Reviewerly/exHarmony huggingface dataset:
Spatiotemporal characteristics of medical extended-reality systems

Emerging uses for extended-reality (XR) head-mounted displays (HMDs) within medical environments include visualizations of medical data across various imaging modalities including radiography, computed tomography, ultrasound, and magnetic resonance images. Rendering medical data in XR environments requires real-time updates to account for user movement within the environment. Unlike stationary 2D medical displays, XR HMDs also require real-time stereoscopic rendering capabilities with high performance graphics processing units. Furthermore, performance depends on the status of added systems including tracking sensor technology, user's input data, and in the case of augmented reality (AR), spatial mapping and image registration. These temporal considerations have implications for the interpretation of medical data. However, methods for the evaluation of their effects on image quality are not yet well defined. The definition of these effects in the context of medical XR devices is at best inconsistent if not completely lacking. In this work, we compare the effects and causes for three classes of XR spatiotemporal characteristics affecting medical image quality: temporal artifacts, luminance artifacts, and spatial mapping artifacts. We describe the XR system components starting from user movement recognized by inertial measurement unit and camera sensors and ending with user perception of the display through the optics of the HMD. We summarize our findings and highlight device performance areas contributing to the different effects.
Breaking down memory walls

Log-Structured Merge-trees (LSM-trees) have been widely used in modern NoSQL systems. Due to their out-of-place update design, LSM-trees have introduced memory walls among the memory components of ...
Diverse Audio-to-Image Generation via Semantics and Feature Consistency

Humans are capable of imagining scene images when hearing ambient sounds. Therefore, audio-to-image synthesis becomes a challenging yet practical topic for both natural language comprehension and image content understanding. In this paper, we propose an audio-to-image generation network by applying the conditional generative adversarial networks. Specifically, we utilize such generative models with the proposed feature consistency and conditional adversarial losses, so that diverse image outputs with satisfactory visual quality can be synthesized from a single audio input. Experimental results on sports audio/visual data verify that the effectiveness and practicality of the proposed method over the state-of-the-art approaches on audio-to-image synthesis.
A Tightly Secure DDH-based Multisignature with Public-Key Aggregation

From the birth of the blockchain technology, multisignatures attract much attention as a tool for handling blockchain transactions. Concerning the application to the blockchain, multisignatures with public-key aggregation, which can compress public keys of signers to a single public key, is preferable to the standard multisignature because the public keys and the signature used in a transaction are stored to verify the transaction later. Several multisignature schemes with public key aggregation are proposed, however, there are no known schemes having a tight security reduction. We propose a first multisignature with public-key aggregation whose security is proven to be tightly secure under the DDH assumption in the random oracle model. Our multisignature is based on the DDH-based multisignature by Le, Yang, and Ghorbani, however, our security proof is different from theirs. The idea of our security proof originates from another DDH-based multisignature by Le, Bonnecaze, and Gabillon whose security proof is tightly one. By tailoring their security proof to a setting which admits the public-key aggregation, we can prove the tight security of our multisignature.
Development of a framework for a collaborative and personalised voice assistant

Virtual assistant is an artificial intelligence (AI) system that understands natural voice commands and completes the task for the user. There is a need for seamless integration of high level general purpose voice assistants such as Google Assistant and Amazon Alexa under a single framework. In this paper, a novel collaborative personalised voice assistant with the ability to interface hardware components for an interactive environment is proposed. The proposed assistant takes the users' voice input as the query and processes it using a natural language processing (NLP) unit which generates intents from the converted text. Based on the type of intent, the NLP unit passes to one of the two services Google Assistant or Amazon Alexa. If the query is related to requesting information, the NLP passes to Google Assistant which gives an appropriate answer as the voice input and/or a query related to controlling hardware objects, will be pass on to Amazon Alexa. To demonstrate our collaborative voice assistant, a servo motor is used as a hardware object which controls the movement of the 5-inch liquid crystal display (LCD). Results prove that the proposed collaborative voice assistant brings together the strengths of Google Assistant and Amazon Alexa in a single framework.
11 Pragmalinguistic variation in L2 Spanish e-mail requests: Learner strategies and instructor perceptions

