Reviewerly/exHarmony huggingface dataset:
A Multi-Target Selection Method for Power Equipment Virtual Assembly System

With the continuous development of virtual assembly technology, virtual assembly system has been widely used in military, medicine, agriculture, industrial production, manufacturing and other fields. In order to improve the operability of power equipment virtual assembly system, a highly applicable multi-target object selection method is essential. Because the power equipment has close assembly relationship, there is widespread occlusion between components. However, the existing object selection methods can&#x2019;t solve the multi-target object selection problem in the case of complete occlusion. Therefore, this paper proposes a multi-target object selection method based on volume region. This method is divided into a fully inclusive selection method and a partially intersecting selection method. The spatial position and size range of the cuboid selection area of the two methods are adjusted by the oculus rift's touch handle. The fully inclusive selection method is to determine whether all eight vertices of the target&#x2019;s cuboid bounding box are within the selection area. If so, the target is determined to be selected. The partially intersecting selection method is to determine whether there are one or more vertices of the eight vertices in the selection area. If so, the target is determined to be selected. The experimental results show that the proposed method has high applicability in the case of semi occlusion, full occlusion and multi-target, which greatly improves the training and teaching efficiency of power equipment virtual assembly system.
Image Deblurring Based on Generative Adversarial Networks

Image deblurring technology uses deep learning method to solve the blurry problem of single image , which is a challenging problem in the field of computer vision. In recent years, the rapid development of deep learning and computer vision has promoted the performance of blur processing algorithm. From the perspective of deep learning, the article studies on the image deblurring problem, and uses convolution neural network to achieve the purpose of image deblurring. Aiming at the problem that the scale of single deblurring using multi-scale network is huge, and the important feature information is not fully used, this paper proposes a deblurring algorithm based on generative adversarial networks. The model uses feature pyramid network as a framework instead of the multi-scale input, which effectively reduces the size of network and accelerates the training speed. In order to make better use of feature information, the attention mechanism and dual scale discriminator are introduced into the network. In order to make the training process more stable, the algorithm improves the discriminator loss, using the least squares and relativistic combination. The experimental results show that the image deblurring algorithm based on the generative adversarial network achieves better restoration effect than other algorithms.
Efficient Framework for Hidden Camera Detection &amp; Jamming using IoT

Hidden Camera so called ‘'Spy Camera” is frequently used to record something without prior knowledge. Usually, the hidden camera will not be visible for objects that are recorded. The use of hidden cameras in public spaces has rapidly increased, and correspondingly the hidden camera recordings may be shared over a network. This research study has proposed a IoT-based architecture for performing camera detection and jamming. The client recognizes the network, where the camera is recording/streaming and jams the camera linked to that network through IoT. The cameras connected to the entire network may be jammed here. An extension to this RFID is introduced to identify the hidden camera signals and forecast the presence of a camera in that location.
Research on Face Recognition Based on Deep Learning Technology in Intelligent Construction Site

It is an urgent problem for construction enterprises to supervise site personnel and reduce potential safety hazards. In order to improve the safety supervision ability of the construction site, combined with the management requirements of the current smart construction site monitoring platform, and based on the analysis of the architecture and functions of the smart construction site monitoring platform, and conducive to the deep learning technology, a smart construction site face recognition system based on convolutional neural network is designed. Recognize the faces of site personnel through convolution neural network, manage and control the site personnel, realize the identification of suspicious targets, realize the intellectualization of video monitoring on the construction site, provide good safety guarantee for site construction, and improve the construction level and management ability of intelligent site production engineering.
Exploratory Research in Knowledge Assistance Learning with Linked Data: A Case on The Education With Historic Military Combat

