Reviewerly/exHarmony huggingface dataset:
Match Analysis 4.0 with Big Data: From Studies to Experiments

The paper provides an overview of the development of match analysis in recent years. Based on technological developments in sensor technology, especially in the field of commercial football, coupled with changes in media preparation of sports games, new types of performance evaluation have been established. The massive increase in available data consolidated under the term 'big data' makes it possible to calculate more complex performance indicators. Based on the positional data of the individual players and the ball, analyses can be significantly faster than with video-based material. Whereas in the past, the focus was on analyzing frequencies of certain game events, it is now possible to calculate specific metrics. These metrics make it possible to portrait the performance of teams and individual players as well as the interaction dynamics between teams. However, it is shown that the actual significance for the performance of many of these new performance indicators (KPIs) is often still insufficiently scientifically proven. In one of the largest big data field studies conducted so far, a big data field study defined various KPIs in professional football and validated them in the first steps. The paper offers an outlook to make hypotheses empirically verifiable through field experiments based on positional data. Such an experimental paradigm would be appealing, as it would be able to generate real data in an 11 vs. 11 football game, theory-guided (not post-hoc testing), reliable, objective with corresponding KPIs, and extremely fast.
Four-intensity measurement-device-independent quantum key distribution protocol with modified coherent state sources

We propose a scheme of double-scanning 4-intensity MDI-QKD protocol with the modified coherent state (MCS) sources. The MCS sources can be characterized by two positive parameters, ξ and c. In all prior works, c was set to be the same for all sources. We show that the source parameter c can be different for the sources in the X basis and those in the Z basis. Numerical results show that removing such a constraint can greatly improve the key rates of the protocol with MCS sources. In the typical experiment conditions, comparing with the key rates of WCS sources, the key rates of MCS sources can be improved by several orders of magnitude, and the secure distance is improved by about 40 km. Our results show that MCS sources have the potential to improve the practicality of the MDI-QKD protocol.
Blockchain-Based Encrypted Image Storage and Search in Cloud Computing

In the traditional cloud computing system, existing ranked image search mechanisms will leak the similarity of search results, even cannot prevent malicious cloud servers from tampering with or forging results. Therefore, with the help of blockchain technology, we propose an encrypted image storage and search system. A threshold encryption method based on the matrix similarity transformation theory is designed to protect the similarity of search results. Besides, a hybrid storage structure based on blockchain and cloud server is designed to ensure the correctness and completeness of search results. Finally, we implement the prototype in Python and Solidity, and conduct performance evaluations on local Ethereum client. The theoretical analysis and performance evaluations using the real-world dataset demonstrate that our proposed system is secure and feasible.
Design and Implementation of an Intelligent Classification and Reliability Prediction Tool based on Cloud Platform

Reliability prediction is of great importance for electronic products design, and an indispensable technique to ensure that electronic components, equipment and the overall system meet their required reliability targets as well. Present methods, handbooks, standards and software tools of reliability prediction were studied and compared. For industrial and commercial electronic equipment applications, the development and demand of reliability prediction tools were analyzed in this paper. Using new-generation information technologies such as cloud computing and machine learning, a lightweight component reliability prediction tool was designed based on the Spring Cloud microservice architecture. In addition, the naive Bayesian algorithm was adopted to achieve intelligent components classification, which enhances prediction efficiency significantly. An example was offered, which verified the effectiveness of the cloud-based reliability prediction tool. Its characteristics of low-cost, rapidly-deployed and easy-to-use were shown, which differ it from traditional prediction tools, and make it suitable for commercial companies and electronic products.
Model-Driven Development of Groupware Systems

Building Collaborative systems with awareness (or groupware) is a very complex task. This article presents the use of the domain specific language CSSL v2.0 - Collaborative Software System Language -built as an extension of UML, using the metamodeling mechanism. CSSL provides simplicity, expressiveness and precision to model the main concepts of collaborative systems, especially collaborative processes, protocols and awareness.The CSSL concrete syntax is defined via a set of editors through which collaborative systems models are created. According to the MDD methodology, models are independent of the implementation platform and are formally prepared to be transformed. The target of the transformation is a web application that provides a set of basic functions that developers can refine to complete the development of the collaborative system. Finally, evaluation, validation and verification of the language is performed, determining that the CSSL tools allow developers to solve central aspects of collaborative systems implementation in a simple and reasonable way.
Prediction, Detection and Recurrence of Breast Cancer Using Machine Learning Based on Image and Gene Datasets

