Reviewerly/exHarmony huggingface dataset:
Loss Optimised Video Captioning using Deep-Lstm,Attention Mechanism and Weighted Loss Metrices

The aim of the video captioning task is to use multiple natural-language sentences to define video content. Photographic, graphical, and auditory data are all used in the videos. Our goal is to investigate and recognize the video's visual features, as well as to create a caption so that anyone can get the video's information within a second. Despite the fact, that phase encoder-decoder models have made significant progress, but it still needs many improvements. In the present work, we enhanced the top-down architecture using Bahdanau Attention, Deep-Long Short-Term Memory (Deep-LSTM) and weighted loss function. VGG16 is used to extract the features from the frames. To understand the actions in the video, Deep-Lstmis paired with an attention system. On the MSVD dataset, we analysed the efficiency of our model, which indicates a major improvement over the other state-of-art model.
Focusing on Possible Named Entities in Active Named Entity Label Acquisition

Named entity recognition (NER) aims to identify mentions of named entities in an unstructured text and classify them into the predefined named entity classes. Even though deep learning-based pre-trained language models achieve good predictive performances, many domain-specific NERtasks still require a sufficient amount of labeled data. Active learning (AL), a general framework for the label acquisition problem, has been used for the NER tasks to minimize the annotation cost without sacrificing model performance. However, heavily imbalanced class distribution of tokens introduces challenges in designing effective AL querying methods for NER. We propose AL sentence query evaluation functions which pay more attention to possible positive tokens, and evaluate these proposed functions with both sentence-based and token-based cost evaluation strategies. We also propose a better data-driven normalization approach to penalize too long or too short sentences. Our experiments on three datasets from different domains reveal that the proposed approaches reduce the number of annotated tokens while achieving better or comparable prediction performance with conventional methods.
Modern Data Science with R

Journal of the Royal Statistical Society: Series A (Statistics in Society)Volume 185, Issue 2 p. 735-736 BOOK REVIEW Modern data science with R , Baumer, Banjamin S., Kaplan, Daniel T., Horten, Nicholas J. Chapman and Hall/CRC Press, ISBN 9780367191498, pp. xvii + 631, 2021 Shalabh, Shalabh shalab@iitk.ac.in Department of Mathematics & Statistics, Indian Institute of Technology Kanpur, IndiaSearch for more papers by this author Shalabh, Shalabh shalab@iitk.ac.in Department of Mathematics & Statistics, Indian Institute of Technology Kanpur, IndiaSearch for more papers by this author First published: 16 November 2021 https://doi.org/10.1111/rssa.12784Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume185, Issue2April 2022Pages 735-736 RelatedInformation
Unsupervised Motion Representation Learning with Capsule Autoencoders

We propose the Motion Capsule Autoencoder (MCAE), which addresses a key challenge in the unsupervised learning of motion representations: transformation invariance. MCAE models motion in a two-level hierarchy. In the lower level, a spatio-temporal motion signal is divided into short, local, and semantic-agnostic snippets. In the higher level, the snippets are aggregated to form full-length semantic-aware segments. For both levels, we represent motion with a set of learned transformation invariant templates and the corresponding geometric transformations by using capsule autoencoders of a novel design. This leads to a robust and efficient encoding of viewpoint changes. MCAE is evaluated on a novel Trajectory20 motion dataset and various real-world skeleton-based human action datasets. Notably, it achieves better results than baselines on Trajectory20 with considerably fewer parameters and state-of-the-art performance on the unsupervised skeleton-based action recognition task.
On Provable Benefits of Depth in Training Graph Convolutional Networks

Graph Convolutional Networks (GCNs) are known to suffer from performance degradation as the number of layers increases, which is usually attributed to over-smoothing. Despite the apparent consensus, we observe that there exists a discrepancy between the theoretical understanding of over-smoothing and the practical capabilities of GCNs. Specifically, we argue that over-smoothing does not necessarily happen in practice, a deeper model is provably expressive, can converge to global optimum with linear convergence rate, and achieve very high training accuracy as long as properly trained. Despite being capable of achieving high training accuracy, empirical results show that the deeper models generalize poorly on the testing stage and existing theoretical understanding of such behavior remains elusive. To achieve better understanding, we carefully analyze the generalization capability of GCNs, and show that the training strategies to achieve high training accuracy significantly deteriorate the generalization capability of GCNs. Motivated by these findings, we propose a decoupled structure for GCNs that detaches weight matrices from feature propagation to preserve the expressive power and ensure good generalization performance. We conduct empirical evaluations on various synthetic and real-world datasets to validate the correctness of our theory.
학습 번역을 바라보는 다양한 시각 : 최근 국내외 연구동향 고찰

