Reviewerly/exHarmony huggingface dataset:
Correction methods for shadow-band diffuse irradiance measurements: assessing the impact of local adaptation

Shadow-bands are a low cost alternative when a precision solar tracker is not available. Adequate precision may be achieved if the measured diffuse irradiance is corrected to account for the sky portion blocked by the shadow-band. The isotropic sky assumption leads to a systematic under estimation of diffuse irradiance. Several correction methods have been proposed to take into account the anisotropic effects. However, their performance at a given site depends on the dominant local climate. In this work, it is shown that the local adaptation of shadow band correction methods results in a significant improvement in the diffuse irradiance measurement's accuracy. Nine well-known correction methods are implemented and tested (both in their original and locally adapted versions) for the Pampa Húmeda region of southeastern South America. In absence of local adaptation, only one of the pre-existing methods improves the simple isotropic model. All locally adapted versions perform similarly well and outperform significantly the original methods. A new model based on the parametrization of Battle's model is proposed. It provides the best performance compared to all locally adapted pre-existing models, under all-sky and discriminated sky conditions.
Design and Implementation of Garbage Classification System Based on Deep Learning

The rapid development of computer vision makes man-machine interaction possible and has a wide application prospect. At present, the Chinese government has implemented the garbage classification policy in various places, and garbage classification has been paid more and more attention by people. But it happens all the time that garbage is misclassified. This essay proposes an image recognition system to help people classify garbage, which can identify different kinds of garbage. The training data set used to train the system is made up of images taken by a camera. The image is preprocessed by rotation, cutting and other methods. Then ResNet50 is selected to train the preprocessed image. The experimental results show that the garbage classification system in this essay can classify garbage effectively and improve the accuracy of garbage classification.
Addressing Visual Search in Open and Closed Set Settings

Searching for small objects in large images is a task that is both challenging for current deep learning systems and important in numerous real-world applications, such as remote sensing and medical imaging. Thorough scanning of very large images is computationally expensive, particularly at resolutions sufficient to capture small objects. The smaller an object of interest, the more likely it is to be obscured by clutter or otherwise deemed insignificant. We examine these issues in the context of two complementary problems: closed-set object detection and open-set target search. First, we present a method for predicting pixel-level objectness from a low resolution gist image, which we then use to select regions for performing object detection locally at high resolution. This approach has the benefit of not being fixed to a predetermined grid, thereby requiring fewer costly high-resolution glimpses than existing methods. Second, we propose a novel strategy for open-set visual search that seeks to find all instances of a target class which may be previously unseen and is defined by a single image. We interpret both detection problems through a probabilistic, Bayesian lens, whereby the objectness maps produced by our method serve as priors in a maximum-a-posteriori approach to the detection step. We evaluate the end-to-end performance of both the combination of our patch selection strategy with this target search approach and the combination of our patch selection strategy with standard object detection methods. Both elements of our approach are seen to significantly outperform baseline strategies.
Inequation Constraint-Handling Technique and Applications in Engineering Optimization

Engineering optimization problems often need to solve constrained optimization, and many methods have been proposed to solve constrained optimization problems over the past few years. Constraint-handling is one of the major concerns when solving constrained optimization problems. This paper presents a new constraint-handling method, which reform inequation constraint as equation constraint without any parameter. In addition, we adopt the penalty function method to balance objective and constraints, and solved unconstrained optimization by heuristic method named novel global harmony search algorithm (NGHS). The NGHS algorithm utilizes position updating and mutation strategy with low probability. After solving two engineering optimization problems, the results demonstrated that the constraint-handing technique is effective, and it can be an efficient alternative on solving constrained optimization problems.
Research on Behavioral Decision Reward Mechanism of Unmanned Swarm System in Confrontational Environment