The Semantic Web is applied to help computers to recognize the contents of documents and improve the efficiency of internet search. In general, the resource description framework (RDF) model is used in the semantic web to describe the links among data to data (or thing to thing), and these are called Linked Data. The Linked Data technique has been applied to many fields such as finance, business, government and education. Additionally, some famous search engines, like Google and Yahoo, also utilize Linked Data technique into their retrieval system. As the Linked Open Data (LOD) project aimed to convert the open data structure into the Linked Data structure for connection and use of different data sources. This study focused on the Linked Data application for expansion of education knowledge based on the related content from Wikipedia. A retrieval system was established to integrate education knowledge and examine the connection between linked knowledge of subject events and other resource objects based on arious disciplines. Furthermore, we discussed the possible development of Linked Data technology in the education in conjunction with the government's open data policy.
An image defogging algorithm of a designed-FCMSPCNN for natural image

In order to address the problem of defogging images of foggy scenes, this paper proposes a parameter-tunable image defogging algorithm based on the FC-MSPCNN model (DFC-MSPCNN). Firstly, the proposed model is used to defogger color fogging images. Secondly, we simple four key parameters and one synaptic weight matrix in the model, including &#x03B1;, &#x03B2;, V, Q and Wijkl, and how they are set. Thirdly, our proposed method for image defogging is derived from dark channel theory and retinex theory. Finally, our proposed model in image defogging aspect brings a good image defogging performance than other popular algorithms.
On Sparsity Measures In Deep Subspace Clustering

Traditional clustering methods groups data points according to attributes such as similarity, continuity, neighbor-hood information, etc. overlooks the structural properties of the data. Consequently, prevalent clustering approaches to below-par performance in real-world applications. Unlike traditional clustering approaches, subspace clustering methods attempt to group datapoints keeping the inherent structure and rank-related properties of the data into account. Despite the rapid growth in deep learning-based approaches, very few works have utilized deep learning for the subspace clustering task. This work introduced an auto-encoder-based deep learning architecture consisting of a self-expressive layer for the deep subspace clustering task. We use smoothed L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , L0.5 and Frobenius norms instead of the actual measures for ease of optimization task. We also explored the efficacy of sparsity measures that characterize the self-representation coefficient matrix of the self-expressive layer. The experiments conducted on standard datasets suggest that the application of efficient sparsity measures improves the performance of the subspace clustering approach and results in superior performance compared to the previous deep subspace clustering approaches.
FWIB: Fuzzy Weighted Iterative Bayes Algorithm

Within the theoretical framework of Naive Bayes(NB), Iterative Bayes(IB), as an important variant, was proposed to alleviate the attribute interdependence problem based on output adjustment technique and shows certain performance enhancement. However, like NB, IB also suffers from biased classification and poor robustness against noise on class imbalanced data. To address these problems, cost-sensitive learning is introduced, and by assigning fuzzy-membership values to training data, we propose a class of Fuzzy Weighted Iterative Bayes algorithms (FWIB-CE, FWIB-HE, FWIB-CL-HL) to integrate more prior information from the sample distribution. The experimental results on ten typical imbalanced datasets show our methods have significant outperformance over NB and IB under G-mean evaluation in classification and have higher robustness against noise interference.
Nonlinear dynamics at the quantum limit in the unbalanced Dicke model

In 1973 Klaus Hepp and Elliott Lieb showed that a tightly confined ensemble of atoms placed in a quantised single-mode optical cavity will have their emission of light enhanced by constructive interference of emission events if the photons emitted are indistinguishable. This effect, known as superradiance, has proven difficult to realise experimentally due to the strong light-matter coupling required to produce such behaviour. However, over the last decade there have been several successful realisations which engineer superradiance by driving Raman transitions between atomic ground states of multilevel atoms. In this case, superradiance emerges via a quantum phase transition mediated by the laser driving power. In this work, we consider a generalisation of this model referred to as the unbalanced Dicke model. It has recently been implemented experimentally and is characterised by two lasers driving two distinct Raman transitions. We show that, in the semiclassical limit of a large number of atoms, the unbalanced Dicke model exhibits a wealth of nonlinear dynamics which describe novel dynamical phases of matter. Examples of nonlinear behaviour observed are pitchfork, saddle-node, and Hopf bifurcations, as well as two types of chaos. We explore the quantum mechanical manifestations of these bifurcations and additional physical effects which appear in the presence of quantum fluctuations. In this way, the model creates a playground in which to explore the onset of classical nonlinear dynamics from an open quantum system.
Dementia-related user-based collaborative filtering for imputing missing data and generating a reliability scale on clinical test scores