This study focuses on pragmalinguistic variation in the e-mail requests of second-year Spanish second language (L2) learners and recipients’ perception of these requests. The data come from a corpus of first language (L1) English (n=239) and L2 Spanish (n=239) e-mails. Requests were analyzed regarding level of directness as well as internal mitigation strategies in both languages. Twenty-six instructors of Spanish completed a 16-item questionnaire that examined the perception of requests with varying degrees of politeness. Results indicated that participants primarily use speaker-oriented requests regardless of the language and that they tend to use direct requests regardless of degree of imposition or language of the e-mail. Participants made more high imposition requests in L1 English and used more internal modification for high imposition requests in both languages. Indirect requests were rated as more polite than direct requests, and requests with no internal modification were perceived as significantly less polite than those with modification.
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks

Curie's principle states that when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them. We demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to articulate many symmetry-relevant scientific questions into simple optimization problems. We prove these properties mathematically and demonstrate them numerically by training a Euclidean symmetry equivariant neural network to learn symmetry-breaking input to deform a square into a rectangle and to generate octahedra tilting patterns in perovskites.
How to Improve the Effects of Foreign Language Education in Korea and China: Focusing on the Improvement of Foreign Language Education Policy

This study examined the current situation of foreign language education , teacher training and education in Korea and China. The analysis and comparison of both countriesâ€™ s educational policies, problems, and improvement strategies revealed aspects requiring attention for both Korea and China. When laying down and enforcing foreign language education policies in the future, more attention should be paid to rigorous systematization of the foreign language curriculum. Furthermore, in the context of increasingly intensifying globalization, it is necessary to diversify foreign language subject options so that students can learn various foreign languages and improve their multicultural awareness. Finally, foreign language teacher training and education should be promoted more intensively to empower them with the competence and qualifications necessary to make them excellent teachers and important agents in delivering foreign language education to learners.
AUTONOMIZATION AS A FORM OF MANAGEMENT OF AN INSTITUTION OF ADDITIONAL EDUCATION: SETTING A PEDAGOGICAL PROBLEM

In the article, the author notes that overthe past decade, reforms have beencarried out in our country related todemocratization, globalization, anddigitalization. However, despite theimplementation of reforms, themanagement system of institutions ofadditional education, in particular, itsmovement towards autonomizationremains unclear. It has been proved thatmost of the studies concerning thephenomenon of autonomization areconcentrated on the managementprocesses of higher education, althoughthis phenomenon should be ahead of thecurve in the entire education system.The need for autonomization of additionaleducational institutions is indisputable,because today our country, focusing onthe best world practices of lifelonglearning, with the help of non-formal /additional education is involved ininnovative processes associated with thenew philosophy of post-formal education.Using the example of various studies, theauthor shows that the implementation ofthe best teaching practices in combinationwith the reliance on the principles ofadherence to the quality of educationgives different results that requirereflection.The author reveals the problem from threesides - political, economic, pedagogical,and focuses on the need to introducereforming the management of additionaleducational institutions in accordance withthe socio-economic requirements ofsociety, relying on the possibility ofachieving the goals of teaching in areasonable society.
Simple non-iterative clustering and CNNs for coarse segmentation of breast cancer whole-slide images

Breast cancer is the dominant cancer among women as it accounts for about one-quarter of all cancer cases in females. The digitized images of Hematoxylin and Eosin (H&amp;E) stained slides of breast cancer specimens carry valuable diagnostic information. However, inspecting these slides manually is a non-trivial task prone to subjective interpretation. Digital pathology (DP) and artificial intelligence (AI) open an opportunity for objective interpretation of the image data. It is challenging to automate the segmentation process in the whole slide images due to the visual complexity of tissue appearance without the need for tedious and time-consuming fine annotations. Many algorithms classify the tissue regions into different types instead of segmenting them, as the classification algorithms require coarse annotations that are easier to acquire. In this paper, we propose a new segmentation framework that combines the simple non-iterative clustering algorithm with a standard convolutional neural network (CNN) classifier to segment whole slide images into different tissue types. In addition, a graph-based post-processing step is applied to improve the framework segmentation performance further. The results show promising improvement to the CNN classifier based coarse segmentation, which would give better feasibility to quantify and study tissues’ mutual relationships.
Teachers’ Confidence in Teaching English Using You*Tube Channel in Early Pandemic Covid-19

