Reviewerly/exHarmony huggingface dataset:
Students' perception on utilizing podcast in learning speaking skill

This research aimed to find out students’ perception of utilizing podcasts in learning speaking skills. The researchers used a qualitative approach and basic interpretative design to explore students’ perceptions. Three students were involved in this study. They are first-grade students of the vocational high school in the academic year 2019/2020. The data was gathered from the reflective journal and semi-structured interview. To obtain the data, the researchers analyzed it by thematic analysis. Results of this research, researchers classified three aspects that are the instruction in the podcast, the benefits of the podcast, and the use of podcast. The findings illustrated that students had a positive perception of the utilization of podcasts in learning speaking. Students perceive that podcast is interesting media because it has many contents or topics that they can listen and practice their speaking skill whenever they want. They also agree that podcast is beneficial for speaking achievement, for example, students can increase their vocabularies and word pronunciation.
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds

In this work, we tackle the problem of category-level online pose tracking of objects from point cloud sequences. For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories. Here the 9DoF pose, comprising 6D pose and 3D size, is equivalent to a 3D amodal bounding box representation with free 6D pose. Given the depth point cloud at the current frame and the estimated pose from the last frame, our novel end-to-end pipeline learns to accurately update the pose. Our pipeline is composed of three modules: 1) a pose canonicalization module that normalizes the pose of the input depth point cloud; 2) RotationNet, a module that directly regresses small interframe delta rotations; and 3) CoordinateNet, a module that predicts the normalized coordinates and segmentation, enabling analytical computation of the 3D size and translation. Leveraging the small pose regime in the pose-canonicalized point clouds, our method integrates the best of both worlds by combining dense coordinate prediction and direct rotation regression, thus yielding an end-to-end differentiable pipeline optimized for 9DoF pose accuracy (without using non-differentiable RANSAC). Our extensive experiments demonstrate that our method achieves new state-of-the-art performance on category-level rigid object pose (NOCS-REAL275) and articulated object pose benchmarks (SAPIEN, BMVC) at the fastest FPS ~12.
Mengoptimalkan Kemampuan Berbahasa Anak Usia Dini Melalui Metode Bercerita

Children's language skills in listening and speaking activities are not optimal, children's vocabulary is still limited, so that children in conveying ideas, answering questions, repeating stories and telling stories about their experiences have not been achieved as expected. The purpose of this research is to analyze the effect of storytelling methods on the development of language skills in early childhood. This type of research is an experimental study with a nonequivalent control group design. The research was carried out in KB 'Aisyiyah Mentari Tuban with 32 children as subjects. The number of subjects was divided into 2 groups, namely the control and experimental groups as a comparison of the development of language skills. Collecting research data using observation and documentation techniques. The validity test with the Pearson correlation and the reliability test with Cronbach Alpha were carried out first then analyzed the data with the t test. The results of this study were proven by using the storytelling method to optimize early childhood language skills. Calculation using the t-test for all indicators&gt; t critical 1.7530, it can be concluded that H0 is rejected, H1 is accepted. This means that there is an effect of storytelling methods for all indicators on children's language skills. Suggestions for educators and PAUD institutions to implement storytelling methods as a method that can stimulate children's language skills.&#x0D; &#x0D; Keywords: Storytelling Method, Language Ability&#x0D;
Mixture-Based 5-Round Physical Attack against AES: Attack Proposal and Noise Evaluation

Physical attacks against cryptographic devices and their countermeasures have been studied for over a decade. Physical attacks on block-cipher algorithms usually target a few rounds near the input or the output of cryptographic algorithms. Therefore, in order to reduce the implementation cost or increase the performance, countermeasures tend to be applied to the rounds that can be targeted by physical attacks. For example, for AES, the conventional physical attacks have practical complexity when the target leakage is as deep as 4 rounds. In general, the deeper rounds are targeted, the greater the cost required for attackers. In this paper, we focus on the physical attack that uses the leakage as deep as 5 rounds. Specifically, we consider the recently proposed 5-round mixture differential cryptanalysis, which is not physical attack, into the physical attack scenarios, and propose the corresponding physical attack. The proposed attack can break AES-128 with data complexity and time complexity of 225.31. As a result, it is clear that the rounds as deep as 5 must be protected for AES. Furthermore, we evaluated the proposed attack when the information extracted from side-channel leakage contains noise. In the means of theoretical analysis and simulated attacks, the relationship between the accuracy of information leakage and the complexity of the attack is evaluated.
Information Technology for Travel Agency

