Reviewerly/exHarmony huggingface dataset:
Evaluation and Design of Power Controller of Two-Axis Solar Tracking by PID and FL for a Photovoltaic Module

Solar trackers represent an essential tool to increase the energy production of photovoltaic modules compared to fixed systems. Unlike previous technologies where the aim is to keep the solar rays perpendicular to the surface of the module and obtain a constant output power, this paper proposes the design and evaluation of two controllers for a two-axis solar tracker, which maintains the power that is produced by photovoltaic modules at their nominal value. To achieve this, mathematical models of the dynamics of the sun, the solar energy obtained on the Earth’s surface, the two-axis tracking system in its electrical and mechanical parts, and the solar cell are developed and simulated. Two controllers are designed to be evaluated in the solar tracking system, one Proportional-Integral-Derivative and the other by Fuzzy Logic. The evaluation of the simulations shows a better performance of the controller by Fuzzy Logic; this is because it presents a shorter stabilization time, a transient of smaller amplitude, and a lower percentage of error in steady-state. The principle of operation of the solar tracking system is to promote the orientation conditions of the photovoltaic module to generate the maximum available power until reaching the nominal one. This is possible because it has a gyroscope on the surface of the module that determines its position with respect to the hour angle and altitude of the sun; a data acquisition card is developed to implement voltage and current sensors, which measure the output power it produces from the photovoltaic module throughout the day and under any weather conditions. The results of the implementation demonstrate that a Fuzzy Logic control for a two-axis solar tracker maintains the output power of the photovoltaic module at its nominal parameters during peak sun hours.
A Novel Emotion Lexicon for Chinese Emotional Expression Analysis on Weibo: Using Grounded Theory and Semi-Automatic Methods

As one of the most popular social media platforms in China, Weibo has aggregated huge numbers of texts containing people's thoughts, feelings, and experiences.Analyzing emotions expressed on Weibo has attracted a great deal of academic attention.Emotion lexicon is a vital foundation of sentiment analysis, but the existing lexicons still have defects such as a limited variety of emotions, poor cross-scenario adaptability, and confusing written and online expressions and words.By combining grounded theory and semi-automatic methods, we built a Weibo-based emotion lexicon for sentiment analysis.We first took a bottom-up approach to derive a theoretical model for emotions expressed on Weibo, and the substantive coding led to eight core emotion categories: joy, expectation, love, anger, anxiety, disgust, sadness, and surprise.Second, we built a new emotion lexicon containing 2,964 words by manually selecting seed words, constructing a word vector model to expand words, and making rules to filter words.Finally, we tested the effectiveness of our lexicon by using a lexicon-based approach to recognize the emotions expressed in Weibo text.The results showed that our lexicon performed better in Weibo emotion recognition than five other Chinese emotion lexicons.This study proposed a method to construct an emotion lexicon that considered both theory and application by combining qualitative research and artificial intelligence methods.Our work also provided a reference for future research in the field of social media sentiment analysis.
Roles of saliency and set size in ensemble averaging

Ensemble statistics are often thought of as a reliable impression of numerous items despite limited capacities to consciously represent each individual. However, whether all items equally contribute to ensemble summaries (e.g., mean) and whether they might be affected by known limited-capacity processes, such as focused attention, is still debated. We addressed these questions via a recently described “amplification effect,” a systematic bias of perceived mean (e.g., average size) towards the more salient “tail” of a feature distribution (e.g., larger items). In our experiments, observers adjusted the mean orientation of sets of items varying in set size. We made some of the items more salient or less salient by changing their size. While the whole orientation distribution was fixed, the more salient subset could be shifted relative to the set mean or differ in range. We measured the bias away from the set mean and the standard deviation (SD) of errors, as it is known to reflect the physical range from which ensemble information is sampled. We found that bias and SD changes followed the shifts and range changes in salient subsets, providing evidence for amplification. However, these changes were weaker than those expected from sampling only salient items, suggesting that less salient items were also sampled. Importantly, the SD decreased as a function of set size, which is only possible if the number of sampled elements increased with set size. Overall, we conclude that orientation summary statistics are sampled from an entire ensemble and modulated by the amplification effect of attention.
Defense against adversarial attacks by low‐level image transformations

