Reviewerly/exHarmony huggingface dataset:
Clustering mining method of large-scale network abnormal data based on selective collaborative learning

In order to improve the accuracy and recall rate of the clustering mining process of large-scale network abnormal data and shorten the time of clustering mining, in this study, a large-scale network anomaly data clustering mining method based on selective collaborative learning is proposed. Through cooperative training and selective ensemble learning, a machine learning anomaly detection model and a strong classifier for large-scale network data are designed, and the correlation variable analysis method is used to obtain the dissimilarity measure of data. The network anomaly data is processed by fuzzy fusion, and the nearest neighbor algorithm is used to realize the clustering mining of large scale network anomaly data. The data clustering mining accuracy of this method reaches 98.16%, the time of data clustering mining is only 2.5 s, and the recall rate of data clustering mining is up to 98.38%, indicating that this method can improve the effect of large-scale network anomaly data clustering mining.
Perspective Chapter: Blockchain Adoption in Food Supply Chain

Modern food supply chain involves numerous stakeholders that are geographically dispersed. This scattered and complex structure impedes the free flow of information throughout the supply chain. Low transparency lays the groundwork for food fraud and delays the implementation of necessary countermeasures when a food contamination incident occurs. Moreover, customers nowadays are increasing demanding in terms of product provenance and sustainability. Under this circumstance, it is urgent to identify effective solutions that can mitigate the concerns of food safety, quality, and fraud. Blockchain has been identified as a promising technology for enabling end-to-end supply chain traceability. This study investigates the main challenges of agri-food supply chain and how blockchain attributes can address these challenges. We also propose an integrative framework of key factors that influence blockchain adoption in the food industry.
AHSM-Net: Unsupervised Stereo Matching Algorithm Based on Attention Mechanism and Hybrid Dilated Convolution

Abstract Too many parameters often accompany the stereo matching algorithm of the convolutional neural network. The dense matching makes the constructed cost volume increase with the resolution, resulting in high occupied memory and processing time. To perform depth estimation more efficiently, this paper proposes a stereo matching algorithm AHSM-Net based on attention mechanism and hybrid dilated convolution (HDC), which uses an unsupervised method to train the stereo matching network end-to-end. This method obviously enhances the consistency of objects. AHSM-Net is trained on the SceneFlow dataset, and a large number of experiments and analyses are carried out on the KITTI2012 and KITTI2015 datasets, which verifies that the method proposed in this paper has great matching accuracy and speed under the condition of unsupervised.
Body part relation reasoning network for human activity understanding

The wide application of vision sensors has brought great opportunities and challenges to the intelligent understanding of image and video data. Research on effective human activity understanding algorithms is of great significance in efficient processing and utilizing the data. At present, existing human activity understanding methods have the defects of low recognition accuracy, high complexity or poor robustness. In view of the above problems, we construct the body part relation reasoning network (BPRRN). It aims to explore and utilize the common-sense knowledge in human bodies to reason human activities. Firstly, ten body part boxes are generated from each human body with the help of pose estimation and part classification functions. Secondly, with the states of each body part, the relationships between different body parts are explored and utilized for constructing the body part relation reasoning module (BPRRM). Finally, the body part relation reasoning features from each frame are adopted for temporal modeling with the temporal relation reasoning module (TRRM). In this way, the context association between video frames are constructed, which further promotes the reasoning ability of BPRRN. The experiments carried out on Stanford 40 and UCF101 demonstrate the effectiveness of the proposed network.
Morphologically Motivated Input Variations and Data Augmentation in Turkish-English Neural Machine Translation

Success of neural networks in natural language processing has paved the way for neural machine translation (NMT), which rapidly became the mainstream approach in machine translation. Significant improvement in translation performance has been achieved with breakthroughs such as encoder-decoder networks, attention mechanism, and Transformer architecture. However, the necessity of large amounts of parallel data for training an NMT system and rare words in translation corpora are issues yet to be overcome. In this article, we approach NMT of the low-resource Turkish-English language pair. We employ state-of-the-art NMT architectures and data augmentation methods that exploit monolingual corpora. We point out the importance of input representation for the morphologically rich Turkish language and make a comprehensive analysis of linguistically and non-linguistically motivated input segmentation approaches. We prove the effectiveness of morphologically motivated input segmentation for the Turkish language. Moreover, we show the superiority of the Transformer architecture over attentional encoder-decoder models for the Turkish-English language pair. Among the employed data augmentation approaches, we observe back-translation to be the most effective and confirm the benefit of increasing the amount of parallel data on translation quality. This research demonstrates a comprehensive analysis on NMT architectures with different hyperparameters, data augmentation methods, and input representation techniques, and proposes ways of tackling the low-resource setting of Turkish-English NMT.
Hybrid Analytic Method for Missing Data Imputation in Medical Big Data

