Reviewerly/exHarmony huggingface dataset:
Water and Energy Balance Model GOES-PRWEB: Development and Validation

In 2009, the University of Alabama-Huntsville configured their GOES satellited-based solar radiation product to include Puerto Rico, the US Virgin Islands (USVI), Dominican Republic, Haiti, Jamaica, and Cuba. The half-hourly and daily integrated data are available at 1 km resolution for Puerto Rico and the USVI and 2 km for Hispaniola, Jamaica, and Cuba. These data made it possible to implement estimates of satellite radiation-based evapotranspiration methods on all of the islands. The use of the solar radiation data in combination with estimates of other climate parameters facilitated the development of a water and energy balance algorithm for Puerto Rico. The purpose of this paper is to describe the theoretical background and technical approach for estimating the components of the daily water and energy balance. The operational water and energy balance model is the first of its kind in Puerto Rico. Model validation results are presented for reference and actual evapotranspiration, soil moisture, and streamflow. Mean errors for all analyses were less than 7%. The water and energy balance model results can benefit such diverse fields as agriculture, ecology, coastal water management, human health, renewable energy development, water resources, drought monitoring, and disaster and emergency management. This research represents a preliminary step in developing a suite of gridded hydro-climate products for the Caribbean Region.
Asynchronous Multi-grained Graph Network For Interpretable Multi-hop Reading Comprehension

Multi-hop machine reading comprehension (MRC) task aims to enable models to answer the compound question according to the bridging information. Existing methods that use graph neural networks to represent multiple granularities such as entities and sentences in documents update all nodes synchronously, ignoring the fact that multi-hop reasoning has a certain logical order across granular levels. In this paper, we introduce an Asynchronous Multi-grained Graph Network (AMGN) for multi-hop MRC. First, we construct a multigrained graph containing entity and sentence nodes. Particularly, we use independent parameters to represent relationship groups defined according to the level of granularity. Second, an asynchronous update mechanism based on multi-grained relationships is proposed to mimic human multi-hop reading logic. Besides, we present a question reformulation mechanism to update the latent representation of the compound question with updated graph nodes. We evaluate the proposed model on the HotpotQA dataset and achieve top competitive performance in distractor setting compared with other published models. Further analysis shows that the asynchronous update mechanism can effectively form interpretable reasoning chains at different granularity levels.
EmbedMask: Embedding Coupling for Instance Segmentation

Current instance segmentation methods can be categorized into segmentation-based methods and proposal-based methods. The former performs segmentation first and then does clustering, while the latter detects objects first and then predicts the mask for each object proposal. In this work, we propose a single-stage method, named EmbedMask, that unifies both methods by taking their advantages, so it can achieve good performance in instance segmentation and produce high-resolution masks in a high speed. EmbedMask introduces two newly defined embeddings for mask prediction, which are pixel embedding and proposal embedding. During training, we enforce the pixel embedding to be close to its coupled proposal embedding if they belong to the same instance. During inference, pixels are assigned to the mask of the proposal if their embeddings are similar. This mechanism brings several benefits. First, the pixel-level clustering enables EmbedMask to generate high-resolution masks and avoids the complicated two-stage mask prediction. Second, the existence of proposal embedding simplifies and strengthens the clustering procedure, so our method can achieve high speed and better performance than segmentation-based methods. Without any bell or whistle, EmbedMask outperforms the state-of-the-art instance segmentation method Mask R-CNN on the challenging COCO dataset, obtaining more detailed masks at a higher speed.
Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation

As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.
A Survey on Goal Recognition as Planning

Goal Recognition is the task of inferring an agent's goal, from a set of hypotheses, given a model of the environment dynamic, and a sequence of observations of such agent's behavior. While research on this problem gathered momentum as an offshoot of plan recognition, recent research has established it as a major subject of research on its own, leading to numerous new approaches that both expand the expressivity of domains in which to perform goal recognition and substantial advances to the state-of-the-art on established domain types. In this survey, we focus on the advances to goal recognition achieved in the last decade, categorizing the resulting techniques and identifying a number of opportunities for further breakthrough research.
Explaining Self-Supervised Image Representations with Visual Probing

