Reviewerly/exHarmony huggingface dataset:
A New Time Series Outlier Pattern Detection Approach based on Markov Model

Time series pattern outlier represents a pattern with abnormal behavior that is significantly different from other patterns in the time series and can induce a bias in the decision-making process related to design, operation, and management. This research focuses on the problem of detecting anomalous patterns from time series data. The main consideration is the fact that normal pattern can be modeled by some statistical models and anomalous patterns cannot. Combined with characteristics of time series data, this research develops an effective and accurate outlier pattern detection approach SAX_ePST to detect anomalous patterns in time series. Experiments indicate the proposed methods are fast and correctly identifying anomalous pattern.
The application of visual technology in the measurement of parts

In order to solve the efficiency problem of on-line shape and position tolerance quality inspection in industrial production, especially for the case that the surface to be tested is rigid plane, based on the relative fixed position of the camera and the part to be measured, the relationship between pixel and actual physical size is obtained by geometric optics principle on the basis of restraining lens distortion, and finally the machine vision is used The surface image information of the part to be measured is obtained by the sensing technology, and the key features are extracted to complete the calculation and analysis. The results show that the measurement period is 7.12ms, the measurement accuracy of part size is 0.1mm. The system has low requirements for hardware resources, and can be deployed online to realize full inspection of parts. The comprehensive performance is better than the current contact parts measurement. It can be seen that this method can realize on-line shape and position tolerance detection of parts, and has a great help to improve the quality of parts production.
A Method for Stabilization of Ground Robot Path Controlled by Airborne Autopilot with Time Delay

The paper addresses the problem of visual navigation of ground robots using a camera positioned at a certain elevation above the confined area. Also, the methods of the stability theory of delay differential equations are used in the study of an actual engineering problem of a ground robot autonomous path. We give a description of autopilot for the stabilization of the ground robot autonomous motion according to desirable path. Indeed, large time delay exists in obtaining by autopilot current information about robot position and orientation, because of big data processing by vision-based (visual) navigation system. Despite this fact, we can prove that autopilot can guarantee a stable desirable path. We demonstrate how to create an appropriate controlling signal for the described information time delay and calculate control parameters for case of polygonal chain path. This path consists of linear motion along with line segments and rotations in vertices.
Whole-chain supervision method of industrial product quality and safety based on knowledge graph

With the rapid improvement of China's industrial production level, there are an increasing number of industrial enterprises and kinds of products. The quality and safety supervision of industrial products is an important step to ensure people's livelihood safety. The current supervision includes a number of processes, such as risk monitoring, public opinion analysis, supervision, spot check and postprocessing. The lack of effective information integration and sharing between the above processes cannot support the implementation of whole-chain regulation well. This paper proposes a whole-chain supervision method of industrial product quality and safety based on a knowledge graph, which integrates massive and complex data of the whole chain and visually displays the relationships between entities in the regulatory process. This method can effectively solve the problem of information islands and track and locate the quality problems of large-scale industrial products.
Pitfalls and Challenges of Blockchain in Supply Chain and Logistics

The potential impact of block chain on supply chain management has been explored by many authors, and indeed, many articles in the popular press extol the benefits of blockchain in supply chain management. In simple terms, blockchain is a distributed ledger system, or a record book, except that it is not maintained by a single person but by anyone who is interested in keeping the records. Since blockchain is a new and revolutionary concept in technology, more techies are interested in it. However, it also has certain disadvantages. The purpose of this study is to explain the challenges and pitfalls of blockchain in supply chain and logistics. Using a systematic literature review method, this chapter identifies 31 publications that discuss the challenges and pitfalls associated with blockchain in supply chain and logistics.
Heterogeneous Graph Representation for Industry Application

Heterogeneous graph (HG) representation is closely related with the real-world applications, as heterogeneous objects and interactions are ubiquitous in many practical systems. HG representation methods deployed in real-world system should consider capturing the complex interactions among objects as well as solving the unique challenges existing in real-world systems, such as large-scale, dynamics, and multi-source information. In this chapter, we focus on summarizing the industrial-level applications with HG representation. Particularly, we introduce several well deployed systems that have demonstrated the success of HG representation techniques in resolving real-world applications, including cash-out user detection, intent recommendation, share recommendation, and friend-enhanced recommendation. For industrial-level applications, we pay more attention on two key components: HG construction with industrial data and graph representation techniques on the HG.
Quantification of Mineral Resources

