Reviewerly/exHarmony huggingface dataset:
Weakly-supervised Fingerspelling Recognition in British Sign Language Videos

The goal of this work is to detect and recognize sequences of letters signed using fingerspelling in British Sign Language (BSL). Previous fingerspelling recognition methods have not focused on BSL, which has a very different signing alphabet (e.g., two-handed instead of one-handed) to American Sign Language (ASL). They also use manual annotations for training. In contrast to previous methods, our method only uses weak annotations from subtitles for training. We localize potential instances of fingerspelling using a simple feature similarity method, then automatically annotate these instances by querying subtitle words and searching for corresponding mouthing cues from the signer. We propose a Transformer architecture adapted to this task, with a multiple-hypothesis CTC loss function to learn from alternative annotation possibilities. We employ a multi-stage training approach, where we make use of an initial version of our trained model to extend and enhance our training data before re-training again to achieve better performance. Through extensive evaluations, we verify our method for automatic annotation and our model architecture. Moreover, we provide a human expert annotated test set of 5K video clips for evaluating BSL fingerspelling recognition methods to support sign language research.
Short-Term Trajectory Prediction for Full-Immersive Multiuser Virtual Reality with Redirected Walking

Full-immersive multiuser Virtual Reality (VR) envisions supporting unconstrained mobility of the users in the virtual worlds, while at the same time constraining their physical movements inside VR setups through redirected walking. For enabling delivery of high data rate video content in real-time, the supporting wireless networks will leverage highly directional communication links that will "track" the users for maintaining the Line-of-Sight (LoS) connectivity. Recurrent Neural Networks (RNNs) and in particular Long Short-Term Memory (LSTM) networks have historically presented themselves as a suitable candidate for near-term movement trajectory prediction for natural human mobility, and have also recently been shown as applicable in predicting VR users' mobility under the constraints of redirected walking. In this work, we extend these initial findings by showing that Gated Recurrent Unit (GRU) networks, another candidate from the RNN family, generally outperform the traditionally utilized LSTMs. Second, we show that context from a virtual world can enhance the accuracy of the prediction if used as an additional input feature in comparison to the more traditional utilization of solely the historical physical movements of the VR users. Finally, we show that the prediction system trained on a static number of coexisting VR users be scaled to a multi-user system without significant accuracy degradation.
Skill-based Model-based Reinforcement Learning

Model-based reinforcement learning (RL) is a sample-efficient way of learning complex behaviors by leveraging a learned single-step dynamics model to plan actions in imagination. However, planning every action for long-horizon tasks is not practical, akin to a human planning out every muscle movement. Instead, humans efficiently plan with high-level skills to solve complex tasks. From this intuition, we propose a Skill-based Model-based RL framework (SkiMo) that enables planning in the skill space using a skill dynamics model, which directly predicts the skill outcomes, rather than predicting all small details in the intermediate states, step by step. For accurate and efficient long-term planning, we jointly learn the skill dynamics model and a skill repertoire from prior experience. We then harness the learned skill dynamics model to accurately simulate and plan over long horizons in the skill space, which enables efficient downstream learning of long-horizon, sparse reward tasks. Experimental results in navigation and manipulation domains show that SkiMo extends the temporal horizon of model-based approaches and improves the sample efficiency for both model-based RL and skill-based RL. Code and videos are available at https://clvrai.com/skimo
Tokens and Distributed Ledgers in Digital Payment Systems

In this article, we consider the roles of tokens and distributed ledgers in digital payment systems. We present a brief taxonomy of digital payment systems that use tokens, and we address the different models for how distributed ledger technology can support digital payment systems in general. We offer guidance on the salient features of digital payment systems, which we comprehend in terms of consumer privacy, token issuance, and accountability for system operators.
Variations on the Choi-Jamiolkowski isomorphism

We address various aspects of a widely used tool in quantum information theory: the Choi-Jamiolkowski isomorphism [A. Jamiolkowski, Rep. Math. Phys., 3, 275 (1972)]. We review different versions of the isomorphism, their properties and propose a unified description that combines them all. To this end, we identify the physical reason for the appearance of the (basis-dependent) operation of transposition in the isomorphism as used in Choi's theorem [M.-D. Choi, Lin. Alg. Appl., 10, 285 (1975)]. This requires a careful distinction between Jordan algebras and the different C*-algebras they arise from, which are distinguished by their order of composition. Physically, the latter encodes a choice of time orientation in the respective algebras, which relates to a number of recent results, including a characterisation of quantum from more general non-signalling bipartite correlations [M. Frembs and A. D\"oring, arXiv:2204.11471] and a classification of bipartite entanglement [M. Frembs, arXiv:2207.00024].
DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving

Vehicle-to-Everything (V2X) network has enabled collaborative perception in autonomous driving, which is a promising solution to the fundamental defect of stand-alone intelligence including blind zones and long-range perception. However, the lack of datasets has severely blocked the development of collaborative perception algorithms. In this work, we release DOLPHINS: Dataset for cOllaborative Perception enabled Harmonious and INterconnected Self-driving, as a new simulated large-scale various-scenario multi-view multi-modality autonomous driving dataset, which provides a ground-breaking benchmark platform for interconnected autonomous driving. DOLPHINS outperforms current datasets in six dimensions: temporally-aligned images and point clouds from both vehicles and Road Side Units (RSUs) enabling both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based collaborative perception; 6 typical scenarios with dynamic weather conditions make the most various interconnected autonomous driving dataset; meticulously selected viewpoints providing full coverage of the key areas and every object; 42376 frames and 292549 objects, as well as the corresponding 3D annotations, geo-positions, and calibrations, compose the largest dataset for collaborative perception; Full-HD images and 64-line LiDARs construct high-resolution data with sufficient details; well-organized APIs and open-source codes ensure the extensibility of DOLPHINS. We also construct a benchmark of 2D detection, 3D detection, and multi-view collaborative perception tasks on DOLPHINS. The experiment results show that the raw-level fusion scheme through V2X communication can help to improve the precision as well as to reduce the necessity of expensive LiDAR equipment on vehicles when RSUs exist, which may accelerate the popularity of interconnected self-driving vehicles. DOLPHINS is now available on https://dolphins-dataset.net/.
PLSS: A Projected Linear Systems Solver

We propose iterative projection methods for solving square or rectangular consistent linear systems Ax = b. Existing projection methods use sketching matrices (possibly randomized) to generate a sequence of small projected subproblems, but even the smaller systems can be costly. We develop a process that appends one column to the sketching matrix each iteration and converges in a finite number of iterations whether the sketch is random or deterministic. In general, our process generates orthogonal updates to the approximate solution xk. By choosing the sketch to be the set of all previous residuals, we obtain a simple recursive update and convergence in at most rank(A) iterations (in exact arithmetic). By choosing a sequence of identity columns for the sketch, we develop a generalization of the Kaczmarz method. In experiments on large sparse systems, our method (PLSS) with residual sketches is competitive with LSQR and LSMR, and with residual and identity sketches compares favorably with state-of-the-art randomized methods.
The Influence of PUBG Mobile Online Game on the English Learning

This study aims to determine the English learning achievement of students playing the PUBG Mobile online game and the effect of the PUBG Mobile online game on students' English learning. The method used in this study was a descriptive design. The participants of this study were selected using purposive sampling technique, in which 15 students from class XIa, and 15 students from class XIb. This study used documents (English test scores) and a Likert scale questionnaire as instruments. This study found that the average English achievement score of class XI MAN 2 Banda Aceh students who played the PUBG Mobile online game was 62, in the unsatisfactory category. Besides, the influence of the PUBG Mobile online game on the English learning of class XI MAN 2 Banda Aceh students had 90% negative effect. Therefore, it can be concluded that the PUBG Mobile online game had a negative impact on the students' English learning achievement.
Predictive Analysis of Spatial Data and Time Series to Predict Earthquake Magnitudes by Using Data Mining Approach

The suggested methodology presented in this work uses data mining to identify seismic zones and time series to forecast earthquake magnitudes. Utilize historical earthquake data gathered from the United States Geological Survey (USGS) and obtained by utilizing hierarchical and fartherst first clustering to predict seismic activity. Latitude and longitude cluster data were used to create a prediction model to forecast the size of upcoming earthquakes in the Nanggroe Aceh Darussalam region and its nearby areas.
An Error Analysis of English Voiceless Plosive Consonants /p/, /t/, and /k/ of Students at SMAN 11 Banda Aceh