This study aims to identify teachers' confidence in teaching English using a You*Tube channel in early pandemic. This study used a qualitative quantitative approach with a survey method. The data sources in this study were 50 teachers from both public and private primary schools in Denpasar who used You*Tube channel as the media in teaching English. The data were collected through questionnaires consisting of 30 closed and open statements which were grouped into four categories. Closed statements consisted of yes/no statements as well as agreement and frequency statements based on a Likert’s five-point scaling. Open statements were intended to provide additional information on the answers to the closed statements that had been given. The data were analyzed qualitatively and presented descriptively using narration supported by diagrams and tables. The result of analysis data showed that teachers' confidence in teaching English using a YouTube channel in the early pandemic was supported by many factors, such as educational background, teaching experience and teacher perspective in online English learning using the You*Tube channel. However, teaching English through the You*Tube channel needs to be used frequently to improve teachers' skills and techniques in using this media for better teaching.
A Memetic Random Key Algorithm for the Balanced Travelling Salesman Problem

This paper considers a variant of the well-known travelling salesman problem. In this variant, the cost of travelling from a vertex to another is an arbitrary value on the real line and the objective is finding a tour with minimum absolute value cost. We propose a memetic random key algorithm for this problem and experiment with different settings of the algorithm. The experiments explore the use of a flexible decoding mechanism and also the frequency of applying local search within the random key algorithm.
Secure Outsourced Private Set Intersection with Linear Complexity

In the context of privacy preserving protocols, Private Set Intersection (PSI) plays an important role due to their wide applications in recent research community. In general, PSI involves two participants to securely determine the intersection of their respective input sets, not beyond that. These days, in the context of PSI, it is become a common practice to store datasets in the cloud and delegate PSI computation to the cloud on outsourced datasets, similar to secure cloud computing. We call this outsourced PSI as OPSI. In this paper, we design a new construction of OPSI in malicious setting under the Decisional Diffie-Hellman (DDH) assumption without using any random oracle. In particular, our OPSI is the first that incurs linear complexity in malicious environment with not-interactive setup. Further, we employ a random permutation to extend our OPSI to its cardinality variant OPSI-CA. In this case, all the properties remain unchanged except that the adversarial model is semi-honest instead of malicious.
Innovation Reform of College English Teaching Management Based on Computer Network Technology

Abstract College English Teaching (CET) is gradually moving towards the direction of personalized and autonomous learning. Based on computer and classroom teaching mode, it not only helps to enhance the dominant position of students in the teaching process, but also helps to improve students’ autonomy, learning motivation and learning efficiency, so it has high research value. Based on this, this paper first analyses the characteristics of CET based on computer network technology, then studies the current situation and problems of CET and management, and finally puts forward the innovation and reform strategy of CET management based on computer network technology.
EMONAS: efficient multiobjective neural architecture search framework for 3D medical image segmentation

Deep learning plays a critical role in medical image segmentation. Nevertheless, manually designing a neural network for a specific segmentation problem is a very difficult and time-consuming task due to the massive hyperparameter search space, long training time and large volumetric data. Therefore, most designed networks are highly complex, task specific and over-parametrized. Recently, multiobjective neural architecture search (NAS) methods have been proposed to automate the design of accurate and efficient segmentation architectures. However, they only search for either the macro- or micro-structure of the architecture, do not use the information produced during the optimization process to increase the efficiency of the search, and do not consider the volumetric nature of medical images. In this work, we propose EMONAS, an Efficient MultiObjective Neural Architecture Search framework for 3D medical image segmentation. EMONAS is composed of a search space that considers both the macro- and micro-structure of the architecture, and a surrogate-assisted multiobjective evolutionary based algorithm that efficiently searches for the best hyperparameters using a Random Forest surrogate and guiding selection probabilities. EMONAS is evaluated on the task of cardiac segmentation from the ACDC MICCAI challenge. The architecture found is ranked within the top 10 submissions in all evaluation metrics, performing better or comparable to other approaches while reducing the search time by more than 50% and having considerably fewer number of parameters.
SG_BIoT: Integration of Blockchain in IoT Assisted Smart Grid for P2P Energy Trading

The demand for energy has increased exponentially over the last decade, directing the trend towards modernizing the electricity requirements using renewable energy resources. The demand has led to the development of smart grid infrastructure which identifies and responds to native electricity requirements using grid automation, intelligent substations, and smart meters. Smart grids have led to the evolution of microgrid technology which allows the user to distribute generation on small scale rather than centralized generation. In microgrids, the centralized control is provided through renewable resources. However, the use of IoT devices in the grid system increases the vulnerability to compromise the grid system because of it’s centralized behavior. This paper proposes a system that provides resilience and security by integrating blockchain in smart grids. The decentralization of smart grid impacts the electricity distribution that drives the energy market towards an optimal resource sharing environment which enables an efficient energy trading scheme with high security. The backtraceability nature of blockchain eases the process of accounting for customers transaction and electricity billing. Thus, electricity can be provided whenever and wherever.
High-Resolution Optical and SAR Image Registration Using Local Self-Similar Descriptor Based on Edge Feature

