online dictionary. Besides, according to the node selection results, the model parameters are updated online recursively by using the elementary transformation of the matrix and the block matrix inverse formula. The proposed method is applied to the artificial data set and UCI data set. The experimental results show that the testing time consumption of the proposed method is at the level of 10-4s and the online detection can be realized. Moreover, compared with the existing mature offline fault detection methods, this method performs better in F1, AUC, G-mean and fault detection rate.
A Population Size Dynamic Reduction Criterion in PSO Algorithms

The size of the population is extremely important when executing a population-based algorithm. Its portioning impacts how much the algorithm will have for exploration and exploitation. An excessively large population can benefit exploration as opposed to exploitation. With a population below ideal, exploration may be impaired, and the algorithm may quickly converge to a local optimum. Unfortunately, the choice of population size is often made empirically, where the user experiences different values, several times, for different problems, without any well-defined criteria, often drawing only on his experience. This type of approach can under-use the algorithm, generating waste in both computational cost and results. In this work, we improve and study an approximation metamodel as a particle reduction criterion for particle swarm algorithms. This metamodel considers that if two particles are relatively close and with similar velocitys, they will tend to the same solution, allowing one of these to be eliminated. Five traditional benchmark problems in the literature in the field of engineering applications were performed and the results analyzed.
Motivational Factors in Ensuring the Transition to Blended Learning of the Subject “Physical Education”

In order to increase interest to PE lessons and follow the modern educational tendencies of development and effective approach to the organization of the educational process, specialists more and more often turn to the search of rational solution in the field of innovative technologies.This study attempts to examine the impact of PE lessons on the development of cross-cultural competencies of learners to switch to forms of blended learning that include a combination of traditional and e-learning with online components.An anonymous survey was conducted among the 2nd year students of the University in 2019/2020 academic year. 405 students from study groups with different sports orientation and students of special medical department took part in the study. The questions concerning the respondents’ attitude to the educational activities of the department were selected for the questionnaire.Methods of theoretical analysis of data from literary sources and Internet resources, questionnaires, determination of the percentage of the responses received to the total number of respondents were used.The results obtained confirm the increased interest in the discipline “Physical Education and Sport”, which is a good basis for the following creative use of physical education means and methods for professional and personal development and physical self-improvement of students.A variety of motivational factors was a prerequisite for the transition to new, modern forms of learning.
Biased random-key genetic algorithms using path-relinking as a progressive crossover strategy

In a biased random-key genetic algorithm, a deterministic decoder algorithm takes a solution represented by a vector of real numbers (random-keys) and builds a feasible solution for the problem at hand. Selection is said to be biased not only because one parent is always a high-quality solution, but also because it has a higher probability of passing its characteristics to its offspring. Path-relinking is a search intensification strategy to explore trajectories connecting high-quality solutions. In this work, we show how path-relinking can be applied in the space of the random-keys and successfully explored as a progressive crossover strategy in biased random-key genetic algorithms. The efficiency of the newly proposed improved crossover strategies, combining multiple crossover operators with the progressive crossover strategy by path-relinking, is illustrated by applications on two problems: the single-round divisible load scheduling problem and the multi-round divisible load scheduling problem. The computational results show that the improved crossover strategies, combining multiple crossover operators with the progressive crossover strategy by path-relinking, are able not only to improve the running times of the original BRKGA, but also to find better solutions in the same running times.
Dissociable Neural Representations of Adversarially Perturbed Images in Convolutional Neural Networks and the Human Brain

Despite the remarkable similarities between convolutional neural networks (CNN) and the human brain, CNNs still fall behind humans in many visual tasks, indicating that there still exist considerable differences between the two systems. Here, we leverage adversarial noise (AN) and adversarial interference (AI) images to quantify the consistency between neural representations and perceptual outcomes in the two systems. Humans can successfully recognize AI images as the same categories as their corresponding regular images but perceive AN images as meaningless noise. In contrast, CNNs can recognize AN images similar as corresponding regular images but classify AI images into wrong categories with surprisingly high confidence. We use functional magnetic resonance imaging to measure brain activity evoked by regular and adversarial images in the human brain, and compare it to the activity of artificial neurons in a prototypical CNN-AlexNet. In the human brain, we find that the representational similarity between regular and adversarial images largely echoes their perceptual similarity in all early visual areas. In AlexNet, however, the neural representations of adversarial images are inconsistent with network outputs in all intermediate processing layers, providing no neural foundations for the similarities at the perceptual level. Furthermore, we show that voxel-encoding models trained on regular images can successfully generalize to the neural responses to AI images but not AN images. These remarkable differences between the human brain and AlexNet in representation-perception association suggest that future CNNs should emulate both behavior and the internal neural presentations of the human brain.
Quantum sensing using thermal photon bunching

Classical sensing techniques such as range finding and clock synchronization use timing modulated light sources to provide the timing correlations needed for their implementation. Corresponding quantum schemes utilise spontaneous parametric down converted light sources to provide the timing correlations without the need for timing modulation, although entangled states are too fragile to be fully exploited presently. Here we demonstrate the use of thermal light as an alternative source for timing correlations, via the photon bunching generated from a laser operating below threshold, and showcase its practical viability by successful range finding measurements.
Deep Learning based Automatic Extraction of Student Performance from Gazette Assessment Data

Everyday millions of files are generated worldwide containing humongous amounts of data in an unstructured format. Most of us come across at least one new document every week, which tells the large volume of data associated with documents. All the data in these documents is in unstructured format which makes it difficult for further processing. The extraction of data from this documents still remains largely a manual effort resulting in higher processing time. A system that could extract the required fields from documents and store them in a structured format automatically will be of much significance. In this paper, we have described an approach for extracting the data from Exam Result Gazette document and then storing it in a CSV file. Mask RCNN model having a backbone of ResNeXt-101-32x8d and Feature Pyramid Network(FPN) has been hypertuned for detecting the required fields. Then PyTesseract Optical Character Recognition System has been used for extracting the data from detected fields. Our proposed system is trained on custom data set created by us and then evaluated on test data to extract the required fields. The overall accuracy of our system is 98.69%. The results indicate that the system could be used for efficiently extracting the required fields from given exam result gazette document.