The purposes of this research are to describe, analyze, and determine the online marketing strategy of domestic travel agent at Bandung Tour Expert. In this case, the authors focused on SWOT analysis to find out the right marketing strategy for the tour packaging. In this research, the data was collected using the observation method, meanwhile the data analysis method used were descriptive qualitative and SWOT analysis. Internet users number are increasing, especially adults with high mobility. For them who often travel outside the city, internet is crucial because it connects them with the travel agent. Nowadays, the convenient use of Internet technology is used to market products and services offered by travel agents, so that it provides many benefits for business travel agents with minimum marketing costs. The results of this study were the use of internet and online marketing had impacts on the increasing profits of the travel agents. The conclusion of this research is Bandung Tour Expert as a company need to improve their variant of tour package to make tourist more interest to visit the company website.
Using Deep Learning for COVID-19 Control: Implementing a Convolutional Neural Network in a Facemask Detection Application

The ongoing COVID-19 pandemic has changed people’s lives in ways that many would not have predicted. In the days, weeks and months since mandatory lockdowns and restrictions came into effect worldwide, people have had to adjust their daily lives in an effort to slow and restrict the spread of the virus -- like regularly sanitising their hands, maintaining social distancing in crowded places, and wearing facemasks. The latter is contentious for some but has been a necessary deterrent in slowing the spread of this virus. There is potential for utilising technology as a supplementary deterrent and monitoring tool to help detect non-compliance of mask wearing. This research investigates the efficacy of AI for such purposes, exploring the applicability of a Convolutional Neural Network (CNN), for predicting if a person in a real time video feed is wearing a facemask. A dataset of over 10,000 images was created to effectively evaluate this research. The CNN developed was tested against the validation dataset to evaluate its performance, the model demonstrated 98.47% accuracy on a varied and balanced dataset.
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image

We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image. This is a challenging problem that goes far beyond the capabilities of current view synthesis methods, which quickly degenerate when presented with large camera motions. Methods for video generation also have limited ability to produce long sequences and are often agnostic to scene geometry. We take a hybrid approach that integrates both geometry and image synthesis in an iterative `\emph{render}, \emph{refine} and \emph{repeat}' framework, allowing for long-range generation that cover large distances after hundreds of frames. Our approach can be trained from a set of monocular video sequences. We propose a dataset of aerial footage of coastal scenes, and compare our method with recent view synthesis and conditional video generation baselines, showing that it can generate plausible scenes for much longer time horizons over large camera trajectories compared to existing methods. Project page at https://infinite-nature.github.io/.
ORCID ID Source of Scholarly Identity: A Pakistani Librarians Awareness

The Open Researcher &amp; Contributor ID (ORCID) ID is a unique author identification for the bibliographical record maintaining source. This is a thematic paper based on qualitative methodology deep sight reviewed different papers about the ORCID ID. Its role in scholarly communication and the publishing industry. The research findings show the awareness of Pakistani scholars about the ORCID and found 5020 ORCID ID account holders which seem very low awareness about the ORCID ID. Libraries and Librarian roles are also discussed about the promotion awareness of author identification sources. This study is helpful to librarians to include ORCID ID in their respective information literacy sessions. Pakistani researcher can secure their profile and avoid duplication of ID’s. This is the first study about the ORCID ID in the Pakistani context it will help to know Pakistani authors, researchers, scholars to known about one of the most emerging author identification tools having 12.1 million author/researcher records.
A Comparative Study of Transformer-Based Language Models on Extractive Question Answering

Question Answering (QA) is a task in natural language processing that has seen considerable growth after the advent of transformers. There has been a surge in QA datasets that have been proposed to challenge natural language processing models to improve human and existing model performance. Many pre-trained language models have proven to be incredibly effective at the task of extractive question answering. However, generalizability remains as a challenge for the majority of these models. That is, some datasets require models to reason more than others. In this paper, we train various pre-trained language models and fine-tune them on multiple question answering datasets of varying levels of difficulty to determine which of the models are capable of generalizing the most comprehensively across different datasets. Further, we propose a new architecture, BERT-BiLSTM, and compare it with other language models to determine if adding more bidirectionality can improve model performance. Using the F1-score as our metric, we find that the RoBERTa and BART pre-trained models perform the best across all datasets and that our BERT-BiLSTM model outperforms the baseline BERT model.
SMART POWER WHEELCHAIR: PROBLEMS AND CHALLENGES OF PRODUCT APPROACH

