Reviewerly/exHarmony huggingface dataset:
The effectiveness of Solar Tracker Use on Solar Panels to The Output of The Generated Electricity Power

Abstract With the aim of optimizing the electrical power output of the solar panels, the solar panels are installed facing perpendicular to the sunlight, then solar panels that can be moved automatically to follow the direction of the sun position. A solar tracker is made PT. Pertamina Plaju RU-3, using 3 units of Solar Power Plant (PLTS) equipped with Solar Tracker, using an actuator that moves solar panels facing the direction from sunrise to sunset. The effectiveness test of the solar tracker on the output power of solar panels is done by adjusting the position 3 unit PLTS against the sunlight, namely at Tracking and Static (Fixed) Position. Arrangements were made in several combinations of positions and measurement of the electrical power output. From the measurement results, obtained a graph that describes the characteristics of changes in output power that are not affected. This is caused by the electrical energy generated by the solar panels only occur optimally from 10 AM until 2 PM. When heading east before 10 AM and west after 2 PM, solar panels absorb solar energy very low. Solar tracker does not increase the efficiency of solar panel power output. Thus the Solar Panel installed in an area close to the equator does not require a Solar Tracker, and it would be better if the solar panel is positioned statically or fixed, with the position of the solar panel facing upright perpendicular to the direction of the sun coming at 12 AM.
Laser Doppler Signal Denoising Based on Wavelet Packet Thresholding Method

In laser Doppler velocimeter (LDV), calculation precision of Doppler shift is affected by noise contained in Doppler signal. In order to restrain the noise interference and improve the precision of signal processing, wavelet packet threshold denoising methods are proposed. Based on the analysis of Doppler signal, appropriate threshold function and decomposition layer number are selected. Heursure, sqtwolog, rigrsure, and minimaxi rules are adopted to get the thresholds. Processing results indicate that signal-to-noise ratio (SNR) and root mean square error (RMSE) of simulated signals with original SNR of 0 dB, 5 dB, and 10 dB in both low- and high-frequency ranges are significantly improved by wavelet packet threshold denoising. A double-beam and double-scattering LDV system is built in our laboratory. For measured signals obtained from the experimental system, the minimum relative error of denoised signal is only 0.079% (using minimaxi rule). The denoised waveforms of simulated and experimental signals are much more smooth and clear than that of original signals. Generally speaking, denoising effects of minimaxi and saqtwolog rules are better than those of heursure and rigrsure rules. As shown in the processing and analysis of simulated and experimental signals, denoising methods based on wavelet packet threshold have ability to depress the noise in laser Doppler signal and improve the precision of signal processing. Owing to its effectiveness and practicability, wavelet packet threshold denoising is a practical method for LDV signal processing.
Automating the Configuration of MapReduce: A Reinforcement Learning Scheme

With the exponential growth of data and the high demand for the analysis of large datasets, the MapReduce framework has been widely utilized to process data in a timely, cost-effective manner. It is well-known that the performance of MapReduce is limited by its default configuration parameters, and there are a few research studies that have focused on finding the optimal configurations to improve the performance of the MapReduce framework. Recently, machine learning based approaches have been receiving more attention to be utilized to auto configure the MapReduce parameters to account for the dynamic nature of the applications. In this article, we propose and develop a reinforcement learning (RL)-based scheme, named RL-MRCONF, to automatically configure the MapReduce parameters. Specifically, we explore and experiment with two variations of RL-MRCONF; one variation is based on the traditional RL algorithm and the second is based on the deep RL algorithm. Results obtained from simulations show that the RL-MRCONF has the ability to successfully and effectively auto-configure the MapReduce parameters dynamically according to changes in job types and computing resources. Moreover, simulation results show our proposed RL-MRCONF scheme outperforms the traditional RL-based implementation. Using datasets provided by MR-Perf, simulation results show that our proposed scheme provides around 50% performance improvement in terms of execution time when compared with MapReduce using default settings.
Progressive Memory Banks for Incremental Domain Adaptation

This paper addresses the problem of incremental domain adaptation (IDA) in natural language processing (NLP). We assume each domain comes one after another, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the domains that we have encountered. We adopt the recurrent neural network (RNN) widely used in NLP, but augment it with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition. The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity. We learn the new memory slots and fine-tune existing parameters by back-propagation. Experimental results show that our approach achieves significantly better performance than fine-tuning alone. Compared with expanding hidden states, our approach is more robust for old domains, shown by both empirical and theoretical results. Our model also outperforms previous work of IDA including elastic weight consolidation and progressive neural networks in the experiments.
Zero-shot Chinese Discourse Dependency Parsing via Cross-lingual Mapping