While pedagogic translation has undergone many ups and downs, the use of translation in English classrooms in Korea has remained constant due to the uniqueness of the Korean education system. As pedagogic translation is receiving attention once again as an effective language learning tool, the time is ripe for examining recent related discussions in the context of English education in Korea. This paper investigated a variety of recent researches both in Translation and English education studies, and witnessed the shift in the focus of pedagogic translation from acquiring linguistic knowledge to cultivating intercultural mediation skills. The study also reviewed the status and methodologies of pedagogic translation described under the two major approaches to language teaching, Grammar Translation Method and Communicative Language Teaching, and finally briefly dealt with English workbook translations for high school students in Korea as a prime example of Korea-specific pedagogic translation.
USING UNSCRIPTED ROLE-PLAY TEACHING ACTIVITY TO IMPROVE STUDENTS’ SPEAKING ABILITY

In learning English, most of the students think that English is a difficult skill. Given that, the writers want to improve the students’ speaking skills in English by using the Unscripted Role Play Teaching Activity. The data from classroom action research is collected by quantitative data (oral test) as main data and qualitative data (notes and documentation) as supporting data. The outcomes show that students have improved in English. It proves by ab average score that the students achieved in cycle 1 and cycle 2. In Cycle 1, the average score was 63.28 with 11 students (31.42%) achieved the standard minimum score whilst the average score in Cycle 2 is 78,28 with 30 students (80.71%) achieved the standard minimum score. In conclusion, the unscripted role-play teaching activity has improved the students’ speaking ability. Keywords :  Unscripted, Role Play, Language Competence
MTAdam: Automatic Balancing of Multiple Training Loss Terms

When training neural models, it is common to combine multiple loss terms. The balancing of these terms requires considerable human effort and is computationally demanding. Moreover, the optimal trade-off between the loss terms can change as training progresses, e.g., for adversarial terms. In this work, we generalize the Adam optimization algorithm to handle multiple loss terms. The guiding principle is that for every layer, the gradient magnitude of the terms should be balanced. To this end, the Multi-Term Adam (MTAdam) computes the derivative of each loss term separately, infers the first and second moments per parameter and loss term, and calculates a first moment for the magnitude per layer of the gradients arising from each loss. This magnitude is used to continuously balance the gradients across all layers, in a manner that both varies from one layer to the next and dynamically changes over time. Our results show that training with the new method leads to fast recovery from suboptimal initial loss weighting and to training outcomes that match or improve conventional training with the prescribed hyperparameters of each method.
Analisis Perbandingan Dampak Kejadian Bencana Hidrometeorologi dan Geologi di Indonesia Dilihat Dari Jumlah Korban (Studi : Data Kejadian Bencana Indonesia 2018): Studi: Data Kejadian Bencana Indonesia 2018

Year 2018 is the year of disaster in Indonesia. Three major disaster occurred in close intervals. The earthquake of West Nusa Tenggara, The earthquake triggered tsunami and liquefaction in Central Sulawesi and The Tsunami of Sundanese Straits have given huge impacts on casualties and damages. Around 2,572 disasters in 2018 caused 4,814 people killed and missing, 21 thousand people injured and 300 houses damaged. Even though hydro-meteorological disaster is the most frequent disaster i.e. 96.8%, geological disaster is the biggest contributor for the impacts of casualties and damages in 2018. Efforts of mitigation and preparedness become steps to reduce the impact on disaster that occurs. The involvement of community leaders in disaster management activities that can eventually affect their attitudes and behavior during a disaster.
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum

This paper proposes a new algorithm -- the \underline{S}ingle-timescale Do\underline{u}ble-momentum \underline{St}ochastic \underline{A}pprox\underline{i}matio\underline{n} (SUSTAIN) -- for tackling stochastic unconstrained bilevel optimization problems. We focus on bilevel problems where the lower level subproblem is strongly-convex and the upper level objective function is smooth. Unlike prior works which rely on \emph{two-timescale} or \emph{double loop} techniques, we design a stochastic momentum-assisted gradient estimator for both the upper and lower level updates. The latter allows us to control the error in the stochastic gradient updates due to inaccurate solution to both subproblems. If the upper objective function is smooth but possibly non-convex, we show that {\aname}~requires $\mathcal{O}(\epsilon^{-3/2})$ iterations (each using ${\cal O}(1)$ samples) to find an $\epsilon$-stationary solution. The $\epsilon$-stationary solution is defined as the point whose squared norm of the gradient of the outer function is less than or equal to $\epsilon$. The total number of stochastic gradient samples required for the upper and lower level objective functions matches the best-known complexity for single-level stochastic gradient algorithms. We also analyze the case when the upper level objective function is strongly-convex.
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales

Extracting temporal relationships over a range of scales is a hallmark of human perception and cognition -- and thus it is a critical feature of machine learning applied to real-world problems. Neural networks are either plagued by the exploding/vanishing gradient problem in recurrent neural networks (RNNs) or must adjust their parameters to learn the relevant time scales (e.g., in LSTMs). This paper introduces DeepSITH, a network comprising biologically-inspired Scale-Invariant Temporal History (SITH) modules in series with dense connections between layers. SITH modules respond to their inputs with a geometrically-spaced set of time constants, enabling the DeepSITH network to learn problems along a continuum of time-scales. We compare DeepSITH to LSTMs and other recent RNNs on several time series prediction and decoding tasks. DeepSITH achieves state-of-the-art performance on these problems.
Contrastive Active Inference

Active inference is a unifying theory for perception and action resting upon the idea that the brain maintains an internal model of the world by minimizing free energy. From a behavioral perspective, active inference agents can be seen as self-evidencing beings that act to fulfill their optimistic predictions, namely preferred outcomes or goals. In contrast, reinforcement learning requires human-designed rewards to accomplish any desired outcome. Although active inference could provide a more natural self-supervised objective for control, its applicability has been limited because of the shortcomings in scaling the approach to complex environments. In this work, we propose a contrastive objective for active inference that strongly reduces the computational burden in learning the agent's generative model and planning future actions. Our method performs notably better than likelihood-based active inference in image-based tasks, while also being computationally cheaper and easier to train. We compare to reinforcement learning agents that have access to human-designed reward functions, showing that our approach closely matches their performance. Finally, we also show that contrastive methods perform significantly better in the case of distractors in the environment and that our method is able to generalize goals to variations in the background.
Dasar Program Perencanaan dan Perancangan Arsitektur (DP3A) Pekalongan Convention Center dengan Konsep Arsitektur Vernakular

Exhibition and conference activities are one of the activities that are useful to improve the level of the economy and the progress of a region. The convention building is one of the things that must exist to facilitate exhibition and conference activities so that it can be facilitated properly. Pekalongan or often called the city of batik is one of the cities in Central Java, known as a city of culture. In addition to locations that are in close contact with the pantura, Pekalongan Regency will host the opening and closing of the International Sufi Ulema Conference or 'Al-Muntada as-Sufy al-Alamy'. The conference is planned to be opened by the Indonesian Minister of Defense, General of the Armed Forces (Ret.) Ryamizard Ryacudu. The conference will be attended by 85 Sufi scholars from abroad and 1,500 from within the country. This area will host the opening of an international Sufi multaqo. This is a gathering of world Sufism scholars. From abroad came 85 Sufi scholars and 1,500 from within the country. This area will host the opening of an international Sufi multaqo. This is a gathering of world Sufism scholars. From abroad came 85 Sufi scholars and 1,500 from within the country. The broad aim of holding this project is to accommodate events that are often or will be held in the city of Pekalongan. Through the comparative descriptive method with two variables as the object of reference, writing and summarizing this concept is expected to produce good planning results.
Latvian Treebank v2.6

Latvian Treebank (LVTB) is in development since 2010. Corpus is annotated manually according to SemTi-Kamols hybrid dependency-constituency grammar model. This version of the treebank contains data used for deriving LVTB version 2.6.
Outdoor Program Based Counseling to Improve Interpersonal Relationship between Special Teachers and Parents of Children with Special Needs in Inclusive School

This study aims to see whether the Outdoor Program Based Counseling method effectively improves interpersonal relationships between Special Assistant Teachers and Parents of Children with Special Needs. This research is quantitative research with an experimental method. Subjects in the study were divided into a control group and an experimental group. Subjects in the experimental group were 28 people. The data obtained in this study used a scale, observation, and interviews which were used as complementary data. The statistical analysis results using the Mann Whitney technique, the significance value obtained is p = 0.004, which means it is smaller than 0.05 (0.004 <0.05). From this value, it can be concluded that the Outdoor Based Counseling Program effectively improves the interpersonal relationships of special assistant teachers and parents of children with special needs in inclusive school. From the calculation of effect size, it can be concluded that the effect of Outdoor Based Counseling Program activities on improving interpersonal relationships has a moderate effect. This situation can be caused by several influencing factors such as the influence of the physical environment and nature on the activities carried out, the characteristics of the trainer or facilitator, the grasping power of the debriefing process, as well as attitude towards activities and awareness of their role as parents or teachers .
Snowflake: Scaling GNNs to high−dimensional continuous control via parameter freezing

