Reviewerly/exHarmony huggingface dataset:
Project Education in Design. Innovations or Traditions?

Abstract The importance of the given article results from active new teaching technologies integrating into the educational system in Russia. The necessity for Russia’s education to shift to up-to-date forms is recognized by everybody in the scientific community. Prospects of project scenarios of acquiring knowledge are evident to everybody. Sooner or later, all persons involved, from professors to students, will have to solve these problems, but in practice, the problems are not so complicated as they seemed at the start of the reforms. Newest technologies in modern education and a historical experience of training specialist in architecture and art will make it possible to solve problems of creating scientific teams based on new cooperation principles. The objective of the given article is to focus the interest of colleagues of all areas of knowledge on the theory and practice of project education in leading scientific schools involved in training specialists in architecture, design, and culture. These areas of education possess a rich experience thanks to many generations of architects, designers, and artists working in decorative art, this experience produces amazing results. A new educational space that is being formed nowadays, puts forward a demand for multifunctional specialists, any experience being appreciated here. A method of solving the problem of shifting to project educational technologies in Russia was stated, among others, in the architectural and design education methodology. Being a work-through principle, project technologies concern all the subjects and meet the demands to form of a complete scope of competences of specialists in these aspects. This article propositions can result in a basis for new project education methods elaboration and fundamentally new educational medium formation. In the first place, these methods are directed to the problem to motivate creative search and form imaginative and project thinking. Nature as a source of inspiration, a factor of shape design and style, and actual problems of preserving it, can become a new content of many projects. A new attitude to the educational space as an integral body will support creating an ideal medium for interaction and development of all participants of the educational process on a permanent basis in future. That is a medium, really beautiful and comfortable for scientific and professional development and perfection.
Biases in machine learning models and big data analytics: The international criminal and humanitarian law implications

Abstract Advances in mobile phone technology and social media have created a world where the volume of information generated and shared is outpacing the ability of humans to review and use that data. Machine learning (ML) models and “big data” analytical tools have the power to ease that burden by making sense of this information and providing insights that might not otherwise exist. In the context of international criminal and human rights law, ML is being used for a variety of purposes, including to uncover mass graves in Mexico, find evidence of homes and schools destroyed in Darfur, detect fake videos and doctored evidence, predict the outcomes of judicial hearings at the European Court of Human Rights, and gather evidence of war crimes in Syria. ML models are also increasingly being incorporated by States into weapon systems in order to better enable targeting systems to distinguish between civilians, allied soldiers and enemy combatants or even inform decision-making for military attacks. The same technology, however, also comes with significant risks. ML models and big data analytics are highly susceptible to common human biases. As a result of these biases, ML models have the potential to reinforce and even accelerate existing racial, political or gender inequalities, and can also paint a misleading and distorted picture of the facts on the ground. This article discusses how common human biases can impact ML models and big data analytics, and examines what legal implications these biases can have under international criminal law and international humanitarian law.
A CCG-based compositional semantics and inference system for comparatives

Comparative constructions play an important role in natural language inference. However, attempts to study semantic representations and logical inferences for comparatives from the computational perspective are not well developed, due to the complexity of their syntactic structures and inference patterns. In this study, using a framework based on Combinatory Categorial Grammar (CCG), we present a compositional semantics that maps various comparative constructions in English to semantic representations and introduces an inference system that effectively handles logical inference with comparatives, including those involving numeral adjectives, antonyms, and quantification. We evaluate the performance of our system on the FraCaS test suite and show that the system can handle a variety of complex logical inferences with comparatives.
Scaling Distributed Training with Adaptive Summation

