Reviewerly/exHarmony huggingface dataset:
Pengaruh Pembelajaran Daring Terhadap Motivasi Belajar Siswa SD

The background of this research is related to the higher demand of the learning process during the Covid-19 Pandemic. The education workforce is required to be creative and innovative so that the Online Learning process can be carried out properly. As the consequentes of all the limitations and challenge, the Online Learning process still need to be maximized more. Due to that problems the students seems to experience the lack of Learning Motivation. The purpose of this research is to find out how much the Online Learning influences the elementary student’s learning motivation. This study uses a Document Study of the results of previous research, the author also uses the Meta Analysis method. The data collection technique was done by examining the Google Schoolar. From the search results, the author analyzed 3 articles. Based on the analysis, it depicted tha Online Learning can decrease the elementary student’s learning motivation. Before Online Learning the average of student learning motivation is 80,8% and after Online Learning the average has decreased to 64,01%. In conclusion, the student’s learning motivation decrease until 16,07%.
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP

Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks.However, annotated data for every target task in every target language is rare, especially for low-resource languages.We propose UXLA a novel unsupervised data augmentation framework for zero-resource transfer learning scenarios.In particular, UXLA aims to solve crosslingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language.At its core, UXLA performs simultaneous selftraining with data augmentation and unsupervised sample selection.To show its effectiveness, we conduct extensive experiments on three diverse zero-resource cross-lingual transfer tasks.UXLA achieves SoTA results in all the tasks, outperforming the baselines by a good margin.With an in-depth framework dissection, we demonstrate the cumulative contributions of different components to its success.
Undergraduate Students’ Device Preferences in the Transition to Online Learning

The global higher education sector has been greatly affected by the COVID-19 pandemic, and the mode of delivery has transformed into a blended learning mode of delivery or fully remote mode. Online delivery significantly demands reliable and stable internet access and technology, at both the lecturer’s and students’ ends. This paper investigates the challenges and barriers to accessibility of technologies used for remote delivery of learning and teaching. The paper also investigates key digital skills students need to help them develop and enhance their technology literacy. A survey was also conducted among 555 university undergraduate students to identify their choice of device to connect to remote learning during the transition to online learning. It was revealed that students used laptops and smartphones considerably and least relied on desktop computers. The results indicate the significance of a device’s portability, built-in network hardware and cost. Further, it identifies the impacts of accessibility of educational technologies on students’ learning experience.
Solving Math Word Problems with Teacher Supervision

Math word problems (MWPs) have been recently addressed with Seq2Seq models by `translating' math problems described in natural language to a mathematical expression, following a typical encoder-decoder structure. Although effective in solving classical math problems, these models fail when a subtle variation is applied to the word expression of a math problem, and leads to a remarkably different answer. We find the failure is because MWPs with different answers but similar math formula expression are encoded closely in the latent space. We thus designed a teacher module to make the MWP encoding vector match the correct solution and disaccord from the wrong solutions, which are manipulated from the correct solution. Experimental results on two benchmark MWPs datasets verified that our proposed solution outperforms the state-of-the-art models.
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel

This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. Isolation kernel outperforms Gaussian kernel in two aspects. First, the use of Isolation kernel in t-SNE overcomes the drawback of misrepresenting some structures in the data, which often occurs when Gaussian kernel is applied in t-SNE. This is because Gaussian kernel determines each local bandwidth based on one local point only, while Isolation kernel is derived directly from the data based on space partitioning. Second, the use of Isolation kernel yields a more efficient similarity computation because data-dependent Isolation kernel has only one parameter that needs to be tuned. In contrast, the use of data-independent Gaussian kernel increases the computational cost by determining n bandwidths for a dataset of n points. As the root cause of these deficiencies in t-SNE is Gaussian kernel, we show that simply replacing Gaussian kernel with Isolation kernel in t-SNE significantly improves the quality of the final visualisation output (without creating misrepresented structures) and removes one key obstacle that prevents t-SNE from processing large datasets. Moreover, Isolation kernel enables t-SNE to deal with large-scale datasets in less runtime without trading off accuracy, unlike existing methods in speeding up t-SNE.
A Random Walk Analysis of Search in Metaheuristics

