Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Collaboration Framework for Teaching Material Creation

Making use of existing resources to create teaching material can save effort and yield better quality. Although there are some resources that can be used freely for educational purposes, most come with significant IP protections. A technology is needed that can allow the usage of copyright restricted resources for collaboration in teaching material generation. We propose a system that provides a full record of multiple authorships and contribution shares when resources are used and such a record supports royalty sharing; its system framework is detailed in this paper. We also exploit the advantage of blockchain technology to bind participants when sharing resources and acknowledge other teachers when their teaching materials are used. In addition, the blockchain-based record can provide distributed management with security, transparency and immutability. Thus, teachers who create teaching materials have sufficient evidence to claim authorship. The smart contract on blockchain has two core functions: registering original material to allow sharing as well as recording multiple authorships of the materials used with contribution distribution. With blockchain as its core, it can be extended with addon functions, such as access management, external file storage and external version control system to support complicate editing activities. We use Remix-Ethereum IDE to implement a prototype. Our contributions are two-fold. First, we propose a blockchain-based framework to support the use of copyrighted resources when creating teaching materials. Second, we demonstrate its feasibility in a prototype implementation.
A Comprehensive Survey on Handwritten Gujarati Character and Its Modifier Recognition Methods

In India, handwritten character recognition is becoming necessity regionalwise due to new education policy 2020. Various technologies are applied to solve the problem in this area like statistical or probability model, support vector machine, Bayes probability model, deterministic finite automaton (DFA), hidden Markov model, and many more which are used. Due to the advancement in machine learning, convolutional neural network is a good solution of HCR which gives more promising results but any new algorithm in machine learning that depends on training data, mathematical function, loss function, and method of evaluation of model. Focusing on past research of handwritten Gujarati character recognition is found that sufficient research is required for modifier level called “Barakshari”. Results obtained in past are limited to character level only. In this paper, our effort is to analyze and summarize previous contributions in the handwritten character recognition for several Indian languages.
Voxel-Based Route-Search Algorithm for Tumor Navigation and Blood Vessel Avoidance

In this study, we propose an algorithm that determines a simple-shaped surgical path from the liver surface to its malignant tumor in 3D voxel space. The method has a good affinity with DICOM (Digital Imaging and Communications in Medicine standards) captured by MRI (Magnetic Resonance Imaging) or CT (computed tomography). It also accounts for voxel density, which reflects the probability of the existence of blood vessels along the cutting path. The algorithm selects a path that avoids high-density voxels and the entangled blood vessels that spawn them.
Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks

We apply generative adversarial convolutional neural networks to the problem of style transfer to underdrawings and ghost-images in x-rays of fine art paintings with a special focus on enhancing their spatial resolution. We build upon a neural architecture developed for the related problem of synthesizing high-resolution photo-realistic image from semantic label maps. Our neural architecture achieves high resolution through a hierarchy of generators and discriminator sub-networks, working throughout a range of spatial resolutions. This coarse-to-fine generator architecture can increase the effective resolution by a factor of eight in each spatial direction, or an overall increase in number of pixels by a factor of 64. We also show that even just a few examples of human-generated image segmentations can greatly improve—qualitatively and quantitatively—the generated images. We demonstrate our method on works such as Leonardo’s Madonna of the carnation and the underdrawing in his Virgin of the rocks , which pose several special problems in style transfer, including the paucity of representative works from which to learn and transfer style information.
Education and Technology

The aim of this paper is to explore what we know about the difficulties from comprehension of digital texts in comparison with traditional texts with a particular focus on new readers. The question we want to investigate is whether the reading of an electronic text is more (or less) difficult than that of a paper text. In the first part of the paper, the object of the study is presented; in the second part research studies in the field are examined through an analysis of the evidence-based literature; in the third and final part, we try to draw some conclusions from data. Do we read more or read less in the time of digital media? Does reading on the screen change the way we understand meanings? And does the way we write change? These are some of the questions that parents and teachers ask themselves to understand what are the correct spaces and times to leave for devices at home, at school, in free time. Research suggests that the question of time is crucial. Maryanne Wolf, a neuroscientist who has been studying the brain that reads for years, noted that reading on the screen ends up inhibiting deep reading in the long run. We run away, looking for some articulations of the text that allow us to synthetically grasp its meaning without taking the time to weigh every single part: the risk is that the ability to understand exactly the meaning of what is being read is compromised. Fast thoughts and slow thoughts should both belong to our cognitive economy: the former are useful in some cases, the latter in others. In fact, however, the speed at which we are progressively more and more required in everyday life, at home as well as in organizations, can mean that we tend to resort more and more frequently, especially to quick thoughts. The real problem, therefore, is not digital, but speed. We need to find a way to slow down because only by slowing down is it possible to activate our slow thoughts.
Studying How to Apply Chatbots Technology in Higher-Education: First Results and Future Strategies