Due to the absence of labeled data, discourse parsing still remains challenging in some languages.In this paper, we present a simple and efficient method to conduct zero-shot Chinese text-level dependency parsing by leveraging English discourse labeled data and parsing techniques.We first construct the Chinese-English mapping from the level of sentence and elementary discourse unit (EDU), and then exploit the parsing results of the corresponding English translations to obtain the discourse trees for the Chinese text.This method can automatically conduct Chinese discourse parsing, with no need of a large scale of Chinese labeled data.
A comprehensive evaluation for the prediction of mortality in intensive care units with LSTM networks: patients with cardiovascular disease

Abstract Intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of electronic health records. We aimed to build a mortality prediction model on a Medical Information Mart for Intensive Care (MIMIC-III) database and to assess whether the use of deep learning techniques like long short-term memory (LSTM) can effectively utilize the temporal relations among clinical variables. The models were built on clinical variable dynamics of the first 48 h of ICU admission of 12,550 records from the MIMIC-III database. A total of 36 variables including 33 time series variables and three static variables were used for the prediction. We present the application of LSTM and LSTM attention (LSTM-AT) model for mortality prediction with such a large number of clinical variables dataset. For training and validation purpose, we have used International Classification of Diseases, 9th edition (ICD-9) codes for extracting the patients with cardiovascular disease, and infections and parasitic disease, respectively. The effectiveness of the LSTM model is achieved over non-recurrent baseline models like naïve Bayes, logistic regression (LR), support vector machine and multilayer perceptron (MLP) by generating state of the art results (area under the curve [AUC], 0.852). Next, by providing attention at each time stamp, we developed a model, LSTM-AT, which exhibits even better performance (AUC, 0.876).
Fairness across network positions in cyberbullying detection algorithms

Cyberbullying, which often has a deeply negative impact on the victim, has grown as a serious issue in online social networks. Recently, researchers have created automated machine learning algorithms to detect Cyberbullying using social and textual features. However, the very algorithms that are intended to fight off one threat (cyberbullying) may inadvertently be falling prey to another important threat (bias of the automatic detection algorithms). This is exacerbated by the fact that while the current literature on algorithmic fairness has multiple empirical results, metrics, and algorithms for countering bias across immediately observable demographic characteristics (e.g. age, race, gender), there have been no efforts at empirically quantifying the variation in algorithmic performance based on the network role or position of individuals. We audit an existing cyberbullying algorithm using Twitter data for disparity in detection performance based on the network centrality of the potential victim and then demonstrate how this disparity can be countered using an Equalized Odds postprocessing technique. The results pave the way for more accurate and fair cyberbullying detection algorithms.
Targeted cyber attacks: how to mitigate the increasing risk

In the past year, it has felt as if cyber security breaches have rarely been out of the press, with more and more reports of organisations and individuals becoming victims of targeted attacks. Sainsbury's, Uber and Argos are just three examples of companies out of more than 100 who fell victim to this summer's Capital One breach. 1 This high-profile hack has led to a potential fine of $500m for the financial giant and the hacker in question has been charged with five years’ imprisonment and over £200,000 in fines.
Computer-aided system for Leukocyte nucleus segmentation and Leukocyte classification based on nucleus characteristics

Differential count of leukocytes plays a consequential role in the determination of diagnostic details of a patient. Conventionally, leukocyte identification is performed manually by skilled medical personnel. But the involvement of humans slows down the task and also affects the process outcome. Designing a computer-aided system to automate this task can reduce the load on the medical personnel and help in attaining precise results in minimal time. This research presents an image-processing technique to segment the Leukocyte Nucleus from the blood smear image and categorize them into their constituent classes based on the features drawn from the segmented region. Mathematical Operations are performed on the color channels of RGB and LAB color spaces to obtain Nucleus-enhanced grayscale Image. Leukocyte Nucleus is segmented using Fuzzy C-Means Clustering. Geometrical, Color and Texture features are extracted from the segmented nucleus. Feed-Forward Neural Network and Support Vector Machine classifiers are employed to classify the leukocytes into its constituent classes. The proposed segmentation technique rendered better segmentation performance compared to the methods reviewed in the literature with an overall segmentation accuracy of 88.1%. The Feed Forward Neural Network classifier yielded an accuracy of 92.8% for Leukocyte classification.
On safety assessment of artificial intelligence

