Reviewerly/exHarmony huggingface dataset:
Application of Tree Based Enhanced Stacking Ensemble Learning

Since it is difficult for machine learning algorithms, such as XGBoost, LightGBM, to extract feature interaction effectively and deep learning algorithms have high time complexity, a tree enhanced stacking integrated model is proposed in this paper, based on tree model feature generation theory and convolutional neural network(CNN) feature extraction theory. Firstly, to effectively extract feature interaction, tree-based-enhanced-stacking generates new features through tree model LightGBM and merges them with the original features. Secondly, to enhance the presentation ability, LightGBM is modified into ML LightGBM with hierarchical structure. Finally, ML_LightGBM, XGBoost, Random Forest and other models are introduced into the stacking integration framework to generate secondary data sets. To enhance the deep feature extraction ability of tree-based-enhanced-stacking, CNN model is used to extract features from the secondary data sets. Compared with ML_LightGBM, XGBoost, Random Forest in two data sets ppdai and default, the results show that the tree enhanced stacking model proposed in this paper has significantly improved in AUC, ACC, KS, loss and other indicators.
An Efficient Facial Expression Recognition System with Appearance-Based Fused Descriptors

Facial expressions have always been defined as the most efficient way to characterize emotions. Therefore, they have been used in different fields of application such as human-computer interaction. With the continuous progress in terms of image processing and machine learning techniques, several new methods are proposed each year. However, their performance still needs to be improved and this task remains challenging. In this paper, we propose an automatic emotion recognition system through facial expressions. We compare the use of two distinct image descriptors namely, Local Binary Pattern and Histogram of Oriented Gradients with two different dimensionality reduction techniques namely, Principal Component Analysis and Locally Linear Embedding. Moreover, we also combine both descriptors in order to improve the system efficiency. For the classification part of the system, we choose to use the multi-class Support Vector Machine classifier for its generalization capabilities to distinguish between the six basic emotions. Finally, we evaluate the performance of the proposed system using three different benchmark facial expression datasets namely, JApanese Female Facial Expression (JAFFE), Karolinska Directed Emotional Faces (KDEF) and Radboud Faces Database (RaFD) that yield promising results with 97.16%, 90.12% and 95.54%, respectively.
Context-Aware Deep Learning with Dynamically Assembled Weight Matrices

Deep neural networks are static by nature, meaning they use a single set of parameters to process all data samples. However, based on the availability of complex real-life data an emergent need for more dynamic networks that adapt to varying conditions seems apparent. To this end, the concept of context information and its integration in deep learning architectures is considered. Contrary to current practices contextual and feature information is considered to be vastly different in nature. A set of definitions and subsequent arguments are given in order to provide the necessary clarification regarding the interpretation of context with the purpose of using all information provide in a maximally efficient way. From this interpretation, a problem statement of context aware deep learning, is constructed and consequently linked to its multiple model and transfer learning solutions. These solutions, however, are in-efficient in their information usage since samples are spread across models. Based on the existence of this multiple model solution, a new approach which integrates contextual information directly into a single context-dependent model is proposed. This single model uses dynamically assembled weight matrices based on the contextual information to process each data sample individually, which allows for the single model to consume and learn from all samples. The corresponding training routine is constructed and evaluated on multiple benchmark problems. Starting with an artificially generated problem on which the methods' ability to model multiple linear classification problems concurrently is confirmed. Followed by a time series forecasting and image classification dataset which both represent different aspects of the introduced problem statement. Evaluations of the proposed method are done and compared to standard context aware implementations based on concatenation and gating. These standard methods implement context information by adding additional parameters in order to try modeling all interactions between the context information and the samples. However, the proposed approach integrates the contextual information directly into the network weights allowing parameter efficient modeling of dynamic contextual behaviour. In both cases the proposed solution outperforms its standard counterparts with significant margins in both evaluation metrics and parameter efficiency. Specifically, for the forecasting problem a mean absolute error improvement of 11 standard deviations and for the image classification an 8\% percent increase in classification accuracy is observed.
An Automatic Classification and Storage Method of Construction Images Based on YOLOv5

Computer vision has a wide range of application prospects in the construction industry, which can improve the management efficiency of the construction site to a large extent. However, the application of machine vision has relatively high requirements for images, and currently there is no good way to obtain images. Most researchers need to build their own databases when conducting research, which hinders the applications of computer vision in construction to a certain extent. A large number of image resources are generated on the construction site every day, but the image information is relatively complicated and messy, and difficult to be used effectively. Therefore, this paper proposes an automatic classification and storage method for construction images, which can effectively automatically classify and format large-scale images. The results show that the classification accuracy for helmets, workers, and excavators is about 70%, which can meet the needs of image classification and storage, and has good application prospects.
Smart Contract: Is it Really Smart in Construction?