Due to different imaging mechanisms, the registration of optical and Synthetic Aperture Radar (SAR) image is a very challenging task. Many optical and SAR registration methods have been proposed. But most of them are for low-to-medium resolution images, and less for high-resolution images. Therefore, this paper proposes a high-resolution optical and SAR image registration method using local self-similar descriptor based on edge feature. Firstly, a Gauss-Gamma bi-windows algorithm is used to extract the edge intensity maps of the images respectively. Its function is to eliminate the non-linear gray-scale difference between SAR and optical images, and also to avoid the interference of isolated speckle noise on feature point extraction. Then, local self-similar descriptor is extracted on the edge intensity map, and descriptor matching is performed using Euclidean distance. Finally, the fast sample consensus algorithm is used to eliminate mismatched point pairs. The experimental results can effectively resist speckle noise and radiation differences, and obtain pixel-level registration accuracy.
The neural networks: application and optimization Application of Levenberg- Marquardt algorithm for Tifinagh character recognition

Levenberg-Marquardt (LM) Optimization is a virtual standard in nonlinear optimization. It is a pseudosecond order method which means that it works with only function evaluations and gradient information but it estimates the Hessian matrix using the sum of outer products of the gradients. In this word, we will applythe Levenberg- Marquardt method for learning and recognition pattern concerned Tifinaghcharacters; this last will be compared to other methods such as Windrow-Hoff (WH). This note reviews the application for Levenberg-Marquardt for network neuronal and also details the algorithm.
The research output of state universities in Vidarbha Region of Maharashtra: A scientometrics study

The present article attempts to present the scientometrics picture of the research output of state universities in the Vidarbha Region of Maharashtra. Continuous growth was observed in the publication output of these universities. Material Science Multidisciplinary and Chemistry Multidisciplinary were two top leading subjects for both the universities. Multi-authorship was seen as the major trend in the authorship pattern. S. J. Dhoble was observed as the most prolific author both in terms of several publications (460) and fractionalization (138.5024) of publications. However, M.K. Rai (31) and S.V. Moharil (31) stand supreme in respective of h-index. The co-authorship network reflects the authors having a minimum of 10 publications. The Journal of Luminescence and Luminescence was identified as the most productive journal out of the combined output. The top ten manuscripts from the combined output have received 6289 citations. Further, author keywords and keywords plus highlights the prominent subjects on which authors from both universities have preferred to write upon.
Why Do Parameter Values in the Zipf-Mandelbrot Distribution Sometimes Explode?

The Zipf-Mandelbrot distribution serves as a mathematical model for ranked frequencies in many areas of scientific research, including linguistics. Many linguistic units, like e.g., words or word n-grams, follow this distribution. However, in some cases, such as for graphemes in linguistics or species abundance and diversity data in biology, the parameters of the Zipf-Mandelbrot distribution are virtually uninterpretable, as their values strongly depend on the precision of numerical methods used to estimate them (values from several tens to several hundreds are not uncommon). It is shown in the paper that these values can be explained by the convergence to the geometric distribution, which forces both parameters of the Zipf-Mandelbrot distribution to increase to infinity while their ratio converges to a constant. Some examples which illustrate this limit behaviour are presented.
Research data management: An evaluation of Indian research funding agencies’ policies and guidelines

Research funding agencies are a major Research Data Management (RDM) stakeholder along with libraries, researchers, managing bodies of institutions, publishers & government bodies. Globally, research funding agencies are increasingly mandating the sharing of research data generated from funded research projects. Indian research funding agencies are still at the nascent level in identifying the value of research data created by their researchers and making policies in terms of data management, sharing and accessibility. This study has been undertaken to identify the policies, requirements and guidelines related to data management and sharing of the Indian research funding agencies regarding research data generated in funded research. Data was collected from the websites of 73 major research funding agencies in India irrespective of discipline. Information from their stated policies guidelines was extracted to identify references related to data sharing, data management, data policies, Data Management Plan (DMP), data deposit and public access. The study findings indicate that 05 research funding agencies/central ministries, Govt. of India have formal policies and guidelines for RDM and sharing. Further analysis of the policies was conducted to identify the coverage, guidelines and data repository elements included. The study findings help in giving better insights for identifying the gap among the available data policies in India with a special reference to the need for developing more policies and guidelines.