Random walks are a useful modeling tool for stochastic processes. The addition of model features (e.g. finite travel in one direction) can provide insight into specific practical situations (e.g. gambler's ruin). A series of random walk experiments are designed to study the effects of selection, exploration, and exploitation during the search processes of metaheuristics. We present a set of random walk conditions which leads to greater movement as the dimensionality of the sampling distributions increases. We then implement a version of Simulated Annealing in a similar search space which also achieves improving performance with increasing dimensionality. Conversely, we show that standard Particle Swarm Optimization has decreasing performance with increasing dimensionality which is consistent with the expected effects of the Curse of Dimensionality. These experiments give us insights into future methods that metaheuristics might be able to employ to defeat the Curse of Dimensionality (in globally convex, continuous domain search spaces).
Leveraging Human Attention in Novel Object Captioning

Image captioning models depend on training with paired image-text corpora, which poses various challenges in describing images containing novel objects absent from the training data. While previous novel object captioning methods rely on external image taggers or object detectors to describe novel objects, we present the Attention-based Novel Object Captioner (ANOC) that complements novel object captioners with human attention features that characterize generally important information independent of tasks. It introduces a gating mechanism that adaptively incorporates human attention with self-learned machine attention, with a Constrained Self-Critical Sequence Training method to address the exposure bias while maintaining constraints of novel object descriptions. Extensive experiments conducted on the nocaps and Held-Out COCO datasets demonstrate that our method considerably outperforms the state-of-the-art novel object captioners. Our source code is available at https://github.com/chenxy99/ANOC.
Revisiting three anonymous two-factor authentication schemes for roaming service in global mobility networks

Designing a secure and efficient anonymous authentication protocol for roaming services in global mobile networks is a hot topic in the field of information security protocols.Based on the widely accepted attacker model, this paper analyzes the security of three representative anonymous authentication protocols in global mobile networks.It is pointed out that: (1) Xu et al.'s protocol cannot resist the claimed offline password guessing attack and mobile user impersonation attack, and do not achieve mobile user untraceability and forward security; (2) Gupta et al.'s protocol cannot resist offline password guessing attacks, and temporary information disclosure attacks; (3) Madhusudhan et al.'s protocol cannot resist mobile user impersonation attack, foreign agent impersonation attack, replay attack, offline password guessing attack and session key disclosure attack, and cannot realize the anonymity and untraceability and forward security of users.It is emphasized that the fundamental reason for the failure of these protocols lies in the violation of the four basic principles of protocol design: Public key principle, Forward security principle, User anonymity principle and Anti offline guessing attack principle.The specific mistakes of these schemes are clarified, and the corresponding correction methods are proposed.
CRITICAL FACTORS OF CLOUD ACCOUNTING ACCEPTANCE AND SECURITY FOR PROSPECTIVE ACCOUNTANTS: TAM EXTENSION

Cloud computing technology can change the business structure, including accounting, through cloud computing. However, cloud accounting has problems, especially those related to data, which affect user acceptance of cloud accounting. Based on these problems, this research aims to test the acceptance of cloud accounting. The object of this research focuses on cloud accounting users to prospective accountants who are still at the undergraduate degree, with a total of 123 respondents. The questionnaire was distributed via google form and then analyzed using PLS using data processing through SmartPLS. The findings of this study indicate that perceived ease of use and security can influence the intention to using cloud accounting. Meanwhile, perceived usefulness not able to control the choice to use cloud accounting. This is because prospective accountants still consider cloud as a learning medium to not guarantee uncertainty in using cloud accounting.
Service Design as a Catalyst for Patient-Centered eHealth Innovation

Today's healthcare industry is confronted with a myriad of challenges amidst emerging trends and opportunities which trigger a paradigm shift in healthcare design from stand-alone products to holistic services. These three dimensions are critical in assessing and managing healthcare, particularly in underserved settings. This study aims to maximize opportunities presented by both design and information and communication technologies to enhance the implementation of integrated people-centered health services. It is a qualitative study conducted across six government health facilities within Nairobi slums in Kenya as a case study of maternal health information services. Co-design-oriented service design research strategy is employed while a representative sample of (n=47) participants is drawn from different stakeholders in the public health sector. An architectural design framework for cloud-based patient-centered health information service is designed to support maternal care in underserved settings. A prototype service (AfyaTab app) is developed as a proof-of-concept of the proposed design solution.
Solving Continuous Control with Episodic Memory