A smart contract is a protocol that can self-execute when predefined conditions are met. This new technology is considered destructive and can transfer the construction industry. In Blockchain 2.0, the combined use of blockchain and smart contracts allows users to express business logic to achieve more advanced transactions. This research aims to critically analyze the challenges, progresses, and benefits of smart contracts in construction through a systematic literature review to address whether it is smart. The findings suggested that numerous progress had been made to address the challenges of smart contracts. Besides, the benefits of smart contracts have attracted the construction industry. The research findings can open the avenue for researchers and construction practitioners to understand the impacts of the salient features of smart contracts and determine appropriate application areas.
A Critical Review of Interaction Design Between Smart Home Devices and the Elderly

Smart home technologies are increasingly mature, which can achieve multiple functions such as real-time health monitoring, household device management, and hazardous events detection based on smart home systems. Smart home systems can provide the elderly in the aging society with promising solutions to maintain independence and quality of life. However, a comprehensive review of the interaction design between smart home devices and the elderly appears to be unavailable. This study aims to address this gap by systematically reviewing 107 relevant peer-reviewed journal papers published from 2000 to 2021. These papers were analyzed in terms of contributing journals and institutions, citations, keyword co-occurrence, keyword burst detection, and keyword term cluster using a scientometric approach. Results and discussion of this study uncover potential means of human-machine interaction design between smart home devices and the elderly and identify research trends for academics and practitioners in their future research and practice.
A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process model

This paper proposes a random finite set (RFS)-based algorithm to deal with the tracking problem of multiple non-rigid extended targets (MNRET) with irregular shapes in the presence of clutter, false alarms and missed detection. The extensions of targets are modelled by spatiotemporal Gaussian process, which is augmented with internal reference point (IRP) modelling the kinematics to construct the state of MNRET. The probability hypothesis density (PHD) filter is employed to propagate the first-order moment of the RFS of MNRET. A suitable predicted likelihood of MNRET for the optimal partition is given, and the filter recursion is presented along with the necessary approximations and assumptions. More importantly, the closed-form implementation and its corresponding smoothing filter are derived by converting the posterior density to Gaussian mixture form. Simulation results show the robustness and effectiveness of the proposed algorithm.
Blind Image Inpainting with Sparse Directional Filter Dictionaries for Lightweight CNNs

Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of image quality and run time. However, neural network strategies typically lack a theoretical explanation, which contrasts with the well-understood theory underlying model-based methods. In this work, we leverage the advantages of both approaches by integrating theoretically founded concepts from transform domain methods and sparse approximations into a CNN-based approach for blind image inpainting. To this end, we present a novel strategy to learn convolutional kernels that applies a specifically designed filter dictionary whose elements are linearly combined with trainable weights. Numerical experiments demonstrate the competitiveness of this approach. Our results show not only an improved inpainting quality compared to conventional CNNs but also significantly faster network convergence within a lightweight network design. Our code is available at https://github.com/cv-stuttgart/SDPF_Blind-Inpainting .
Geometric Transformations

Geometric operations modify the spatial (positional) relationships among the objects in an image and include translation, rotation, scaling, and more general actions that warp the contents of an image. A geometric operation simply copies an image in such a way that the pixel locations change. The spatial mapping function specifies where each input pixel moves to in the output image and thus defines the geometric operation and the effect it will have on the image. A mapping of input quadrilaterals into output rectangles is often used to specify complex transformations. Interpolation is used when output pixels do not fall upon the sampling grid. Nearest neighbor, bilinear, cubic, and higher-order interpolation algorithms are common choices. Geometric operations are useful to remove spatial distortion, to bring different images of the same specimen into registration, and to stitch together overlapping images of large specimens.
Image Digitization and Display