Deep neural networks (DNNs) are vulnerable to adversarial examples, which can fool classifiers by maliciously adding imperceptible perturbations to the original input. Currently, a large number of research on defending adversarial examples pay little attention to the real-world applications, either with high computational complexity or poor defensive effects. Motivated by this observation, we develop an efficient preprocessing module to defend adversarial attacks. Specifically, before an adversarial example is fed into the model, we perform two low-level image transformations, WebP compression and flip operation, on the picture. Then we can get a de-perturbed sample that can be correctly classified by DNNs. WebP compression is utilized to remove the small adversarial noises. Due to the introduction of loop filtering, there will be no square effect like JPEG compression, so the visual quality of the denoised image is higher. And flip operation, which flips the image once along one side of the image, destroys the specific structure of adversarial perturbations. By taking class activation mapping to localize the discriminative image regions, we show that flipping image may mitigate adversarial effects. Extensive experiments demonstrate that the proposed scheme outperforms the state-of-the-art defense methods. It can effectively defend adversarial attacks while ensuring only slight accuracy drops on normal images.
Imbalanced Data Fault Diagnosis Based on an Evolutionary Online Sequential Extreme Learning Machine

To quickly and effectively identify an axle box bearing fault of high-speed electric multiple units (EMUs), an evolutionary online sequential extreme learning machine (OS-ELM) fault diagnosis method for imbalanced data was proposed. In this scheme, the resampling scale is first determined according to the resampling empirical formulation, the K-means synthetic minority oversampling technique (SMOTE) method is then used for oversampling the minority class samples, a method based on Euclidean distance is applied for undersampling the majority class samples, and the complex data features are extracted from the reconstructed dataset. Second, the reconstructed dataset is input into the diagnosis model. Finally, the artificial bee colony (ABC) algorithm is used to globally optimize the combination of input weights, hidden layer bias, and the number of hidden layer nodes for an OS-ELM, and the diagnosis model is allowed to evolve. The proposed method was tested on the axle box bearing monitoring data of high-speed EMUs, on which the position of the axle box bearings was symmetrical. Numerical testing proved that the method has the characteristics of faster detection and higher classification performance regarding the minority class data compared to other standard and classical algorithms.
Knowledge Graph Embedding Based Collaborative Filtering

Along with the rapidly increasing massive online data, recommender systems have been used as an effective approach for filtering useful information, which have been widely adopted in many web applications.In recent years, new techniques such as neural network and deep learning have demonstrated its effectiveness in studies of recommender systems.In this paper, we propose a novel approach for collaborative filtering with implicit feedback based on knowledge graph embedding.The basic ideal is to model the interactions between users and items as an interaction knowledge graph with a single relation, whose vector representation is learned through knowledge graph embedding.Base on the learned representation, the collaborative filtering problem is converted into link prediction in the interaction knowledge graph.KGECF, a neural network for knowledge graph embedding based collaborative filtering, is proposed based on this ideal and the RotatE knowledge graph embedding model.Experimental results on five datasets with different characteristics show that the KGECF model achieves the state-of-the-art (SOTA) performance on all datasets.And unlike other SOTA models that have obvious performance drops on AMusic and AToy datasets, our model's performance is very stable across all datasets.
Controllable Image Processing via Adaptive FilterBank Pyramid

Traditional image processing operators often provide some control parameters to tweak the final results. Recently, different convolutional neural networks have been used to approximate or improve these operators. However, in those methods, one single model can only handle one operator of a specific parameter value and does not support parameter tuning. In this paper, we propose a new plugin module, “Adaptive Filterbank Pyramid”, which can be inserted into a backbone network to support multiple operators and continuous parameter tuning. Our module explicitly represents one operator with one filterbank pyramid. To generate the results of a specific operator, the corresponding filterbank pyramid is convolved with the intermediate feature pyramid produced by the backbone network. The weights of the filterbank pyramid are directly regressed by another sub-network, which is jointly trained with the backbone network and adapted to the input parameter, thus enabling continuous parameter tuning. We applied the proposed module for a large variety of image processing tasks, including image smoothing, image denoising, image deblocking, image enhancement and neural style transfer. Experiments show that our method is generalized to different types of image processing tasks and different backbone network structures. Compared to the single-operator-single-parameter baseline, our method can produce comparable results but is significantly more efficient in both training and testing.
Improve YOLOv3 using dilated spatial pyramid module for multi-scale object detection