The term “Mineral Resources” has changed significantly in meaning especially since the turn of the millennium. Until the mid 1990s, a resource simply implied the existence of mineralization which might become potentially economic. In more recent times, a Mineral Resource cannot be declared unless there is some justification for expecting the deposit to be profitably exploited as of the date of the study. The term “Reserve” is now applied to a resource which can be proven to be economic through the inclusion of all mining and economic parameters from access to rehabilitation. Since the economic climate is extremely variable, this discussion will concentrate on the “technical” quantification of in situ mineralization. The quantification methods discussed here relate to those parameters for which an average (or total) is a suitable estimator. However, the techniques can also be adapted for categorical variables, such as lithology. Some variables from deposits such as oil and gas, which depend on flow characteristics and connectivity, are not suitable for these techniques. A reading list is included and many informative resources are available on the internet (cf. www.kriging.com).
Digital Competence of Higher Education Learners in the Context of Covid-19 Triggered Online Learning

The spread of COVID-19 caused wide scale disruptions in the educational sector across the globe. Digital education involving use of digital tools, virtual platforms and online learning is viewed as one of the viable alternatives to continue the academic activities in such a backdrop. Higher education institutions have largely switched to this new mode of learning and are still continuing with the digital mode in many parts of the world, owing to the enduring pandemic threat. However, competence of the learners to engage effectively in the online classes and the influence of their socio-economic background on this competence were not given adequate consideration. The present study was an effort to glean into these aspects as they are crucial to the success of digital education. The study was conducted 833 students comprising of graduate, post graduate and Ph.D. students of an Agricultural University to assess their digital competencies and factors affecting an effective participation in online classes. The Digital Competence Framework 2.0 of EU Science Hub (DIGCOMP) was adapted and used for this study. Our findings suggest that the learners have a satisfactory level of competence in most of the aspects of digital competence. The results of a Tukey difference in the mean test reveals that digital competence among the learners vary significantly based on their gender, economic profile and academic level. This finding can be attributed to the difference in their socio- economic background, which confirms digital divide among learners. Our findings have implications for the design of policies for digital higher education and institutional management for ensuring effective learner participation particularly for the higher education institutions in developing countries.
Effect of Lean Software Development on the Small Organizations Using LSD Approach

Choosing an appropriate SDLC (software development life cycle) model is an issue that is being faced by the software development organizations initially, that makes software development organizations select an appropriate SDLC model to intercept the change and make projects successful for the user, client, and stakeholders. This leads to a software development model that meets the project management triple constraints for successful project management. LSD (lean software development) is one of them which is being used for software development is now a days. It&#x2019;s a service-oriented approach that is an operational and implementation-based choice for development. The objective of this paper purposes to examine the surviving writings on LSD characteristics, their strengths, weaknesses and to understand the features/principles. It also delivers the goals, challenges, and boundaries of LSD and then analyzes the gathered data statistically. The survey method is used to gather data and is validated by the questionnaire on quality criteria. Results conclude that LSD is one of the best approaches used by growing or small organizations to meet triple constraints and making projects successful on a good average.
Knowledge Production on Fourth Industrial Revolution in the ECOWAS Region in Africa: A Scientometric Analysis

The study measured the trend of research output relating to the fourth industrial revolution (4IR) in the ECOWAS region in Africa. It examined the year wise growth, the breadth of collaboration and impact of collaboration on the productivity of research compared to the world’s average. The study reports the findings of a scientometric study of 4IR knowledge production culled from the broad field of specialization in computer science research in all ECOWAS states. This data was extracted from the Scopus Database using “SciVal” an online analytic tool that allows visualization of research, benchmarking for comparison and analysis of strategic partnerships (collaborations) among actors in a research system. The analysis from the study provided information on volume and growth of knowledge supply on 4IR. It also provided information on the breadth of collaboration by ECOWAS member states on 4IR and the impact of the collaboration on research productivity of the ECOWAS region.
Cubature Kalman Filtering for Dynamic Pointing Rotary Steerable System Based on Dynamic Event-Triggering Mechanism

