Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Regression Models in Solar PV Forecasting

Solar energy is increasingly becoming one of the promising sources of renewable energy for versatile purposes including residential and commercial applications. Forecasting of the power output of solar photovoltaic (PV) systems with high accuracy in the short term is of significant importance for production, delivery, and storage in the energy market. This work aims to produce reliable short-term forecasting using machine learning methods such as Linear Regression, Random Forest Regression, and Decision Tree Regression. Results show 99% percent accuracy for the proposed models. The work also detects faults and abnormalities occurring in Solar PV panels with the help of results thereby enhancing the production.
Localization and Segmentation of Galaxy Morphology Based on Mask R-CNN

Segmentation and accurate positioning of galaxies is one of the important steps in the study of survey galaxy images. However, thanks to the development of large astronomical observation equipment, astronomy has entered the era of big data, so it is difficult to use traditional methods to solve this demand. As an effective method of massive data analysis, deep learning has great potential in astronomical image processing. Therefore, this paper proposes a method based on Mask R-CNN to locate and segment galaxy morphology. The dataset from the Galaxy Park Project is first preprocessed to address the problems of overfitting and underfitting. Then transfer learning and decay learning rate strategies are used to train the model. Finally, the experimental results show that the trained model can be used to locate and segment four types of commonly used galaxy images. The mAP@0.5 can achieve an accuracy of 93%, so we can determine the type, location and boundary information of galaxies with relatively high accuracy.
Research on a Collaborative Filtering Method Introduced into Knowledge Graph

In our society, recommendation technology is more popular in Commercial, AI, and other related fields with the further development of the Internet. Information retrieval and filtering research is the initial origin of recommendation systems. The most used filtering type by RS at the beginning is collaborative, content-based, and demographic. Collaborative filtering (CF) is the most popular recommendation technology, it usually filters similarly interesting users' thoughts and choices to find aimed users' most likely future goods. However, the technical conditions and situation changed with the progressed technology and changed demands; the CF technology cannot be suitable for the latest conditions and situations. We introduce and apply knowledge graphs (KG) in CF technology to solve issues. The Knowledge Graph is a kind of technology that can reflect the structure relationship between different entities. Knowledge Graph attracts more attention from professional areas as a form of structural human knowledge, which constantly argues the user relationship's effects on recommendation results. This article finds three main measuring factors from Knowledge Graph and composed correlation vectors based on these factors: Relationship Strength Vector, Arrow Direction Vector, and Entity-relationship similarity vectors. To improve the CF recommendation systems, the Authors first use the latest three vectors to infer entity similarity, then compose with the traditional collaborative filtering technology to get a new method. Finally, there are some experiments to prove the effectiveness of this method. This method can further improve and change the recommendation order, letting the recommendation result has higher similarity with the actual users.
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning

Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially private federated learning (DP-FL). While the unsuitability of batch normalization for these domains has already been shown, the impact of other normalization methods on the performance of federated or differentially private models is not well-known. To address this, we draw a performance comparison among layer normalization (LayerNorm), group normalization (GroupNorm), and the recently proposed kernel normalization (KernelNorm) in FL, DP, and DP-FL settings. Our results indicate LayerNorm and GroupNorm provide no performance gain compared to the baseline (i.e. no normalization) for shallow models in FL and DP. They, on the other hand, considerably enhance the performance of shallow models in DP-FL and deeper models in FL and DP. KernelNorm, moreover, significantly outperforms its competitors in terms of accuracy and convergence rate (or communication efficiency) for both shallow and deeper models in all considered learning environments. Given these key observations, we propose a kernel normalized ResNet architecture called KNResNet-13 for differentially private learning. Using the proposed architecture, we provide new state-of-the-art accuracy values on the CIFAR-10 and Imagenette datasets, when trained from scratch.
Data Redaction from Pre-trained GANs

Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness. The common way to mitigate this is to re-train them differently from scratch using different data or different regularization - which uses a lot of computational resources and does not always fully address the problem. In this work, we take a different, more compute- friendly approach and investigate how to post-edit a model after training so that it “redacts”, or refrains from outputting certain kinds of samples. We show that redaction is a fundamentally different task from data deletion, and data deletion may not always lead to redaction. We then consider Generative Adversar-ial Networks (GANs), and provide three different algorithms for data redaction that differ on how the samples to be redacted are described. Extensive evaluations on real-world image datasets show that our algorithms out-perform data deletion baselines, and are capable of redacting data while retaining high generation quality at a fraction of the cost of full re- training,
Short-term prediction of photovoltaic power based on improved Wile Horse Optimizer

According to the volatility of photovoltaic output, a multi- kernel extreme learning machine (MKELM) based on improved Wile Horse Optimizer is proposed. First, historically similar day data sets were generated using fuzzy C mean clustering (FCM). Secondly, in view of the problem that MKELM parameters are difficult to determine, the improved Wile Horse Optimizer (IWHO) is used to optimize the kernel parameters and regularization coefficient of MKELM, which enhances the generalization ability of the model. Taking the measured data as an example, the results show that the proposed prediction model can achieve satisfactory prediction accuracy under different weather conditions, compared with the traditional support vector machine, BP neural network and long and short-term neural network error index, which effectively improves the prediction accuracy of photovoltaic output.
Incomplete multiview subspace clustering algorithm based on self-representation and consistent learning

Incomplete multiview clustering is an important topic in machine learning due to ubiquitous incompleteness of view in the real data in life. The incompleteness of views is prevalent in real life. In this paper, we propose an incomplete multiview subspace learning method based on self-representation learning and graph fusion. The proposed method construct the robust graph of each view through the self-expression and semi-nonegative matrix decomposition, and learn a consistent representation of all views by Laplace co-regularization based on the low-dimensional representation of the each view. Through the learned consistent representation, the incomplete multiview can be divided by k-means. Experiments is implemented on 3 benchmark datasets and the superior results validate the effectiveness of the proposed method.
Vertical Text Recognition System Based on Attention Mechanism

This paper designs a vertical text recognition system based on the attention mechanism, which can recognize not only horizontal text but also vertical text. The system is divided into two parts: recognition and web. The recognition part includes the generation of data sets, image preprocessing and text recognition. The text recognition is divided into two processes: encoding and decoding. The web part mainly displays visually to facilitate the user experience. Because the vertical text data set is scarce, the generated data set is used here to train the neural network. The size of the training set is 750,000 images, and the test set has 75,000 images. The recognition accuracy of the test on the generated test set has reached 98%.
Real Time Automatic Sign Language Translation Machine Learning &amp; IoT

The easiest method for interacting with the deaf is through sign language. The inclusion of characteristics and regional dialect diversity in this study collections have been the major obstacles in most American sign language research. Learning sign language will help you communicate with them. Learning frequently takes place in peer groups. For learning signs, there is not enough research resources available. Hence, mastering sign language is a challenging procedure. The initial phase of learning sign language is Moreover, when no equivalent sign is given or the signer is illiterate, finger spellings are employed in sign language. The bulk of sign language education programmes currently on the market depend heavily on expensive sensing devices.
Smart Contract Project Management Framework (SCPMF) ‒ A Conceptual Model

Smart Contract Project Management Framework (SCPMF) is a proposed conceptual model that combines smart contract technology with project management principles to enhance transparency, accountability, and security in project management. This framework has the potential to revolutionize project management by enabling real-time monitoring of project progress, automated contract execution, and immutable record-keeping. This paper provides an overview of the SCPMF, induding its applications in project planning, resource management, and contract management. The paper also discusses the benefits and challenges of using SCPMF in project management. The benefits indude increased efficiency, reduced costs, and improved collaboration, while the challenges include scalability, interoperability, and regulatory compliance. The paper concludes by highlighting some of the current trends in SCPMF-based project management and offering recommendations for future research. Overall, the paper demonstrates the potential for SCPMF to transform project management and suggests that further research and development in this area could lead to significant improvements in project outcomes.
Activity Monitoring and Alert System for Elderly People in Smart Homes