Effectively and efficiently recognizing multi-scale objects is one of the key challenges of utilizing deep convolutional neural network to the object detection field. YOLOv3 (You only look once v3) is the state-of-the-art object detector with good performance in both aspects of accuracy and speed; however, the scale variation is still the challenging problem which needs to be improved. Considering that the detection performances of multi-scale objects are related to the receptive fields of the network, in this work, we propose a novel dilated spatial pyramid module to integrate multi-scale information to effectively deal with scale variation problem. Firstly, the input of dilated spatial pyramid is fed into multiple parallel branches with different dilation rates to generate feature maps with different receptive fields. Then, the input of dilated spatial pyramid and outputs of different branches are concatenated to integrate multi-scale information. Moreover, dilated spatial pyramid is integrated with YOLOv3 in front of the first detection header to present dilated spatial pyramid-You only look once model. Experiment results on PASCAL VOC2007 demonstrate that dilated spatial pyramid-You only look once model outperforms other state-of-the-art methods in mean average precision, while it still keeps a satisfying real-time detection speed. For 416 × 416 input, dilated spatial pyramid-You only look once model achieves 82.2% mean average precision at 56 frames per second, 3.9% higher than YOLOv3 with only slight speed drops.
Lithium and Brine Geochemistry in the Salars of the Southern Puna, Andean Plateau of Argentina

Abstract The Andean plateau is a small region of South America extending between northwest Argentina, southwest Bolivia, and northern Chile. It concentrates the largest global resources of lithium brines in its numerous salars. Of these, the giant salars in Bolivia and Chile have been relatively well studied; however, only little is known about the smaller but numerous salars in the Argentine Puna region. In this article, we present the results of the first regional-scale reconnaissance exploration of the 12 major salars situated in the southern part of the Puna plateau (24°S–26°30’S). Hydrochemical data indicate that the shallowest brines are characterized by highly variable Li concentrations, with mean Li grades ranging between 57 and 570 mg L−1, and mean Li/Mg ratios from 0.01 to 1.24. A survey of the brine chemistry of the salars across the Puna plateau, including its northern part, has revealed the absence of a geographical pattern in Li+ grade distribution. However, a comparison among mean Li+ grades, Li+/Mg2+ ratios, and the sizes of all salars allows an estimation of their Li mining potential. Specifically, the salt pan of Arizaro represents the highest potential, mainly due to its size; Antofalla-Botijuelas has a large surface and promising Li/Mg ratios for Li recovery via brine evaporation, though its elongated shape is a constraint; and Pastos Grandes, Pozuelos, and Rincón have encouraging Li grades, interesting salar sizes, and relatively easy access. Olaroz, Cauchari, and Hombre Muerto contain the highest Li+ grades in brines of the Argentine Puna and embody the most interesting perspectives of the Argentine plateau in a regional context. Salar sizes could be related to maximum Li+ grade of brines. Larger salars would then be expected to contain brines with higher Li+ grades than smaller ones, which could be considered as a useful criterion for surveys of brine-type deposits.
Augmented Reality System and Maintenance of Oil Pumps

Qualification of employees who operate technological processes directly influences the safety of production. However, the employees'' qualification cannot completely exclude human factor. Today, there are many technologies that can minimize or eliminate human factor impact on production safety ensuring. The augmented reality technology is an example of this technology. Nowadays, the augmented reality technologies and industrial technologies integration process moves to a new level of development. These technologies have huge experience, which has been accumulated in a long period of time. -This new level turns available by this experience combination and integration; it brings additional profit to the enterprise and can be a basis for completely new technologies. This paper shows an example of combination of augmented reality technology and oil pumps maintenance. For researching of efficiency of augmented reality system for oil pump maintenance, the laboratory unit with Grundfos vertical electric centrifugal pump (CR15-4 A-FGJ-AE-HQQE) was used. The laboratory unit is a physical model of one of the continuous oil processes. The oil pump of this laboratory unit is object of this research. The algorithm of servicing of oil pump was developed. The test of system and algorithms were carried out with four groups of people: the first one had only instructions to use on hand, the second one only used the internal recommendations of the system, the third one used only the help of an expert, and the fourth used internal recommendations and, if necessary, contacted the expert. The results show the efficiency and actuality of augmented reality technology for maintenance of industrial equipment, especially for the equipment operated in remote Arctic conditions.
Online Teaching Resources

Because of the global COVID-19 pandemic during the spring of 2020, colleges and universities found themselves rapidly moving all courses to online modalities, a format that many instructors were not experienced in or comfortable with. Academic librarians were called upon to help support faculty in this sudden transition, as well as migrating their own library services and instruction online. For many, it felt like a mad scramble to find best practices for online teaching, or even just “good-enough” practices, as well as specific tools that instructional faculty and librarians could utilize to keep their students engaged and learning during such a trying time. At the time of this writing, some universities have announced that they will continue to hold most, if not all, courses online in the fall 2020 term. Resources to support online teaching are critical now, and they will continue to be important going forward even after the global pandemic subsides. The reviews in this column cover a variety of online instructional tools and instructional support sites. The resources reviewed range from providing best practices for online pedagogy, creating engaging formative assessment tools, developing video discussion boards, creating interactive images, using virtual bulletin boards for student collaboration, and ensuring online content meets accessibility requirements. These resources will be useful for librarians and instructional faculty as they continue to teach in the online environment.
An Adaptive Colour Calibration for Crowdsourced Images in Heritage Preservation Science