Unmanned swarm system is regarded as a multi-agent system. At present, many kinds of research use deep reinforcement learning algorithms to solve the problem of multi-agent autonomous behavioral decision. The multi-agent deep deterministic policy gradient (MADDPG) algorithm proposed by Google's DeepMind team pioneered the application of multiple agents in collaborating and confronting complex scenarios, and can be used to solve the problem of generating an autonomous strategy for unmanned swarm in continuous action space. Experiments show that this algorithm has an unsatisfactory convergence and stability in a competitive environment. The key to multi-agent strategy optimization is the design of the reward function. Thus, the paper improves the algorithm of the reward model for multi-agent behaviors in the confrontational environment. First, the advantages of the MADDPG algorithm for implementing multi-agent behavioral decision in a confrontational environment and the training and execution framework of the reward function are introduced. Second, a per-distance reward mechanism and the addition of distance parameters are proposed to improve the efficiency of the agents' behavioral decision and solve the delayed reward problem. The global sharing value reward strategy is changed to the experience pool sharing value reward strategy to avoid the phenomenon of lazy agents and improve the efficiency of the agents' cooperation. Finally, after placing the improved reward mechanism into the MADDPG algorithm, experimental results show that the improved reward function has improved convergence, the algorithm training cycle is shortened, the function curve stabilizes faster, and multiple agents have better performance in the confrontational environment. This paper provides reference methods for intelligent behavioral decisions of the unmanned swarm system.
Wireless Signal Fngerprint Extraction Based on Convolutional Deep Belief Network

Signal Fingerprint is a physical layer based wireless security algorithm desinged for recognizing legal devices. The non-ideality of transmitting circuit will lead to small but unique distortions in the signal emitted by this circuit. Signal fingerprint is aiming at extracting those unique distortions from received signal in order to identify the related radiofrequency circuit and wireless device. Traditional signal fingerprint extraction depends on carefully designed formula whose application scope is narrow and the accuracy cannot be guaranteed. Supervised deep learning based signal fingerprint extraction enjoys high accuracy and can be deployed in many different circumstances. But the large amount labeled data required in training stage is a strong requirement that cannot be easily met. Besides, the network might be sensitive to noise. To solve those problems, this paper proposes a Convolutional Deep Belief Network (CDBN) based signal fingerprint extraction algorithm. The signal fingerprint extraction is performed by network itself with the spectrum of signal as input. So the accuracy can be improved and application scope can be widen. Due to the unsupervised essence of CDBN, the constraint of labeled data is relieved.
Manual Operated Paper Recycling Machine

The production of enormous quantities of waste papers is evident in any large institution, particularly educational institutions such as schools or universities. It's also feasible to make good use of recycled paper (craft papers, registers etc). So, rather than throwing away the waste papers, recycling them makes sense. A paper-recycling machine that may be operated manually was conceived and built. This was done to allow for the conversion of waste paper into a useful product in the home. The machine unit's design has been created in accordance with all essential component requirements. In addition, the overall cost is minimal, and there is no requirement for power.
SMOTE Using Border-points and Expectation-maximization Algorithm for Imbalanced Data Classification

Class imbalance refers to a classification problem which occurs when the class distribution is skewed in the dataset. It is known that class imbalance degrades the predictive performance of classifiers. As a solution to this problem, we propose GBL-SMOTE, a modified method of SMOTE technique considering the clusters and their border-points. GBL-SMOTE solves the class imbalance problem in the learning stage by generating artificial observations at the cluster boundary of minority class observations. In this study, we create an artificial class imbalanced dataset to show the effect of GBL-SMOTE compared to original SMOTE and other SMOTE-variants. In addition, through experiments on the publicly available data, it was shown as an experiment that learning a support vector machine using GBL-SMOTE has better prediction performance than using other oversampling techniques.
Progressive Stochastic Binarization of Deep Networks

We propose a stochastic binarization scheme for deep networks that approximates scalar products of weights and activations using progressive sampling of stochastic shifts. This representation has bounded relative error and thereby permits high accuracies at moderate sampling costs. Further, it allows for the first time a fully dynamic and localized control of accuracy. This not only enables a choice of accuracy at run-time, but also provides a new tool for adaptively focusing computational attention. A reference implementation is provided under a free license (https://github.com/JGU-VC/progressive_stochastic_binarization).
Evaluation of the Neural-network-based Method to Discover Sets and Representatives of Nonlinearly Dependent Variables

It is desired in a variety of fields to identify which variables are dependent, and variable dependence measures have been studied. The majority of such measures detect a linear or a certain range of nonlinear dependence between paired variables. To go beyond them, a method based on Neural Network Regression, Group Lasso, and Information Aggregation has been proposed in our past study. It can detect a wide range of nonlinear dependences among multi variables and discover the sets and representatives of the detected dependences. Its fundamental effectiveness has already been examined using synthesized artificial datasets containing a single dependence. For further evaluation in the present study, we conducted an experiment using those containing multi dependences. The proposed method succeeded in discovering the sets and representatives, and its performance was robust to data size and noise rate. The experimental results suggested that the proposed method works well for difficult tasks to handle multi dependences.
A fast feature point extraction method for optical tracking system