Health care gives the life expectancy to each individual. Good health services procure the health of individuals. Presently, in 2021, the Health Care Index of India is 66.25 which is very low as compared to the expected HCI which should be 118.68. There are two type of diseases which affect the health of a person (a) communicable and (b) non-communicable. Cancer is a non-communicable disease which is a leading cause of death. This disease is caused by the uncontrolled division of abnormal cell in a part of body. There are various types of cancer like lung, breast, pancreas, ovary, colon and rectum. Among them, breast cancer is the second major cause of death in female. According to GLOBOCAN report 2018, breast cancer incidents around the world are 2,088,849, and in India, it is 162468. As per the National Breast Cancer Organization, breast cancer stages are of four types. In this review, we have mentioned about the noble technique of multivariant dataset and semi-supervised technique combined with hybrid feature selection method for the detection, prognosis and recurrence of breast cancer. There are two types of data through which breast cancer can be detected: one is genes and another one is images. These types can be analyzed by the collection of datasets for getting the best result for preventing the breast cancer. Genes in our DNA are the basic cause of mutation which causes breast cancer, and with the help of images, we get the scan of tumor. An image can be benign or malignant. Various researchers have worked on single dataset only which also have a drawback of false-positive results, but no researcher has used the multivariant technique to predict breast cancer, so there is a need to use the multivariant dataset together which will be a combination of images and genes dataset with feature selection method of machine learning to increase the level of accuracy and to predict the breast cancer. This review paper is divided into two parts, first part consists of image dataset for the diagnosis, prediction and recurrence of breast cancer, and second part consists of genes dataset with different machine learning techniques used by different researchers. Aim of this review paper is to analyze which machine learning technique is suitable when we combine two different datasets together with single feature selection method on both datasets and which will improve the accuracy rate for the detection, prediction and recurrence of breast cancer in females.
Effective motivational factors and comprehensive study of information security and policy challenges

The protection of information is a crucial feature and plays an important part in securing the business of a company. To preserve their value and credibility, organizations are expected to protect their knowledge and data. The goal of this chapter's comprehensive literature review is to incorporate information security administration as an integrated approach to balance information security policy with organizational goals. That review established the need for such an implementation of a comprehensive information security governance (ISG) structure that (1) ties the aims and security of the organization, (2) discusses each area of planning, control, and enforcement, (3) ensures the adherence of procedures and policy guidelines, and (4) guarantees continuous monitoring and enforcement. A literature search revealed the key obstacles in developing a curriculum for information security management. The study established seven areas with 27 crucial achievements in establishing an appropriate system for information security management.
A unified framework of deep unfolding for compressed color imaging

Traditional iterative-based reconstruction algorithms for compressed color imaging often suffer from long reconstruction time and low reconstruction accuracy at extreme low-rate subsampling. This paper proposes a model-driven deep learning framework for compressed color imaging. In the training step, extract the image blocks at the same position of the R, G, and B channel images as the ground truth, and then, singular value decomposition is performed on the measurement matrix to obtain the optimized measurement matrix and low-dimensional measurements; afterward, the ground-truth and optimized measurements are utilized to construct a large amount of training data pairs to train an ‘end-to-end’ deep unfolding model for compressed color imaging. In the test step, the single pretrained model is used to reconstruct high-quality images from optimized low-dimensional compressed measurements for each channel and synthesize a color image. Numerical experiments demonstrate that our proposed unified framework can achieve high accuracy and real-time reconstruction for the color image at extremely low subsampling rate.
Sufficiently Informative and Relevant Features: An Information-Theoretic and Fourier-Based Characterization

