Reviewerly/exHarmony huggingface dataset:
How to Accelerate Capsule Convolutions in Capsule Networks

How to improve the efficiency of routing procedures in CapsNets has been studied a lot. However, the efficiency of capsule convolutions has largely been neglected. Capsule convolution, which uses capsules rather than neurons as the basic computation unit, makes it incompatible with current deep learning frameworks' optimization solution. As a result, capsule convolutions are usually very slow with these frameworks. We observe that capsule convolutions can be considered as the operations of `multiplication of multiple small matrics' plus tensor-based combination. Based on this observation, we develop two acceleration schemes with CUDA APIs and test them on a custom CapsNet. The result shows that our solution achieves a 4X acceleration.
English Video Blogging (E-Vlog) in Teaching Speaking: Technology Practices in Real Teaching

Speaking is one important skill that needs to be acquired by students. It is one activity that students do in almost of their study. However, speaking is the most difficult skill to learn. Most of the students in second grade are still low in speaking ability. The students failed in speaking because they come across some factors such as they cannot express their ideas, opinion, feeling, and experience in speaking well, and the teacher uses methods which are still dissatisfaction until the students feel bored. Therefore, the researchers are interested in using the media in teaching speaking namely English Video Blogging. The objective of the research is to find out whether the use of E-Vlog is able to improve speaking ability of the students of the Second Grade of SMK Negeri 4 Sidrap. The researchers applied quasi-experimental method, with two group pre-test and post-test design. The population of the research was the second grade students of SMK Negeri 4 Sidrap which consisted of four classes (47 students). The sample was drawn using cluster random sampling technique. The research instrument used was speaking test. The result of the data analysis shows that the students’ speaking ability improved significantly. It is proved by the mean score of pre-test was smaller than the post-test. It shows that using E-Vlog can improve significantly the students’ speaking ability of the second grade of SMK Negeri 4 Sidrap.
Correction to: Implementing a real-time image captioning service for scene identification using embedded system

A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-10867-3
SOLUSI PENDEKATAN PERSAMAAN GELOMBANG FRAKSIONAL NON LINEAR MENGGUNAKAN NEW VERSION OF OPTIMAL HOMOTOPY ASYMPTOTIC METHOD

Non-linear differential equations with fractional derivative order are mathematical models that are widely used in modeling physical phenomena, one of the applications of these models is non-linear fractional wave equations. Many methods for solving non-linear fractional partial differential equations, one of which is the New Version of Optimal Homotopy Asymptotic Method which is developed by Liaqat Ali in 2016. The author will use this method to solve non-linear fractional wave equations predetermined, so that the convergence of function of the approximation solution non-linear fractional wave equation can be observed and it can be observed that the function of approximation solution of non-linear fractional wave equation solution using the New Version of Optimal Homotopy Asymptotic Method is simple and has a value error using Mean Absolute Percentage Error which is categorized very well
간호대학생의 코로나19에 대한 불안, 위험인식, 정보이해력 및 감염예방행위 수행간의 관계

This study aimed to explore anxiety, risk perception, information literacy, and performance level of infection prevention practices against coronavirus disease (COVID-19) among nursing students during the pandemic. A descriptive online survey was conducted, targeting a total of 224 nursing students from June 8, 2020 to 14, 2020. The collected data were analyzed using descriptive statistics, t-test, analysis of variance, and correlation analysis using SPSS 26.0. The nursing students’ level of anxiety was 6.86 out of 10, which was normal. Their information literacy level and performance level of infection prevention practices were 3.50 out of 4 and 4.44 out of 5, respectively, and these scores were considered high. The higher the level of anxiety, risk perception, and information literacy, the higher the level of infection prevention practices. The level of anxiety with respect to infection and risk perception may increase when new types of infectious diseases such as COVID-19 break out. Therefore, it is necessary to promote the accurate understanding of the infectious diseases by providing sufficient information. Accurate understanding of new infectious diseases can reduce the risk of transmission as it encourages people to thoroughly perform infection prevention practices.
Few-Shot Transformation of Common Actions into Time and Space

This paper introduces the task of few-shot common action localization in time and space. Given a few trimmed support videos containing the same but unknown action, we strive for spatio-temporal localization of that action in a long untrimmed query video. We do not require any class labels, interval bounds, or bounding boxes. To address this challenging task, we introduce a novel few-shot transformer architecture with a dedicated encoder-decoder structure optimized for joint commonality learning and localization prediction, without the need for proposals. Experiments on our reorganizations of the AVA and UCF101-24 datasets show the effectiveness of our approach for few-shot common action localization, even when the support videos are noisy. Although we are not specifically designed for common localization in time only, we also compare favorably against the few-shot and one-shot state-of-the-art in this setting. Lastly, we demonstrate that the few-shot transformer is easily extended to common action localization per pixel.
An Adversarial Human Pose Estimation Network Injected with Graph Structure

Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods. In this paper, we design a novel generative adversarial network (GAN) to improve the localization accuracy of visible joints when some joints are invisible. The network consists of two simple but efficient modules, Cascade Feature Network (CFN) and Graph Structure Network (GSN). First, the CFN utilizes the prediction maps from the previous stages to guide the prediction maps in the next stage to produce accurate human pose. Second, the GSN is designed to contribute to the localization of invisible joints by passing message among different joints. According to GAN, if the prediction pose produced by the generator G cannot be distinguished by the discriminator D, the generator network G has successfully obtained the underlying dependence of human joints. We conduct experiments on three widely used human pose estimation benchmark datasets, LSP, MPII and COCO, whose results show the effectiveness of our proposed framework.
The Effect of Genogram Technique on Students’ Career Decision in MAN 2 Kota Makassar

This study is aimed to (1) describe the implementation of genogram technique in students’ career decision, (2) investigate students’ career decision, and (3) investigate the effect of genogram technique on students’ career decision. This study applied quantitative approach and implemented quasi experimental design as well as Pretest-Posttest Control Group Design to 20 samples. Data collection techniques applied in this research were career decision scale, observation, and interview. This study implemented analytical statistic descriptive non parametric wilcoxon signed ranks test as data analysis technique. This study found that (1) there were low category of genogram technique implementation to affect students’ career decision in MAN 2 Kota Makassar before the treatment, while its high category found after the treatment, (2) students’ career decision in MAN 2 Kota Makassar was raised after genogram technique, and (3) genogram technique can increase students’ career decision in MAN 2 Kota Makassar.
LibGuides: The Fourth Industrial Revolution: General support and useful tools

Professor Klaus Schwab is convinced that we are at ‘the beginning of a revolution that is fundamentally changing the way we live, work and relate to one another.’ He calls this revolution the Fourth Industrial Revolution.
Reinforcement Learning with Temporal Logic Constraints for Partially-Observable Markov Decision Processes

This paper proposes a reinforcement learning method for controller synthesis of autonomous systems in unknown and partially-observable environments with subjective time-dependent safety constraints. Mathematically, we model the system dynamics by a partially-observable Markov decision process (POMDP) with unknown transition/observation probabilities. The time-dependent safety constraint is captured by iLTL, a variation of linear temporal logic for state distributions. Our Reinforcement learning method first constructs the belief MDP of the POMDP, capturing the time evolution of estimated state distributions. Then, by building the product belief MDP of the belief MDP and the limiting deterministic B\uchi automaton (LDBA) of the temporal logic constraint, we transform the time-dependent safety constraint on the POMDP into a state-dependent constraint on the product belief MDP. Finally, we learn the optimal policy by value iteration under the state-dependent constraint.
Spatial and Temporal Splitting Heuristics for Multi-Robot Motion Planning

In this work, we systematically examine the application of spatio-temporal splitting heuristics to the Multi-Robot Motion Planning (MRMP) problem in a graph-theoretic setting: a problem known to be NP-hard to optimally solve. Following the divide-and-conquer principle, we design multiple spatial and temporal splitting schemes that can be applied to any existing MRMP algorithm, including integer programming solvers and Enhanced Conflict Based Search, in an orthogonal manner. The combination of a good baseline MRMP algorithm with a proper splitting heuristic proves highly effective, allowing the resolution of problems 10+ times than what is possible previously, as corroborated by extensive numerical evaluations. Notably, spatial partition of problem fusing with the temporal splitting heuristic and the enhanced conflict based search (ECBS) algorithm increases the scalability of ECBS on large and challenging DAO maps by 5--15 folds with negligible impact on solution optimality.
On-device Federated Learning with Flower.

Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud. Despite the algorithmic advancements in FL, the support for on-device training of FL algorithms on edge devices remains poor. In this paper, we present an exploration of on-device FL on various smartphones and embedded devices using the Flower framework. We also evaluate the system costs of on-device FL and discuss how this quantification could be used to design more efficient FL algorithms.
Jeju April 3 Incident and Supporting Police Force - A Case Study of 『Prelude to Glory of Gangwon Police Warrior』 and A Photo to Commemorate the Dispatch of Troops Belonging to the Gyeongbuk Special Police Force -

