Reviewerly/exHarmony huggingface dataset:
Unconditionally Fast Secure Multi-party Computation with Multi-depths Gates Using Pre-computed Information

In secure multi-party computation (MPC), n participants execute secure communication in a circuit to compute any given function using their private inputs such that the system does not reveal any information about their inputs. Computing a share of n-inputs ( $$n>2$$ ) multiplication gates with various multiplicative depths has been an important subject in this research field as it increases the round complexity using, for example, Beaver’s triples method. That is because just the shares of the multiplication gates with the same depth can be computed each time of implementing the existing MPC protocols, and thus, the communication rounds of a circuit with different multiplicative levels increase. In this paper, we present a secure protocol which enables computing a share of simultaneous n-inputs multiplication gates as well as the addition gate in just one round of online computation phase. Therefore, our protocol enables computing a share of any given function in just one round of computation which would result in fast computation and gives an improvement on the current MPC systems. To achieve it, we employ the technique of (Theory of cryptography conference. Springer, pp 213-230, [2]), based on hyper-invertible matrices, for generating pre-computed shares of random values. Our protocol has the unconditionally security against a coalition of t parties controlled by a passive adversary with the communication complexity $$O(n^2)$$ for computing a share of n-inputs multiplication with different depths.
The integrated approach to the development of the research skills of middle school pupils in the process of chemistry learning

The implementation of the research approach has been considered as a means of the development of key and subject competencies, abilities and skills of pupils of the middle school in the whole in the process; a percentage range is indicated of results of diagnostics of components of research skills of middle-class pupils at chemistry lessons. Basic key components in the structure of the research skills and abilities are identified: cognitive, active and self-regulating. To assess this potential, a comprehensive system of educational and learning tasks in a certain sequence has been proposed, according to the formation of research skills as both special and subject. Subject skills include applying different mental actions, the clearly defined stages of further work, ability to predict the results of the experiment, to prove own opinion and ability to organize own work for the communication in the team. The special tasks include to plan a short-term experiment; to analyze and argue the proposed hypotheses; to draw conclusions upon the experiment results. According to the results of the research, it is possible to draw a conclusion about the insufficient level of formation of research skills, the development of which more depends on the ability, skills and capacity of the teacher, as a qualified professional, who possesses qualities to organize educational and research work, is able to manage the educational process. Three directions of development of research skills of pupils have been developed and implemented, the basic stages of the educational process organization directed to skills formation are determined, complex system of exercises have been implemented, which corresponds to requirements of research training and helps to form appropriate skills of pupils for process of studying chemistry. As a result of joint work in the achievements of program, key and subject competencies, results of training within various kinds of research activity of pupils have been shown. Key words: research skills, key and subject competencies, ability, skills and capacity, complex system of exercises
AttKGCN: Attribute Knowledge Graph Convolutional Network for Person  Re-identification

Discriminative feature representation of person image is important for person re-identification (Re-ID) task. Recently, attributes have been demonstrated beneficially in guiding for learning more discriminative feature representations for Re-ID. As attributes normally co-occur in person images, it is desirable to model the attribute dependencies to improve the attribute prediction and thus Re-ID results. In this paper, we propose to model these attribute dependencies via a novel attribute knowledge graph (AttKG), and propose a novel Attribute Knowledge Graph Convolutional Network (AttKGCN) to solve Re-ID problem. AttKGCN integrates both attribute prediction and Re-ID learning together in a unified end-to-end framework which can boost their performances, respectively. AttKGCN first builds a directed attribute KG whose nodes denote attributes and edges encode the co-occurrence relationships of different attributes. Then, AttKGCN learns a set of inter-dependent attribute classifiers which are combined with person visual descriptors for attribute prediction. Finally, AttKGCN integrates attribute description and deeply visual representation together to construct a more discriminative feature representation for Re-ID task. Extensive experiments on several benchmark datasets demonstrate the effectiveness of AttKGCN on attribute prediction and Re-ID tasks.
Light &amp; Shadow AR App

The Light and Shadow App is an immersive photography studio lighting workshop in Augmented Reality. The App allows photo enthusiasts to virtually step into a full-size photo lighting studio and visualize the placement of studio lights for portrait photography in three dimensions. With the Light and Shadow App, users can use smartphones or iPads, and Augmented Reality, to move around the photo studio scenes and study the position of lights from different points of view. The App was originally developed for university level photography students at Kwantlen Polytechnic University during the covid-19 restrictions of 2020 and 2021 when in-person learning was not possible. The goal of the app is to recreate the learning environment of the photo studio and allow students to visualize the effect of lighting on the portrait in an interactive and immersive format.
GRANULAR NETWORK TRAFFIC CLASSIFICATION FOR STREAMING TRAFFIC USING INCREMENTAL LEARNING AND CLASSIFIER CHAIN