A fundamental challenge in learning is the presence of nonlinear redundancies and dependencies in the data. To address this, we propose a Fourier-based approach to characterize feature redundancies, in unsupervised learning, and feature-label dependencies, in the supervised variant of the problem. We first develop a novel Fourier expansion for functions (more generally stochastic mappings) of correlated binary random variables. This is a generalization of the standard Fourier expansion on the Boolean cube beyond product probability spaces. As an important application of this analysis, we investigate learning with feature subset selection. In the unsupervised variant of this problem, we characterize feature redundancies via the Shannon entropy and group the features into sufficiently informative and redundant. Then, we make a connection to the proposed Fourier expansion and derive an upper bound on the joint entropy. Based on that, we propose a measure to quantify feature redundancies and present an unsupervised learning algorithm. We test our method on various real-world and synthetic datasets and demonstrate improvements on conventional unsupervised feature selection techniques. Then, we investigate the supervised feature subset selection and reformulate it in the Fourier domain. Bridging the Bayesian error rate with the Fourier coefficients, we demonstrate that the Fourier expansion provides a powerful tool to characterize nonlinear feature-label dependencies. Further, we introduce a computationally efficient measure for selecting relevant features. Via a theoretical analysis, we show that our proposed measure finds provably <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">asymptotically optimal</i> feature subsets. Lastly, we present an algorithm based on this measure and via numerical experiments demonstrate its improvements on various supervised feature selection algorithms.
Comparative Analysis of Different Machine Learning Algorithms in Classification

This paper aims to find a relatively better method to deal with the classification problems of different data sets by exploring other different machine learning methods. The main research direction is to analyze the performance difference of the machine learning method in the data classification of Gaussian Distribution, Images, Voice, Text, and Excel to find the better classification method for the corresponding data set. This study mainly used three machine learning methods: k-nearest neighbors algorithm (KNN), Support-vector machine (SVM), and neural network. The final test results show that for relatively simple discrete data, such as Excel, KNN has the best effect. In addition, the SVM and KNN methods for Images data are inferior to neural networks. For the remaining two data classifications, these three machine learning methods have reasonable accuracy rates.
Large Scale Data Anonymisation for GDPR Compliance

Abstract General Data Protection Regulation (GDPR) has been in place since May 2018 to give EU citizens more control over their personal data, applying principles like security and privacy by design. One of the most powerful tools to allow data processing while being compliant with data protection regulations is anonymisation, a procedure that consists of transforming data in such a way that makes no longer possible the re-identification of the data subjects. This chapter describes how anonymisation can be performed at a large scale, addressing common challenges to become GDPR compliant.
A knowledge-enriched ensemble method for word embedding and multi-sense embedding

Representing words as embeddings has been proven to be successful in improving the performance in many natural language processing tasks. Different from the traditional methods that learn the embeddings from large text corpora, ensemble methods have been proposed to leverage the merits of pre-trained word embeddings as well as external semantic sources. In this paper, we propose a knowledge-enriched ensemble method to combine information from both knowledge graphs and pre-trained word embeddings. Specifically, we propose an attention network to retrofit the semantic information in the lexical knowledge graph into the pre-trained word embeddings. In addition, we further extend our method to contextual word embeddings and multi-sense embeddings. Extensive experiments demonstrate that the proposed word embeddings outperform the state-of-the-art models in word analogy, word similarity and several downstream tasks. The proposed word sense embeddings outperform the state-of-the-art models in word similarity and word sense induction tasks.
RFNet: Reverse Fusion Network With Attention Mechanism for RGB-D Indoor Scene Understanding

RGB-D indoor multiclass scene understandingis a pixelwise task that interprets RGB-D images using depth information to improve the RGB features for higher performance. We propose a novel asymmetric encoder structure for RGB-D indoor scene understanding that uses a reverse fusion network (RFNet) with an attention mechanism and a simplified feature extraction block. Specifically, the pre-trained ResNet34 and VGG16 networks (two asymmetric input streams) are used as the backbone for the information extraction paths as well as additive fusion and attention modules that further enhance network performance. The strong feature extraction ability of classical networks and the advantages of two-way reverse fusion enable this novel semantic segmentation network to narrow the gap between low- and high-level features, such that the features are better merged for segmentation. We achieved segmentation performances (MIoU) of 53.5% and 50.7% on the SUN RGB-D and NYUDv2 datasets, respectively, thereby outperforming other state-of-the-art approaches.
Estimation of roof photovoltaic potential and carbon reduction potential based on semantic segmentation method