Stochastic gradient descent (SGD) is an inherently sequential training algorithm--computing the gradient at batch $i$ depends on the model parameters learned from batch $i-1$. Prior approaches that break this dependence do not honor them (e.g., sum the gradients for each batch, which is not what sequential SGD would do) and thus potentially suffer from poor convergence. This paper introduces a novel method to combine gradients called Adasum (for adaptive sum) that converges faster than prior work. Adasum is easy to implement, almost as efficient as simply summing gradients, and is integrated into the open-source toolkit Horovod. This paper first provides a formal justification for Adasum and then empirically demonstrates Adasum is more accurate than prior gradient accumulation methods. It then introduces a series of case-studies to show Adasum works with multiple frameworks, (TensorFlow and PyTorch), scales multiple optimizers (Momentum-SGD, Adam, and LAMB) to larger batch-sizes while still giving good downstream accuracy. Finally, it proves that Adasum converges. To summarize, Adasum scales Momentum-SGD on the MLPerf Resnet50 benchmark to 64K examples before communication (no MLPerf v0.5 entry converged with more than 16K), the Adam optimizer to 64K examples before communication on BERT-LARGE (prior work showed Adam stopped scaling at 16K), and the LAMB optimizer to 128K before communication on BERT-LARGE (prior work used 64K), all while maintaining downstream accuracy metrics. Finally, if a user does not need to scale, we show LAMB with Adasum on BERT-LARGE converges in 30% fewer steps than the baseline.
UTILIZING EYE-TRACKING TECHNOLOGY AND DATA TO AUGMENT UX-UI WEB DEVELOPMENT CURRICULUM

This research article provides the reader with a fundamental understanding of User Experience – User Interface (UX-UI). And how the adoption of eye-tracking technology in the classroom environment would augment or impact students' learning of UI/UX design. Included is an overview of eye-tracking technologies and applications as a background for better understanding. Suggest incorporating student research, developing students' critical thinking skills, and exploring data collection in your UI/UX curriculum. This report will conclude with a focus on resources and an outline for developing an eye-tracking research model as a case study for web development practicum. This article is a resource for instructors to improve perceptions of User Experience (UX). In developing an eye-tracking component in your web development - user experience curriculum, these hands-on research labs offer students the opportunity to understand how individuals access websites and provide activities for designing better ways for people to interface with computers. This curriculum model includes references to eye-tracking technologies, research topics, and examples of how eye-tracking data is collected.
The impact of scaling on Support Vector Machine in Breast Cancer Diagnosis

By using support vector machine (SVM) and the grid technique Badr et al. [1] introduced new scaling techniques on the data set Wisconsin from UCI machine learning with a total 569 rows and 33 columns.These scaling techniques overcame the standard normalization techniques.In this paper, three new scaling techniques are proposed by using SVM and the grid technique on the the data set Wisconsin from UCI machine learning with a total 569 rows and 32 columns.These scaling techniques are: (i) de Buchet for p = ( ∞) (ii) Lp-norm for p = (∞) (iii) Entropy .Experimental results show that SVM with new scaling techniques achieves 98.60 % , 98.42 % and 98.42 % accuracy against to the standard normalization by 96.49 %.
A Pedagogical Framework with Integration of TPACK for Mobile Interactive System in Teaching Mathematics

Although there is a variety of technology available in 21st century, the way classes and lesson being conducted are still mostly remaining the same which is teaching via one-way communication. One-way communication teaching process surfastces issues like lack of interaction where minimal discussion is going on during teaching session, Since the interaction between teachers and students are fairly poor, limited classroom activity can be expected such as “Question and Answer” which lead to the increment of boredom in classes and lessons among students. As the teaching process is leaning towards instructional, students’ feedback is usually being overlooked. Hence, since the availability of educational technology is getting more common nowadays, the integration of technology in classroom is encouraged changing the teaching and learning environment including knowledge delivery method from teacher perspective. Introducing mobile interactive system allows teachers deliver their knowledge differently, however, they are required to adapt and get familiar with the educational technology for improving teaching experience. Therefore, Technological Pedagogical Content Knowledge (TPACK) framework is applied in this paper to assess teachers using the seven elements of the framework. This paper is aimed to address the issues faced by conventional classroom and identify the effectiveness of teachers conducting classes using educational technology with the application of TPACK framework. Proposed TPACK framework is formed with the integration of three elements which are teachers’ efficiency, students’ performance and students’ engagement representing the outcome of current TPACK framework.
An Improved Feedback Network Superresolution on Camera Lens Images for Blind Superresolution

