Reviewerly/exHarmony huggingface dataset:
KNOWLEDGE SHARING AND INFORMATION QUALITY AS MEDIATED BY MOBILE LEARNING SYSTEM USAGE OF SENIOR HIGH STUDENTS

This study aimed to determine the mediating effect of mobile learning system-usage of Davao City National High School senior high students between knowledge sharing and information quality. A stratified random sampling method was adopted, with 319 students as respondents. The researcher collected the data using a Google form delivered to the respondents. The results of a non-experimental quantitative mediation analysis using a validated questionnaire, mean, regression techniques, and Pearson r revealed a significant relationship between knowledge sharing, information quality, and mobile learning system usage. The findings revealed that senior high students' level of knowledge sharing was high. Likewise, the level of information quality of senior high students was high. In addition, the Level of Mobile Learning System Usage suggested that the outcome was high. Since the mediating effect causal steps approach in this study revealed a non-significant relationship between knowledge sharing and information quality, there was a complete mediation on the effect of mobile learning system usage on the relationship between knowledge sharing and information quality. As a result, mobile learning systems influence students' knowledge sharing and information quality, and vice versa.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0939/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Designing Convolution Neural Network Architecture by utilizing the Complexity Model of the Dataset

Convolutional Neural networks (CNN) have been utilized in a wide variety of areas, with a high degree of performance. The design of the CNN has been a general problem addressed mainly by using the experts to design the architecture of the CNN model, which is quite inefficient. In recent times there has been the need to develop the CNN architecture automatically, resulting in multiple approaches that have been utilized for this process. In this paper, we give an overview of the multiple approaches utilized for automatic CNN architecture design and also propose a method to design the architecture, which utilizes the dataset itself for the process of designing the architecture of the CNN. The method shows good performance and in terms of the resources required to generate the architecture has significantly better performance than state-of-the-art models.
A SURVEY ON THE DIFFICULTIES IN WRITING ESSAYS OF ENGLISH MAJORED SOPHOMORES AT TAY DO UNIVERSITY, VIETNAM

This thesis “A study on the difficulties in writing essays of English-majored sophomores at Tay Do University” was conducted to examine the difficulties that second-year students majoring in English often met in writing essays. For this purpose, the participants in this study were 100 English majored sophomores in classes 14A, 14B and 14C at Tay Do University. The interview with 3 extended statements for English majored teachers and a questionnaire with 25 closed statements for students is used as instruments to collect the data. The data from the interview and questionnaire were collected to make clear and prove the problems that the students have had in writing essays. The study figured out some difficulties about background knowledge, vocabulary, grammar structure, idea arrangement and orthography in writing essays faced by English majors sophomores at Tay Do University. Besides, this study also helps students realize their challenges and improve their writing essays.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0939/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Explainable AI via Learning to Optimize

Indecipherable black boxes are common in machine learning (ML), but applications increasingly require explainable artificial intelligence (XAI). The core of XAI is to establish transparent and interpretable data-driven algorithms. This work provides concrete tools for XAI in situations where prior knowledge must be encoded and untrustworthy inferences flagged. We use the "learn to optimize" (L2O) methodology wherein each inference solves a data-driven optimization problem. Our L2O models are straightforward to implement, directly encode prior knowledge, and yield theoretical guarantees (e.g. satisfaction of constraints). We also propose use of interpretable certificates to verify whether model inferences are trustworthy. Numerical examples are provided in the applications of dictionary-based signal recovery, CT imaging, and arbitrage trading of cryptoassets. Code and additional documentation can be found at https://xai-l2o.research.typal.academy.
Quantum Dictionaries without QRAM

This paper presents an efficient gate-level implementation of a quantum dictionary: a data structure that can store a superposition of mappings from keys to values. The dictionary is stored as a fixed-length list of sorted address-value pairs, where the length of the list is the maximum number of entries that can be put in the dictionary. An addressed value can be extracted from (or injected into) the dictionary using $C \cdot (V + 2.5A) + O(V + A + C)$ expected Toffoli gates and $O(V + A)$ auxiliary qubits (where $C$ is the maximum capacity, $A$ is the address width, and $V$ is the value width).
OSSGAN: Open-Set Semi-Supervised Image Generation