초록 · 키워드 목차 오류제보하기 Although data such as "Jeju 4 · 3 Incident Fact-finding Report" (2003), "Jeju 4 3 Incident Additional Fact-finding Report I"(2019), and various data collections generally describe or organize the development of Jeju 4 · 3 Incident, most of the police records are based on domestic newspaper articles. In particular, the cheering police unit is directly linked to the damage caused by the operation, so it is necessary to secure literature data, but only a few. In the meantime, I newly obtained data on the Jeju April 3rd Incident from the police and carefully considered it. First of all, according to the 『Prelude to Glory of Gangwon Police Warrior』, Gangwon Police Corps was dispatched twice in May and July 1948, but the operation plan was changed during the first dispatch, and the second mission was carried out. However, such a simple record alone shows that although no one knows when or where the operation was conducted, it was also conducted in the middle of the village, disguised as civilian clothes, and carried out combat operations. Next, the unit in the "Photo to commemorate the dispatch of troops belonging to the Gyeongbuk Special Police Force" is a platoon, and the timing of the dispatch was reported to the media when the cheering police units of the Seoul Metropolitan Police Agency and the 8th(Jeonnam) Police Agency were dispatched in early October. The troops were also wearing Japanese long-sleeved armor, and firearms were also carrying 11-year-old Japanese light machine guns and U.S. M3 machine guns, indicating that the unit was armed to respond effectively in combat as a guerrilla force. Anyway, I hope that this article will actively collect new data in the future, and further revitalize follow-up research, providing an opportunity to shed light on the Jeju April 3 incident once again. #제주 4 · 3사건 #사건사 #국가폭력 #응원경찰부대 #강원경찰전사 #특별경찰대 #Jeju April 3 Incident #Incident history #National Violence #Supporting Police Force #Gangwon Police Combat History #Special Police Force Ⅰ. 머리말Ⅱ. 제주 4 · 3사건 위원회의 발간 자료 검토Ⅲ. 『강원경찰전사 영광의 서곡 제1집』Ⅳ. 경북 특별경찰대 소속 부대의 파견 기념사진Ⅳ. 맺음말참고문헌ABSTRACT
Wasserstein GANs for Generation of Variated Image Dataset Synthesis

Deep learning networks required a training lot of data to get to better accuracy. Given the limited amount of data for many problems, we understand the requirement for creating the image data with the existing sample space. For many years the different technique was used to develop data fixtures to improve modelling and training efficiently with the advent of GAN we were now able to get close to real data. However, the standard GANs require a lot of effort in training and not cost-efficient. A more practical way of training GANs is Wasserstein GAN. They can be used for efficient generating for data taking the training sample space. Better representation GANs with WGANS solved the problem of learning a probability density. In this paper, we now use WGANs for classification training data given the sample data. We intend to deal with the following objectives. (a)To consider the sample space for the training data to mock with WGAN. (b)To build WGAN in combination with the network for classification and evaluating the models' performance. (c)To compare WGAN and standard GAN for knowing the increased accuracy of the classifier.
Saez, V y otros. (2019) (Cord.). Educación de la Mirada: Experiencias, reflexiones y desafíos pedagógicos de la formación en Medios, Comunicación y Tecnologías en Argentina. Argentina: Facultad de Ciencias Sociales. Universidad de Buenos Aires

espanolEn un mundo abarrotado de tecnologias, de nuevos modos de comunicacion y de nuevas formas de transmision de la informacion cabe preguntarse ?De que nos sirve como sociedad tantos dispositivos electronicos, tantas redes sociales, tanta tecnologia digital? “Educacion de la Mirada” plantea algunas respuestas que combinan el punto de vista social con el educativo. A traves de trabajos de distintos investigadores de tematicas variadas, este libro se propone abordar los diferentes aspectos que abarcan un tema tan complejo como la educacion mediatica. El punto central en el que todos los trabajos hacen hincapie es la necesidad de fomentar una mirada critica de los consumos mediaticos, asi como promover la produccion dentro de los medios por parte de los estudiantes. Por ultimo, tambien se resalta el rol que las nuevas tecnologias pueden tener para lograr una transformacion social en pos de una mayor igualdad de oportunidades. EnglishIn a world overcrowded with technologies, new modes of communication and new forms of information transmission, it is worth asking: What is the use of so many electronic devices, so many social networks, so much digital technology as a society? Education of the Look raises some answers that combine the social point of view with the educational one. Through the work of different researchers on various topics, this book aims to address the different aspects that cover such a complex subject as media education. The central point in which all the works emphasize is the need to promote a critical view of media consumption, as well as to promote production within the media by the students. Finally, the role that new technologies can have to achieve a social transformation towards greater equality of opportunities is also highlighted.
Distinctive Context Sensitive and Hellinger Convolutional Learning for Privacy Preserving of Big Healthcare Data