Digitization is the process that generates a digital image by measuring and quantizing an optical image. Display is the process of generating a visible image by interpolating a digital image. A number of factors affect the quality of the digital and displayed images produced. The different components in the imaging chain should be well matched to each other and to the projects that the system will be expected to support. Factors that can degrade image quality in the digitizing process are loss of detail, noise, aliasing, shading, photometric nonlinearity, and geometric distortion. Factors that affect displayed image quality include displayed image size (native resolution), pixel size and shape, photometric linearity, and noise. The resolution of a microscope image digitizer is limited by the pixel size and spacing and by the wave nature of light (the diffraction limit). Noise tends to reduce the effective photometric resolution of both digitizers and displays. Geometric distortion affects the size, shape, and position of objects in an image.
Object Measurement

This chapter covers the basic techniques for extracting quantitative measurement data from the objects contained in a digital image. Object measurements are normally computed from the binary representation of a segmented object or from the gray level intensity distribution within the object boundary. They quantify the size, distance, pose, shape, intensity, and texture of an object. Size measures include the area and perimeter of objects. Pose measures the location and orientation of objects. Shape measures include circularity, thinness, elongation and measures derived from moments. Intensity measures include optical density, contrast, and methods based on the gray level histogram. Object texture can be measured using statistical or power spectrum approaches. Objects can also be described using compact descriptors such as a chain codes, the medial axis transform, and graphs.
Morphological Image Processing

Morphological image processing operates by probing an image with a structuring element and either filtering or quantifying the image according to the manner in which the structuring element fits each object in the image. It is an alternative to classical methods for image enhancement and segmentation, and for object measurement and classification. Morphological processing has applications in image filtering, segmentation, and pattern recognition for both binary and grayscale images. Primitive operators that have intuitive physical analogies can be combined to create complex operators. Morphological processing is particularly well suited for use in digital computers. Useful techniques include erosion, dilation, opening, closing, filtering, skeletonization, and top-hat, edge-off, and watershed transforms.
Improving the detection of community smells through socio‐technical and sentiment analysis

Abstract Open source software development is regarded as a collaborative activity in which developers interact to build a software product. Such a human collaboration is described as an organized effort of the “social” activity of organizations, individuals, and stakeholders, which can affect the development community and the open source project health. Negative effects of the development community manifest typically in the form of community smells, which represent symptoms of organizational and social issues within the open source software development community that often lead to additional project costs and reduced software quality. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational, social, and emotional aspects to provide an automated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational–social and emotional symptoms that characterize the existence of community smell instances in a software project. We build a multi‐label learning model to detect 10 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi‐label problems into several single‐label problems, which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 143 open source projects. The statistical tests of our results show that our approach can detect community smells with an average F‐measure of 93%, achieving a better performance compared to different state‐of‐the‐art techniques. Furthermore, we investigate the most influential community‐related metrics to identify each community smell type.
Research on UAV Teaching Application and Technological Innovation with 5G Technology and Development of High-pole Throwing Hydrangea

The technical innovation of high pole throwing Hydrangea is increasing and this study compares with the existing projection technology theory from two aspects of teaching and competition, promotes the corresponding improvement of competition rules and referee law enforcement. This paper studies (1) The history and culture of Ethnic Zhuangs' embroidered ball and the development process of modern throwing embroidered ball by using the method of literature review, (2) Studies the technical characteristics of high-pole throwing Hydrangea by using the method of mathematical analysis, (3) Compares the landing points of two main Hydrangea throwing techniques after passing through the circle by using UAV aerial photography of Hydrangea flying tracks, (4) The Self-learning Systems and Pattern Recognition and Exploitation for Multimedia Asian Information Processing in which the disputes existing in the process of competition rules and referee enforcement. The research shows that: the competition rules and the referee's decision are controversial, the existing projection teaching technology lacks practical guidance for high-level competition, innovative projection technology is divided into three matching situations, the best shot angle range is 64°&lt;α &lt;72°; and the shot speed range is 13.04m/s &lt; V0 &lt; 13.70m/s. Suggestions: According to the observation and analysis of the competition scene using UAV and Internet and other science and technology, improve the teaching and competitive level of Hydrangea throwing, improve the competition rules, enhance the referee's law enforcement ability so as to improve the competitive level of national sports, and take the standardized and international road to inherit the excellent national sports culture, and provide new development ideas for the popularization and promotion of national traditional sports and the inheritance of excellent national traditional sports culture.
Breaking CaptchaStar Using the BASECASS Methodology

