Reviewerly/exHarmony huggingface dataset:
Pembelajaran Bahasa Indonesia secara Daring pada Masa Pandemi Covid-19

The Covid-19 pandemic has forced Indonesian language lecturers to use internet-assisted technology. The worry arises because all lecturers have not mastered professional skills in using assisted internet technology, affecting the quality of Indonesian language learning, especially at the Ganesha University of Education. This qualitative research discusses (1) the learning tools used by Indonesian language lecturers and (2) the process of Indonesian learning during the Covid-19 Pandemic era. The subjects of this research are Indonesian language lecturers at the Ganesha University of Education. Data collection techniques were carried out using observation, open interviews, and documentation studies. The data analysis technique used the Milles and Huberman analysis model and the TPACK principle. The results showed that (1) Indonesian language learning during the Covid-19 pandemic era had been planned in advance through the preparation of learning tools; and (2) Indonesian lecturers were able to combine aspects of technology skills, pedagogy, and content knowledge in learning carried out both synchronously and asynchronously.
Evaluation of Domain-Specific Word Vectors for Biomedical Word Sense Disambiguation

Among medical applications of natural language processing (NLP), word sense disambiguation (WSD) estimates alternative meanings from text around homonyms. Recently developed NLP methods include word vectors that combine easy computability with nuanced semantic representations. Here we explore the utility of simple linear WSD classifiers based on aggregating word vectors from a modern biomedical NLP library in homonym contexts. We evaluated eight WSD tasks that consider literature abstracts as textual contexts. Discriminative performance was measured in held-out annotations as the median area under sensitivity-specificity curves (AUC) across tasks and 200 bootstrap repetitions. We find that classifiers trained on domain-specific vectors outperformed those from a general language model by 4.0 percentage points, and that a preprocessing step of filtering stopwords and punctuation marks enhanced discrimination by another 0.7 points. The best models achieved a median AUC of 0.992 (interquartile range 0.975 - 0.998). These improvements suggest that more advanced WSD methods might also benefit from leveraging domain-specific vectors derived from large biomedical corpora.
Textual Dataset Classification Using Supervised Machine Learning Techniques

Text classification has been a significant domain of study and research because of the increased volume of text datasets and documents available in digital format. Text classification is one of the major approaches used to arrange digital information via automatically allocating text dataset records or documents into predetermined classes depending on their contents. This paper proposes a technique that implements supervised machine learning algorithms such as KNN, Decision tree, Random Forest, Bernoulli Naive Bayes, and Multinomial Naive Bayes classifiers to classify a dataset into distinct classes. The proposed technique combines the above-mentioned machine learning classifiers with the TF-IDF feature extraction method as a vector space model to achieve more precise classification results. The proposed technique yields high accuracy, precision, recall, and f1-measure metric values for all the implemented classifiers. After comparing the obtained results of different classifiers, it is found that the Random Forest classifier is the best algorithm used to classify the textual dataset records with the highest accuracy value of 0.9995930.
VEHICLE INSTANCE SEGMENTATION WITH ROTATED BOUNDING BOXES IN UAV IMAGES USING CNN

Abstract. Vehicle instance segmentation is a major but challenging task in aerial remote sensing applications. More importantly, the current majority methods use horizontal bounding boxes which does not tell much about the orientation of vehicles and often leads to inaccurate mask proposals due to high background to foreground pixel-ratio. Given that the orientation of vehicles is important for numerous applications like vehicle tracking, we introduce in this paper a deep neural network to detect and segment vehicles using rotated bounding boxes in aerial images. Our method demonstrates that rotated instance segmentation improves the mask predictions, especially when objects are not axis aligned or are touching. We evaluate our model on the ISPRS benchmark dataset and our newly introduced UAV dataset for vehicle segmentation and show that we can significantly improve the mask accuracy compared to instance segmentation using axis-aligned bounding boxes.
Sign Language Detection with CNN

Abstract: Sign language is one of the oldest and most natural forms of language for com- munication, but since most people do not know sign language and interpreters are very difficult to come by, we have come up with a real time method using neural networks for fingerspelling based American sign language. In our method, the hand is first passed through a filter and after the filter is applied the hand is passed through a classifier which predicts the class of the hand gestures. Our method provides 95.7% accuracy for the 26 letters of the alphabet.
Path reinforcement learning planning of nuclear power plant UAVs based on dynamic target probability distribution