Most of the recent advances in image superresolution (SR) assume that the blur kernel during downsampling is predefined (e.g., Bicubic or Gaussian kernel), but it is a difficult task to make it suitable for all the realistic images. In this paper, we propose an Improved Superresolution Feedback Network (ISRFN) which is designed free to predefine the downsampling blur kernel by dealing with real-world HR-LR image pairs directly without downsampling process. We propose ISRFN by modifying the layers and network structures of the famous Superresolution Feedback Network (SRFBN). We trained the ISRFN with the Camera Lens Database named City100, which produced the HR and LR on the same lens, respectively, free for downsampling, so our proposed ISRFN is free to estimate the blur kernel. Due to different camera lens (smartphone and DSLR) databases, we perform two series of experiments under two camera lenses-based City100 databases, respectively, to choose the optimum network structures; experiments make it clear that different camera lens-based databases have different optimum network structures. We also compare our two ISRFNs with the state-of-the-art algorithms on performance; experiments show that our proposed ISRFN outperforms other state-of-the-art algorithms.
IDENTITIES IN EFL CREATIVE WRITING IN INDONESIA

Creative writing is created for aesthetic rather than informative purposes and its creation is greatly influenced by the writers’ background. Thus, how the writers’ perception of themselves or their identities are expressed in the creative writing products. As such, creative writing in the English as a foreign language (EFL) context may offer a space for foreign language learners to express their perceived identities. In this study, an analysis of two short stories written by students of the English Letters Department, Universitas Sanata Dharma, Yogyakarta, Indonesia, revealed that the students’ perceived identities were used as the resources for their creative writing outputs. In the first short story entitled “Calling Him Back”, through the internal conflicts of the main character, the student writer expressed her doubt as to which identities to claim. In the second short story entitled “Happy Birthday”, the student writer exploits her identities as a literature student when she did a research on novels with Indian background. The data were gathered through document analysis on the two short stories and correspondences with the student writers. These two short stories were evidence that foreign language learners utilized different aspects of their multifaceted identities as the resources for their creative writing outputs.
Generator Polynomial Matrices of Reversed and Reversible Quasi-Cyclic Codes

Quasi-cyclic (QC) codes over the finite field ${\mathbb{F}_q}$ correspond to certain ${\mathbb{F}_q}[x]$-modules. We describe a QC code $\mathcal{C}$ by a generator polynomial matrix. The code obtained by reversing the codewords of $\mathcal{C}$ is called the reversed code of $\mathcal{C}$ and denoted by ℛ. If$\mathcal{R} = \mathcal{C}$, then C is a reversible code. In this work, we prove a formula for a generator polynomial matrix of the reversed code ℛ of a prescribed QC code $\mathcal{C}$. Then, we present a necessary and sufficient condition in terms of the proven formula to ensure the reversibility of any QC code. Moreover, we characterize the reduced generator polynomial matrices of reversible QC codes. As an application, we apply our theoretical results to QC codes with the best known parameters. Computer search is used to show the existence of reversible QC codes that achieve the upper bounds on the minimum distance of linear codes. Several binary reversible QC codes with the best known parameters are provided.
Multi-step, Learning-Based, Semi-supervised Image Registration Algorithm

This paper presents a contribution to the Learn2Reg challenge organized jointly with the MICCAI 2020, more specifically, to the task related to inter-patient hippocampus registration in magnetic resonance images. The proposed algorithm is a multi-step, learning-based, and semi-supervised procedure. The method consists of a sequentially stacked U-Net-like architecture, trained in alternation. The method was ranked as the second-best (for the hippocampus registration task) in terms of the combined challenge evaluation criteria.
The convergence of Internet of Things, Blockchain and Connected Vehicles: Conceptual Advantages and Disadvantages of a new Cooperative Intelligent Transportation System

This paper intends to explore the convergence of some technological innovations that could lead to new cooperative Intelligent Transportation Systems (ITS). The technologies that might soon converge and lead to some new developments are: the Blockchain Technology (BT) concept, Internet of Things (IoT) and Connected and Automated Vehicles (CAV). Advantages and disadvantages of the new concepts founding a new ITS system are discussed in this conceptual paper. Blockchain technology has been recently introduced and many research ideas have been presented for application in the transportation sector. In this paper, we discuss a system that is based on a dedicated blockchain, able to involve both drivers and city administrations in the adoption of promising and innovative technologies that will create cooperation among connected vehicles. The proposed blockchain-based system can allow city administrators to reward drivers when they are willing to share travel data. The system manages in a special way the creation of rewards which are assigned to drivers and institutions participating actively in the system. Moreover, the system allows keeping a complete track of all transactions and interactions between drivers and city management on a completely open and shared platform. The main idea is to combine connected vehicles with BT to promote Cooperative ITS use, a better use of infrastructures and a more sustainable eco-system of cryptocurrencies. A short description of BT is introduced to evidence energy problems of sustainability in the implementation of Proof of Work (PoW) that is adopted by many blockchains.
Use of information and communication technology in virtual education and its correlation with the emotional intelligence of teachers in Ecuador in a covid-19 context

