Reviewerly/exHarmony huggingface dataset:
Quantum Rewinding for Many-Round Protocols

We investigate the security of succinct arguments against quantum adversaries. Our main result is a proof of knowledge-soundness in the post-quantum setting for a class of multi-round interactive protocols, including those based on the recursive folding technique of Bulletproofs. To prove this result, we devise a new quantum rewinding strategy, the first that allows for rewinding across many rounds. This technique applies to any protocol satisfying natural multi-round generalizations of special soundness and collapsing. For our main result, we show that recent Bulletproofs-like protocols based on lattices satisfy these properties, and are hence sound against quantum adversaries.
HBA optimized Efficient CNN in Human Activity Recognition

A HAR can be a significant test in different applications, counting human–computer connectivity. Hence, in this paper, developing an efficient background subtraction and a deep learning model aimed at classifying human actions from the videos. The main motive of the research work can be developed efficient technique for identifying human activities in videos using a Convolutional Neural Network (CNN) and hybrid feature extraction techniques. The projected CNN classifier can be developed and combined with the Honey Badger Algorithm (HBA) and CNN in the process of feature extraction. The projected classifier is utilized to identify human actions such as bending, walking and so on. HBA can be consumed to optimize the weighting parameters which enhance the performance of CNN. The projected approach is validated considering Weizmann and KTH datasets. Additionally, the performance metrics are considered to justify the performance of the projected technique such as specificity, sensitivity and accuracy.
Boosting Knowledge Graph Generation from Tabular Data with RML Views

A large amount of data is available in tabular form. RML is commonly used to declare how such data can be transformed into RDF. However, RML presents limitations that lead, in many cases, to the need for additional preprocessing using scripting. Although some proposed extensions (e.g., FnO or RML fields) address some of these limitations, they are verbose, unfamiliar to most data engineers, and implemented in systems that do not scale up when large volumes of data need to be processed. In this work, we expand RML views to tabular sources so as to address the limitations of this mapping language. In this way, transformation functions, complex joins, or mixed syntax can be defined directly in SQL queries. We present our extension of Morph-KGC to efficiently support RML views for tabular sources. We validate our implementation adapting R2RML test cases with views and compare it against state-of-the-art RML+FnO systems showing that our system is significantly more scalable. Moreover, we present specific examples of a real use case in the public procurement domain where basic RML mappings could not be used without additional preprocessing. Resource type: Software framework License: Apache 2.0 DOI: 10.5281/zenodo.7385488 URL: https://github.com/morph-kgc/morph-kgc
Interpretability-Guided Human Feedback During Neural Network Training

When models make wrong predictions, a typical solution is to acquire more data related to the error: an expensive process known as active learning. Our supervised classification approach combines active learning with interpretability so the user can correct such mistakes during the model’s training. At the end of each epoch, our training pipeline shows examples of mistaken cases to the user, using interpretability to allow the user to visualise which regions of the images are receiving the model’s attention. The user can then guide the training through a regularisation term in the loss function. This approach differs from previous works where the user’s role was to annotate unlabelled data since, in this proposal, the user directly influences the training procedure through the loss function. Overall, in low-data regimens, the proposed method returned lower loss values in the predictions made for all three datasets used: 0.61, 0.47, 0.36, when compared with fully automated training methods using the same amount of data: 0.63, 0.52, 0.41, respectively. We also observed higher accuracy values in two datasets: 81.14% and 92.58% over the 78.41% and 92.52% seen in fully automated methods.
Genetic Algorithm Based Service Broker Policy to find Optimal Datacenters in Cloud Services

In modern-day computing, cloud services are widely used in every aspect of life. So, user satisfaction depends on the effectiveness and efficiency of cloud services. Service broker policy of the cloud maintains the effectiveness and efficiency of cloud services. Service broker policy provides the rules and norms based on which a data center is selected for a userbase request. This paper proposes a genetic algorithm-based service broker policy that provides the optimal sequence of data centers for different userbases based on their requirements. This research aims to find an optimal data center for userbases that can achieve user satisfaction by minimizing the cloud service’s response time and data processing time. We have experimented with our proposed genetic algorithm-based service broker policy in the CloudAnalyst platform based on different real-world scenarios. Simulation results indicate that our proposed genetic algorithm outperforms existing traditional algorithms.
JUMLA-QSL-22: A Novel Qatari Sign Language Continuous Dataset