In this article, we present fundamental design flaws of CaptchaStar. We also present a full analysis using the BASECASS methodology that employs machine learning techniques. By means of this methodology, we find an attack that bypasses CaptchaStar with almost 100% accuracy.
A Statistical Approach to Analyzing Turkish Morphology

Morphological analyzers are essential components of Turkish language processing pipelines due to complex and rich morphology of Turkish language. Previous work usually focuses on two-level description of Turkish morphology, thus leads to lexicon and two-level rules oriented FST implementations. However, two-level based implementations are not robust to spelling errors and new loan words entering the Turkish lexicon. In this paper, we introduce a statistical approach to analyzing Turkish word forms by training and comparing two seq2seq models on our annotated dataset. We approach analyzing Turkish word forms as a machine translation problem, where the source sequence is a Turkish word form and the target sequence is a sequence of morphemes. Evaluating on three testsets of informal written language word forms, we show that our approach offers a robust approach to analyzing the Turkish words and proposes a strong baseline for sequential modeling of Turkish morphology.
Automatic Modulation Classification Based on Machine Learning

Automatic Modulation Classification (AMC) is a rapidly evolving technology, which can be employed in software defined radio structures, such as for military communication. Machine Learning can provide novel and efficient technology for modulation classification, especially for systems working in low Signal to Noise Ratio (SNR). For this work, a dynamic modulation classification system without phase lock is trialed. The signals are captured with different SNR and duration. Traditional Machine Learning based on the mathematical features is compared with Deep Learning based on the constellations. Based on these two methods, a hybrid model is provided. This model involved the novel Deep Learning at first and the feature classification as a supplement, which achieves good performance at low SNR area.
A Lightweight Blockchain-Based Trust Management Framework for Access Control in IoT

Blockchain technology has provided lots of significant benefits in terms of security, auditability, immutability and anonymity. Following on from these remarkable features, blockchain technology has been incorporated with lots of non-monetary applications including the Internet of Things (IoT). On the other hand, blockchain can be used for trust management for IoT. However, a major challenge is to find an appropriate light weight consensus algorithm that can be implemented in IoT devices, which have suffered limited computational resources. Building upon the idea of using blockchain as the basic framework, this chapter proposes a lightweight blockchain-based trust management framework that is suitable for IoT devices. Our framework is built upon high resource devices to form the underlying Peer-to-Peer (P2P) network. In addition, we use the smart contract mechanism to generate a trustworthy environment for IoT devices. With the trust evaluation approach, we propose a reputation-based consensus algorithm which can significantly decrease the mining time. Moreover, the verification mechanism can incorporate with the reputation approach to reduce the processing time of block verification. Simulations have demonstrated that our framework achieves low delay time, high Transactions Per Second (TPS) and less processing time compared with relevant baselines. More importantly, our framework shows that it is resilient to several security attacks in blockchain systems.
Review of Cyber Security for Power Trading and Communication Systems

The trading and communication systems of the wholesale energy market are an essential part of critical national infrastructure. If adversaries were to exploit the vulnerabilities in the wholesale energy trading and communication system, they could disrupt electricity generation and supply nationally, resulting in a devastating chain reaction. In this context, this study provides a review of deployments of security mechanisms for energy market trading and communication systems. This helps to understand the current security controls and challenges better and shines a light on potential research that can be conducted to make trading and communication systems more secure. This review is categorised into four themes: (1) security technologies that can be applied to energy trading and call audit systems, (2) blockchain technology that can be applied to protect energy trading and auditing services, (3) communication technology (voice over IP and video conferencing) that operates in the cloud, and (4) network performance and security management for voice over IP and video conferencing systems. This review investigates the use of blockchain technology that has increasingly emerged in a microgrid (peer-to-peer) energy trading and reveals a gap in using blockchain for macrogrid national energy trading. This study also emphasises the importance of balancing network security and performance when systems are hosted in the cloud.