Optical tracking systems (OTS) mainly consist of multiple cameras and optical target. A number of markers, at least three, are attached to the target. Position and motion posture of the target are obtained by photographing those markers. A small part of the image is occupied by marker. If markers are searched by traversing the whole image, huge expenditure and extravagant computing resources will be caused. A fast feature point extraction method is described in this paper to reduce computational burden. It combines marker prediction, perspective projection, nearest neighbor fast seed point search algorithm, region growing algorithm, and gray centroid method. Compared with the traditional whole image traversal method, the marker extraction speed is improved by hundreds or even thousands of times, which is verified by trinocular vision tracking experiment. The method is suitable for OTS.
Data optimization for large batch distributed training of deep neural networks

Distributed training in deep learning (DL) is common practice as data and models grow. The current practice for distributed training of deep neural networks faces the challenges of communication bottlenecks when operating at scale, and model accuracy deterioration with an increase in global batch size. Present solutions focus on improving message exchange efficiency as well as implementing techniques to tweak batch sizes and models in the training process. The loss of training accuracy typically happens because the loss function gets trapped in a local minima. We observe that the loss landscape minimization is shaped by both the model and training data and propose a data optimization approach that utilizes machine learning to implicitly smooth out the loss landscape resulting in fewer local minima. Our approach filters out data points which are less important to feature learning, enabling us to speed up the training of models on larger batch sizes to improved accuracy.
Mobile Application Puzzle and Its’ Effect to the Learners Learning Outcomes in Earth Science

Abstract This study aimed to determine the performance, attitude and interactivity of Grade 11 High School learners in Earth Science before and after the Jigsaw Puzzle Mobile App when utilized as instructional material. This also gathered data on the effect of Jigsaw Puzzle Mobile App and reflections of the respondents. It was conducted at Fisheries and Marine Science High School, Iloilo State College of Fisheries Main Tiwi Campus from August to October 2018. Fifty-two (52) purposively chosen grade 11 learners were the respondents of the study. The quasi-experimental research design was used. Data were gathered using the test, guide questions, attitudinal and reflection. Data were analyzed using the mean and t-test. The result showed that there was an improvement in the performance of the learners as a whole based on their pre-test and post-test scores. The attitude of the learners before the conduct of the study as a whole, as to sex and as to track was “positive” respectively but after the study male and according to the track was “very positive” respectively while a female has “positive” attitude. The interactivity of the learners as a whole was “average”, according to sex and track respectively except STEM with “high interactivity”. There was a highly significant difference in the performance and the attitude of the learners before and after the jigsaw puzzle mobile app was introduced. The presence of the jigsaw puzzle mobile app in the classroom was found to be useful, enjoyable and challenging. This also let the learners develop their sense of deep concentration, critical thinking skills and psychomotor coordination.
Protection Method of Network Data Privacy Security Issues Based on Blockchain Technology

As a new model of Internet-based computers, blockchain technology has always been a hot spot for scholars and the industry. Although blockchain technology has been developed for many years, data security and reliable computing are still the main challenges facing the current blockchain technology applications. In order to solve this problem, many researchers have conducted a lot of research on it and proposed many models, including data integrity verification and secure multi-part calculation. However, most of these solutions face problems such as high computational complexity or lack of scalability. This paper proposes a data release method based on differential privacy protection for the problems of the release of static data sets and the release of dynamic data, and uses the built platform for experimental verification. Aiming at the privacy leakage problem faced by the multi-level and fine-grained search of big data, this paper proposes an incremental data indexing strategy based on conceptual lattice granular deduction, and builds a complete data retrieval service system that supports differentiation on the cloud platform architecture, it also supports practical search modes such as multi-keyword ranking search, similarity search and fuzzy search, and systematic research on the protection methods of network data privacy security issues under blockchain technology. The experimental results surface: Compared with other methods, the algorithm in this paper has greatly reduced information loss under the same privacy requirements, and the algorithm has high execution efficiency. This shows that the results of this study can provide ideas for the research of big data security and privacy protection, and have certain reference significance.
Contribution Principal Component Analysis to Optimizing Data by Reducing Product Data on Transaction