This paper proposes the first large-scale and annotated Qatari sign language dataset for continuous sign language processing. This dataset focuses on phrases and sentences commonly used in healthcare settings and contains 6300 records of 900 sentences. The dataset collection process involves diverse participants, including both hearing-impaired individuals and sign interpreters, to capture variations in signing styles, speeds, and other linguistic nuances. The data collection setup integrates advanced technology, including true depth cameras, to comprehensively record signing movements from various angles. The collected dataset is rich in content, encompassing different signing variations and linguistic intricacies. The dataset is publicly available in IEEE Dataport. The paper also analyzes the data captured to understand the trends and patterns within the data. As the global population with hearing difficulties continues to grow, there is a pressing need for effective sign language recognition systems to bridge the communication gap between the deaf and non-deaf communities and the introduction of the JUMLA-QSL-22 dataset constitutes a significant stride toward addressing this imperative need.
Reinforcement Learning Under Partial Observability Guided by Learned Environment Models

Reinforcement learning and planning under partial observability is notoriously difficult. In this setting, decision-making agents need to perform a sequence of actions with incomplete information about the underlying state of the system. As such, methods that can act in the presence of incomplete state information are of special interest to machine learning, planning, and control communities. In the scope of this paper, we consider environments that behave like a partially observable Markov decision process (POMDP) with known discrete actions, while assuming no knowledge about its structure or transition probabilities. We propose an approach for reinforcement learning (RL) in such partially observable environments. Our approach combines Q-learning with IoAlergia, an automata learning method that can learn Markov decision processes (MDPs). By learning MDP models of the environment from the experiences of the RL agent, we enable RL in partially observable domains without explicit, additional memory to track previous interactions for dealing with ambiguities stemming from partial observability. We instead provide the RL agent with additional observations in the form of abstract environment states. By simulating new experiences on a learned model we extend the agent’s internal state representation, which in turn enables better decision-making in the presence of partial observability. In our evaluation we report on the validity of our approach and its promising performance in comparison to six state-of-the-art deep RL techniques with recurrent neural networks and fixed memory.
Fusion Blossom: Fast MWPM Decoders for QEC

The Minimum-Weight Perfect Matching (MWPM) decoder is widely used in Quantum Error Correction (QEC) decoding. Despite its high accuracy, existing implementations of the MWPM decoder cannot catch up with quantum hardware, e.g., 1 million measurements per second for superconducting qubits. They suffer from a backlog of measurements that grows exponentially and as a result, cannot realize the power of quantum computation. We design and implement a fast MWPM decoder, called Parity Blossom, which reaches a time complexity almost proportional to the number of defect measurements. We further design and implement a parallel version of Parity Blossom called Fusion Blossom. Given a practical circuit-level noise of 0.1%, Fusion Blossom can decode a million measurement rounds per second up to a code distance of 33. Fusion Blossom also supports stream decoding mode that reaches a 0.7 ms decoding latency at code distance 21 regardless of the measurement rounds.
Research on Decision Tree Application in Data of Fire Alarm Receipt and Disposal

The paper adopts decision tree algorithm in SPSS to mine the collected data sample of fire alarm receipt and disposal in a city in 2014, primarily verifies the validity of decision tree method in the analysis of data of fire alarm receipt and disposal, and briefly analyzes the mining results. The results show that, there exists categorical relationship in descriptive attributes of the data of city fire alarm receipt and disposal, which has guiding significance for fire alarm receipt and disposal, meanwhile, makes primary exploration into application of data mining in analyzing data of fire alarm receipt and disposal.
الصعوبات التي تحول دون استخدام طلبة كلية العلوم التربوية في الجامعة الاردنية للتعلم الالكتروني

This study aimed to identify the difficulties that may prevent the use of students of the Faculty of Educational Sciences at the University of Jordan for e learning. The study included a random sample of (150) students, constituting 8% of the study population of 1772. The questionnaire developed by using a descriptive survey method. The analysis based on descriptive statistical analysis, One Way Anova analysis, and T-test.
How to…

Abstract This month Simon Tappin, Editor of Companion, discusses how to approach the patient with abnormalities in these important electrolytes.
Multi-source weak supervision for saliency detection

The high cost of pixel-level annotations makes it appealing to train saliency detection models with weak supervision. However, a single weak supervision source usually does not contain enough information to train a well-performing model. To this end, we propose a unified framework to train saliency detection models with diverse weak supervision sources. In this paper, we use category labels, captions, and unlabelled data for training, yet other supervision sources can also be plugged into this flexible framework. We design a classification network (CNet) and a caption generation network (PNet), which learn to predict object categories and generate captions, respectively, meanwhile highlight the most important regions for corresponding tasks. An attention transfer loss is designed to transmit supervision signal between networks, such that the network designed to be trained with one supervision source can benefit from another. An attention coherence loss is defined on unlabelled data to encourage the networks to detect generally salient regions instead of task-specific regions. We use CNet and PNet to generate pixel-level pseudo labels to train a saliency prediction network (SNet). During the testing phases, we only need SNet to predict saliency maps. Experiments demonstrate the performance of our method compares favourably against unsupervised and weakly supervised methods and even some supervised methods.
Wendy Carlos’s Xenharmonic Keyboard