This paper tries to find the best condition to use chatbots (conversational agents) in higher-education studies after pilots carried out at the University of Granada (Spain). Our aim, along with the rest of partners in EDUBOTS -an Erasmus + European Project which counts with two pedagogical chatbots-, is to improve students’ engagement in class, as well as reducing the existing gap between them and their teachers. In this paper we present the results of a previous survey carried out among the students with the intention of laying out a plan of possible effective applications of this technology in the classroom in the near future, if possible during the next project pilot. The survey helps us confirm the reasons for the learning outcomes in the carried out pilots, as well as identify the targets for future application of chatbot technology.
Machine Learning Techniques in IoT Applications: A State of The Art

The present innovation of Internet of Things (IoT) provides intelligent and innovative services by being connected with numerous devices within network. The applications of IoTs have increased rapidly, it requires massive data storage, capable devices along with intelligent data processing techniques. IoT has got many applications; however, this covers smart household devices such as light-lamps, charging adapter, electric meter, fridge, microwave oven, air conditioner, temperature sensor, smoke-detecting sensor, Internet Protocol camera to more classy devices such as radio frequency-ID gadgets, heartbeat counters, accelerometer, parking-lot sensors, variety of other automobile sensors, and so on. According to IoT Analytics estimates, there were roughly 9.5 billion connected IoT devices at the end of 2019, and numerous overviews have been distributed that spread to the various IoT networks. Machines/software agents may make optimum choices for a given situation to optimize the output of a system based on forward/feedback.
PolyDNN Polynomial Representation of NN for Communication-Less SMPC Inference

The structure and weights of Deep Neural Networks (DNN) typically encode and contain very valuable information about the dataset that was used to train the network. One way to protect this information when DNN is published is to perform an interference of the network using secure multi-party computations (MPC). In this paper, we suggest a translation of deep neural networks to polynomials, which are easier to calculate efficiently with MPC techniques. We show a way to translate complete networks into a single polynomial and how to calculate the polynomial with an efficient and information-secure MPC algorithm. The calculation is done without intermediate communication between the participating parties, which is beneficial in several cases, as explained in the paper.
An Applicative Survey on Few-shot Learning

Background: Learning discriminative representation from large-scale data sets has made a breakthrough in decades. However, it is still a thorny problem to generate representative embedding from limited examples, for example, a class containing only one image. Recently, deep learning- based Few-Shot Learning (FSL) has been proposed. It tackles this problem by leveraging prior knowledge in various ways. Objective: In this work, we review recent advances of FSL in a perspective of high-dimensional representation learning. The results of the analysis can provide insights and directions for future work. Methods: We first present the definition of general FSL. Then, we propose a general framework for the FSL problem and give the taxonomy under the framework. We survey two FSL directions: learning policy and meta-learning. Results: We review the advanced applications of FSL, including image classification, object detection, image segmentation and other tasks etc., as well as the corresponding benchmarks to provide an overview of recent progress. Conclusion: In future work, FSL needs to be further studied in medical images, language models and reinforcement learning. In addition, cross-domain FSL, successive FSL and associated FSL are more challenging and valuable research directions.
Improved Canny-based algorithm for image edge detection

Edge detection is one of the most central branches in the field of image processing. The traditional edge detection algorithm is studied for its poor resistance to pretzel noise and poor adaptability of threshold selection. An improved image edge detection algorithm based on Canny is proposed, which firstly combines improved adaptive median filtering to reduce image noise. Then, the local adaptive thresholds are used to detect the edges of the image. The comparison of experimental results shows that the improved algorithm, which addresses the shortcomings of the traditional Canny algorithm, is better at removing the interference of pepper noise and preserving the image edge details.
Ming Shan Digital Experience

The Ming Shan Digital Experience is an immersive installation designed to support meditation in the context of a new Taoist center. Its creation confronted current academic literature on digital technology for meditation with the practical and cultural requirements of Taoist practice. Quantitative and qualitative learnings show the effectiveness of multimodal biofeedback on individual and collective meditative experience. Now instated in the Taoist center, the installation opens new perspectives for combining digital technology with ancient practice.
CAREER CAPITAL. CONCEPTUAL APPROACHES