In modern networks, network visibility is of utmost importance to network operators. Accordingly, granular network traffic classification quickly rises as an essential technology due to its ability to provide high network visibility. Granular network traffic classification categorizes traffic into detailed classes like application names and services. Application names represent parent applications, such as Facebook, while application services are the individual actions within the parent application, such as Facebook-comment. Most studies on granular classification focus on classification at the application name level. Besides that, evaluations in existing studies are also limited and utilize only static and immutable datasets, which are insufficient to reflect the continuous and evolving nature of real-world traffic. Therefore, this paper aims to introduce a granular classification technique, which is evaluated on streaming traffic. The proposed technique implements two Adaptive Random Forest classifiers linked together using a classifier chain to simultaneously produce classification at two granularity levels. Performance evaluation on a streaming testbed setup using Apache Kafka showed that the proposed technique achieved an average F1 score of 99% at the application name level and 88% at the application service level. Additionally, the performance benchmark on ISCX VPN non-VPN public dataset also maintained comparable results, besides recording classification time as low as 2.6 ms per packet. The results conclude that the proposed technique proves its advantage and feasibility for a granular classification in streaming traffic.
Critical Digital Pedagogy – Innovative Model

Like any kind of innovation, innovation in pedagogy takes new ideas and practices and brings them together in new ways to solve problems that presently do not have adequate solutions. Developing new pedagogical models, however, involves both the identification of problems and the suggestion of novel practices. The full power of pedagogy and pedagogical innovations, however, can only be evaluated by considering all the things the pedagogies are trying to achieve. The research and history of pedagogy from Plato to the modern day necessitate that critical pedagogy must consider all of these different themes of creativity simultaneously: connectivity, reflection, extension, embodiment, personalization, and scale. This approach helps create learning content and learning environments that consider all learning styles and learners’ various social and cultural backgrounds.
Oral Histopathological Photomicrograph Classification Using Deep Learning

The burden of oral cancer on the whole world in general and India in specific is increasing in an exponential order. Oral cancer is cancer that originates in the mouth region. The gold standard for diagnosing oral cancer is histopathological analysis. Hematoxylin and Eosin is the most common stain used in pathology labs as it helps in the staging and classification of most biopsy tissues. As this analysis is done manually, it allows errors in terms of misclassification. We have proposed a novel method to classify these Hematoxylin and Eosin stained photomicrographs through the fusion of the predictions of some of the most popular deep learning models. The deep learning models used in our experimental analysis include the winners and runners up of the Imagenet Large Scale Visual Recognition Challenge. Authors have achieved a successful classification of the photomicrographs into benign and malignant classes with the accuracy of 100% on the data consisting of 21 benign and 98 malignant images of 100x magnification.
Analysis of Recurrent Neural Network and Convolution Neural Network Techniques in Blood Cell Classification

Medical imaging is a process of obtaining photographic images of the human body for the purpose of diagnostics and further treatment. White blood cells are the cells that fight any disease that might want to attack the human body; they are the cells that form the immune system of the body. There are five different types of white blood cells responsible for attacking different invaders, like bacteria and parasites, among others. Recent times have seen medical imaging being used for identification of the different types of white blood cells being produced by the body. Machine learning and deep learning, which have revolutionised many important sectors, have been researched in the field of medical imaging as well. Many research works have been carried out in order to classify the different types of white blood cells, like neutrophils, eosinophils, lymphocytes, and monocytes. Proper identification will lead to better diagnosis of blood-related diseases. Many machine leaning and deep learning techniques have been successfully employed and have been experimented with for this purpose. Deep learning techniques, recurrent neural network and convolution neural network, have been employed for better classification. So, this chapter proposes to conduct a detailed study of different deep learning techniques used for classification of blood cell type and to analyse their effectiveness in producing accurate classification results.
Network Topology Classification in SDN Ecosystem using Machine Learning

To meet the increasing network demands of enterprise environments and data centers, traditional network architectures have been replaced by software-enabled hardware devices for developing agile, dynamic, and programmable networks. Software Defined Networking (SDN) is a new paradigm shift that abstracts network design and infrastructure in the software and then implements it by using software across hardware devices. SDN is used to build and manage the network in a customized way. SDN architecture offers network virtualization, network programmability, and flexibility by decoupling control and data planes which further enriches the network performance. As such, the SDN controller is a tactical control point in SDN. An SDN normally allows data flow control to the switches and/or routers and the rationale of the application's logic for deploying intelligent networks. SDN with Machine Learning (ML) and Artificial Intelligence (AI) techniques build network models, which essentially can take decisions based on self-learning and self-management capabilities. Accurate classification of topology is of prime importance to satisfy future network prerequisites like unpredictable traffic patterns, dynamic scaling, flexibility, and centralized control. The controller needs to have exact information about the topology of the network in order to configure and manage the network. Subsequently, topology classification is an important component of any Software Defined Network architecture. This paper presents the classification of topologies using different supervised ML algorithms. The accuracy obtained from Support Vector Machine (SVM) and Classification and Regression (CART) is 95% and 90% respectively. The experimental results show that according to the k Cross-Fold validation technique, SVM algorithm has been found to be the most accurate amongst the other ML algorithms with a mean accuracy value of 85%.
DEVELOPING SNAKES AND LADDERS MEDIA TO TEACH SPEAKING SKILL