This research was conducted based on the phenomenon happened in learning environment. The students have low ability in pronunciation. They got difficulty in pronouncing English words including plosive voiceless consonants sound. Therefore, this manuscript sheds lights on the errors produced in pronouncing plosive voiceless consonants sound. To find out the types of error and to find out the percentages of errors which students made in pronouncing plosive voiceless consonants /p/, /t/ and /k/ with aspirated and unaspirated. This research was conducted based on descriptive qualitative research design. The participants were 20 female tenth graders at SMAN 11 Banda Aceh, class of X IS 2. In collecting the data, a sheet containing 30 aspirated and unaspirated consonants /p/, /t/, and /k/ words was distributed, and then the students pronounced the words while their voice recorded. All recorded sound produced by participants checked thoroughly, they were played one by one. The transcriptions were used to find out how many errors produced. The researcher identified the words on transcription which produced an error, the researcher identified the student’s errors and after identifying the data, and finally the errors were classified. In this way, the researcher determined the types of error that belong to each type of error. Then, the errors that committed by the students put into the distribution table of errors to find out the number of errors each of the student committed. After classifying the data, the researcher calculated the students’ errors and made the total for each error by counting the errors to get the total of each error. The result of the research showed that there were three types of errors those are aspirated /h/ sounds as unaspirated, unaspirated sound as aspirated /h/ and all sounds unaspirated. The total of errors were 372 errors’ items which made by the students. The most common errors were all sounds unaspirated, which was 167 errors’ items or 44.9% made by the students, aspirated sounds as unaspirated was 131 items or 35.2% and unaspirated sounds as aspirated was 74 items of errors or 19.9%.
An Analysis of Mechanical Errors in Writing Skill

Writing skill is one of the important language skills for students. The purpose of this study was to find out the kinds of mechanical errors produced by the 10th grade students at SMAN 11 Banda Aceh. The method used was descriptive quantitative research design. The population comprised 204 students at the 10th grade of SMAN 11 Banda Aceh, while the samples were 30, selected by using cluster random sampling. This study used test as an instrument for data collection. In analyzing the data, the researcher used two steps, namely identifying of mechanical errors and classifying them based on their types. The results showed that the students produced 582 errors in total, which were categorized into three types: errors of punctuation, errors of capitalization, and errors of spelling. In details, the students made 236 errors of punctuation (40, 54%), 312 errors of capitalization (53, 60%), and 34 errors of spelling (5, 84%). The most dominant type of mechanical errors made by students was the errors of capitalization.
Using Refutation Technique to Increase Students’ Reading Comprehension at the Second Grade Students of Sma Hasanuddin Gowa

The aims of this study was to find out whether or not refufation technique can increase the students’ reading comprehension of the second grade students of SMA Hasanuddin Gowa. This research employed pre-experimental method which one class was given pre-test, treatment and post-test. The population of this research was the second grade students of SMA Hasanuddin Gowa. The total sample consisted of 24 students and it is used purposive sampling technique. The instrument was reading text in order to measure the students’ reading comprehension skill by using refutation technique. The result of the data showed that by using reputation technique in reading comprehension was effective in improving the students’ reading skill. In other words, the finding indicated that using refutation technique in teaching reading comprehension was able to improve the students’ skill in reading. Based on the result of the data showed that there was a significant difference between the pre-test mean score (45.04), and the post-test mean score (71.83). It can be concluded that the use of refutation technique in reading comprehension can be improved at SMA Hasanuddin Makassar.
AN IN-DEPTH SURVEY ON ONLINE SHOPPING THROUGH GESTURE RECOGNITION USING DEEP LEARNING

The recent advances in communication capabilities and the proliferation of electronic devices has been effective in improving the online landscape by a large margin.There has been introduction of innovative utilizations of the internet platform in the field of shopping has led to the development and success of ecommerce websites.These websites allow an individual to make purchases from the comfort of their homes.This is incredibly useful and highly convenient practice that makes shopping an easier and highly rewarding experience online.But the problem with these applications is the lack of an effective accessibility features that allow for the disabled people to do shopping.Therefore, there is a need for an approach that can provide the effective interaction and navigation of the e-commerce websites through the use of gestures.For this purpose the computer vision approach is being utilized to capture the gesture and allow for navigating the shopping application through the use of gestures.The presented approach achieves effective gesture based shopping through the implementation of Convolutional Neural networks.This methodology will be elaborated in the upcoming editions of this research article.
21st Century Technology Literacy in EFL Textbook: A Need Analysis

Background: Despite living in the modern era, the level of Indonesian students’ technology literacy is still low, especially in the stage of junior high school. Aim: This research attempts to investigate what 21st century English as Foreign Language (EFL) textbook need in order to develop students’ technology literacy since the book is the most recognized medium for learning and being technology literate will also help students learn English better. Method: The textbook chosen for this research is the one published by the Ministry of Education, When English Rings a Bell, for grade 8 of junior high school. As for the data, they were taken from every chapter’s directions. They were analysed by implementing content analysis to detect the presence of technology literacy indicators in the textbook’s directions. Findings: Based on the results of the analysis, EFL textbook directions need the author’s initiative to modify them so that they can fit the ICT development indicators by adding more sentences related to applying presentation software usage and sharing information through online platforms. It is because the more literate the students in operating, accessing, managing, integrating, and evaluating modern technologies, especially ICT, the bigger their chances become in learning English successfully.
Perceptron Collaborative Filtering