Medical doctors may struggle to diagnose dementia, particularly when clinical test scores are missing or incorrect. In case of any doubts, both morphometrics and demographics are crucial when examining dementia in medicine. This study aims to impute and verify clinical test scores with brain MRI analysis and additional demographics, thereby proposing a decision support system that improves diagnosis and prognosis in an easy-to-understand manner. Therefore, we impute the missing clinical test score values by unsupervised dementia-related user-based collaborative filtering to minimize errors. By analyzing succession rates, we propose a reliability scale that can be utilized for the consistency of existing clinical test scores. The complete base of 816 ADNI1-screening samples was processed, and a hybrid set of 603 features was handled. Moreover, the detailed parameters in use, such as the best neighborhood and input features were evaluated for further comparative analysis. Overall, certain collaborative filtering configurations outperformed alternative state-of-the-art imputation techniques. The imputation system and reliability scale based on the proposed methodology are promising for supporting the clinical tests.
Social Media Sentiment Analysis- A Relative Study on Twitter Dataset

Sentiment is an opinion of the person which a person expresses on the particular social media site. To get to know the intentions of the person on a particular thing, calculating the sentimental value of the person's review is must. TSA-Twitter Sentiment Analysis is tool which will be calculating the subjective and polarized value of the particular tweet apart from it, it will also fetch the recently posted tweets on the particular topic and will also calculate their sentimental value which will help to identify on which topic positive or negative tweets are being made along side it will give graphical representation of tweets distribution as well. TSA will also tell the sentimental value of the user enter text regardless of that being a real time tweet on twitter.
Recognizing Objects from on-Board Vehicle Footage to Build an Educational Foundation for Undergraduate Research in Computer Vision

As the world is getting more connected and processing power increases, the need for undergraduate students to have a solid foundation in machine learning and computer vision is becoming standard [1] [2]. In this paper, we develop a foundation for future computer vision and machine learning work through use of open-source software that can be used on personal and embedded devices. This foundation helps prepare students entering the workforce who will be working in autonomous vehicle safety and car control features.
Closed-form camera pose and plane parameters estimation for moments-based visual servoing of planar objects

Image moments are global descriptors of an image and can be used to achieve control-decoupling properties in visual servoing. However, only a few methods completely decouple the control. This study introduces a novel camera pose estimation method, which is a closed-form solution, based on the image moments of planar objects. Traditional position-based visual servoing estimates the pose of a camera relative to an object, but the pose estimation method directly estimates the pose of an initial camera relative to a desired camera. Because the estimation method is based on plane parameters, a plane parameters estimation method based on the 2D rotation, 2D translation, and scale invariant moments is also proposed. A completely decoupled position-based visual servoing control scheme from the two estimation methods above was adopted. The new scheme exhibited asymptotic stability when the object plane was in the camera field of view. Simulation results demonstrated the effectiveness of the two estimation methods and the advantages of the visual servo control scheme compared with the classical method.
Machine Learning based Efficient Facial Expression Recognition Algorithm

Face expressions are the essential part of social communication. As the technology advances, there is a need of automated system which can recognize facial expressions with good accuracy. This research aims to develop a machine learning based system that can detect the human facial expressions by considering different database images. On the basis of provided datasets, the system can be able to easily integrate various features such as facial expression recognition, face detection, and classification of expression. It can detect and categorize facial expressions based on their basic characteristics. Basic facial expressions which are recognized by the proposed algorithm are angry, disgust, fear, happy, neutral, sad and surprise.
BeeAE: effective aspect term extraction with artificial bee colony