Recently introduced self-supervised methods for image representation learning provide on par or superior results to their fully supervised competitors, yet the corresponding efforts to explain the self-supervised approaches lag behind. Motivated by this observation, we introduce a novel visual probing framework for explaining the self-supervised models by leveraging probing tasks employed previously in natural language processing. The probing tasks require knowledge about semantic relationships between image parts. Hence, we propose a systematic approach to obtain analogs of natural language in vision, such as visual words, context, and taxonomy. We show the effectiveness and applicability of those analogs in the context of explaining self-supervised representations. Our key findings emphasize that relations between language and vision can serve as an effective yet intuitive tool for discovering how machine learning models work, independently of data modality. Our work opens a plethora of research pathways towards more explainable and transparent AI.
Interactive Math E-Book: An Alternative Learning Resources for 21st Century Learners

The objective of this research is to create an interactive math e-book that can be used as learning material for middle school students.The development procedure used is ADDIE(Analysis, Implementation, Development, Evaluation, and Design) model.The instruments used were expert validation questionnaires, student response questionnaires, and learning outcomes tests.This study involved three content experts, three media experts, and60 students.The results showed that achievement scores obtained from content experts 87.50%, media experts 90%, student responses 88.57%, and student learning outcomes 84.33%.These achievement score shows that the math e-book is eligible and can be used in the learning process.Other findings are known that the achievement score of urban student learning outcomes is significantly better than other students.It can be concluded that the math e-book is more suitable as learning resource for urban students.
Hand-drawn sketch recognition with a double-channel convolutional neural network

Abstract In hand-drawn sketch recognition, the traditional deep learning method has the problems of insufficient feature extraction and low recognition rate. To solve this problem, a new algorithm based on a dual-channel convolutional neural network is proposed. Firstly, the sketch is preprocessed to get a smooth sketch. The contour of the sketch is obtained by the contour extraction algorithm. Then, the sketch and contour are used as the input image of CNN. Finally, feature fusion is carried out in the full connection layer, and the classification results are obtained by using a softmax classifier. Experimental results show that this method can effectively improve the recognition rate of a hand-drawn sketch.
Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

Although the number of solutions in combinatorial optimization problems (COPs) is finite, some problems grow exponentially and render exact approaches unfeasible. So, approximate methods, such as heuristics, are customary. Each heuristic usually specializes in specific kinds of problems. Hence, other approaches seek to merge their strengths. One of them is selection hyper-heuristics. However, they usually provide scarce information about their sensitivity. Illumination algorithms may fix this issue since they focus on exploration rather than exploitation while preserving the best solutions under different criteria. Still, literature falls short when merging both approaches, representing a knowledge gap. This work tests the feasibility of using an illumination algorithm, MAP-Elites (ME), for tuning a sequence-based selection hyper-heuristic model for Balanced Partition problems. We choose ME since other researchers have successfully applied it to a different COP. So, we may achieve a hyper-heuristic that represents the best combination of heuristics while simultaneously gaining intel on the performance of diverse alternatives. Our approach operates by creating a multi-dimensional map, where each design variable represents the application of a heuristic. Afterward, ME generates mutated sequences and tests them to determine if they represent a better-performing solution. We consider 1500 instances that include easy and hard instances, analyzed under different scenarios to test our approach. We also include limit instances that are neither easy nor hard. Our resulting data support the proposed approach, as it performs toe-to-toe with a synthetic oracle and may even outperform it. This represents an outstanding result, since a brute-force approach is needed to achieve such an oracle. So, merging ME and hyper-heuristics is a path worth pursuing. We also present how each parameter affects the model performance and identify the critical and virtually irrelevant ones. This serves as the groundwork for future works that focus on exploiting the most relevant parameters.
Intention to Use Microsoft Teams in the Online Learning System for Students of Universitas Tarumanagara During the COVID-19 Pandemic