This article considers approaches to studying the career capital in the interdisciplinary career studies from the point of view of the creating conditions issues for professional development of employees. The concept was formed within the framework of the interdisciplinary career research, based on the concepts of the human, social and cultural capital, as one of the tools for understanding new processes in the field of labor. The content of those processes determines, among other things, the growing variety of career models and the need to adapt them to organizational strategies. Attention is focused on studying those concepts in two principal directions: considering it as a tool for management practices in modern organizations and considering it in the perspective of the career theory development. Conclusions are drawn that approaches to considering career capital are based on interdisciplinary interaction and understanding of the multilevel context of a career in the organizations research. Groups of environmental infrastructure factors are identified for the development of professional promotion opportunities; professional training of employees; individual career success.
Constructing Orientable Sequences

This paper describes new, simple, recursive methods of construction for <i>orientable sequences</i>, i.e. periodic binary sequences in which any <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-tuple occurs at most once in a period in either direction. As has been previously described, such sequences have potential applications in automatic position-location systems, where the sequence is encoded onto a surface and a reader needs only examine <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> consecutive encoded bits to determine its location and orientation on the surface. The only previously described method of construction (due to Dai <i>et al.</i>) is somewhat complex, whereas the new techniques are simple to both describe and implement. The methods of construction cover both the standard &#x2018;infinite periodic&#x2019; case, and also the aperiodic, finite sequence, case. Both the new methods build on the Lempel homomorphism, first introduced as a means of recursively generating de Bruijn sequences.
Joint spatiotemporal regularization and scale-aware correlation filters for visual tracking

Correlation filters (CF)-based tracking methods have drawn extensive attention due to their impressive performance in visual object tracking (VOT) challenges. However, the existing CF-based trackers incur the inherent problems of boundary effect and filter degradation. Meanwhile, the scale variants of targets result in tracking-drift problems, which degrade the tracking accuracy significantly. To address the aforementioned problems, a tracking model named spatiotemporal regularization and scale-aware correlation filters (STR-SACF) is proposed. The STR-SACF model consists of two CFs, namely translation filter and scale filter (SF). The translation filter improves the accuracy of target localization, whereas the SF achieves the optimal and fast scale estimation. The proposed tracking model can improve the tracking accuracy while reducing computational complexity. Moreover, a spatiotemporal regularization term is introduced into the translation filter and SF to suppress boundary effect and filter degradation. The proposed model is effectively optimized based on the alternating direction method of multipliers algorithm. Experimental results on four challenging tracking benchmarks, i.e., OTB2013, OTB2015, TC128, and VOT2016, demonstrate the superiority of the proposed tracker compared with more than 20 state-of-the-art trackers.
Proving SIFA Protection of Masked Redundant Circuits

Implementation attacks like side-channel and fault attacks pose a considerable threat to cryptographic devices that are physically accessible by an attacker. As a consequence, devices like smart cards implement corresponding countermeasures like redundant computation and masking. Recently, statistically ineffective fault attacks (SIFA) were shown to be able to circumvent these classical countermeasure techniques. We present a new approach for verifying the SIFA protection of arbitrary masked implementations in both hardware and software. The proposed method uses Boolean dependency analysis, factorization, and known properties of masked computations to show whether the fault detection mechanism of redundant masked circuits can leak information about the processed secret values. We implemented this new method in a tool called Danira, which can show the SIFA resistance of cryptographic implementations like AES S-Boxes within minutes.
Covenants Without the Sword: Market Incentives for Cybersecurity Investment

Two decades of economics research has repeatedly made the assertion that organizations as well as individuals do not have adequate incentive to invest in cybersecurity. Absent security, associated costs are imposed on third parties rather than producers of insecurity. Cybersecurity is thus a private good with externalities, one that will require regulation to prevent market failure. Underlying this body of research is the assumption that all organizations have the same business drivers, a similar attack surface, and a uniformly informed consumer base. This paper questions these assumptions and outlines seven naturally occurring incentives for organizations to invest in cybersecurity. Furthermore, I provide examples of how these incentives have driven investment in cybersecurity across different sectors. While the applicability of these incentives differs both across and within sectors, any cybersecurity public policy interventions must consider the resulting nuances. Cybersecurity covenants established absent the sword of regulation may be both more effective and sustainable, as they evolve with the experience and exposure of the stakeholders.
Linguistically-driven Selection of Difficult-to-Parse Dependency Structures