In this paper, an adaptive image calibration technique suitable for cultural heritage surveillance in controlled environments is proposed. Heritage pieces, as objects important to conserve, are prone to degradation and thus in the need of a constant watch in order to assess the moment where maintenance is required, preferably in a non-invasive way. By means of a crowdsourcing approach, we take as surveillance cues the pictures visitors take of the piece under surveillance to avoid the need of a permanent human effort on monitoring. In this paper we propose a novel image calibration approach destined to monitor the state of a cultural heritage piece taking as information cues photographs of different sources. Our technique aims to surpass the colorimetry difference between pictures taken by different cameras, performing an adaptive linear interpolation having a given display with a set of colours as reference values, thus transforming the initial colour space of any picture to a fixed one. This is performed in the RGB domain. Lastly, the pictures are transformed to the CIELAB space to extract the colour coordinates and measure the overall objective quality of the calibration. Acting like this allows the heritage monitoring of its possible degradation along time in an effective way without incurring in expensive resources.
Arabic Opinion Mining Using Combined CNN - LSTM Models

In the last few years, Sentiment Analysis regarding customers' reviews in order to comprehend the opinion polarity on social media has received considerable attention.However, the improvement of deep learning for sentiment analysis relating to customer reviews in Arabic language has received less attention.In fact, many users post and jot down their reviews in Arabic daily, so we ought to shed more light on Arabic sentiment analysis.Most likely all previous work depends on conventional classification techniques, such as KNN, Naï ve Bayes (NB), etc.But in this work, we implement two deep learning models: Long Short Term Memory (LSTM) and Convolution Neural Networks (CNN), in addition to three traditional techniques: Naï ve Bayes, K-Nearest Neighbor (KNN), Decision trees for sentiment analysis and compared the experimental results.Also, we offer a combined model from CNN and Recurrent Neural Network (RNN) architecture where this model collects local features through CNN as the input for RNN for Arabic sentiment analysis of short texts.An appropriate data preparation has been conducted for each utilized dataset.Our Conducted experiments for each dataset against traditional machine learning classifier; KNN, NB, and decision trees and regular deep learning models; CNN and LSTM, has resulted in impressive performance using our proposed combined (CNN-LSTM) model with an average accuracy of 85,83%, 86,88% for HTL and LABR datasets respectively.
Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Breast cancer, a common cancer type, is a major health concern in women. Recently, researchers used convolutional neural networks (CNNs) for medical image analysis and demonstrated classification performance for breast cancer diagnosis from within histopathological image datasets. However, the parameter settings of a CNN model are complicated, and using Breast Cancer Histopathological Database data for the classification is time-consuming. To overcome these problems, this study used a uniform experimental design (UED) and optimized the CNN parameters of breast cancer histopathological image classification. In UED, regression analysis was used to optimize the parameters. The experimental results indicated that the proposed method with UED parameter optimization provided 84.41% classification accuracy rate. In conclusion, the proposed method can improve the classification accuracy effectively, with results superior to those of other similar methods.
Towards Formal Fairness in Machine Learning

One of the challenges of deploying machine learning (ML) systems is fairness. Datasets often include sensitive features, which ML algorithms may unwittingly use to create models that exhibit unfairness. Past work on fairness offers no formal guarantees in their results. This paper proposes to exploit formal reasoning methods to tackle fairness. Starting from an intuitive criterion for fairness of an ML model, the paper formalises it, and shows how fairness can be represented as a decision problem, given some logic representation of an ML model. The same criterion can also be applied to assessing bias in training data. Moreover, we propose a reasonable set of axiomatic properties which no other definition of dataset bias can satisfy. The paper also investigates the relationship between fairness and explainability, and shows that approaches for computing explanations can serve to assess fairness of particular predictions. Finally, the paper proposes SAT-based approaches for learning fair ML models, even when the training data exhibits bias, and reports experimental trials.
Analysis of uPort Open, an Identity Management Blockchain-Based Solution

Recently, academics and professionals proposed a substantial number of identity management solutions based on blockchain technology. Among them, uPort appeared promising, being considered for both personal and administrative operations. This paper analyzes the open-source version uPort Open in terms of risk delegation and private key recovery of the mobile application, smart contract security of the uPort identity registry, and usage and on-chain transaction analytics.