Episodic memory lets reinforcement learning algorithms remember and exploit promising experience from the past to improve agent performance. Previous works on memory mechanisms show benefits of using episodic-based data structures for discrete action problems in terms of sample-efficiency. The application of episodic memory for continuous control with a large action space is not trivial. Our study aims to answer the question: can episodic memory be used to improve agent's performance in continuous control? Our proposed algorithm combines episodic memory with Actor-Critic architecture by modifying critic's objective. We further improve performance by introducing episodic-based replay buffer prioritization. We evaluate our algorithm on OpenAI gym domains and show greater sample-efficiency compared with the state-of-the art model-free off-policy algorithms.
Enhance Image as You Like with Unpaired Learning

Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version. In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and normal-light training images without any correspondence. By formulating this ill-posed problem as a modulation code learning task, our network learns to generate a collection of enhanced images from a given input conditioned on various reference images. Therefore our inference model easily adapts to various user preferences, provided with a few favorable photos from each user. Our model achieves competitive visual and quantitative results on par with fully supervised methods on both noisy and clean datasets, while being 6 to 10 times lighter than state-of-the-art generative adversarial networks (GANs) approaches.
Optimized loss functions for object detection and application on nighttime vehicle detection

The loss function is a crucial factor that affects the detection precision in the object detection task. In this paper, we optimize both two loss functions for classification and localization simultaneously. Firstly, we reconstruct the classification loss function by combining the prediction results of localization, aiming to establish the correlation between localization and classification subnetworks. Compared to the existing studies, in which the correlation is only established among the positive samples and applied to improve the localization accuracy of predicted boxes, this paper utilizes the correlation to define the hard negative samples and then puts emphasis on the classification of them. Thus the whole misclassified rate for negative samples can be reduced. Besides, a novel localization loss named MIoU is proposed by incorporating a Mahalanobis distance between the predicted box and target box, eliminating the gradients inconsistency problem in the DIoU loss, further improving the localization accuracy. Finally, the proposed methods are applied to train the networks for nighttime vehicle detection. Experimental results show that the detection accuracy can be outstandingly improved with our proposed loss functions without hurting the detection speed.
Rethinking Training Data for Mitigating Representation Biases in Action Recognition

The purpose of this study is to train spatiotemporal 3D convolutional neural networks (3D CNNs) that properly leverage temporal information to recognize actions. Though 3D CNNs are an effective framework in action recognition, some studies showed the biases of video datasets for generic action recognition lead 3D CNNs to recognize not dynamic motions but static cues, such as objects, scenes, and people. On the other hand, video datasets for fine-grained action recognition, which classifies various actions in a specific domain, are expected to have small biases compared with the datasets for generic action recognition. In this study, we examine the biases of various video datasets, which include both generic and fine-grained action recognition tasks, for training 3D CNNs. Based on the results of experiments, the following conclusions could be obtained: (i) The representation biases learned from fine-grained action recognition datasets are smaller than those of generic action recognition datasets. (ii) The models pretrained on fine-grained action recognition datasets, of which the biases are small, leverage temporal information to recognize actions rather than static information. (iii) The models that leverage temporal information achieve better performance on fine-grained action recognition whereas the performance of the models pretrained on biased datasets is better on generic action recognition. We should evaluate models on both generic and fine-grained recognition datasets to properly evaluate their performance.
Layer-Assisted Neural Topic Modeling over Document Networks

Neural topic modeling provides a flexible, efficient, and powerful way to extract topic representations from text documents. Unfortunately, most existing models cannot handle the text data with network links, such as web pages with hyperlinks and scientific papers with citations. To resolve this kind of data, we develop a novel neural topic model , namely Layer-Assisted Neural Topic Model (LANTM), which can be interpreted from the perspective of variational auto-encoders. Our major motivation is to enhance the topic representation encoding by not only using text contents, but also the assisted network links. Specifically, LANTM encodes the texts and network links to the topic representations by an augmented network with graph convolutional modules, and decodes them by maximizing the likelihood of the generative process. The neural variational inference is adopted for efficient inference. Experimental results validate that LANTM significantly outperforms the existing models on topic quality, text classification and link prediction..
A system for equity: enhancing STEM education during a pandemic