This paper focuses on intelligent assistant for power wheelchair (PW) usage in home conditions. Especially in the context of PW intelligent assistant as a consumer product. The main problematic aspects and challenges of smart PW in real application are noted. The approach to formation of system requirements and their classification is offered. The research results proposed and implemented in the ongoing Mobilis project for smart PW. Further prospects of research and development are noted. Also, it is stated that the implementation of smart PW technology opens possibilities to effective integration with new control methods (including brain-computer interfaces).
Combinatorial optimization through variational quantum power method

The power method (or iteration) is a well-known classical technique that can be used to find the dominant eigenpair of a matrix. Here, we present a variational quantum circuit method for the power iteration, which can be used to find the eigenpairs of unitary matrices and so their associated Hamiltonians. We discuss how to apply the circuit to combinatorial optimization problems formulated as a quadratic unconstrained binary optimization and discuss its complexity. In addition, we run numerical simulations for random problem instances with up to 21 parameters and observe that the method can generate solutions to the optimization problems with only a few number of iterations and the growth in the number of iterations is polynomial in the number of parameters. Therefore, the circuit can be simulated on the near term quantum computers with ease.
Towards Evolutionary Emergence

With the upcoming era of large-scale, complex cyber-physical systems, also the demand for decentralized and self-organizing algorithms for coordination rises.Often such algorithms rely on emergent behavior; local observations and decisions aggregate to some global behavior without any apparent, explicitly programmed rule.Systematically designing these algorithms targeted for a new orchestration or optimization task is, at best, tedious and error prone.Suitable and widely applicable design patterns are scarce so far.We opt for a machine learning based approach that learns the necessary mechanisms for targeted emergent behavior automatically.To achieve this, we use Cartesian genetic programming.As an example that demonstrates the general applicability of this idea, we trained a swarm-based optimization heuristics and present first results showing that the learned swarm behavior is significantly better than just random search.We also discuss the encountered pitfalls and remaining challenges on the research agenda.
Peak flood volume and its suspended sediment at various rainfall in Kedungbulus catchment in Gombong, Central Java, Indonesia

Flood is a natural disaster that frequently happens and causes many material and immaterial losses. During flooding, the suspended sediment is carried along by the streamflow. The amount of sediment transported varies and depends on natural and anthropogenic factors. Limited studies have been conducted regarding the relationship between peak flood volume and its sediment content. Therefore, a study with the purpose to understand the relationship of rainfall characteristics, peak flood volume, and suspended sediment was undertaken in Kedungbulus Catchment in Gombong, Central Java, Indonesia. The size of Kedungbulus catchment is 37.8 km2. To collect the required data, an automatic stream water level recorder was installed in the outlet of the catchment. In addition, an automatic and two conventional rain gauges were set up inside the catchment. Hydrograph and statistical analysis were conducted on 2016-2017 data. The results showed that during the study period, the highest peak flood volume occurred on October 8, 2016. The flood duration was 490 minutes, with the time to peak was 135 minutes. At the highest peak flood volume, the stream water was 5,091,221 m3, and the suspended sediment was around 2,394 tons. Rainfall depth significantly affects the peak flood volume and its suspended sediment. The rainfall intensity and Antecedent Soil Moisture Content (ASMC) weakly correlate with peak flood volume and its suspended sediment content.
Digital Literacy of Doctoral Program Student in Sports Education, Semarang State University

E-learning in new normal era is a product of transformation from conventional to digital-based learning.Students' digital literacy will affect the success of achievement in learning system.The purpose of this research is to identify students' digital literacy in order to support the implementation of e-learning in Sports Education Doctoral Program at Semarang State University.The method used is descriptive with a quantitative approach.56 pupils as subjects of the study, likewise sampling quota applied as the sampling technique.The data analysis used is descriptive analysis.The results shows that students mastery on various e-learning system platforms was in the "Medium" category, mastery of various social media as part of the "High" category of e-learning, mastery of video conference applications in the "Medium" category, and mastery of the supporting media for making assignment is "High" category either.
Understanding Dimensional Collapse in Contrastive Self-supervised Learning

Self-supervised visual representation learning aims to learn useful representations without relying on human annotations. Joint embedding approach bases on maximizing the agreement between embedding vectors from different views of the same image. Various methods have been proposed to solve the collapsing problem where all embedding vectors collapse to a trivial constant solution. Among these methods, contrastive learning prevents collapse via negative sample pairs. It has been shown that non-contrastive methods suffer from a lesser collapse problem of a different nature: dimensional collapse, whereby the embedding vectors end up spanning a lower-dimensional subspace instead of the entire available embedding space. Here, we show that dimensional collapse also happens in contrastive learning. In this paper, we shed light on the dynamics at play in contrastive learning that leads to dimensional collapse. Inspired by our theory, we propose a novel contrastive learning method, called DirectCLR, which directly optimizes the representation space without relying on an explicit trainable projector. Experiments show that DirectCLR outperforms SimCLR with a trainable linear projector on ImageNet.
Sampling from Arbitrary Functions via PSD Models