In this paper, the state estimation problem of dynamic point-the-bit rotary steerable system based on event-triggered cubature Kalman filter is studied. According to the known system model, a dynamic event-triggering mechanism is designed to send signals when the measured values of the system meet the event triggering conditions. Then, an event-triggered cubature Kalman filtering method is proposed and the upper bound of the filter error covariance can be guaranteed. Finally, simulation experiments show that the algorithm is effective.
MobileNet based Chinese Chess recognition with Xavis Platform

The disadvantages of convolutional neural networks are that they cannot run on mobile devices and embedded devices due to their large memory requirements and large amounts of computation. With a small loss of accuracy, the lightweight neural network greatly reduces the number of parameters and the amount of computation compared with the ordinary convolution neural network. In order to use robot arms accurately grasp the chess pieces and place on the right place of the Chinese chess board, the chess pieces should be recognized and classified quickly in real time. In this paper, the MobileNet is embedded into the Xavis platform, which is combined with the robot arms to realize the recognition of Chinese chess. Firstly, convolutional neural network methods are introduced. Then network structure of MobileNet is analyzed, and the pattern recognition method is shown. Xavis platform with MobileNet is further investigated. And the dataset of Chinese Chess is collected. At last, experiments on the self-made Chinese chess data set show that the MobileNet has good chess classification ability. It lays a solid foundation for the grasping work of the robot arms. Next, we will further improve the method of Chinese Chess recognition with Xavis Platform and compare the proposed schedule with state-of-art methods.
3D location estimation of light sources in room-scale scenes

In this paper we present our on-going work on light source estimation in room-scale scenes for more photorealistic experiences. Our unique input is an up-to-date textured 3D mesh of a real uncontrolled environment obtained using a consumer mobile device. We base our approach on the detection of real shadows in a single RGB-D image rendered for a top viewpoint. Contrary to prior art, our approach does not consider any object-based segmentation, neither simplifying assumptions on the scene geometry or on poorly textured surfaces. The 3D locations of light sources are automatically estimated, and for now, the lighting model is completed with intensity values obtained interactively through a GUI displaying augmentations on the scanned scene. This lighting model can then be reused to light the MR scene coherently during mobile experiences. Results on various indoor and outdoor scenes show the beginnings of a promising work. To illustrate the complexity of the problem and to make the community aware of the importance of a correct lighting on user perception, we also fairly show how slightly inaccurate light estimation results or incomplete geometry knowledge can go completely unnoticed in some simple cases but can also deeply impact the final rendering photorealism in some other cases.
Wireless Controlled Wide Range Tuneable CCT Lighting System Using Three Component Colour Mixing

This paper proposes a novel approach of wireless controlled, tenable correlated colour temperature (CCT) lighting system with high colour rendering index (CRI) using the concept of colour mixing. Using Grassmann’s colour mixing theory, phosphor coated warm white LED source (CCT 2800 K) is mixed with the blue and green LED light sources to meet the requirement of high CRI and high value of red object identification parameter R9. A detailed mathematical formulation is derived for easy controlling of variable CCT and illuminance and a flowchart of wireless control scheme is incorporated and experimentally validated in this article. As a result, the locus of the blended light source is very close to the Planckian locus on CIE1931 chromaticity diagram with a minimum uv D value of +0.0005. This proposed system provides a very wide range CCT starting from 2800 K to 15000 K with a high CRI value of 94 at CCT 5000 K. Another feature of this blended light source is the true colour identification parameter of red object (R9), which is also increased to 93 at CCT 8000 K. This system has very wide application in industries like textile, food processing etc., in museum, art gallery, and medical application like surgical lighting for its high CRI and R9 value with wide range of CCTs.
Intelligent Encryption Model of Financial Information Based on Big Data

The conventional intelligent encryption model of financial information has the problem of imperfect database performance, which leads to the long decryption time of the model. An intelligent encryption model of financial information based on big data is designed. Record and manage the operation financial status, obtain the financial information management requirements, use big data technology to build an embedded database, describe the equipment use space, decipher the control content of observation information, identify information security threat factors, build an intelligent encryption model and generate the user's private key. Experimental results: The average decryption times of the financial information intelligent encryption model in this paper and the other two encryption models are 1.732 s, 2.364 s and 2.478 s, respectively, which shows that the financial information intelligent encryption model integrating big data technology has better performance.
MESH Networks to Optimize the Quality of Internet Service via WiFi in University Institutions