In this paper we discuss how systems with Artificial Intelligence (AI) can undergo safety assessment. This is relevant, if AI is used in safety related applications. Taking a deeper look into AI models, we show, that many models of artificial intelligence, in particular machine learning, are statistical models. Safety assessment would then have t o concentrate on the model that is used in AI, besides the normal assessment procedure. Part of the budget of dangerous random failures for the relevant safety integrity level needs to be used for the probabilistic faulty behavior of the AI system. We demonstrate our thoughts with a simple example and propose a research challenge that may be decisive for the use of AI in safety related systems.
Portuguese Language Models and Word Embeddings: Evaluating on Semantic Similarity Tasks

Deep neural language models which achieved state-of-the-art results on downstream natural language processing tasks have recently been trained for the Portuguese language. However, studies that systematically evaluate such models are still necessary for several applications. In this paper, we propose to evaluate the performance of deep neural language models on the semantic similarity tasks provided by the ASSIN dataset against classical word embeddings, both for Brazilian Portuguese and for European Portuguese. Our experiments indicate that the ELMo language model was able to achieve better accuracy than any other pretrained model which has been made publicly available for the Portuguese language, and that performing vocabulary reduction on the dataset before training not only improved the standalone performance of ELMo, but also improved its performance while combined with classical word embeddings. We also demonstrate that FastText skip-gram embeddings can have a significantly better performance on semantic similarity tasks than it was indicated by previous studies in this field.
Cache-Based Multi-Query Optimization for Data-Intensive Scalable Computing Frameworks

In modern large-scale distributed systems, analytics jobs submitted by various users often share similar work, for example scanning and processing the same subset of data. Instead of optimizing jobs independently, which may result in redundant and wasteful processing, multi-query optimization techniques can be employed to save a considerable amount of cluster resources. In this work, we introduce a novel method combining in-memory cache primitives and multi-query optimization, to improve the efficiency of data-intensive, scalable computing frameworks. By careful selection and exploitation of common (sub)expressions, while satisfying memory constraints, our method transforms a batch of queries into a new, more efficient one which avoids unnecessary recomputations. To find feasible and efficient execution plans, our method uses a cost-based optimization formulation akin to the multiple-choice knapsack problem. Extensive experiments on a prototype implementation of our system show significant benefits of worksharing for both TPC-DS workloads and detailed micro-benchmarks.
Data augmentation and semi-supervised learning for deep neural networks-based text classifier

User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance.
Optimizing the spatial spread of a quantum walk

We devise a protocol to build one-dimensional time-dependent quantum walks, maximizing the spatial spread throughout the procedure. We allow only one of the physical parameters of the coin-tossing operator to vary, i.e., the angle $\ensuremath{\theta}$, such that for $\ensuremath{\theta}=0$ we have the ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\ensuremath{\sigma}}}_{z}$, while for $\ensuremath{\theta}=\ensuremath{\pi}/4$ we obtain the Hadamard gate. The optimal $\ensuremath{\theta}$ sequences present nontrivial patterns, with mostly $\ensuremath{\theta}\ensuremath{\approx}0$ alternating with $\ensuremath{\theta}\ensuremath{\approx}\ensuremath{\pi}/4$ values after increasingly long periods. We provide an analysis of the entanglement properties, quasienergy spectrum, and survival probability, providing a full physical picture.
Evaluation of Innovative Readiness of Teaching Staff of Universities

The results of a study of innovative readiness of teachers from nine universities of Samara, Saratov, Bashkortostan, Yekaterinburg, Ulyanovsk are presented, in which 2036 university teachers took part. The work used the methodology for assessing the psychological climate in the teaching staff A.N. Lutoshkina and V.V. Panteleeva, T.P. Knysheva. As mathematical and statistical methods for processing the obtained data, we used discriminant analysis, a one-sample λ-Kolmogorov-Smirnov test, and a nonparametric Spearman rank correlation coefficient. The hypothesis of the presence of various levels of innovative readiness of the teaching staff of universities is considered. A direct relationship has been established between indicators of innovative readiness and the socio-psychological climate in the teaching staff, as well as positive relationships between the components of innovative readiness. Different levels of innovative readiness of the teaching staff of universities (high, normal, optimal) were identified. The results can be used in the process of focused work on the psychological and pedagogical support of innovative activities of university teachers.
Fine-Grained Action Recognition by Motion Saliency and Mid-Level Patches