Recent research has shown that graph neural networks (GNNs) can learn policies for locomotion control that are as effective as a typical multi-layer perceptron (MLP), with superior transfer and multi-task performance [55, 20]. However, results have so far been limited to training on small agents, with the performance of GNNs deteriorating rapidly as the number of sensors and actuators grows. A key motivation for the use of GNNs in the supervised learning setting is their applicability to large graphs, but this benefit has not yet been realised for locomotion control. We show that poor scaling in GNNs is a result of increasingly unstable policy updates, caused by overfitting in parts of the network during training. To combat this, we introduce SNOWFLAKE, a GNN training method for high-dimensional continuous control that freezes parameters in selected parts of the network. SNOWFLAKE significantly boosts the performance of GNNs for locomotion control on large agents, now matching the performance of MLPs while offering superior transfer properties.
Nihongo Hiragana Now (A Mobile Learning Application for Japanese Language Level 1 that translate Hiragana to Romaji and English)

This report is about the development of mobile application for Japanese Language Level 1 in UNIMAS. The project aims to develop a mobile application for the students that currently take Japanese Language Level 1. This application provides an innovative approach for interactive learning environments by utilizing digital technologies among undergraduates’ students which can help the them to gain an understanding in Japanese learning. By using this application, students can learn Japanese anytime, anywhere and even outside the class session. Nihongo Hiragana Now can enhance students learning skill as it provides quiz and audio for memorising, listening and pronouncing during the learning sessions. Even though the proposed application is simple, but surely will give a huge impact on those students who use this application. Before the development process of this application is conducted, the existing mobile applications that are related to this application are reviewed to gather the features and functionality that are suitable and practicable.
Neural Anaphora Resolution in Dialogue

We describe the systems that we developed for the three tracks of the CODI-CRAC 2021 shared task, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. Our team ranked second for entity coreference resolution, first for bridging resolution, and first for discourse deixis resolution.
Deep Incremental Image Classification Method Based on Double-Branch Iteration

To solve the catastrophic forgetting problem caused by incremental learning,a deep incremental image classification method based on double-branch iteration is proposed.The primary network is utilized to store the acquired old class knowledge,while the branch network is exploited to learn the new class knowledge.The parameters of the branch network are optimized by the weight of the primary network in the incremental iteration process.Density peak clustering method is employed to select typical samples from the iterative dataset and construct retention set.The retention set is added into the incremental iteration training to mitigate catastrophic forgetting.The experiments demonstrate the better performance of the proposed method.
El panorama de los profesores universitarios en Colombia respecto a sus competencias digitales

espanolLas tecnologias de la informacion y la comunicacion (TIC), en lo que respecta a temas de educacion actual, estan inmersas en distintos aspectos, que traen consigo nuevos retos a traves de los cuales se persiguen como objetivos el incentivar, impulsar y mejorar las actividades de ensenanza-aprendizaje. El profesor busca desarrollar habilidades digitales a traves de su practica profesional,lo cual le permite afrontar desafios y de esta forma prepararse y preparar a sus estudiantes para esta nueva sociedad del conocimiento y de esta manera adquirir competencias con enfoque digital, comprendiendolas como una oportunidad de crecimiento profesional. El presente proyecto investigativo se realizo bajo el enfoque descriptivo y relacional que aporta a la comprension de la informacion, determinando causales socio-demograficas, competencias actitudinales, de conocimiento y habilidad, aportando elementos de valor para el desarrollo del proceso de aprendizaje aprovechando al maximo los recursos que ofrecen las TIC, se encontro que no hay diferencias en el desarrollo de las competencias por sexo y por edad y que la creacion de contenidos se convierte en una oportunidad para mejorar. Englishnformation and communication technologies (ICT), with regard to current education issues, are immersed in different aspects, which bring with them new challenges through which the objectives of encouraging, promoting and improving activities are pursued. teaching-learning. The teacher seeks to develop digital skills through his professional practice, which allows him to face challenges and in this way prepare and prepare his students for this new knowledge society and in this way acquire skills with a digital approach, understanding them as an opportunity to professional growth. This research project was carried out under the descriptive and relational approach that contributes to the understanding of the information, determining socio-demographic causes, attitudinal, knowledge and skill competencies, providing valuable elements for the development of the learning process, making the most of the resources offered by ICT, it was found that there are no differences in the development of competencies by sex and age and that the creation of content becomes an opportunity to improve
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting

We consider interpolation learning in high-dimensional linear regression with Gaussian data, and prove a generic uniform convergence guarantee on the generalization error of interpolators in an arbitrary hypothesis class in terms of the class's Gaussian width. Applying the generic bound to Euclidean norm balls recovers the consistency result of Bartlett et al. (2020) for minimum-norm interpolators, and confirms a prediction of Zhou et al. (2020) for near-minimal-norm interpolators in the special case of Gaussian data. We demonstrate the generality of the bound by applying it to the simplex, obtaining a novel consistency result for minimum l1-norm interpolators (basis pursuit). Our results show how norm-based generalization bounds can explain and be used to analyze benign overfitting, at least in some settings.