Since industrialization, the global surface temperature has been on an upward trend, and so far it has had a serious impact on the global ecosystem and socio-economic environment. Therefore, it is necessary to reduce carbon emissions through global joint efforts, maintain the stability of the surface temperature not exceeding 2°C, and achieve global carbon neutrality as soon as possible. Solar energy is the cleanest renewable energy and has good prospects for sustainable development in the future. Installing solar photovoltaic (PV) systems on the roofs of buildings has become the most extensive method of utilizing solar energy. In this study, we used the semantic segmentation method to estimate the solar photovoltaic potential based on identifying roofs from remote sensing images, and then combined the photovoltaic module parameters derived from building features with solar radiation data to evaluate the solar photovoltaic potential. We did experiments in Haizhou Economic Development Zone, Lianyungang City, Jiangsu Province. The results show that the number of available roofs of the photovoltaic system is 671, the available roof area is 925,810 square meters, and the annual photovoltaic power generation potential of the study area is 119,300.97 MW$\cdot$h/year, which has a large solar photovoltaic potential. The method to perform precise calculation of specific rooftop solar PV potential developed in this study will guide the formulation of energy policy for solar PV in the future.
Automatic Identification of High Impact Bug Report by Test Smells of Textual Similar Bug Reports

Bug reports are written by the software stakeholders to track software defects and vulnerabilities. Since Software Quality Assurance (SQA) resources are limited, developers tend to resolve High-Impact Bugs (HIB) in advance. Prior research identified HIBs by analyzing the textual information in bug reports. However, they only consider textual information instead of the root cause of bugs, such as code quality. Since prior study revealed software test smells (i.e., sub-optimal test code implementation) are related to bug proneness, we intend to measure test smell distribution in textual similar bug reports to identify HIB reports. We first construct an effective model, which outperforms the baseline by 29.3% in terms of AUC-ROC. Secondly, we use SHAP to compute the importance of test smell features. Finally, we conduct an empirical survey to discuss the relationship between test smell and HIB reports. Result shows that Assertion Roulette and Conditional Test Logic test smell are important factors in distinguishing the types of bug reports.
Learning language to symbol and language to vision mapping for visual grounding

Visual Grounding (VG) is a task of locating a specific object in an image semantically matching a given linguistic expression. The mapping of the linguistic and visual contents and the understanding of diverse linguistic expressions are the two challenges of this task. The performance of visual grounding is consistently improved by deep visual features in the last few years. While deep visual features contain rich information, they could also be noisy, biased and easily over-fitted. In contrast, symbolic features are discrete, easy to map and usually less noisy. In this work, we propose a novel modular network learning to match both the object's symbolic features and conventional visual features with the linguistic information. Moreover, the Residual Attention Parser is designed to alleviate the difficulty of understanding diverse expressions. Our model achieves competitive performance on three popular datasets of VG.
Inference of Geological Material Groups Using Structural Monitoring Sensors on Excavators

In mining, correctly characterising geological grades is very important as it directly relates to ore quality assessment and downstream processing. Significant effort has been placed into creating the geological block models for the mine site, through site exploration and in-lab assay analysis. Yet the blasting, digging and various processing can cause non-negligible movement of material and invalid prediction of material content due to the now obsolete model. On the other hand, it is well known to excavator operators that digging effort is closely related to the hardness or lumpiness of the material underneath, and therefore this may be exploited to indicate the material type post blasting. This paper proposes a method that can automatically infer the geological material types of mined material during excavation at the digging location by applying machine learning methods to the force, energy and kinematic information collected from sensors mounted on the diggers. Therefore, the digging equipment is being used as a sensor for this purpose. Conversely, we also show how knowledge of material type can lead to accurate prediction of digging effort category. Further, per bucket material information can be utilised throughout the material movement pipeline. A case study was conducted in a test region at Pilbara iron ore deposit situated in the Brockman Iron Formation of the Hamersley Province, Western Australia. Initial results show strong level of inter-dependency between sensor measurements and excavated material type, demonstrating the potential of material inference at the bucket level.
Detection System for Construction Image Classification Based on Deep Learning Models

Nowadays, image recognition and classification methods are widely used in various industries such as automatic driving, face recognitions. However, the classification of constructions is rarely studied in the past. In recent years, Image recognition and classification are mainly based on Convolutional neural networks (CNNs) which is one of the representative algorithms of deep learning. CNNs can greatly reduce the error rate of image classification. Therefore, we proposed a self-defined CNN to do image recognition and classification for some images of buildings, roads, bridges and so on. We collected pictures from Google image manually as our dataset. For our model, we first constructed the convolutional layer with filter size of 32 kernels, and then created a pooling layer to extract features. We repeated this process two more time and gradually adding the number of kernels to reduce sampling and the value of training. In the end, we constructed a fully-connected layer. Lastly, our method can achieve 0.2034 in loss, 0.9065 in accuracy, 0.8675 in validation loss and 0.7059 in validation accuracy, which is a relatively high level and acceptable in many real applications.