Summary.Wendy Carlos is an American composer and electronic musician who constructed three closely related musical scales she called α, β, and γ. These scales are xenharmonic, i.e., unrelated to the familiar 12-tone scale. However, the 5 black and 7 white keys contained in one octave on a standard keyboard have a strong analogy with representations of the α and β scales contained in one perfect fifth on the γ scale. We explore this analogy using idea from algebra and number theory, and find that Carlos’s three scales arise naturally from the theory of continued fractions. Moreover, an infinite family of similarly “nice” musical scales arises from the same construction.
ANALISIS GAMBARAN KOLABORASI PETUGAS KESEHATAN DALAM PENURUNAN BERAT BADAN PASIEN STROKE ISKEMIK

Stroke is a disorder of local or extensive brain function that occurs suddenly, quickly, and can cause death.Obesity is known as one of the causes of hypertension and stroke.Body Mass Index (BMI) is a simple tool to monitor the nutritional status especially that is related to underweight and overweight status.The purpose of this study was to determine the analysis of the picture of collaboration between health workers in reducing the weight of patients with ischemic stroke in general hospital of Makassar.This research used descriptive design.Samples were taken by purposive sampling with 10 stroke patients who had BMI > 25.This study is conducted by observations and interviews.The instrument used an observation sheet containing demographic data, measurements of blood pressure, weight, and height.Health worker collaboration in reducing stroke patients' weight is more towards health education to patients and families.Health education in the form of a recommended diet such as high fiber, low salt, adequate mineral water intake, and increase physical activity, if there are no contraindications to medical diagnosis by a doctor.Weight loss requires a process and the changes are not immediately visible in a short time, it depending on the length of stay in the hospital, so that consistency in diet and physical activity is needed at home.
중서서원의 종교교육이 윤치호의 개종에 미친 영향 연구

This study illustrates the influence of the mission school's religious education on Chi-ho Yun's conversion. From 1885 to 1888, Yun moved to Shanghai and studied in the Anglo-Chinese College run by Methodist Episcopal Church, South. The study reveals that the Anglo-Chinese College had eight years of curriculum and Yun entered into the third grade. Next, the study finds that the college followed curriculum typical of mission schools in Asia during the late 19th century: They used the Bible in every class, had religious services every morning and afternoon, and had a Bible class every Saturday. The study also finds that Yun went to church for the first time in his life on February 8, 1885, influenced by Rev. W. W. Royall—not on February 15, 1885, led by Rev. Y. J. Allen as previously argued. Missionaries paid great attention to Yun, who was an influential exile from a country that the M.E.C.S Church had yet to enter, and made every effort to lead him to Christianity. Thus, the study emphasizes the strategic approach taken by the mission school in converting Yun, which created a particular environment for him to explore and develop his personal interest in the religion and modern thought.
Text Compression for Myanmar Information Retrieval

Myanmar word segmentation is an important task for construction of dictionary file for Myanmar information retrieval and Myanmar text compression. Although Myanmar word segmentation using dictionary and orthography has been existed for Myanmar language, the performance of word segmentation depends on the coverage of the dictionary and training dataset and can cause out of vocabulary (OOV) problem, leading to lower precision and recall in information retrieval. And to compress Myanmar text, words in text needs to be recognized first. In this paper, we propose a new method for Myanmar word segmentation by local statistical dataset without the use of any additional data (e.g., training corpus) and new compressed Myanmar Information Retrieval (MIR) model which used End Tagged Dense Code (ETDC) text compressed method. The experimental results showed that the method can improve evaluation of vocabulary file with precision 75%, recall 87%, F-measure 80% and average compression ratio is 32% of texts for Myanmar language.
한국어 예비교원의 국외 ‘사전 실습’ 프로그램 개발과 실제 -일본(도쿄, 오사카) 지역을 중심으로-