Abstract: While multivariate logistic regression classifiers are a great way of implementing collaborative filtering - a method of making automatic predictions about the interests of a user by collecting preferences or taste information from many other users, we can also achieve similar results using neural networks. A recommender system is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. A perceptron or a neural network is a machine learning model designed for fitting complex datasets using backpropagation and gradient descent. When coupled with advanced optimization techniques, the model may prove to be a great substitute for classical logistic classifiers. The optimizations include feature scaling, mean normalization, regularization, hyperparameter tuning and using stochastic/mini-batch gradient descent instead of regular gradient descent. In this use case, we will use the perceptron in the recommender system to fit the parameters i.e., the data from a multitude of users and use it to predict the preference/interest of a particular user
Low-Cost Video Super-Resolution Assisted by Event Signals

Video super-resolution is a widely studied topic and has been achieving improving its definition in the past decades, especially with the development of deep learning techniques. However, most high-quality super-resolution algorithms are computing-intensive and cannot be readily adopted in computational-resource-limited platforms, such as unmanned aerial vehicles (UAVs). Considering that the fidelity at the contours of moving objects is more important than that in the static background, in terms of both visual quality and quantitative metrics, in this paper, we introduce an event camera to discriminate moving contours with a static background and propose a low-cost patch-specific video super-resolution. We formulate a 0–1 knapsack resource allocation problem to achieve high-quality video super-resolution at a low cost. Specifically, we apply a computing-intensive neural network in the surrounding pixels of moving contours while using a pruned version at the static regions for acceleration. According to the simulation results, the proposed resource allocation algorithm shortens the running time without degeneration of super-resolution performance.
Multi‐directional feature refinement network for real‐time semantic segmentation in urban street scenes

Abstract Efficient and accurate semantic segmentation is crucial for autonomous driving scene parsing. Capturing detailed information and semantic information efficiently through two‐branch networks has been widely utilised in real‐time semantic segmentation. This study proposes a network named MRFNet based on two‐branch strategy to solve the problem of accuracy and speed of segmentation in urban scenes. Many real‐time networks do not comprehensively consider contextual information from sub‐regions in different directions and at different scales. To handle this problem, a Multi‐directional Feature Refinement Module (MFRM) which has three sub‐paths to capture information at different scales and directions is proposed. And MFRM reduces computation by using strip pooling and dilated convolution operations. In particular, the authors propose a Feature Cross‐guide Aggregation Module to aggregate detailed information and contextual information through the mutual guidance of detailed information and semantic information. This module guides the extraction of feature maps in a more precise direction. Experiments on Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a balance between accuracy and inference speed. Specially, on single 1080Ti GPU, our method yields 78.9% mean intersection over union (mIoU) and 77.4% mIoU at speed of 144.5 frames per second (FPS) and 120.8 FPS on Cityscapes and CamVid datasets respectively.
Performance-Efficiency Comparisons of Channel Attention Modules for ResNets

Abstract Attention modules can be added to neural network architectures to improve performance. This work presents an extensive comparison between several efficient attention modules for image classification and object detection, in addition to proposing a novel Attention Bias module with lower computational overhead. All measured attention modules have been efficiently re-implemented, which allows an objective comparison and evaluation of the relationship between accuracy and inference time. Our measurements show that single-image inference time increases far more (5–50%) than the increase in FLOPs suggests (0.2–3%) for a limited gain in accuracy, making computation cost an important selection criterion. Despite this increase in inference time, adding an attention module can outperform a deeper baseline ResNet in both speed and accuracy. Finally, we investigate the potential of adding attention modules to pretrained networks and show that fine-tuning is possible and superior to training from scratch. The choice of the best attention module strongly depends on the specific ResNet architecture, input resolution, batch size and inference framework.
Education scientific poster training with Canva assistance for UPI Purwakarta students

Scientific poster training with Canva aims to increase students knowledge and skills in making scientific posters as well as being a competition for participants in implementing their new skill. The method used is to form a training concept as well as a scientific poster competition involving the role of LEPPIM management and members as the committee. This training and competition activity was attended by 20 students online using the Zoom Meeting platform and the Google Form link as a means to collect works. The result of this training is increasing the knowledge and sportsmanship of the participants in competing to produce scientific poster works.