The development of communication technology currently plays a very important role in fulfilling human life.The current Covid-19 pandemic situation makes communication technology indispensable in the world of education for the implementation of distance learning systems, such as the use of Microsoft Teams.The use of Microsoft Teams as an educational system platform at Universitas Tarumanagara can be a solution to overcome obstacles in the learning process during the Covid-19 pandemic.This study aims to discuss Intention To Use in using Microsoft Teams for online learning systems.Universitas Tarumanagara students during the Covid-19 pandemic.The main theory in this research is the Technology Acceptance Model which is used to examine the effect of technology acceptance on the use of Microsoft Teams.This study uses a quantitative approach with the method of analysis of the Structural Equation Modeling (SEM) path using SmartPLS3 software.This study used a survey method with data collection using an online questionnaire (self-administered questionnaire) with 123 respondents.The results of this study indicate that the variables Perceived Ease of Use, Perceived Usefulness, Attitude Towards Using have a significant effect on Intention To Use, so that the Technology Acceptance Model in using Microsoft Teams is accepted.
An Efficient feature selection algorithm for the spam email classification

The existing spam email classification systems are suffering from the problems of low accuracy due to the high dimensionality of the associated feature selection (FS) process. But being a global optimization process in machine learning, FS is mainly aimed at reducing the redundancy of dataset to create a set of acceptable and accurate results. This study presents the combination of Chaotic Particle Swarm Optimization (PSO) algorithm with Artificial Bees Colony (ABC) for the reduction of features dimensionality in a bid to improve spam emails classification accuracy. The features for each particle in this work were represented in a binary form, meaning that they were transformed into binary using a sigmoid function. The features selection was based on a fitness function that depended on the obtained accuracy using SVM. The proposed system was evaluated for performance by considering the performance of the classifier and the selected features vectors dimension which served as the input to the classifier; this evaluation was done using the Spam Base dataset and from the results, the PSO-ABC classifier performed well in terms of FS even with a small set of selected features.
Comparison of Effectiveness of Stemming Algorithms in Indonesian Documents

Stemming is a process to determine basic word with some rules.In Bahasa Indonesia, the way is to eliminate prefixes, infixes, suffixes, or combination of prefixes and suffixes in derivative words.Several stemming algorithms for Bahasa Indonesia have been developed.But their effectiveness has not been studied.In this study, these three stemming algorithms will be compared.We used 900 affixes to conduct the comparison.Each word is searched for their basic words using the three algorithms.The basic word resulted then referred to KBBI or Indonesian dictionary to see whether they are right.Comparison process of stemming show that Sastrawi's could do the best stemming that 95,2% of the affix words tested could be root words.The Nazief & Adriani Algorithm resulted 92,4%, while Arifin Setiono's finished at 89%.It could state that Arifin Setiono's needs a lot of improvement because many affixed words could not return to the root word.
Non-Gaussian photonic state engineering with the quantum frequency processor

Non-Gaussian quantum states of light are critical resources for optical quantum information processing, but methods to generate them efficiently remain challenging to implement. Here we introduce a generic approach for non-Gaussian state production from input states populating discrete frequency bins. Based on controllable unitary operations with a quantum frequency processor, followed by photon-number-resolved detection of ancilla modes, our method combines recent developments in both frequency-based quantum information and non-Gaussian state preparation. Leveraging and refining the K-function representation of quantum states in the coherent basis, we develop a theoretical model amenable to numerical optimization and, as specific examples, design quantum frequency processor circuits for the production of Schr\"{o}dinger cat states, exploring the performance tradeoffs for several combinations of ancilla modes and circuit depth. Our scheme provides a valuable general framework for producing complex quantum states in frequency bins, paving the way for single-spatial-mode, fiber-optic-compatible non-Gaussian resource states.
Learning multimodal word representation with graph convolutional networks

Multimodal models have been proven to outperform text-based models on learning semantic word representations. According to psycholinguistic theory, there is a graphical relationship among the modalities of language, and in recent years, the graph convolution network (GCN) has been proven to have substantial advantages in the extraction of non-European spatial features. This inspires us to propose a new multimodal word representation model, namely, GCNW, which uses the graph convolutional network to incorporate the phonetic and syntactic information into the word representation. We use a greedy strategy to update the modality-relation matrix in the GCN, and we train the model through unsupervised learning. We evaluated the proposed model on multiple downstream NLP tasks, and various experimental results demonstrate that the GCNW outperforms strong unimodal baselines and state-of-the-art multimodal models. We make the source code of both models available to encourage reproducible research.
Active sensing with artificial neural networks