The paper illustrates a novel methodology meeting a twofold goal, namely quantifying the reliability of automatically generated dependency relations without using gold data on the one hand, and identifying which are the linguistic constructions negatively affecting the parser performance on the other hand. These represent objectives typically investigated in different lines of research, with different methods and techniques. Our methodology, at the crossroads of these perspectives, allows not only to quantify the parsing reliability of individual dependency types, but also to identify and weight the contextual properties making relation instances more or less difficult to parse. The proposed methodology was tested in two different and complementary experiments, aimed at assessing the degree of parsing difficulty across (a) different dependency relation types, and (b) different instances of the same relation. The results show that the proposed methodology is able to identify difficult-to-parse dependency relations without relying on gold data and by taking into account a variety of intertwined linguistic factors. These findings pave the way to novel applications of the methodology, both in the direction of defining new evaluation metrics based purely on automatically parsed data and towards the automatic creation of challenge sets.
Study of different performance measures and their relations in satellite‐based and terrestrial quantum communication

Rapid developments in quantum communication are paving the way forward for more advanced technologies. Conventional channels like optical fibres have been a great resource in recent decades. However, they suffer largely from exponential transmissivity decays and are not suitable for very long-distance communication. Satellite links along with quantum cryptography can be the foundation of modern quantum communication systems. This work presents an overview of the hybrid quantum repeater with a detailed analysis of different parametric relations in satellite-based communication from experimental data. The link efficiency between the satellite-based scheme and terrestrial scheme is also compared, emphasising the satellite-based scheme's importance for quantum communication. Finally, we present a performance analysis of different quantities and derive relations between them.
Cybersecurity and cyber risk in integrated and management reports of key service operators

Purpose: The scope of interactive information processed and exchanged through cyberspace has grown exponentially. Therefore, there is a need to develop cybersecurity that protects this space against both internal and external threats, as well as to work out an appropriate reporting system on the cybersecurity model operating in the company. The aim of the paper is to identify and assess the disclosures on cybersecurity and cyber risk in the integrated and management reports of selected companies listed on the Warsaw Stock Exchange. Methodology: The study focused on the integrated and management reports of 17 selected companies identified as operators of so-called key services. The representative sample was chosen through purposive sampling. This process was preceded by a preliminary analysis of companies listed in the WIG 30 Index, drawing on the number of integrated reports prepared by the operators of key services. The research involved an analysis of the literature and legal regulations, as well as the structure and scope of information on cybersecurity reported by the surveyed companies, along with the deductive method. The results of the analysis showed that only some companies present information on existing cyber risks and cybersecurity, while information is scattered in different parts of the business reports and non-comparable due to the lack of a unified data structure. It was noted that the reports do not contain detailed information on the activities in the field of cybersecurity, which makes it impossible to perform a multifaceted and multisectoral assessment of the results reported by the entities. Originality: The paper builds on and thus complements the scientific achievements in the field of non-financial reporting, including the business model, by identifying the shortcomings related to reporting on how to protect companies against the risk related to cyber threats in the reports to date. The study also confirms the need to improve the content of business reports with quantitative and qualitative information in this regard
Nearly constant acceleration model for state estimation in the range‐Doppler plane

The problem of motion modelling in the range-Doppler (R-D) plane as well as range and Doppler estimation for the Cartesian nearly constant acceleration motion, which is a common manoeuvering motion, is investigated. The temporal evolution equation is derived based on the state vector consisting of target range, Doppler and derivatives of the product of range and range rate versus time. In this way, the measurement equation of range and Doppler measurements can be maintained in a desirable linear-Gaussian structure. Based on the non-linear state equation and the linear measurement equation, the unscented Kalman filter is adopted to tackle the non-linear filtering problem. The corresponding filter initialisation method is developed based on the two-point differencing method. Explicit expressions of the initial state estimates and the initial covariance matrix are presented in analytic forms where the correlation among the state components is handled properly. The posterior Cramer–Rao lower bound (PCRLB) is provided for state estimation in the R-D plane. Comprehensive comparisons of the proposed method against the existing R-D state estimation methods using approximate models, Cartesian state estimator and PCRLB are carried out in simulations to demonstrate the validity and correctness of the proposed motion model and estimation method.