The rapid ageing of the population poses difficulties for elderly people who live alone. It gets more difficult for people who live in rural areas. As a result, there is an immediate need for real-time monitoring via the internet of things (IoT). In this work, An activity monitoring and alert system are proposed, which monitors health parameters such as ECG, oxygen amount in the blood, fall detection, and the person’s activity. The individual monitors his or her heart disease using various wearables. The chest strap is used to read the ECG signal, the wrist strap is used to check the oxygen level in the blood, and the footwear is used to detect falls. To reduce false alarms, three modules are integrated. For activity monitoring, multiple sensor nodes are placed throughout the house. Any deviation from the parameters sends a message to the cloud database, alerting family members, doctors, and emergency services. The proposed system is useful for providing medication and assistance on time.
Object Classification Using A rtificial I ntelligence Technique s i n Autonomous Vehicles

Autonomous or self-driving vehicles are self- decision systems that must choose their next course of action when an object or other vehicle is spotted on the road. Smooth driving is crucial for self-driving cars to avoid causing other road users to become stranded in a traffic jam. For autonomous vehicles, understanding traffic objects is crucial. The self-driving automobile can perform the required action after identifying an object’s precise class. Self-driving cars should be extra cautious when passing big vehicles like buses or trucks if they are identified. Consequently, one of the key capabilities of self-driving automobiles is object identification. Computer vision tasks like object classification can be useful for object recognition. Deep learning is a key subfield of machine learning and crucial for a number of computer vision applications such object detection, localization, and classification. In this paper, we present a model for real-time object classification using the Convolutional Neural Network (CNN) of AI approach. It includes a thorough discussion of various deep learning techniques for classifying objects in RGB pictures and LiDAR point clouds.
BPEL process defects prediction using multi-objective evolutionary search

Web services are becoming increasingly popular technologies for modern organizations to improve their cooperation and collaboration through building new software systems by composing pre-built services. Such services are typically composed and executed through BPEL (Business Process Execution Language) processes. Like any other software artifact, such processes are frequently changed to add new or modify existing functionalities or adapt to environmental changes. However, poorly planned changes may introduce BPEL process design defects known as anti-patterns or defects. The presence of defects often leads to a regression in software quality. In this paper, we introduce an automated approach to predict the presence of defects in BPEL code using Multi-Objective Genetic Programming (MOGP). Our approach consists of learning from real-world instances of each service-based business process defect (i.e., anti-pattern) type to infer prediction rules based on the combinations of process metrics and their associated threshold values. We evaluate our approach based on a dataset of 178 real-world business processes that belong to various application domains, and a variety of BPEL process defect types such as data flow and portability defects. The statistical analysis of the achieved results shows the effectiveness of our approach in identifying defects compared with state-of-the-art techniques with a median accuracy of 91%.
Improvement accuracy in deep learning: An increasing neurons distance approach with the penalty term of loss function

The increasing use of neural networks for solving complex tasks has emphasized the need to optimize their performance. In recent years, the development of neural networks has enabled them to tackle increasingly challenging problems for applications in various fields. Existing methods, such as L1 and L2 penalty terms, improve the neural network performance by reducing the neurons' weight and removing some neurons' output. However, these algorithms reduce the number of active neurons and the theoretical maximum capacity of the neural network. A loss function penalty term can improve the network performance by guiding the gradient descent direction of neurons during the training process. Moreover, it does not need to alter the network structure, increase the data, or change the training process. In this study, a novel algorithm is proposed to improve the performance of the network by adding a new loss function penalty term. The algorithm proposed in this study enhances the accuracy and anti-interference capabilities of neural networks by increasing the number of active neurons based on the finding that identical neurons extract identical features from the data, while distinct neurons extract unique features. The neurons are treated as hyperspace points and the distance between them is converted into repulsive forces to increase the distance between these points. To verify the validity of the proposed algorithm, we tested it on image classification datasets (CIFAR-100 and CALTECH-256), text datasets (SST-2 and THUCNews), and an image detection dataset (PASCAL VOC). The results show that the proposed algorithm can improve the network performance for a variety of neural network structures, data types, and task types. This algorithm has demonstrated its effectiveness in robustness against data noise and compatibility with pre-trained models such as Faster R-CNN, YOLO, and RetinaNet.
Active learning in multi-label image classification with graph convolutional network embedding