We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set. Unlike the existing semi-supervised image generation task, where unlabeled data only contain closed-set samples, our task is more general and lowers the data collection cost in practice by allowing open-set samples to appear. Thanks to entropy regularization, the classifier that is trained on labeled data is able to quantify sample-wise importance to the training of cGAN as confidence, allowing us to use all samples in unlabeled data. We design OSSGAN, which provides decision clues to the discriminator on the basis of whether an unlabeled image belongs to one or none of the classes of interest, smoothly integrating labeled and unlabeled data during training. The results of experiments on Tiny ImageNet and ImageNet show notable improvements over supervised BigGAN and semi-supervised methods. Our code is available at https://github.com/raven38/OSSGAN.
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer

The current state-of-the-art for few-shot cross-lingual transfer learning first trains on abundant labeled data in the source language and then fine-tunes with a few examples on the target language, termed target-adapting. Though this has been demonstrated to work on a variety of tasks, in this paper we show some deficiencies of this approach and propose a one-step mixed training method that trains on both source and target data with \textit{stochastic gradient surgery}, a novel gradient-level optimization. Unlike the previous studies that focus on one language at a time when target-adapting, we use one model to handle all target languages simultaneously to avoid excessively language-specific models. Moreover, we discuss the unreality of utilizing large target development sets for model selection in previous literature. We further show that our method is both development-free for target languages, and is also able to escape from overfitting issues. We conduct a large-scale experiment on 4 diverse NLP tasks across up to 48 languages. Our proposed method achieves state-of-the-art performance on all tasks and outperforms target-adapting by a large margin, especially for languages that are linguistically distant from the source language, e.g., 7.36% F1 absolute gain on average for the NER task, up to 17.60% on Punjabi.
Dynimp: Dynamic Imputation for Wearable Sensing Data through Sensory and Temporal Relatedness

In wearable sensing applications, data is inevitable to be irregularly sampled or partially missing, which pose challenges for any downstream application. An unique aspect of wearable data is that it is time-series data and each channel can be correlated to another one, such as x, y, z axis of accelerometer. We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point’s missingness with nearest neighbors along feature axis and then feeding the data into a LSTM-based denoising autoen-coder which can reconstruct missingness along the time axis. We experiment the model on the extreme missingness scenario (> 50% missing rate) which has not been widely tested in wearable data. Our experiments on activity recognition show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
Explainable Federated Learning: A Lifecycle Dashboard for Industrial Settings

As the adoption of Federated Learning (FL) in the manufacturing industry grows and systems get increasingly complex, a need to inspect their behavior arises. Stakeholders of the FL process want a more transparent system to understand the current state and analyze how its performance changed over time. However, current representation approaches are often not designed for industrial applications and do not cover the entire FL model lifecycle. We propose the Lifecycle Dashboard, which considers the different requirements and perspectives of industrial stakeholders by visualizing information from the FL server. In addition, our representation approach is generic enough to be applied to different use cases and industries. We evaluate the Lifecycle Dashboard in a semi-structured expert interview, show improvements in the understandability of FL systems, and discuss possible use cases in the industry.
SHOP: A Deep Learning Based Pipeline for Near Real-Time Detection of Small Handheld Objects Present in Blurry Video

While prior works have investigated and developed computational models capable of object detection, models still struggle to reliably interpret images with motion blur and small objects. Moreover, none of these models are specifically designed for handheld object detection. In this work, we present SHOP (Small Handheld Object Pipeline), a pipeline that reliably and efficiently interprets blurry images containing handheld objects. The specific models used in each stage of the pipeline are flexible and can be changed based on performance requirements. First, images are deblurred and then run through a pose detection system where areas-of-interest are proposed around the hands of any people present. Next, object detection is performed on the images by a single-stage object detector. Finally, the proposed areas-of-interest are used to filter out low confidence detections. Testing on a handheld subset of Microsoft Common Objects in Context (MS COCO) demonstrates that this 3 stage process results in a 70 percent decrease in false positives while only reducing true positives by 17 percent in its strongest configuration. We also present a subset of MS COCO consisting solely of handheld objects that can be used to continue the development of handheld object detection methods. https://github.com/spider-sense/SHOP
Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning

The idea of federated learning is to train deep neural network models collaboratively and share them with multiple participants without exposing their private training data to each other. This is highly attractive in the medical domain due to patients' privacy records. However, a recently proposed method called Deep Leakage from Gradients enables attackers to reconstruct data from shared gradients. This study shows how easy it is to reconstruct images for different data initialization schemes and distance measures. We show how data and model architecture influence the optimal choice of initialization scheme and distance measure configurations when working with single images. We demonstrate that the choice of initialization scheme and distance measure can significantly increase convergence speed and quality. Furthermore, we find that the optimal attack configuration depends largely on the nature of the target image distribution and the complexity of the model architecture.
SOCIAL MEDIA AS MODERN PLATFORM TO CREATE AWARENESS OF SONOBUDOYO AND SANDI MUSEUMS IN YOGYAKARTA INDONESIA

Museum awareness through social media has been developing gradually.A previous study related to the topic found that museum social media was not well managed.It was related to the human resources competence in social media management even now it is defeated by better solution to have consultant or outsourcing.Good museum management will certainly have positive impact on the number of visits.However, not all museums have enough number of human resources and also have clear procedures for the use of related social media to increase the number of visits.The general objective of this study is to describe the maximum use of social media in order to increase the number of museum visits in Yogyakarta that specifically focuses on the types of social media and the strategies used to build awareness and interest in visiting the museum.The research method is a mixed qualitative quantitative approach in Sonobudoyo and Sandi museums in Yogyakarta, Indonesia as both museums have active social media.The respondents were thirty-nine Instagram followers of Sonobudoyo and thirty-eight followers of Sandi's Instagram.This study concludes that the two museums have good implementation of Social Media Marketing through Instagram and there is an increase in the number of visits after the social media used and entertainment content are well managed as the best strategy.It is suggested that the result of this study can provide solutions and input for other museums in Indonesia to become the preferred tourism destination to develop social media through the right Social Media Marketing (SMM) strategy to attract attention and interest of visitors, especially young people as the targets set by the government.
Finite sequences representing expected order statistics

Characterizations of finite sequences $\beta_{1}<\cdots<\beta_{n}$ representing expected values of order statistics from a random sample of size $n$ are given. As a by-product, a characterization of binomial mixtures, when the mixing random variable is supported in the open interval $(0,1)$, is presented; this enables the exact description of the convex hull of the open binomial curve, as well as the open moment curve.
3T-1R Analog Write and Digital Read of MRAM for RNG and Low Power Memory Application

This work represents integration of MTJ with 30nm FinFET for low voltage analog write operations and readout optimization for the p-bit or true random number generator (TRNG), where the induced p-bit, the probabilistic state of the magnetic tunnel junction (MTJ), is detected within only a single computational period. The period contains two sub-cycles: write and joined read & reset cycles. The operation with MTJ becomes stochastic, independent after calibrating at the desired working point against the factors, which can induce the signal deviations, e.g. temperature, material degradation or external magnetic field.
FindFacts: A Scalable Theorem Search

The Isabelle Archive of Formal Proofs (AFP) has grown to over 500 articles in late 2019. Meanwhile, finding formalizations in it has not exactly become easier. At the time of writing, the site-specific AFP google search and the Isabelle find_theories resp. find_consts commands (that only work on imported theories) are still the only tools readily available to find formalizations in Isabelle. We present FindFacts, a novel domain-specific search tool for formal Isabelle theory content. Instead of utilizing term unification, we solve the problem with a classical drill-down search engine. We put special emphasis on scalability of the search system, so that the whole AFP can be searched interactively.
ConvTract: A Convolutional-based Framework for Tractography

ConvTract is a data-driven tractography pipeline.
Study on test method of multi-angle static ash holdup of cigarette combustion by full vision