Compared to other traditional datasets, medical data has several hidden challenges. In fact, the possibility of missing values for certain attributes presents a great dispute for data mining researchers to make correct medical decisions. In this paper, a hybrid scheme combining the k-means method and regression analysis is proposed. A combination of these two analytical methods allows to find the best distributional model of numerical data in space and helps to predict missing data. Applied to medical data (diabetes dataset), the proposed model predicts the values with a minor error rate, which is considered very satisfactory.
Path planning algorithm of dual arm space intelligent robot

In order to solve the cooperation problem between the two arms of the dual arm robot, improve the effect and accuracy of the path planning of the intelligent robot, in this study, a new path planning algorithm for dual arm space (DAS) intelligent robot is designed. By analyzing the kinematics performance of DAS intelligent robot, the operation law of DAS intelligent robot is determined. By analyzing the collision that needs to be considered during the operation of DAS intelligent manipulator, a collision detection method based on capsule envelope is proposed to detect the collision of obstacles in the running path. Finally, the path planning model of DAS intelligent robot is designed by using immune genetic algorithm. The experimental results show that the path planned by this method can avoid obstacles and dangerous areas, and the accuracy of path planning for the existing environment is always higher than 90%. With the increase of iteration times, the planning time is relatively stable, about 5.5 seconds. The proposed method can enhance the effect and accuracy of path planning, effectively shorten the time of path planning, and improve the work efficiency of DAS intelligent robot.
Trajectory tracking control method of mobile robot based on AVRX operating system

In order to improve the trajectory tracking control effect of mobile robot, a mobile robot trajectory tracking control (TRC) method based on AVRX operating system is proposed. The principle of AVRX operating system is analyzed and the hardware structure of mobile robot is designed. The input-output relationship of mobile robot trajectory is determined according to multivariable fuzzy controller and the wrong data of mobile robot trajectory tracking information is eliminated by radar point cloud measurement. The TRC model of mobile robot is designed through Euler Lagrange equation to realize the TRC of mobile robot. The experimental results show that the trajectory tracking accuracy of mobile robot is as high as 97.9%, the trajectory tracking time is 16.2 s, and the trajectory tracking error of mobile robot is only 0.04. It shows that the TRC effect of mobile robot is improved, which provides a reference for the trajectory tracking control of mobile robots.
Multi-disciplinary optimization of a space re-entry vehicle using skeleton-based integral soft objects

In the present paper, a parametric procedure for the shape modelling and optimization of a next generation winged re-entry vehicle using Skeleton-Based Integral Soft Objects is presented. Detailed aerodynamic bodies are efficiently generated just “inflating” a parametric flat domain representing the aeroshape planform composed by discretized panels. Specifically, a potential field generated by a set of mathematical primitives and related field functions is used to alter nodal coordinates of the flat planform domain. In such mode, several geometrical features as canopy, cabin outline, dihedral angles, winglets, and vertical tails can be easily modelled, added, or suppressed. Specialized Skeleton-Based Integral Soft Objects operators are used to produce both smooth and sharp transitions between different sub-component surfaces as required. Consequently, huge geometrical variations of the aerodynamic shape can be then efficiently generated using a very limited number of high-sensitivity parameters. The suggested modelling procedure has been tested performing several multi-disciplinary shape optimizations driven by rapid engineering evaluators, for a minimum mass/maximum cross range design of a concept vehicle that re-enters from Low Earth Orbit, up to a controlled horizontal landing on runway. Additionally, Computational Fluid Dynamics evaluations have been performed on optimum design candidates to validate the real performance of the proposed aeroshape configurations. Results comparison has highlighted slight differences within the range 5-10%, thus confirming the reliability of the design of the aircraft aeroshape.
SAR Image Denoising using MMSE Techniques

Synthetic aperture radar (SAR) provides many advantages over optical remote sensing, principally the all-weather and all-day acquisition capability. For this reason, SAR images have been exploited for many applications such as forestry, agriculture, disaster monitoring, sea/ice monitoring. However, the main limitation in SAR images is the contamination with the multiplicative speckle noise. The speckle damages the radiometric quality of SAR images and contracts the performance of information extraction techniques. Many methods have been proposed in the literature to reduce speckle noise. These methods, however, must avoid degrading the useful information in the SAR images, such as textures, local mean of backscatter, and point targets. The minimum mean square error (MMSE) techniques have been largely applied in SAR image speckle denoising. The objective of this chapter is to review and give new insights into the MMSE denoising of SAR images. In particular, the performances of three MMSE-based techniques which are the commonly applied Lee sigma filter and the newly introduced iterative MMSE (IMMSE) filter, and the infinite number of looks prediction (INLP) filter are studied.
A Relook on Impact of Digitization for Education