Effective extraction of human body parts and operated objects participating in action is the key issue of fine-grained action recognition. However, most of the existing methods require intensive manual annotation to train the detectors of these interaction components. In this paper, we represent videos by mid-level patches to avoid the manual annotation, where each patch corresponds to an action-related interaction component. In order to capture mid-level patches more exactly and rapidly, candidate motion regions are extracted by motion saliency. Firstly, the motion regions containing interaction components are segmented by a threshold adaptively calculated according to the saliency histogram of the motion saliency map. Secondly, we introduce a mid-level patch mining algorithm for interaction component detection, with object proposal generation and mid-level patch detection. The object proposal generation algorithm is used to obtain multi-granularity object proposals inspired by the idea of the Huffman algorithm. Based on these object proposals, the mid-level patch detectors are trained by K-means clustering and SVM. Finally, we build a fine-grained action recognition model using a graph structure to describe relationships between the mid-level patches. To recognize actions, the proposed model calculates the appearance and motion features of mid-level patches and the binary motion cooperation relationships between adjacent patches in the graph. Extensive experiments on the MPII cooking database demonstrate that the proposed method gains better results on fine-grained action recognition.
Comparison of Traditional Machine Learning and Deep Learning Approaches for Sentiment Analysis

With the advent of deep learning paradigms, many new algorithms have been flourishing which can be leveraged for the task of sentiment analysis. In this paper, we present an experimental comparison of traditional classification algorithms—Naive Bayes, Logistic Regression, Random Forest using bag of words model along with modern deep learning algorithms like Recurrent Neural Networks with Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) on reviews and tweets using three different datasets to classify a binary output, based on positive or negative polarity of the sentiment. We compare the results to find which model provides the best accuracy and analyze reason behind the differences in accuracies. The goal is to explore natural language processing (NLP) models trained on a certain dataset and then benchmark their respective performances, thereby ultimately demonstrating that deep learning algorithms prove to be more effective. We also state how the deep learning models outperform the traditional models and analyze them in detail.
Balancing Privacy Rights and the Production of High-Quality Satellite Imagery

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTBalancing Privacy Rights and the Production of High-Quality Satellite ImageryMegan M. CofferMegan M. CofferCenter for Geospatial Analytics, North Carolina State University, Raleigh, North Carolina 27513, United StatesMore by Megan M. Cofferhttp://orcid.org/0000-0003-3188-4729Cite this: Environ. Sci. Technol. 2020, 54, 11, 6453–6455Publication Date (Web):May 11, 2020Publication History Received15 April 2020Published online11 May 2020Published inissue 2 June 2020https://doi.org/10.1021/acs.est.0c02365Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views5804Altmetric-Citations4LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-AlertsSUBJECTS:Analytical apparatus,Atmospheric chemistry,Imaging,Planets,Sensors Get e-Alerts
Smartphone tapping vs. handwriting: A comparison of writing medium

&lt;p align="left"&gt;Mobile-learning (m-learning), or mobile-assisted language learning (MALL), has been the object of a great deal of research over the last twenty years. However, empirical work in this area has largely failed to produce generalizable conclusions due to variation in methodology, target feature, and task-type (Burston, 2014, 2015). As schools in Japan begin to join the growing number of classrooms worldwide using mobile-based assignments, this study examined how Japanese EFL students’ writing task production differed depending on writing medium (i.e., handwritten on paper vs. tapped on a smartphone). Writing samples were collected from &lt;em&gt;N &lt;/em&gt;= 1,449 participants, divided into smartphone- or paper-based groups, across a spectrum of English proficiencies. Handwritten submissions were found to be significantly longer than those composed on a smartphone (&lt;em&gt;p &lt;/em&gt;&amp;lt; .001, &lt;em&gt;d &lt;/em&gt;= .54), with differences being more pronounced for learners of higher proficiency than lower ones. Significance and effect sizes steadily dropped from &lt;em&gt;p &lt;/em&gt;&amp;lt; .001, &lt;em&gt;d &lt;/em&gt;= .66 for advanced learners to &lt;em&gt;p &lt;/em&gt;= .168, &lt;em&gt;d &lt;/em&gt;= .38 for beginners. These results indicate that care must be taken in designing m-learning activities, and that students must be given adequate training in smartphone-input skills (i.e., tapping) and time to acclimate before using such tasks for high-stakes assessments.&lt;/p&gt;