Purpose The purpose of this paper is to report the change in students' STEM perceptions in two different informal learning environments: an online STEM camp and a face-to-face (FTF) STEM camp. Design/methodology/approach For this quasi-experimental study, 26 students participated in an online STEM summer camp and another 26 students participated in the FTF STEM camp. Students from each group took the same pre- and post-STEM Semantics Survey documenting their perceptions of the individual STEM fields and of STEM careers. Wilcoxon Signed-Rank tests, Mann–Whitney U tests and corresponding effect sizes were used to compare the pre- and post-scores within and between the camps. Findings Results indicate that both camps produce similar outcomes regarding STEM field and career perceptions. However, analysis of all statistical values indicates that the online STEM camp can produce a larger positive influence on STEM field perceptions and the FTF camp can produce a larger positive influence on STEM career perceptions. Research limitations/implications This suggests that STEM camps, both online and in-person, can improve students' perceptions of the STEM fields and of STEM careers. Implications from this study indicate that modifications of informal learning environments should be based on the type of learning environment. Originality/value This manuscript discusses the development and impact of an online STEM camp to accommodate for the sudden onset of the COVID-19 pandemic and the inability to hold an in-person STEM camp. These results may influence the curriculum and organization of future online and FTF STEM camps.
Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequences

Visual information extraction (VIE) has attracted increasing attention in recent years. The existing methods usually first organized optical character recognition (OCR) results in plain texts and then utilized token-level category annotations as supervision to train a sequence tagging model. However, it expends great annotation costs and may be exposed to label confusion, the OCR errors will also significantly affect the final performance. In this paper, we propose a unified weakly-supervised learning framework called TCPNet (Tag, Copy or Predict Network), which introduces 1) an efficient encoder to simultaneously model the semantic and layout information in 2D OCR results, 2) a weakly-supervised training method that utilizes only sequence-level supervision; and 3) a flexible and switchable decoder which contains two inference modes: one (Copy or Predict Mode) is to output key information sequences of different categories by copying a token from the input or predicting one in each time step, and the other (Tag Mode) is to directly tag the input sequence in a single forward pass. Our method shows new state-of-the-art performance on several public benchmarks, which fully proves its effectiveness.
Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

With the advent of the artificial intelligence era, target adaptive tracking technology has been rapidly developed in the fields of human-computer interaction, intelligent monitoring, and autonomous driving. Aiming at the problem of low tracking accuracy and poor robustness of the current Generic Object Tracking Using Regression Network (GOTURN) tracking algorithm, this paper takes the most popular convolutional neural network in the current target-tracking field as the basic network structure and proposes an improved GOTURN target-tracking algorithm based on residual attention mechanism and fusion of spatiotemporal context information for data fusion. The algorithm transmits the target template, prediction area, and search area to the network at the same time to extract the general feature map and predicts the location of the tracking target in the current frame through the fully connected layer. At the same time, the residual attention mechanism network is added to the target template network structure to enhance the feature expression ability of the network and improve the overall performance of the algorithm. A large number of experiments conducted on the current mainstream target-tracking test data set show that the tracking algorithm we proposed has significantly improved the overall performance of the original tracking algorithm.
H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning

The longstanding goals of federated learning (FL) require rigorous privacy guarantees and low communication overhead while holding a relatively high model accuracy. However, simultaneously achieving all the goals is extremely challenging. In this paper, we propose a novel framework called hierarchical federated learning (H-FL) to tackle this challenge. Considering the degradation of the model performance due to the statistic heterogeneity of the training data, we devise a runtime distribution reconstruction strategy, which reallocates the clients appropriately and utilizes mediators to rearrange the local training of the clients. In addition, we design a compression-correction mechanism incorporated into H-FL to reduce the communication overhead while not sacrificing the model performance. To further provide privacy guarantees, we introduce differential privacy while performing local training, which injects moderate amount of noise into only part of the complete model. Experimental results show that our H-FL framework achieves the state-of-art performance on different datasets for the real-world image recognition tasks.