Due to various circumstance, the online teaching learning method has been introduced in all organization. It has been observed that most of the students benefit from the new technology rather than traditional teaching learning approach. In digital platforms, learners can gain knowledge through Google Meet, Zoom, etc. Students and teachers have become comfortable in digital teaching learning systems as there are no time constraints in the learning methodology. In the online teaching learning method, learners from a place can communicate with tutors from anywhere. This chapter provides the comparative analysis between the traditional approach and online teaching learning method. At the end of the chapter, it also shows that a digital division has been created among different categories of learners for making their assessment.
Meta-learning for emerging applications: Finance, building materials, graph neural networks, program synthesis, transportation, recommendation systems, and climate science

This chapter investigates 17 influential contributions to the field, all of which can be divided into several taxonomies according to their tasks to solve: credit card fraud and economic inequality, graph meta-learning, traffic state prediction, climate change, building material defect recognition, program synthesis, etc. Each section covers the following perspectives: Which practice areas are of emerging interest to the meta-learning community? What is the current status of the existing research in each of these application areas? What and why are meta-learning approaches favorable among different practice areas? How do these approaches work theoretically and practically? Most pieces of research recursively track related bibliographies in papers accepted at premier academic conferences: AAAI, ICML, ICRA, ICRL, IJCAI, CVPR, KDD, and NeurIPS.
Meta-reinforcement learning

Meta-reinforcement learning (Meta-RL) is an ambitious technology step toward general AI learning as automatically and efficiently as, or even exceeding, human beings. In this chapter, initially, the basics of deep reinforcement learning and Meta-RL are introduced: basic components, simulated environments, limitations, early development, and formalism. Three fundamental components (i.e., a memory, a Meta-RL paradigm, and a Markov decision processes distribution) is explored in the following sections: Section 7.3 discusses different approaches regarding memory. Even as Meta-RL is just emerging, the passion from the researcher is dramatically increasing while a variety of Meta-RL methods has been proposed for multiple scenarios. These are investigated in Section 7.4. Section 7.5 examines the reward function and environments for the Meta-RL setup. Besides the above essential components during Meta-RL training periods, a standardized benchmark for broad Meta-RL assessments and evolution is reviewed. Visual navigation is investigated with couple meta-learning strategies. The chapter ends up with an evaluation of pros and cons of Meta-RL.
Meta-learning basics and background

This chapter contains a review of the concepts and paradigms involved in the background of meta-learning. Starting from the theoretical formalization of meta-learning, Section 1.2 presents an intro-level picture of this emerging technology. The fundamental knowledge of general machine learning is described in Section 1.3. Section 1.4 examines the development and critical characteristics of deep learning technology. As similar methods that are usually compared with meta-learning, transfer learning and multitask learning are discussed in section 1.5. Section 1.6 dives into few-shot learning (including zero-shot and one-shot learning) to indicate its relationship with meta-learning. Sections 1.7 and 1.8 recap, separately, the other side of artificial intelligence—a probabilistic model and Bayesian inference—for better understanding and clarification of the scope of meta-learning.
Meta-learning for natural language processing

This chapter explores meta-learning application in natural language processing. This chapter investigates 20 advanced pieces of research, all of which can be divided into seven taxonomies according to their tasks to solve: semantic parsing, sentiment analysis, machine translation, dialogue system, knowledge graph (including named entity recognition and question answering), relation extraction, and emerging topics (e.g., domain-specific word embedding, multilabel classification, compositional generalization, query suggestion). Numerous attempts are discussed across various research areas, including domain adaption, few-shot/zero-shot learning, and a lifelong learning setting. Each section outlines the definition of relevant terminology, early attempts, current status, multiple research lines, milestones of model architecture, distribution and general purposes, remarkable techniques, objective function and pseudocode of critical methods, regular datasets, and benchmarks. Finally, a summary compares deep learning with meta-learning, and presents further discussion of meta-approaches based on natural language processing.
Smart real-time multi-camera people locating system

The article presents a multi-camera video monitoring system for rooms, which allows for real-time tracking of the places where individual people are. The system uses easily accessible, cheap cameras (e.g. installed in smartphones). Video stream is sent via IP protocol to a central server. The server performs image analysis using the YOLO neural network. The places where the detected people are located are marked on the virtual room plan that can be accessed using web browser.
Working with MongoDB