This work develops a path reinforcement learning planning algorithm for unmanned aerial vehicles (UAVs) based on the probability distribution of dynamic targets. The proposed algorithm enables the effective interception of aerial dynamic invasive targets into nuclear power plants. In accordance with the status information of the invasive target, the target probability distribution is calculated by the probability diffusion principle and the target's possible location is deduced based on the obtained distribution. On this basis, the action space based on path point transfer rules is established and the dynamic updating mechanism of the reward function based on the target probability distribution is designed. Using $Q$-learning for continuous path optimization, a UAV path planning framework based on the target probability distribution and reinforcement learning is constructed, so as to realize the UAV path planning. Simulation results indicate that the proposed method can enable the autonomous path planning of UAVs for intercepting aerial dynamic invasive targets into nuclear power plants when the target point changes.
Reinforcement Learning on Social Chabot to Improve the Semantic Variation

Artificial intelligence (AI) in the fields of conversation and language has experienced immense growth as a result of the large quantities of text corpora available for training models. This paper discusses conversational AI dialogue systems concerning the components of natural language processing (NLP) and reinforcement learning that function together to produce a human-like response. The types of conversational AI, namely, task-oriented systems, question-answering agents, and social chatbots, are also expanded on to provide a systematic review. Reinforcement learning has a crucial role in resolving errors in existing conversational AI models and allows for a factor of exploration in the art of communication. The trial-and-error nature of reinforcement learning agents ensures that irrelevant patterns in a text corpus are not memorized. The implementation of reinforcement learning and NLP tools to create social chatbots with emotional intelligence brings researchers one step closer to mimicking human conversation.
PSYCHO-EMOTIONAL CLIMATE TO REDUCE THE RISK OF DROP OUT IN THE CONTEXT OF HIGHER EDUCATION

The high number of students who have dropped out of higher education prompts to find out both the reasons for dropping out and the causes of dissatisfaction. The psycho-emotional climate of an educational institution is considered to be one of the indicators of the quality of the institution's organizational culture and performance, which determines its pedagogical effectiveness. Therefore, at the moment when the idea of institutional accreditation seriously marks its place in the accreditation process, there is a growing interest in research, the subject of which is the psycho-emotional climate of a higher education institution as a pedagogical resource. The aim of this publication is to identify the psycho-emotional risks of drop out among the students. The research methodology consists of a set of qualitative data obtained by surveying 50 students who have expressed an intention to drop out. The study data were analysed using the qualitative data processing program NVivo 12.0. The study analysed and described the theoretical framework of the psycho-emotional climate and identified the main risks of drop out. It is concluded that the psycho-emotional climate is an essential component of students' desire to continue their studies, as it promotes the institutional sense of belonging and learning achievements. The results of this study complement existing research with qualitative data, operationalizing psycho-emotional support in higher education settings.
Dynamic Texture Analysis of Water Flow Video Using Temporal Binary Patern

This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior gives a severe challenge to distinguish the inliers and outliers, and wrong loop closures can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, which does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. The traditional high-dimensional consistency matrix is decomposed into the sub-matrix blocks corresponding to the overlapping trajectory area. Building on this logic, a clustering method involving topology correctness of inter-robot loop closures is introduced to split the entire measurement set into multiple clusters. We define the weight function to find the maximum cardinality subset with topologically correct and consistent, then convert the weight function to a maximum clique problem in the graph and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.
Forecasting International Tourist Arrivals in Indonesia Using SARIMA Model

Tourism is an important sector that significantly contributes to the economy, so the tourism sector is a priority development program. International tourist arrivals indirectly contribute to the country's economic growth. The government has an important task to increase the number of foreign tourist visits. One way to encourage an increase in foreign tourist arrivals is by forecasting. In general, the time series data for the arrival of foreign tourists has a seasonal pattern. The forecasting method that can model seasonal data is SARIMA. This study aims to predict the arrival of foreign tourists in Indonesia using the SARIMA model. Forecasting results show that the appearance of foreign tourists to Indonesia has increased every period.
Rising declaratives in Veneto dialects