This study aims to develop snakes and ladders media, which is integrated in English to determine the level of attractiveness and feasibility of using it, and to find out the response of students after using the snake and ladder media integrated with English. The research method adapts to the development model developed by (Borg and Gall, 1989). The study used the following seven steps: (1) potential problems, (2) data collection, (3) product design, (4) product validation, (5) product revision, (6) product trial, (7) product revision. The data collection tools used are: material expert questionnaires and media expert questionnaires as well as educator response questionnaires to determine product feasibility, student response questionnaires are used to determine product attractiveness. The results of the research that have been developed obtained a design in the form of a snake and ladder media integrated with English in classroom learning, obtaining a feasibility percentage of 4.2 by material experts, 3.9 by media experts, and 4.4 by educators. The percentage of participants' responses to the attractiveness of the product obtained 6.6. Based on the results of the description above, the product developed by the researcher is feasible to be used as a learning medium.
Safe deployment of a reinforcement learning robot using self stabilization

In toy environments like video games, a reinforcement learning agent is deployed and operates within the same state space in which it was trained. However, in robotics applications such as industrial systems or autonomous vehicles, this cannot be guaranteed. A robot can be pushed out of its training space by some unforeseen perturbation, which may cause it to go into an unknown state from which it has not been trained to move towards its goal. While most prior work in the area of RL safety focuses on ensuring safety in the training phase, this paper focuses on ensuring the safe deployment of a robot that has already been trained to operate within a safe space. This work defines a condition on the state and action spaces, that if satisfied, guarantees the robot’s recovery to safety independently. We also propose a strategy and design that facilitate this recovery within a finite number of steps after perturbation. This is implemented and tested against a standard RL model, and the results indicate a significant improvement in performance.
Benchmarked

During the 22nd week of this year (30 May - 3 June 2022), we had the honor to be part of the Benchmarked: Optimization Meets Machine Learning workshop held at the Lorentz center in Leiden, the Netherlands. The workshop was organized by some of the most renowned scientists from the field of optimisation and machine learning, Carola Doerr, Mike Preuss, Marc Schoenauer, Thomas Stützle, and Joaquin Vanschoren. The theme of the workshop was benchmarking at the intersection of machine learning and optimization, as this is a very important yet generally overlooked subject. It was also a perfect occasion for bringing together the brightest minds in the field to discuss the topic. We, as beginning PhD students, were very lucky to participate in this prestigious workshop and it gave us the opportunity to meet many colleagues in real life for the first time. In this post we'd like to take you along through an educational and very fun week.
The meaning of ‘relevance’: Korean native speakers’ acceptance judgment and interpretation of relevance conditionals

This paper mainly aims to investigate the interpretation of relevance conditionals in Korean. To this end, an acceptability judgment test was conducted on Korean native speakers, and the results show that Korean speakers accept relevance conditionals only when the consequent clause provides relevant information with respect to the action-relevant decision problem derived from the antecedent clause. Given this finding, this paper suggests the interpretation of Korean relevance conditionals based on the Kratzerian interpretation method of ordinary indicative conditionals with some modifications for Korean relevance conditionals. Under the current analysis, it is assumed that the antecedent clause functions as a restrictor of a modal base, just like ordinary conditional clauses. Also, it is claimed that a (potential) action-relevant decision problem is derived from the antecedent clause, and as a result, the addressee’s desire to solve the decision problem by deciding which action should be performed ranks at the top of the addressee’s effective preference; then the consequent clause provides information that helps the addressee to choose the best action among the possible actions that compose the decision problem.
The Method of Literature Education for Students in Arts and Physical Education - Focused on Writing a Group Plan

