Reviewerly/exHarmony huggingface dataset:
Deep graph learning for spatially-varying indoor lighting prediction

Lighting prediction from a single image is becoming increasingly important in many vision and augmented reality (AR) applications in which shading and shadow consistency between virtual and real objects should be guaranteed. However, this is a notoriously ill-posed problem, especially for indoor scenarios, because of the complexity of indoor luminaires and the limited information involved in 2D images. In this paper, we propose a graph learning-based framework for indoor lighting estimation. At its core is a new lighting model (dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph Convolutional Network (GCN) that infers the new lighting representation from a single LDR image of limited field-of-view. Our lighting model builds 128 evenly distributed SGs over the indoor panorama, where each SG encoding the lighting and the depth around that node. The proposed GCN then learns the mapping from the input image to DSGLight. Compared with existing lighting models, our DSGLight encodes both direct lighting and indirect environmental lighting more faithfully and compactly. It also makes network training and inference more stable. The estimated depth distribution enables temporally stable shading and shadows under spatially-varying lighting. Through thorough experiments, we show that our method obviously outperforms existing methods both qualitatively and quantitatively.
Constructing marine expert management knowledge graph based on Trellisnet-CRF

Creating and maintaining a domain-specific database of research institutions, academic experts and scholarly literature is essential to expanding national marine science and technology. Knowledge graphs (KGs) have now been widely used in both industry and academia to address real-world problems. Despite the abundance of generic KGs, there is a vital need to build domain-specific knowledge graphs in the marine sciences domain. In addition, there is still not an effective method for named entity recognition when constructing a knowledge graph, especially when including data from both scientific and social media sources. This article presents a novel marine science domain-based knowledge graph framework. This framework involves capturing marine domain data into KG representations. The proposed approach utilizes various entity information based on marine domain experts to enrich the semantic content of the knowledge graph. To enhance named entity recognition accuracy, we propose a novel TrellisNet-CRF model. Our experiment results demonstrate that the TrellisNet-CRF model reached a 96.99% accuracy rate for marine domain named entity recognition, which outperforms the current state-of-the-art baseline. The effectiveness of the TrellisNet-CRF module was then further demonstrated and confirmed on entity recognition and visualization tasks.
The Parallelization and Optimization of K-means Algorithm Based on MGPUSim

Although the k-means algorithm has been parallelized into different platforms, it has not yet been explored on multi-GPU architecture thoroughly. This paper presents a study of parallelizing k-means on a novel MGPUSim architecture, including its parallel execution mechanism, architecture design, etc. In addition, it proposes an optimization method “O-kmeans” to initialize the selection of clustering centers by first finding the centroids of the samples and then dividing the initialized clustering centers with centroids, thus solving the problem of poor clustering effect of the k-means algorithm when the data size is large. The performance of this algorithm is tested with both real and synthetic datasets. The experimental results show that:(1) The proposed O-kmeans algorithm performs well on the MGPUSim. It can achieve a 26.74×–62.92× speedup for real data sets, which is better than the CUDA implementation of kernel k-means. (2) In synthetic datasets, by conducting controlled variable experiments at varying data sizes and data dimensions, and different clustering centers. We find that the algorithm has higher stability and good processing speed on MGPUSim.
Joint visual template and natural language for robust visual tracking

At present, the target of interest in visual tracking is given in the form of a bounding box. Due to the randomness of the target shape, the bounding box may contain a lot of non-target information. When encountering complex tracking scenarios, the performance of the tracker reduces severely. To address this problem, in this letter, the authors propose a novel tracking framework based on the joint of the visual template and natural language (VNTrack) to alleviate the impact of bounding box ambiguity. Specifically, the authors first use a pre-trained language model to extract the features of the language description of the target. Then, a feature alignment module is designed to align and enhance the visual template feature and natural language feature. In addition, the authors design a multimodal query module to fuse the visual template, natural language, and search region information. Experimental results over tracking benchmarks with language annotations show that the proposed VNTrack is competitive among the state-of-the-art trackers.
Feature selection based on discriminative power under uncertainty for computer vision applications