MongoDB is one of the most popular document-oriented NoSQL databases. Spring Data MongoDB provides support for working with MongoDB with a consistent Spring-based programming model similar to Spring Data JPA. Spring Boot provides a starter for Spring Data Mongo, which makes it even easier to use by implementing its autoconfiguring mechanism.
Building REST APIs Using Spring Boot

REST is an architectural style for building distributed systems that provide interoperability between heterogeneous systems. The need for REST APIs has increased significantly with the drastic increase in mobile devices. It became logical to build REST APIs and let web and mobile clients consume the API instead of developing separate applications.
Reactive Programming Using Spring WebFlux

Modern IT business needs have changed significantly compared to a few years ago. The amount of data that is being generated from various sources like social media sites, IoT devices, sensors, and the like is humongous. The traditional data processing models may not be suitable for processing such a huge volume of data. Even though we have better hardware support these days, many of the existing APIs are synchronous and blocking APIs, which become bottlenecks to better throughput.
Multi-Target Multi-Camera Aerial Re-identification by Convex Hull Topology

This paper discusses the re-identification (Re-ID) of objects of interest (OOI) across multiple cameras. In the first part of this paper, we propose a novel technique to associate and re-identify objects between multiple aerial cameras across a corresponding frame by considering the topological (layout) sequence of ground objects calculated using computational geometry. This sequence relied on the invariant properties of projective transformation, i.e. collinearity, point of intersection, and cross ratio. The associated objects are then used for homography estimation to generate the homography matrix needed for subsequent comparison between object trajectories for re-identification across cameras and across time using an earlier developed technique with the results shown in the second part. By applying both the novel topology-based and our previous homography trajectory matching approaches, our initial field test shows successful object re-identification with Precision around 90-95%. Additionally, with the combination of both techniques, the full pipeline of object Re-ID for different cameras is completed, allowing the potential for automated Re-ID of objects for moving cameras.
Attention Empowered Feature-level Radar-Camera Fusion for Object Detection

Safe autonomous driving can not be realizable unless with robust environment perception. However, robustness can only be guaranteed if the system functions reliably in all weather conditions with any darkness level, while capturing all corner cases. Perception has heavily relied on cameras for object detection purposes. Due to operating at visible-light frequencies, there exists a plethora of corner cases for a sole camera-based perception system. In this paper, radar data is constructively fused with the RGB images for improving perception performance. Radar data that is in the form of point cloud is pre-processed by domain conversion from a bird-eye-view perspective into an image coordinate system. These alongside with RGB images from the camera are given as inputs to our proposed fusion network, which extracts the features of each sensor independently. These features are then fused to perform a joint detection. The robustness in adverse conditions like fog is validated via synthetically foggified images for different levels of fog densities. A channel attention module is integrated into the fusion network, which helps to prevent the drop in performance up to a fog density of 25. The network is trained and tested on NuScenes [1] dataset. Our proposed fusion network is capable of outperforming the other state-of-the-art radar-camera fusion networks by at least 8%.
The architecture design and application of IP SDN controller system

In the era of SDN/NFV technology, in order to meet the goal of cloud network convergence and cloud digital transformation, this paper proposes an IP SDN controller architecture design and implementation scheme. According to the characteristics of loose coupling and flexible deployment of each module, the system architecture of IP SDN controller is designed to realize the decoupling of four layers: application layer, service abstraction layer, core network function layer and protocol adaptation layer. In addition, as a common module, the plug-in development platform of the controller provides the micro-service governance, plug-in framework, continuous integration and continuous delivery tool chain involved in the above four layers. Through key capability research and development, deployment and implementation verification and technical standardization, IP SDN controller, as the core capability base of a new generation of cloud network operation system, enables cloud network integration.
Research on speech recognition technology based on Wisdom Classroom

Nowadays, Wisdom classroom has many image recognition functions, but there are still large gaps and shortcomings in speech recognition. How to give real-time feedback to teachers and students on the discussion records and knowledge notes taught by teachers in class. Solving this problem requires real-time speech recognition. The number of training samples in the system is small. Through the integration training of multiple data sets. The improved CNN-BiGRU algorithm is used to fully extract speech features. Through CTC-beam-search get the optimal recognition result.
Compensating radar rotation in target tracking

For applications where the vessel carrying the radar is highly maneuverable, or the radar rotation frequency is low, it may be unreasonable to approximate the radar measurements as arriving simultaneously. This paper identifies effects caused by radar rotation and shows that these effects can degrade tracking performance. We present methods for mitigating the undesirable effects and apply these to a Joint Integrated Probabilistic Data Association (JIPDA) tracker. Results on simulated data show that the proposed methods give significant performance benefits.