이 글은 예체능 계열 학생들을 위한 전공 연계 글쓰기의 일환으로 모둠 기획서 쓰기를 시도한 것이다. 모둠 기획서 쓰기는 예체능 계열 학생들이 전공과 연계하여 실기와 다른 방향에서 진로를 고민할 수 있도록 하여 글쓰기의 필요성을 인식하고 흥미를 유발하기 위해 도입한 글쓰기 방법이다. 모둠은 5명 내외로 편성한다. 기획서는 팸플릿을 만드는 정도의 분량과 내용으로한다. 대주제는 교수자가 제시하고 모둠별로 소주제를 결정한다. 기간, 장소, 주최 및주관 등의 요소는 교수자가 제한하고, 모둠 기획서 쓰기의 핵심이라 할 수 있는 기획의도와 작가 및 작품 소개 등은 모둠별 토론과 교수자의 피드백을 통해 완성시켜 나간다. 부기해야 할 사항은 기타를 활용하며, 표지도 제작하도록 한다. 모둠 기획서가완성되면 발표와 지정토론을 통해 상호피드백을 실시한다. 이와 같은 사항들은 같은예술 계열이라고 해도 학과마다 차이가 있으므로 그러한 점을 충분히 반영한다. 모둠 기획서 쓰기는 학생들이 자신의 전공과 관련하여 실습 이외에 새로운 각도에서 상상력을 발휘하여 진로를 탐색할 수 있으며, 이를 통해 글쓰기의 필요성을 인식하고 흥미롭게 글쓰기를 해볼 수 있다는 데서 의의가 있다. 또한 모둠 글쓰기는 협업을 전제하는 바 앞으로 실기 이외의 부분에서 협동하여 결과물을 제출하는 연습도 경험할 수 있었다. 그러나 여기에서는 체육 계열의 학생들을 대상으로 실제로 적용해 보지 못했다는한계가 있다. 이는 추후의 과제로 남긴다.This study is an attempt to write a group plan as part of a major-linked writing project for students in arts and physical education. Composing a group plan was introduced to make arts and physical education students interested in writing and to connect with their majors so that they can consider their careers in different directions through practical skills. The group consists of five people. The plan is based on the amount and contents of the pamphlet. The big themes are presented by the professor and determines the sub-themes by group. The professor shall limit elements such as duration, location, organization and supervision. The planning intentions, writers and introduction, which are the core of writing a group plan, are completed through discussions by group members and feedback from professors. Professors teach composition with consideration of the difference between majors, even if they are in the same category of arts and physical education.
A Study on the Situation and Possibility of High School Literature Clubs

본 논문은 창의적 체험활동으로서의 고등학교 문학 동아리의 운영 실태를 살펴보고 동아리 활동에서 글쓰기의 자율성이 가지는 의미를 탐색해보고자 한다. 이를 위하여 전국의 57개 고등학교를 대상으로 문학 동아리 설치 여부를 조사하였고, 4명의 현역 작가를 대상으로 고교 시절 문학 동아리 활동 사례를 설문 조사하여 동아리 활동의 유형을 알아보고 그 활동이 실제 작품 활동에 끼치는 영향을 분석하였다. 글쓰기와 관련한 고등학교 동아리는 여전히 교과학습에 종속되어 자발성과 지속성을 획득하지 못하고 있는 것으로 판단된다. 창의적 체험활동 시행 이후 동아리 구성에 형식적인 자율성은 부여되었으나 글쓰기의 자율성은 심각하게 훼손되어 있다. 입시 위주의 교육 현실에서 글쓰기 동아리는 창의적인 아우라를 확산하지 못하고 있다. 학생중심의 문학공동체로서 문학 동아리가 활성화되기 위해서는 지금보다 학생들의 자주성을 강화하고 지역사회와의 연계, 글쓰기 프로그램의 개발 등이 요구된다.This paper aimed to investigate the operational status of the high school literature club as a creative experience activity, and to examine its goals and possibilities. For this purpose, the study investigated the possibility of establishing literary clubs in 57 high schools across the country. In addition, information was obtained from four active writers for details of literary club activities in their high school days. The types of club activities undertaken by them, and the effects of the activities on actual writing activities were analyzed. It is concluded that high school clubs related to writing are still subordinate to coursework and are not able to obtain autonomy and sustainability. In some cases, after the implementation of creative experiential activities, formal autonomy was given for organizing the club, but the autonomy of writing was severely restricted. Writing clubs are not able to spread a creative atmosphere in an educational environment that concentrates on entrance examinations. In order for literary clubs to be activated as student-centered literary communities, it is necessary to strengthen students’ autonomy, connect with the local community, and develop writing programs.
The analysis of the nested structure of named entities in Korean

This paper analyzes the hierarchical structures of named entities in the NIKL Named Entity Corpus, which is annotated with 553,830 flat named entity tags. This study will be a base for developing a method to build a Korean nested named entity corpus. The flat version of named entity recognition identifies mentions as linear spans. The nested named entity approach analyzes the hierarchical internal structure of named entities which may consist of smaller component named entities. We extracted candidate mentions for the nested named entity analysis from the NIKL Named Entity Corpus and classified them into three categories: serial named entities, complex named entities, and phrases with a named entity head. These candidates were reviewed manually to be selected as the target of nested named entity analysis. Finally, we discussed the span and the internal structure of named entities and proposed principles and guidelines for the construction of the Korean nested named entity corpus.