Feature selection is a prolific research field, which has been widely studied in the last decades and has been successfully applied to numerous computer vision systems. It mainly aims to reduce the dimensionality and thus the system complexity. Features have not the same importance within the different classes. Some of them perform for class representation while others perform for class separation. In this paper, a new feature selection method based on discriminative power is proposed to select the relevant features under an uncertain framework, where the uncertainty is expressed through a possibility distribution. In an uncertain context, our method shows its ability to select features that can represent and discriminate between classes.
A Study of Learning Issues in Feedforward Neural Networks

When training a feedforward stochastic gradient descendent trained neural network, there is a possibility of not learning a batch of patterns correctly that causes the network to fail in the predictions in the areas adjacent to those patterns. This problem has usually been resolved by directly adding more complexity to the network, normally by increasing the number of learning layers, which means it will be heavier to run on the workstation. In this paper, the properties and the effect of the patterns on the network are analysed and two main reasons why the patterns are not learned correctly are distinguished: the disappearance of the Jacobian gradient on the processing layers of the network and the opposite direction of the gradient of those patterns. A simplified experiment has been carried out on a simple neural network and the errors appearing during and after training have been monitored. Taking into account the data obtained, the initial hypothesis of causes seems to be correct. Finally, some corrections to the network are proposed with the aim of solving those training issues and to be able to offer a sufficiently correct prediction, in order to increase the complexity of the network as little as possible.
Little-YOLOv4: A Lightweight Pedestrian Detection Network Based on YOLOv4 and GhostNet

Nowadays, the areas such as intelligent-assisted driving, intelligent monitoring, pedestrian analysis, and attitude detection are developing rapidly. Inseparable from these fields is pedestrian detection technology. In order to deploy pedestrian detection algorithms on devices with limited hardware resources, a recognition algorithm that takes into account both performance and speed is required. Thus, a Little-YOLOv4 network structure is proposed, where the GhostNet is used to extract image features and the PANet is ameliorated by adding BiFPN path fusion which can integrate richer semantic features and preserve spatial information. The DO-DConv and DSC take place of standard convolution and the ReLU6 replaces the Leaky ReLU, which reduce the computational cost. The squeeze-and-excitation network is added to YOLOv4 head network, which could greatly reduce the interference information. The pedestrian detection results show that the mAP is 90.11% and the FPS is 79 by using Little-YOLOv4. The Little-YOLOv4 network structure could achieve a good compromise between algorithmic accuracy and speed.
The Relationship between 21st-Century Instructional Leadership and Teachers' Self-Efficacy

A teacher's self-efficacy does not only affects the teacher's performance as an educator but also directly affects the learning processes of the students.Therefore, it is important to look into the things that affect a teacher's self-efficacy.Several factors can influence the level of self-efficacy of a teacher, including the leadership style and practices of school leaders.This study aims to identify the level of the 21st-century instructional leadership practices, the level of teacher self-efficacy and the relationship between the two variables.This study was conducted by using a quantitative method which is a survey design.The results of the study were obtained from 252 teachers working in primary schools in the Wilayah Persekutuan Putrajaya who were selected using the simple random selection method.Based on the data that has been analysed, the findings show that the level of the 21st-century instructional leadership practices and the level of teacher self-efficacy are at a very high level and the relationship between the two variables is strong (r = 0.634).The results of this study are expected to contribute to the efforts of stakeholders including ministries, education departments and education bureaus as well as schools to improve the quality of education.For further research, the researcher suggests a more comprehensive and in-depth study involving leaders at all levels as well as teachers in schools, to determine the leadership style of school leaders and the factors that influence teachers' self-efficacy.
Design of Enterprise Financial Information Fusion Sharing System Based on Blockchain Technology

In the process of global economic integration, the rapid development of various industries not only brings various opportunities but also presents new challenges to enterprises. With the continuous expansion of enterprise scale, more and more enterprises join the trend of international competition, and the internal system of enterprise group organization is complex, resulting in the weakening of the management and control ability of the enterprise itself and the decline of operating efficiency. In order to improve this situation, the fusion and sharing of financial information has become a necessary development strategy to optimize management, strengthen internal control systems, and improve business performance in the era of big data. Technology promotes the development of productivity, and with the continuous maturity of computer technology, blockchain technology comes into being. Blockchain technology is of great significance in promoting industrial transformation and technological innovation. The development of the new era should strengthen the basic research of blockchain and regard blockchain as an important breakthrough for independent innovation of core technologies. This work first analyzes the necessity and feasibility of applying blockchain technology to the enterprise financial information fusion and sharing system. Then, it specifically describes how the blockchain technology is applied to the enterprise financial information fusion and sharing system, and improves the enterprise financial information fusion and sharing system through the blockchain technology. This forms an enterprise financial information fusion and sharing system between companies, which improves the limitations and disadvantages of the current financial sharing platform. Finally, using the AHP to analyze and compare the indicators of the traditional financial sharing model and the financial information fusion sharing model under the blockchain technology, it is concluded that the blockchain technology brings positive changes to the enterprise financial information fusion sharing model.
Glove Defect Detection Via YOLO V5