The fitness of behaving agents depends on their knowledge of the environment, which demands efficient exploration strategies. Active sensing formalizes exploration as reduction of uncertainty about the current state of the environment. Despite strong theoretical justifications, active sensing has had limited applicability due to difficulty in estimating information gain. Here we address this issue by proposing a linear approximation to information gain and by implementing efficient gradient-based action selection within an artificial neural network setting. We compare information gain estimation with state of the art, and validate our model on an active sensing task based on MNIST dataset. We also propose an approximation that exploits the amortized inference network, and performs equally well in certain contexts.
Proof-of-Reputation: An Alternative Consensus Mechanism for Blockchain Systems

Blockchains combine other technologies, such as cryptography, networking, and incentive mechanisms, to enable the creation, validation, and recording of transactions between participating nodes. A consensus algorithm is used in a blockchain system to determine the shared state among distributed nodes. An important component underlying any blockchain-based system is its consensus mechanism, which principally determines the performance and security of the overall system. As the nature of peer-to- peer(P2P) networks is open and dynamic, the security risk within that environment is greatly increased mostly because nodes can join and leave the network at will. Thus, it is important to have a system that can check against malicious behaviour. In this work, we propose a reputation-based consensus mechanism for blockchain-based systems, Proof-of-Reputation(PoR) where the nodes with the highest reputation values eventually become part of a consensus group that determines the state of the blockchain.
An Empirical Study to Understanding Students Continuance Intention Use of Multimedia Online Learning

The purpose of this study was to assess students' ongoing intentions towards online multimedia learning such as perceived usefulness, ease of use, and flow experience.The sample of this study was 523 students who used off-campus/online learning resources and examined the content of online learning resources and their multimedia aspects.The Extended of Technology Acceptance Model (TAM) was used to predict students' continuing intentions.The results showed that students' intentions were positively influenced by their perceived usefulness, ease of use, and flow experience.It is suggested that the designer of multimedia online learning should be more specific in determining the target users to receive and cultivate a more positive sustainable intention.
Hue-Preserving Saturation Improvement in RGB Color Cube

This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement.
Between Shor and Steane: A Unifying Construction for Measuring Error Syndromes

Fault-tolerant quantum error correction requires the measurement of error syndromes in a way that minimizes correlated errors on the quantum data. Steane and Shor ancilla are two well-known methods for fault-tolerant syndrome extraction. In this Letter, we find a unifying construction that generates a family of ancilla blocks that interpolate between Shor and Steane. This family increases the complexity of ancilla construction in exchange for reducing the rounds of measurement required to fault tolerantly measure the error. We then apply this construction to the toric code of size $L\ifmmode\times\else\texttimes\fi{}L$ and find that blocks of size $m\ifmmode\times\else\texttimes\fi{}m$ can be used to decode errors in $O(L/m)$ rounds of measurements. Our method can be applied to any Calderbank-Shor-Steane code and presents a new direction for optimizing fault-tolerant quantum computation.
Extracting Key Traffic Parameters from UAV Video with On-Board Vehicle Data Validation

The advantages of UAV video in flexibility, traceability, easy-operation, and abundant information make it a popular and powerful aerial tool applied in traffic monitoring in recent years. This paper proposed a systematic approach to detect and track vehicles based on the YOLO v3 model and the deep SORT algorithm for further extracting key traffic parameters. A field experiment was implemented to provide data for model training and validation to ensure the accuracy of the proposed approach. In the experiment, 5400 frame images and 1192 speed points were collected from two test vehicles equipped with high-precision GNSS-RTK and onboard OBD after completion of seven experimental groups with a different height (150 m to 500 m) and operating speed (40 km/h to 90 km/h). The results indicate that the proposed approach exhibits strong robustness and reliability, due to the 90.88% accuracy of object detection and 98.9% precision of tracking vehicle. Moreover, the absolute and relative error of extracted speed falls within ±3 km/h and 2%, respectively. The overall accuracy of the extracted parameters reaches up to 98%.