The collection and effectiveness of sensitive Big Data have grown with Information Technology (IT) development. While using sensitive Big Data to acquire relevant information, it becomes indispensable that irrelevant sensitive data are reduced to safeguard personal information in healthcare sector. Many privacy-preserving strategies have been applied in the recent years using quasi-identifiers (QI) for applications like health services. However, privacy preservation over quasi-identifiers is still challenging in the context of Big Data because most datasets were of huge volume. Existing methods suffer from higher time consumption and lower data utility because of dynamically progressing datasets. In this paper, an efficient Distinctive Context Sensitive and Hellinger Convolutional Learning (DCS-HCL) is introduced to ensure privacy preservation and achieve high data utility for big healthcare datasets. First, Distinctive Impact Context Sensitive Hashing model is designed for the given input Big Dataset where both the distinctive and impact values are identified and applied to Context Sensitive Hashing. With this, similar QI-classes are mapped to evolve the computationally efficient anonymyzed data. Second, Hellinger Convolutional Neural Privacy Preservation model is presented to preserve the privacy of the sensitive unstructured data. This is performed by hashing QI-class values, weight updation and bias in CNN to increase the accuracy and to reduce the information loss. Evaluation results demonstrate that with proposed method with large-volume unstructured datasets improved performance of run time, data utility, information loss and accuracy significantly over existing methods.
Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context Disambiguation

We describe the Uppsala NLP submission to SemEval-2021 Task 2 on multilingual and cross-lingual word-in-context disambiguation. We explore the usefulness of three pre-trained multilingual language models, XLM-RoBERTa (XLMR), Multilingual BERT (mBERT) and multilingual distilled BERT (mDistilBERT). We compare these three models in two setups, fine-tuning and as feature extractors. In the second case we also experiment with using dependency-based information. We find that fine-tuning is better than feature extraction. XLMR performs better than mBERT in the cross-lingual setting both with fine-tuning and feature extraction, whereas these two models give a similar performance in the multilingual setting. mDistilBERT performs poorly with fine-tuning but gives similar results to the other models when used as a feature extractor. We submitted our two best systems, fine-tuned with XLMR and mBERT.
The role of Efficient User Interface Design in Modern Human-Computer Interaction

In this paper we addressed the best methods and recommendations for the creation of an effective user interface (UI). We have also presented a case study of Android-UI design from its inception to its present state. Simple and effective UI design strengthens human-computer interaction in a very fundamental way-enhancing that enhances the machine-man relationship. The impact of powerful UI design on businesses and its role in increasing the Human-Computer Interaction bar are also important in this regard we also address the effects and benefits of efficient UI design.
Dogfight: Detecting Drones from Drones Videos.

As airborne vehicles are becoming more autonomous and ubiquitous, it has become vital to develop the capability to detect the objects in their surroundings. This paper attempts to address the problem of drones detection from other flying drones. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity variations, and occlusion make this problem quite challenging. In this scenario, region-proposal based methods are not able to capture sufficient discriminative foreground-background information. Also, due to the extremely small size and complex motion of the source and target drones, feature aggregation based methods are unable to perform well. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach employing spatio-temporal attention cues. During the first stage, given the overlapping frame regions, detailed contextual information is captured over convolution feature maps using pyramid pooling. After that pixel and channel-wise attention is enforced on the feature maps to ensure accurate drone localization. In the second stage, first stage detections are verified and new probable drone locations are explored. To discover new drone locations, motion boundaries are used. This is followed by tracking candidate drone detections for a few frames, cuboid formation, extraction of the 3D convolution feature map, and drones detection within each cuboid. The proposed approach is evaluated on two publicly available drone detection datasets and outperforms several competitive baselines.
The Return of Lexical Dependencies: Neural Lexicalized PCFGs

In this paper we demonstrate that $\textit{context free grammar (CFG) based methods for grammar induction benefit from modeling lexical dependencies}$. This contrasts to the most popular current methods for grammar induction, which focus on discovering $\textit{either}$ constituents $\textit{or}$ dependencies. Previous approaches to marry these two disparate syntactic formalisms (e.g. lexicalized PCFGs) have been plagued by sparsity, making them unsuitable for unsupervised grammar induction. However, in this work, we present novel neural models of lexicalized PCFGs which allow us to overcome sparsity problems and effectively induce both constituents and dependencies within a single model. Experiments demonstrate that this unified framework results in stronger results on both representations than achieved when modeling either formalism alone. Code is available at https://github.com/neulab/neural-lpcfg.
Efficiency improvement of a time-of-arrival quantum random number generator

The bit generation algorithm of a quantum random number generator based on photon arrival time differences is improved in terms of the bit generation efficiency and bit generation rate. The method is analyzed mathematically, proving that, in theory, the improved generator still produces random numbers of high quality without the need of resorting to postprocessing algorithms. The effects of nonidealities are quantitatively analyzed to provide bounds for the maximum tolerable deviations from uniformity. Experimental investigations are conducted on a prototype, showing that the method is capable of increasing the rate of bit generation by more than 47% on the same physical architecture.