The Romance dialects spoken in Veneto, North-East of Italy, have been described as possessing 'lilting cadences' [1].Such cadences have only been reported in anecdotal terms, also by native speakers, who perceive and describe their dialects as marked by a final 'melodic tilt'.In our paper, we aim at examining the phonetic correlates of this trait.We recorded 30 dialectal speakers from Venice, Padua, and Gazzolo (near Verona), eliciting declarative sentences (N=900) across distinct three informational categories.The 3 areas differ not only in terms of geographic distribution but also by relative sociolinguistic prestige in the region.Utterances were first auditorily and visually inspected, and subsequently examined using Generalized Additive Mixed Models.The results indicated that broad focus statements in Gazzolo dialect, the most rural variety, often end with a final rise, a tonal feature possibly contributing to the lilting cadence.After providing an acoustic description of the rise, we discuss the theoretical and sociolinguistic implications of the data.Theoretically, rising declaratives are cross-linguistically seldom and can represent counter-examples for the Frequency code.From a sociolinguistic perspective, the Gazzolo rise may index the speakers not only geographically but also socially.
CONVERSATIONS: Conversation with a prominent propagator

opinion Free Access Share on CONVERSATIONS: Conversation with a prominent propagator: Monica McGill Authors: David P. Bunde Knox College Knox CollegeView Profile , Zack Butler Rochester Institute of Technology Rochester Institute of TechnologyView Profile , Christopher L. Hovey University of Colorado Boulder University of Colorado BoulderView Profile , Cynthia Taylor Oberlin College Oberlin CollegeView Profile Authors Info & Claims ACM InroadsVolume 13Issue 2June 2022 pp 14–18https://doi.org/10.1145/3524045Online:17 May 2022Publication History 0citation107DownloadsMetricsTotal Citations0Total Downloads107Last 12 Months107Last 6 weeks14 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
The continuous-discrete variational Kalman filter (CD-VKF)

We consider the filtering problem of estimating the state of a continuous-time dynamical process governed by a nonlinear stochastic differential equation and observed through discrete-time measurements. As the Bayesian posterior density is difficult to compute, we use variational inference (VI) to approximate it. This is achieved by seeking the closest Gaussian density to the posterior, in the sense of the Kullback-Leibler divergence (KL). The obtained algorithm, called the continuous-discrete variational Kalman filter (CD-VKF), provides implicit formulas that solve the considered problem in closed form. Our framework avoids local linearization, and the estimation error is globally controlled at each step. We first clarify the connections between well known nonlinear Kalman filters and VI, then develop closed form approximate formulas for the CD-VKF. Our algorithm achieves state-of-the-art performances on the problem of reentry tracking of a space capsule.
A Fast Image Encryption based on Linear Feedback Shift Register and Deoxyribonucleic acid

Abstract Recently with the proliferating rates of digital data and the need of sending data through the internet, protection of the contents is crucial. This article proposes a quick method based on a combination of Linear Feedback Shift Register (LFSR) and Deoxyribonucleic acid (DNA) and Tinkerbell chaotic function. This method not only has high protection but also provides high encryption speed. First, a bit shift method is implemented in permutation phase. Shift numbers are determined by LSFR in each step. Then in each step of the permutation phase, specific numbers of pixels are delivered for final encryption in diffusion phase. In this phase, DNA as well as Tinkerbell index converts gray level of image pixels. The results show high security, incredible speed and good resistance against prevailing attacks.
Analisis Komparatif Algoritme Machine Learning dan Penanganan Imbalanced Data pada Klasifikasi Kualitas Air Layak Minum

&#x0D; Abstract. Water is essential for survival. Currently, there are requirements to monitor, assess, and classify water quality to understand the impact of industrialization. The water quality classification process has been carried out using traditional methods such as WQI and Storet, and machine learning methods. Imbalanced data in machine learning method can make this method have a tendency to predict the majority class and become biased. In addition, using all features in the classification process can degrade classification performance and lead to high computation time. To overcome the above-mentioned problems, this study proposes several approaches, included resampling the data to be balanced, determined the most suitable and contributing features, and compared the performance of machine learning algorithms in classifying potable water. The results of handling unbalanced data and implementing feature selection were able to provide increased work on the algorithm, especially the accuracy metric reached 24.8% from previous study. The most optimal algorithm performance was obtained from Random Forest with 87% of precision, 84% of recall, 16% of Miss rate, 85% of F-measure, and 85% of test accuracy, while used seven best features. However, another important aspect is the smallest Miss rate, which was 15%, obtained from Decision Tree algorithm.&#x0D;
SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN

Abstract. Remote sensing images of Earth observation with high spatial resolution and high temporal resolution are critical for the application of remote sensing technology in Megacities.With the development of Smart City,more demands which are still difficult to be perfectly satisfied on the spatial resolution and temporal resolution of remote sensing images have been put forward.This paper studies the use of SRGAN which means Super-Resolution using a Generative Adversarial Network (a network structure that uses the loss function considering the perceptual loss and the adversarial loss to improve the spatial resolution of remote sensing images) for super-resolution reconstruction of single remote sensing image.It is able to enhance the spatial resolution of remote sensing images and improve the depth and breadth of remote sensing images.We adjust the reasonable parameters and network structure for our research by analysing the SRGAN in the network architecture, the perceptual loss and the adversarial loss.A super-resolution model is obtained by training with aerial photogrammetry images whose spatial resolution are 0.1 meter in Shanghai.We find the improved SRGAN has a good performance in in remote sensing image super-resolution by comparing the super-resoved images with real high-resolution images in visual perception, spatial position mapping accuracy and chromaticity spatial information. In addition, it is proved that the trained model is also effective to deal with Worldview-2 and SuperView-1 satellite images whose spatial resolution are 0.5 m. Our research shows that our method which can effectively realize the super-resolution of remote sensing images has great potential in the application of remote sensing technology such as urban mapping and changes monitoring.
An efficient approach to measure the difficulty degree of practical programming exercises based on student performances

This study examines the generality of easy to hard practice questions in programming subjects. One of the most important contributions is to propose four new formulas for determining the difficulty degree of questions. These formulas aim to describe different aspects of difficulty degree from the learner's perspective instead of the instructor's subjective opinions. Then, we used clustering technique to group the questions into three easy, medium and difficult degrees. The results will be the baseline to consider the generality of the exercise sets according to each topic. The proposed solution is then tested on the data set that includes the results of the two subjects: Programming Fundamentals, Data Structures and Algorithms from Ho Chi Minh City University of Technology. The most important result is to suggest the instructors complete various degrees according to each topic for better evaluating student's performance.
Optimal compression of quantum many-body time evolution operators into brickwall circuits

Near term quantum computers suffer from a degree of decoherence which is prohibitive for high fidelity simulations with deep circuits. An economical use of circuit depth is therefore paramount. For digital quantum simulation of quantum many-body systems, real time evolution is typically achieved by a Trotter decomposition of the time evolution operator into circuits consisting only of two qubit gates. To match the geometry of the physical system and the CNOT connectivity of the quantum processor, additional SWAP gates are needed. We show that optimal fidelity, beyond what is achievable by simple Trotter decompositions for a fixed gate count, can be obtained by compiling the evolution operator into optimal brickwall circuits for the $S = 1/2$ quantum Heisenberg model on chains and ladders, when mapped to one dimensional quantum processors without the need of additional SWAP gates.
Personalized Vocabulary Learning using Automatic Speech Recognition

Memorizing vocabulary is a crucial aspect of learning a new language. Although computerized adaptive learning systems have successfully improved the process of vocabulary learning by tailoring learning procedures to the needs of individual learners, the majority of such systems are limited to typing-based learning and do not allow for speech practice. Here, we aim to examine the possibilities for personalized speech-based learning. We combined a response-time based algorithm for adaptive item scheduling that was originally developed for typing-based learning with automatic speech recognition technology and tested the system with 50 participants. We show that typing- and speech-based learning result in similar learning outcomes and that using an adaptive scheduling algorithm improves learning performance relative to traditional learning in both modalities. These results can inform the development of language learning systems that—unlike traditional systems—focus on one of the most important aspects of learning a language: to practice speech.
Big–Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors

This paper investigates the energy savings that near-subthreshold processors can obtain in edge AI applications and proposes strategies to improve them while maintaining the accuracy of the application. The selected processors deploy adaptive voltage scaling techniques in which the frequency and voltage levels of the processor core are determined at the run-time. In these systems, embedded RAM and flash memory size is typically limited to less than 1 megabyte to save power. This limited memory imposes restrictions on the complexity of the neural networks model that can be mapped to these devices and the required trade-offs between accuracy and battery life. To address these issues, we propose and evaluate alternative ‘big–little’ neural network strategies to improve battery life while maintaining prediction accuracy. The strategies are applied to a human activity recognition application selected as a demonstrator that shows that compared to the original network, the best configurations obtain an energy reduction measured at 80% while maintaining the original level of inference accuracy.