In order to evaluate and study the static ash holdup of cigarette combustion from various angles, an objective and accurate real-time measurement method of static ash holdup of cigarette combustion is provided. The image acquisition of the test sample during the static combustion of the cigarette is evenly distributed around the test cigarette through 3 cameras, and the camera lens position is perpendicular to the axial direction of the cigarette. Through the simultaneous shooting of 3 sides of a single cigarette in the static combustion state, the image tracking acquisition is carried out in real time, so as to complete the 3-side full vision acquisition of the static ash holdup of the cigarette test sample combustion. The experimental results show that there are great differences among the three groups of static gray holdup data collected by three-side full vision, so it is necessary to take the average value of three-side photography.
Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic Data

Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. This, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed in-formation for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. This is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://bit.ly/3jN1pJJ
A study of tree-based methods and their combination

Tree-based methods are popular machine learning techniques used in various fields. In this work, we review their foundations and a general framework the importance sampled learning ensemble (ISLE) that accelerates their fitting process. Furthermore, we describe a model combination strategy called the adaptive regression by mixing (ARM), which is feasible for tree-based methods via ISLE. Moreover, three modified ISLEs are proposed, and their performance are evaluated on the real data sets.
Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications

Vehicle-to-Everything (V2X) communications provide opportunities for information sharing among vehicles, edge servers, and cloud services. By the collection and extraction of sensing information from vehicles, such as communication quality or free space size, the edge server in V2X communications can improve its sensing and perception coverage. However, the collection of sensing data from vehicles consumes a large amount of wireless resources and computing resources at the edge server. The objective of this study is to extract object sensing information from vehicles, including the minimum or maximum of the sensing values, with low resource consumption and with high scalability. We propose a method that transforms the extraction of sensing information into a two-level procedure that includes (1) the local sharing and extraction of sensing information among vehicles and (2) the efficient extraction of sensing information at the edge server. Moreover, hybrid communication methods are employed at vehicles, with a short range of communication between vehicles to reduce the consumption of wireless resources for the local sharing of sensing data. The evaluation results show that the proposed method highly reduces the number of reports from the vehicles to the edge server, with a small amount of network resource consumption and scalability.
Building New Tools Outside the Browser: A New Look at Custom Stand-Alone Applications for Earth Data Access and Visualization

Today, the debate between online services and installable applications is often characterized as an either-or, winner-take-all situation. In the earth sciences, how-ever, due to data handing and visualization needs, a combination of online, service-oriented architecture (SOA) tools and custom installable applications holds the most promise if these data resources are to be used by a greater and more diverse user population. The Data Information and Application Layer (DIAL) technology demonstrates the value of custom application authoring for earth data access and visualization. The relative merits of online tools and custom applications are discussed as well as their complementary uses by various user populations.
Detection of diseases using facial features with Deep transfer learning

There is always a relation between face and diseases that leads the idea of facial diagnosis. The aim of this project is to predict the diseases using 2 Dimensional facial images. In this study, we are dealing with diagnosis of single and multiple illnesses such as Beta-thalassemia, hyperthyroidism, Down syndrome, leprosy and vitiligo. Accuracy for face recognition is over 90%. In practical collecting disease specific images are complex, expensive and time taking process. Therefore, collecting data sets for face recognition is hard and complicated. So, in deep transfer learning applications we can do facial diagnosis with a small dataset which will be easy process where we can train the pre trained model.
A real-time recognition model of teachers' voice and emotion for college online education

In order to improve the recognition rate of teachers' voice emotion recognition, this paper proposes a new model of teachers' voice emotion recognition for online education in colleges and universities. In the process of online education, there are some errors and noises in Teachers' voice, which need to be corrected and denoised. On this basis, teachers' voice emotion features are selected for dimensionality reduction, based on the reduced teachers' voice emotion feature parameters. The template of teacher's speech emotion recognition is constructed to realize the real-time recognition of teacher's speech emotion. The experimental results show that, under different experimental objects, the accuracy of the proposed model is much higher than that of the contrast model, which proves that the proposed model has good performance of emotion recognition.