Malaysia is one of the biggest producers and exporters of gloves in the world. To meet and exceed the customer’s expectation, a predictive defect model is necessary to minimize the defect glove. There are three crucial parts to develop an effective defect glove detection model, which are data collection, model development and model evaluation. The data provided should be good quality, the algorithm for developing the model should reach high accuracy and high inference time due to the fast glove production line, and the developed model must compare to the other quality model to prove its robustness and effectiveness. This paper focuses on employing the YOLO V5 model for glove defect detection as well as investigating the efficiency of other several deep learning approaches. The dataset collected in this research was 493 images with three classes which are normal glove, tear glove and unstripped glove. To avoid overfitting due to the small amount of dataset, argumentation processes such as saturation, exposure and noise were applied to increase the dataset number to 1148 images. Data were then split to 70:20:10 for the training-validation-test ratio. The parameter setup was 100 epochs with 129 iterations. The YOLO V5 was compared with Scaled YOLO V4, Detectron2 and EfficientDet by the training time, model size, accuracy, and inference time. In conclusion, the best model was YOLO V5 because it reached the lowest training (0.259 hour) and inference time (0.0095 seconds), smallest model size (14418kb) and highest accuracy (mAP = 0.9951).
Reliable Service -Oriented Architecture for Nasa ’s Mars Exploration Rover Mission

CIPs in their laboratories, homes, and offices.Users are securely connected through the internet.Since the mission took place at the time of Mars, the CIP displayed current time in different time zones of the planet Mars and Earth and introduced staff and event schedules with Martian time scales.In this paper, we present the MER and CIP mission summaries.We show how the CIP helped to meet some of the mission needs and how people used it.We discuss the criteria for choosing its architecture, web services (current time ratio between planet Earth and planet Mars, conversion of Martian (Sol) and Earth Day, and direct connection to planet Mars) and describe how developers made the software so reliable.CIP reliability did not come by chance, but was the result of several key design decisions.
Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports

We aim to reduce overfitting and model overconfidence by distilling the knowledge of an ensemble of deep learning models into a single model for the classification of cancer pathology reports.We consider the text classification problem that involves 5 individual tasks. The baseline model consists of a multitask convolutional neural network (MtCNN), and the implemented ensemble (teacher) consists of 1000 MtCNNs. We performed knowledge transfer by training a single model (student) with soft labels derived through the aggregation of ensemble predictions. We evaluate performance based on accuracy and abstention rates by using softmax thresholding.The student model outperforms the baseline MtCNN in terms of abstention rates and accuracy, thereby allowing the model to be used with a larger volume of documents when deployed. The highest boost was observed for subsite and histology, for which the student model classified an additional 1.81% reports for subsite and 3.33% reports for histology.Ensemble predictions provide a useful strategy for quantifying the uncertainty inherent in labeled data and thereby enable the construction of soft labels with estimated probabilities for multiple classes for a given document. Training models with the derived soft labels reduce model confidence in difficult-to-classify documents, thereby leading to a reduction in the number of highly confident wrong predictions.Ensemble model distillation is a simple tool to reduce model overconfidence in problems with extreme class imbalance and noisy datasets. These methods can facilitate the deployment of deep learning models in high-risk domains with low computational resources where minimizing inference time is required.
A Hierarchical Heterogeneous Graph Attention Network for Emotion-Cause Pair Extraction