In many areas of applied statistics and machine learning, generating an arbitrary number of independent and identically distributed (i.i.d.) samples from a given distribution is a key task. When the distribution is known only through evaluations of the density, current methods either scale badly with the dimension or require very involved implementations. Instead, we take a two-step approach by first modeling the probability distribution and then sampling from that model. We use the recently introduced class of positive semi-definite (PSD) models, which have been shown to be efficient for approximating probability densities. We show that these models can approximate a large class of densities concisely using few evaluations, and present a simple algorithm to effectively sample from these models. We also present preliminary empirical results to illustrate our assertions.
Nézőpont kérdése. A konstruálás pragmatikai vonatkozásairól

Drawing on the theoretical assumptions of cognitive linguistics, the paper takes steps towards a systematic cognitive-functional interpretation of pragmatic phenomena by highlighting the pragmatic aspects and significance of construal.Its key question is how the process of construal can be described in the context of language use interpreted as social cognitive activity.The paper foregrounds it as an essential feature of language use how discourse participants exploit the perspective nature of linguistic symbols while conceptually construing their experiences.Accordingly, construal is interpreted in terms of i. the adaptability of social cognition, ii. the intersubjectivity of attention directing, and iii. the perspectivity of contextualization.
Emergency remote teaching in linguistics during the early COVID-19 pandemic

In the spring of 2020, the COVID-19 pandemic forced many higher education institutions to transition suddenly to emergency remote teaching. This paper describes the results of a survey that the Linguistic Society of America carried out on the response to this situation among teachers and learners in the linguistics community. We consider what teachers tried, what students found helpful, and what got overlooked.
An Algorithmic Approach to Establish a Lower Bound for the Size of Semiring Neural Networks

Semiring neural networks have been introduced as a recurrent neural network-type representation of weighted automata with the potential to learn a recognizable series.Whether a given semiring neural network actually can or cannot compute a recognizable series, however, depends on the size of the network.Therefore, it is desirable to determine whether a proposed size is too small before initiation of the training procedure.Here, we present an algorithm that achieves this in polynomial time.As there is a one-to-one correspondence between semiring neural networks and weighted automata, our algorithm can also be used to derive lower bounds for the size of a recognizing automaton.Our algorithm complements previous work in this area as it works over commutative zero-sum-free semirings.
RecLVQ: Recurrent Learning Vector Quantization

Learning Vector Quantizers (LVQ) and its cost-functionbased variant called Generalized Learning Vector Quanitzation (GLVQ) are powerful, yet simple and interpretable classification models.Even though GLVQ is an effective tool for classifying vectorial data, it cannot handle raw sequence data of potentially different lengths.Usually, this problem is solved by manually engineering fixed-length features or by employing recurrent networks.Therefore, a natural idea is to incorporate recurrent units for data processing into the GLVQ network structure.The processed data can then be compared in a latent space for classification decisions.We demonstrate the ability of this approach on illustrative classification problems.
A Neural Anthropometer Learning from Body Dimensions Computed on Human 3D Meshes

Human shape estimation has become increasingly important both theoretically and practically, for instance, in 3D mesh estimation, distance garment production and computational forensics, to mention just a few examples. As a further specialization, Human Body Dimensions Estimation (HBDE) focuses on estimating human body measurements like shoulder width or chest circumference from images or 3D meshes usually using supervised learning approaches. The main obstacle in this context is the data scarcity problem, as collecting this ground truth requires expensive and difficult procedures. This obstacle can be overcome by obtaining realistic human measurements from 3D human meshes. However, a) there are no well established methods to calculate HBDs from 3D meshes and b) there are no benchmarks to fairly compare results on the HBDE task. Our contribution is twofold. On the one hand, we present a method to calculate right and left arm length, shoulder width, and inseam (crotch height) from 3D meshes with focus on potential medical, virtual try-on and distance tailoring applications. On the other hand, we use four additional body dimensions calculated using recently published methods to assemble a set of eight body dimensions which we use as a supervision signal to our Neural Anthropometer: a convolutional neural network capable of estimating these dimensions. To assess the estimation, we train the Neural Anthropometer with synthetic images of 3D meshes, from which we calculated the HBDs and observed that the network's overall mean estimate error is 20.89 mm (relative error of 2.84 %). The results we present are fully reproducible and establish a fair baseline for research on the task of HBDE, therefore enabling the community with a valuable method.