Abstract Principal component analysis is to analyze the observation data table into a new data table that has the same correlation. And the aim is to simplify the previously complex observation data so that it is easier to process or analyze. The dataset used is transaction data which is often used by the association method in sales analysis, where the data taken consists of 1397 types of products sold in 1200 transactions. In this data, there are products that have very small sales, which means that the percentage of these products has very little effect on the future process, namely sales analysis with the association method. Therefore the authors try to optimize the data to become ready to use data by reducing products that have a small percentage value that affects research for the dataset. And on this occasion the author uses the main component analysis method to reduce products or form products that can represent the entire dataset without reducing the quality of the data for analysis. From the results of research conducted on transaction data, there was a product decline of 65.21%, where the products totaling 1397 were reduced to 486 products that could represent without reducing their value.
P‐27: ML‐Based Eye Tracking for Augmented Reality Heads‐Up Displays (AR HUDs)

3D Augmented Reality (AR) Heads‐up Displays (HUDs) have the potential of overlaying virtual objects at the correct locations with accurate motion parallax. Accurate overlays require tracking the pupils of the driver's eyes. We developed an ML‐based pupil tracking system based on a convolutional neural network (CNN) to find the precise location of the pupils.
Towards to Integrate a Multilayer Machine Learning Data Fusion Approach into Crisis Classification and Risk Assessment of Extreme Natural Events

Nowadays, one of the most critical challenges is the ongoing climate change, which has multiple and significant impacts on human life in financial and environmental levels. As the adverse effects of unexpected destructive natural extreme events, such as loss of human lives and property, will become more frequent and intensive in the future, especially in developing countries, the efficient confront is required in a holistic manner. Hence, there is an urgent need to develop novelty tools to enhance awareness and preparedness, assess risks and support decision-making, aiming to increase social resilience to climate changes. This work suggests a unified multilayer framework that encapsulates machine learning techniques in the risk assessment process for analysing and fusing dynamically heterogeneous information obtained from the field.
Barter Machine: Defier of the Money

Direct bartering allows us to do trading of assets with or without the use of money. We contribute a smart contract written in Solidity language for the Ethereum blockchain that implements a distributed and autonomous direct barter exchange operated by crowds. Since ERC20 smart contract tokens are widely used for initial coin offerings (ICOs), our implementation provides autonomous bartering services for ERC20 tokens. Non-fungible ERC721 token as well as Ethereum Name Service (ENS) name bartering are also supported. Finding feasible bartering solution for bids involving multiple tokens is NP-hard in general. Our exchange just provides escrow and swapping services. It assumes feasible solutions are submit ted by bartering problem solvers from the crowd who run a solver locally on their machines. Bartering problem solvers are incentivized for submitting solutions to the autonomous exchange by awarding them with excess tokens that are left over after granting the bidders the tokens they requested in their bids. Our system, called BarterMachine, can perhaps be used to facilitate a global barter economy. BarterMachine prototype is available for testing on the Ethereum Ropsten network.
QnA System on Educational Textbooks: Digital Library Doubt Support System

The domain of content and knowledge on any particular topic of someone's interest is ever - growing. This broadening of the knowledge base of a subject combined with the easy accessibility to the Internet through various devices has resulted in easy access to the loads of content when a search related to the topic is made. But this gigantic collection of data also possesses a challenge. One needs to make some efforts to find the desired information. For this purpose, an individual might be required to go through a number of documents until he/she finds the answer that they craved for.This paper centers around a model to deal with this issue and helps in finding the relevant and precise answer to a query raised. To fulfill this purpose the model uses Natural Language Processing methods combined with knowledge graphs. The paper comes to a conclusion by presenting the performance of the model built.
Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning and range-domain filtering-based approach

In minimally invasive surgery, laparoscopy video can be corrupted by haze, noise, oversaturated illumination, and other factors. These adverse internal environmental effects in turn impact the subsequent processing, such as segmentation, detection, and tracking the object or region of interest. Enhancement of each frame of video by considering color channels independently gives birth to unintended phantom colors due to its ignorance of the psychovisual correspondence. On the other hand, addressing each frame separately without honoring the interframe correspondence results in a video having impulsive transitions of psychovisual perception and unacceptable quality enhancement. To address all the aforementioned, we have proposed a novel algorithm to ensure enhancement of video with faster performance. The proposed cross color dominant deep autoencoder (C2D2A) uses the strength of (a) a bilateral filter that addresses the one-shot filtering of images both in the spatial neighborhood domain and psychovisual range; (b) a deep autoencoder that can learn salient patterns. The domain-based color sparseness has further improved the performance, modulating a classical deep autoencoder to a color dominant deep autoencoder. The work has shown promise not only as a generic framework of quality enhancement of video streams but also in addressing performance. This in turn improves the image/video analytics such as segmentation, detection, and tracking the objects or regions of interest.