Recently, graph neural networks (GNN), due to their compelling representation learning ability, have been exploited to deal with emotion-cause pair extraction (ECPE). However, current GNN-based ECPE methods mostly concentrate on modeling the local dependency relation between homogeneous nodes at the semantic granularity of clauses or clause pairs, while they fail to take full advantage of the rich semantic information in the document. To solve this problem, we propose a novel hierarchical heterogeneous graph attention network to model global semantic relations among nodes. Especially, our method introduces all types of semantic elements involved in the ECPE, not just clauses or clause pairs. Specifically, we first model the dependency between clauses and words, in which word nodes are also exploited as an intermediary for the association between clause nodes. Secondly, a pair-level subgraph is constructed to explore the correlation between the pair nodes and their different neighboring nodes. Representation learning of clauses and clause pairs is achieved by two-level heterogeneous graph attention networks. Experiments on the benchmark datasets show that our proposed model achieves a significant improvement over 13 compared methods.
Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the security threats and mitigation for AI-powered applications in NextG networks have not been investigated deeply in academia and industry due to being new and more complicated. This paper focuses on an AI-powered IRS implementation in NextG networks along with its vulnerability against adversarial machine learning attacks. This paper also proposes the defensive distillation mitigation method to defend and improve the robustness of the AI-powered IRS model, i.e., reduce the vulnerability. The results indicate that the defensive distillation mitigation method can significantly improve the robustness of AI-powered models and their performance under an adversarial attack.
AVOA-Based Tuning of Low-Cost Fuzzy Controllers for Tower Crane Systems

This paper proposes the African Vultures Optimization Algorithm (AVOA)-based tuning of low-cost fuzzy controllers for the payload position control of tower crane systems. The fuzzy controllers are built around first order discrete-time intelligent Proportional-Integral (iPI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. The parameters of the fuzzy controllers are optimally tuned using the recent metaheuristic AVOA, which solves an optimization problem with the cost function defined as the sum of squared control error multiplied by time, and its variables are the controller tuning parameters. The control system performance improvement is proved in terms of applying only five iterations of AVOA, and the comparison with other metaheuristic algorithms that solve the same optimization problem is carried out on the basis of real-time experimental results.
The Initialization of Flexible K-Medoids Partitioning Method Using a Combination of Deviation and Sum of Variable Values

This research proposed a new algorithm for clustering datasets using the Flexible K-Medoids Partitioning Method.The procedure is divided into two phases, selecting the initial medoids and determining the partitioned dataset.The initial medoids are selected based on the block representation of a combination of the sum and deviation of the variable values.The relative positions of the objects will be separated when the sum of the values of the p variables is different even though these objects have the same variance.The objects are selected flexibly from each block as the initial medoids to construct the initial groups.This process ensures that any identical objects will be in the same group.The candidate of final medoids is determined randomly by selecting objects from each initial group.Then, the final medoids were identified based on the combination of objects that produces the minimum value of the total deviation within the cluster.The proposed method overcomes the empty group that may arise in a simple and fast k-medoids algorithm.In addition, it overcomes identical objects in the different groups that may occur in the initialization of the simple k-medoids algorithm.Furthermore, the artificial data and six real datasets, namely iris, ionosphere, soybean small, primary tumor, heart disease case 1 and zoo were used to evaluate this method, and the results were compared with other algorithms based on the initial and final groups' performance.The experiment results showed that the proposed method ensures that no initial groups are empty.For real datasets, the adjusted Rand index and clustering accuracy of the final groups of the new algorithm outperforms the other methods.
Multi-Document Text Summarization Using Deep Belief Network

Recently, there is a lot of information available on the Internet, which makes it difficult for users to find what they're looking for. Extractive text summarization methods are designed to reduce the amount of text in a document collection by focusing on the most important information and reducing the redundant information. Summarizing documents should not affect the main ideas and the meaning of the original text. This paper proposes a new automatic, generic, and extractive multi-document summarizing model aiming at producing a sufficiently informative summary. The idea of the proposed model is based on extracting nine different features from each sentence in the document collection. The extracted features are introduced as input to the Deep Belief Network (DBN) for the classification purpose as either important or unimportant sentences. Only, the important sentences pass to the next phase to construct a graph. The PageRank algorithm is used to assign scores to the graph sentences. The sentences with high scores were selected to create a summary document. The performance of the proposed model was evaluated using the DUC-2004 (Task2) dataset using ROUGE more. The experimental results demonstrate that our proposed model is more effective than the baseline method and some state-of-the-art methods, Where ROUGE-1 reached 0.4032 and ROUGE-2 to 0.1021.