The present study proposes a mixed investigation of a non-experimental transactional correlational type with a descriptive scope in order to demonstrate the level of knowledge and use of Information and Communication Technology (ICT) in virtual education and its correlation with Emotional Intelligence (EI) of 485 teachers of initial, primary and secondary level In addition, it aims to reveal the context in which the teaching-learning process is being developed in relation to the COVID-19 emergency The results show that there are few ICT skills being utilized Additionally, most teachers lack technological infrastructure and internet access This study also indicates that female teachers have less emotional strengths and there is a very low correlation between the use of ICT and EI Finally, it is highlighted that the WhatsApp application has been transformed from a means of instant communication to a learning platform © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao All rights reserved
A scalable eigenspace-based fuzzy c-means for topic detection

Purpose The aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection. Design/methodology/approach The eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM. Findings Our simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM. Originality/value This research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.
Implementation Of Pohlig-Hellman Algorithm And Steganography Combination Of First Of File (Fof) And End Of File (EOF) For File Security: Implementation Of Pohlig-Hellman Algorithm And Steganography Combination Of First Of File (Fof) And End Of File (EOF) For File Security

The method of steganography First of File (FOF) has an additional lines at the beginning of image and End of File (EOF) method has an additional lines at the bottom of the image. To reduce the suspicion then FOF and EOF methods are combined produces stego image that has border at the top and bottom. To improve the security of message Pohlig-Hellman cryptographic algorithm is used. Pohlig-Hellman algorithm uses two keys (n and e) to process the encryption. Key n is a prime number algorithm tested with Lehmann. Key e have the legal conditions 1 <e <? (n) where ? (n) = n-1. Stego image produced has additional lines at the top and bottom of the image, it is because the ciphertext is inserted at the beginning and end of the image matrix. The result shows that the encryption process takes longer than the decryption process. Processing 129 character message encryption process takes 78 milliseconds and the decryption process takes just 16 milliseconds. Cover image of size 148 KB becomes 150 KB after embedding process of 1132 characters ciphertext.
Cryptoassets in private law

Following Satoshi Nakamoto's famous Bitcoin Whitepaper and the subsequent success of the Bitcoin network, cryptocurrencies have emerged as a new asset class. As an area of constant innovation, the cryptocurrency space is expanding very quickly. The surrounding policy debates keep shifting and legal development is trying to keep pace. This chapter charts the emergence of cryptocurrencies with a focus on their underlying technical properties and provides an overview of pertinent current legal issues. It discusses the most recent iterations and tries to anticipate the legal challenges that are likely to arise in the future.
Personalization of Search Results Representation of a Digital Library

The process of discovering appropriate resources in digital libraries within universities is important, as it can have a big effect on whether retrieved works are useful to the requester. The improvement of the user experience with the digital library of the University of Novi Sad dissertations (PHD UNS) through the personalization of search results representation is the aim of the research presented in this paper. There are three groups of PHD UNS digital library users: users from the academic community, users outside the academic community, and librarians who are in charge of entering dissertation data. Different types of textual and visual representations were analyzed, and representations which needed to be implemented for the groups of users of PHD UNS digital library were selected. After implementing these representations and putting them into operation in April 2017, the user interface was extended with functionality that allows users to select their desired style for representing search results using an additional module for storing message logs. The stored messages represent an explicit change in the results representation by individual users. Using these message logs and ELK technology stack, we analyzed user behavior patterns depending on the type of query, type of device, and search mode. The analysis has shown that the majority of users of the PHD UNS system prefer using the textual style of representation rather than the visual. Some users have changed the style of results representation several times and it is assumed that different types of information require a different representation style. Also, it has been established that the most frequent change to the visual results representation occurs after users perform a query which shows all the dissertations from a certain time period and which is taken from the advanced search mode; however, there is no correlation between this change and the client’s device used.