Active learning has achieved considerable success in sample selection for deep learning models and has been widely used to address the issue of high-cost sample annotation. However, most of the existing active learning methods focus on single-label image classification and have limited use in multi-label scenarios. To address this issue and take advantage of label associations, we propose an active learning model based on the graph convolutional network (GCN) embedding and loss prediction network. Specifically, we construct a heterogeneous information network (HIN) that uses GCN embeddings to learn multiple label associations, as well as associations between images and labels. We also use a loss prediction network to predict target losses of unlabeled inputs. In addition, we propose a dynamic active coefficient to adjust the proportion of active learning gradually in the training process. Comprehensive multi-label image classification experiments with limited training labels are conducted on the MS-COCO, VOC 2007, and NUS-WIDE datasets. The comparison results demonstrate the superiority of our method compared with conventional methods in terms of both classification accuracy and convergence speed.
Integration of sparse and continuous data sets using machine learning for core mineralogy interpretation

In earth science, integrating noninvasive continuous data streams with discrete invasive measurements remains an open challenge. We address such a problem — that of predicting whole-core mineralogy using discrete measurements with the help of machine learning. Our targets are sparsely sampled mineralogy from X-ray diffraction, and features are continually sampled elemental oxides from X-ray fluorescence. Both data sets are acquired on a core cut from a Mississippian-age mixed siliciclastic-carbonate formation in the U.S. midcontinent. The novelty lies in predicting multiple classes of output targets from input features in a small multidimensional data setting. Our workflow has three salient aspects. First, it shows how single-output models are more effective in relating selective target-feature subsets than using a multi-output model for simultaneously relating the entire target-feature set. Specifically, we adopt a competitive ensemble strategy comprising three classes of regression algorithms — elastic net (linear regression), XGBoost (tree-based), and feedforward neural networks (nonlinear regression). Second, it shows that feature selection and engineering, when done using statistical relationships within the data set and domain knowledge, can significantly improve target predictability. Third, it incorporates k-fold cross-validation and grid-search-based parameter tuning to predict targets within 4%–6% accuracy using 40% training data. Results open doors to generating a wealth of information in energy, environmental, and climate sciences where remotely sensed data are inexpensive and abundant but physical sampling may be limited due to analytic, logistic, or economic issues.
Cooperative linear regression model for image set classification

In image set classification, affine hull based models have achieved good performance. However, these models only focus on holistic images and ignore the local information of images. To incorporate local information, this paper develops a cooperative operator and proposes a cooperative linear regression model (CLRM) for image set classification. Specifically, CLRM first proposes a block partitioning strategy to obtain local information. Image sets and local information are both modeled as affine hulls. Then, a linear regression model embedded with cooperative operator is proposed to incorporate different information and compute the distance between the different affine hulls. Finally, a combined distance measure is proposed to incorporate different information for classification. Extensive experiments on different recognition tasks such as face recognition on image/video based databases and object classification show that the proposed cooperative operator can improve the performance of the model and the proposed CLRM achieves competitive performance compared with the state-of-the-art classifiers.
GFR: Generic feature representations for class incremental learning

Class incremental learning (CIL) aims to continuously learn new classes while maintaining discrimination for old classes with sequentially coming data. Due to the lack of old-class samples, existing CIL methods fail to learn discriminative representations for both old and new classes simultaneously, resulting in a severe performance drop in old classes, which is the well-known catastrophic forgetting phenomenon. Different from most existing works, we facilitate CIL by learning generic feature representations that perform well in seen and unseen classes. Specifically, we prove that representations with a substantial number of significant singular values benefit CIL via better old knowledge reservation. However, the overly uniform singular value spectrum will hurt the discrimination of current tasks. Furthermore, we propose that increasing the embedding dimension can enhance the number of significant singular values and validate this assumption from two perspectives: adopting different pooling techniques and devising a wider network. Meanwhile, we also prove that satisfactory current task accuracy and old knowledge reservation can be achieved simultaneously. Finally, the simple yet effective generic feature representation regulation (GFR) is devised and incorporated into two baselines. Extensive experiments are conducted on CIFAR100, ImageNet-Subset, and ImageNet. The results show that the proposed method boosts the performance of both baselines with a large margin (2.00%-9.58% on CIFAR100, 0.68%-7.10% on ImageNet-Subset and 1.18%-5.04% on ImageNet) which outperforms existing SOTAs.