In order for pre-service Korean teachers to acquire the qualification of Korean language teachers, it is necessary to complete the ‘Korean practicum’ course, which is the 5th part of Korean Language education curriculum. It is good to have many opportunities for early field experience and practicum in the educational field in order to become a competent teacher. Unfortunately, there are not as many practical activities given to them. Therefore, pre-service Korean teachers can experience Korean language education abroad through prior training program before taking the ‘Korean Practicum’ course. In addition, they can train talented Korean language teachers while cultivating global leadership and further expanding their understanding of other cultures through various learners. This study first reviews the course and contents of the NCATE to develop a prior training program for overseas, and then compares and analyzes with the ‘Korean Practicum’. Based on this, we developed an overseas Korean language prior training program for pre-service Korean teachers and operate this program mainly in the Tokyo and Osaka area. Futhermore, I will look at the composition and role of the manpower(career teacher, coordinator for practice, head and teacher in charge of practice at agency)related to the prior training. (KyungHee University)
2019 한국 문화연구, 현황과 과제

This paper discusses the changes in the studies of Korean culture since 2010’s. Cultural studies based on Korean literature have three interdisciplinary fields(categories): the research of Korean modern literary history via cultural studies, the studies of Korean modern cultural history, and the studies of ordinary culture. I evaluate the current status of these fields, and examine the tasks in the aspects of institutions and contents. Recently the research of Korean modern literary history via cultural studies left remarkable outcomes especially in the studies of literature and culture in 1960’s and 1970’s, translation culture since the Liberation, female literature and feminism view on the reinterpretation of literature history, the studies of people’s literature(Minjung literature) and labor literature, and the studies of history of cold war culture. The studies of Korean literature will continue to push back the boundaries of ‘literature’ and to transcend nationalism, elitism and androcentrism.Meanwhile ‘literature to culture’ in Korea has kept pace with ‘literature to knowledge.’ In other words, the studies of intellectual history, university history, and cultural history have actively developed on the basis of national research projects. In addition, cultural studies are gradually expanding its boundaries with new methodology, and undeveloped materials and fields. The studies of ordinary culture hold many institutional and theoretical tasks. I looked into the theoretical tasks of the studies of culture stressing on political economic and technoculture paradigm. Cultural studies should become a practical knowledge, going beyond ideological criticism that is easy to be trapped in convention or restoration, and it should create many practical interventions in terms of political economics, scientific technology, and urbanization. Making move conscious efforts to build more camps and strategies for next generations of scholous in the studies of culture will bring a solution to the crisis of the studies of culture.
Data Augmentation from Sketch

State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content. We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives.
국민안전과 인권보호를 위한 테러방지법 개선방안 연구

The Republic of Korea is an area of the National Assembly of Terrorism. There are also few religious conflicts. Police are strictly managing gun guns, artillery guns, and gunpowder, which are the means to use terrorism. To date, there has never been a direct attack from international terrorist group...
3·1운동과 공화주의 - 중첩, 응축, 비약

The people of Joseon experienced tremendous overlay, compression and a social leap forward towards change around March of 1919. It is not that a single, large event determined the next century of a nation. However, dramatic changes and a leap forward took place at the time, as if a hundred years had been compressed into that period. Such compression and overlay that led the Joseon society to leap forward was possible as people experienced firsthand a social movement through the March 1st Movement. Compressed times brought out different aspects of the society and the energy was ultimately channeled towards one direction via the joint action of shouting out ‘Manse’: it came to be acknowledged as a tool of confirming that one belonged to a political community of a nation. The leadership of the March 1st Movement, including the initial 33 activists and student leaders, had not prepared any specific guidelines for the general public to follow. Yet, the ‘Manse’ movement spread voluntarily and quickly along with the aspiration that the nation must regain independence as a political entity. The consensus spread whereby the act of shouting out ‘Manse’ will bring forth independence and that any and all Joseon people must cry out ‘Manse’. The different experiences and events on the Korean Peninsula in March and April of 1919 all compressed down to and are channeled towards one direction in sync. With the passing of time, this part of history became a solid part of Korean identity, along with democracy and the representativeness of the people, as part of our political ethics.
The kindness behavior management in kindness service application using tree structure

Abstract Kindness service application is an application which helps the students in kindness behavior implementations. This application is designed as an intermediary between kindness service providers and those who need kindness service, so it constructs a two-way relationship between the students. In addition, this application can also motivate the students to compete to do good things, as those who often doing kindness will be given a rate by the admin. The process of kindness behavior that had occurred will be designed to form a kindness tree. The management of this process is designed by using tree structure method. The tree structure will set the formation of kindness behavior in the kindness service application. Therefore, kindness behavior that had occurred will form into a whole kindness tree eventhough it provides the features to add or remove the behaviors of kindness.