Nowadays the need to be connected through the internet in a more reliable and faster way is indispensable, Mesh networks are a great connectivity solution because with its configurable and self-regenerating mesh technology it can be implemented in urban and rural areas and university campuses offering favorable results to thousands of users. In this article we propose the design of a Mesh network to optimize the quality of internet service via WIFI in university educational facilities, for this we took as a model the Faculty of Computer Science of the Technical University of Manabi, taking as initial data the operation of the current network, in order to perform the design of the Mesh network and then the simulation of the same, for this we used the simulation tool Riverbed Modeler Academic Edition 17.5, the same one that allows to show viable results by means of modules and graphs for a future implementation benefiting teaching and administrative students of this faculty.
Adversarial Domain Adaptation via Class Correlation

Adversarial learning has been embedded in deep networks as a transferable representation of domain adaptation. The existing adversarial domain adaptation methods may not pay attention to the correlation between categories. In this paper, we propose an adversarial domain adaptation based on class correlation. In addition to the commonly used adversarial loss, we added a loss based on class correlation to reduce the probability of misclassification. We prevent the diversity of the network from decreasing by restricting the eigenvalues of the class correlation matrix.
An Improved Dark Channel Prior Method for Dusty Image Restoration

Abstract For the images which were obtained in a dusty environment, because of the tiny particles suspended in the atmosphere absorb and scatter the light, there is serious haze in the images. The haze can directly cause colour changes and the loss of image details. As a result, a lot of efficient features in the dusty image were concealed or weakened by the haze, which created difficulties to obtain a natural and clear restored image, and has brought serious impact for aerospace, automatic driving and traffic monitoring in the dusty environment. An improved restoration method based on dark channel prior theory and gradient domain guided filtering was proposed to remove the haze and restore the dusty image. According to the characteristics of the dusty images, a specific guided image was adopted to make gradient domain guided filtering for the dark channel image firstly, so that the transmittance image can be better refined than that in the dark channel prior method, and then the image can be restored according to the image degradation model. Experiments show that the visual effect of the restored image is natural and clear, and can retain most effective features, which lays a foundation for computer vision-based target monitoring and recognition in the dusty environment.
Generating Characteristic Summaries for Entity Descriptions

Graph-structured data describing entities and their properties has become a notable component of the Web. With the increasing size of data graphs, an entity is often associated with too many property values to be entirely shown to the user, thereby requiring a compact but characteristic summary to present its most distinguishing features. This paper aims to automatically generate such characteristic entity summaries for human users. To achieve it, we exploit the informativeness of property values by analyzing the data graph using information theory. To improve the utility of information carried by a summary, we learn it from a text corpus. To reduce the information redundancy of a summary, we perform logical reasoning and measure similarity with statistical support. We formalize the entity summarization problem considering these factors as combinatorial optimization problems to solve. Experiments based on a real data graph and hand-crafted gold standards show that our approach improves on two state-of-the-art approaches in F-measure by 20.63%-38.79%.
CIraCLoss: Intra-class Distance Loss Makes CNN Robust

Convolutional neural networks(CNN) are vulnerable to adversarial samples, which poses a threat to application in some scenes. This paper proposes a novel loss function to improve the robustness of CNN—CIraCLoss, which combines the intra-class distance loss function (IntraCLoss) and the cross-entropy loss function (CELoss). In the training stage, the IntraCLoss encourages each feature extracted by CNN to be close to its intra-class center. With this feature space distribution, the adversarial sample needs a larger intensity of attack so that its feature keeps away from the intra-class center. Therefore, IntraCLoss can make CNN more robust to defend against adversarial attacks. The results on the CIFAR10 and MNIST datasets show that CIraCLoss, which is mainly affected by IntraCLoss, can reduce the DBI index of the feature space and also reduce the fooling rates of models. In addition, our method can be applied to different network structures and has good generalization.
Pedestrian Attribute Recognition Based On Deep Learning : A Survey

With the continuous development of intelligent video surveillance, the research on pedestrian attributes has attracted widespread attention. The paper aims to introduce the existing pedestrian attribute recognition based on deep learning. We summarized the existing work on pedestrian attribute recognition. The research of deep learning on pedestrian attribute recognition in terms global-based, locally-based, of GCN-based and attention mechanisms. At the same time, we introduced the related background and concepts of pedestrian attribute recognition. Then, we introduced its commonly used datasets and evaluation criteria. At the end, we summarize the current research hotspots and future research directions.