Abstract Aspect terms are opinion targets for people to express and understand opinions in reviews. Aspect terms extraction is an essential subtask in aspect-level sentiment analysis. To extract aspect terms from a sentence, existing methods mainly focus on context features generated by pre-trained models. However, these models either neglect the crucial implicit linguistic features, e.g., post-of-tag, head, and head dependency, or fail to explore sufficient valuable features for aspect term extraction, which lead to the deficiency in aspect term extraction task. To address the challenges, in this paper, we propose a novel and effective framework for aspect term extraction by integrating both contextual and linguistic features with the artificial bee colony-based feature selection method. Firstly, a novel variant of artificial bee colony is designed to identify the most valuable linguistic features to reduce the high sparsity and dimensionality of the raw dataset. Next, the selected features and context embeddings are integrated to improve the performance of aspect extraction. Finally, extensive experiments are conducted on real-world datasets, and the results exhibit that our proposed framework can outperform the competitive baselines. Compared with the latest baselines, the proposed framework achieves the comparatively higher F 1 scores of 80.7%, 84.7%, 72.2%, and 74.8% on the four groups of datasets. Furthermore, the ablation study shows that the proposed method with the designed feature selection module significantly outperforms the method with the original artificial bee colony, having 4.15%, 4.4%, 4.4%, and 3.2% improvements in F 1 score on all the four datasets, respectively.
Medical endoscopic image classification based on IN-RESCA-SVM+

In this paper, an effective method of capsule endoscope image classification based on IN-RESCA-SVM+ is proposed. The inception-residual network is adopted in this method, and the channel attention mechanism is integrated into the residual network to extract the image features, and then the improved SVM+ classifier based on SVM(Support vector machine) is used for classification, which can make full use of the features of the deep neural network to better classify the endoscope images, thus achieving the effect of automatically classifying the endoscope images by computer and improving the diagnosis efficiency of doctors. We use WCE dataset to test our algorithm, and the maximum top1 precision 98.4% is obtained.
Analysing Paradigms for Managing Product Development: Conventional, Agile and Hybrid Approaches

Abstract As the complexity of products and their development processes increases, a trend emerged where companies try to manage the complexity through implementing agile practices on all or on some levels of the development process. It is not yet clear if an agile approach is the solution or under which circumstances it can be most effective in the development of physical products. This paper aims to compile the information from existing empirical and meta-studies to give an overview of the strengths and weaknesses of conventional, agile and hybrid paradigms.
Related-Key Multiple Impossible Differential Cryptanalysis on Full-Round LiCi-2 Designed for IoT

LiCi-2 is an ultralightweight block cipher designed for constrained IoT devices. It is a successor of LiCi and has even better performance in both software and hardware implementation. In this paper, based on the idea of related-key multiple impossible differential cryptanalysis, a key recovery attack on full-round LiCi-2 is proposed. First, an interesting property is revealed that, with a single bit difference in the related key, a 10-round differential character with probability of 1 exists on LiCi-2. With an automatic approach, the boundaries of impossible differential distinguishers in terms of single-key setting and related-key setting are explored. Under our construction method, the longest length is 8 rounds for single-key setting and 18 rounds for related-key setting. Finally, based on these 18-round distinguishers, a 25-round key recovery attack is proposed with adding 3 rounds before and 4 rounds after the distinguisher. Our attack needs one related key. The time complexity for our attack is O(2123.44), the memory complexity is O(294), and the data complexity is O(260.68). As far as we know, no full-round attack has previously been reported on LiCi-2.
The Fusion of Feature for Person Re-identification Based on Siamese Network

This paper proposes Fusion-based Siamese Network (FSN), a new deep learning architecture that targets at learning global and local features convergently for person re-identification (re-ID). This paper demonstrates the advantages of combining global and local features, which is embodied in indicators such as rank-1 and mAP. At the same time, it is noteworthy that FSN is based on siamese network, which leads innovation in image retrieving. Siamese network consists of two identical sub-networks. In the training process, a pair of images are input into the sub-networks at the same time. Through training, FSN updates the weights according to the triplet loss. FSN can solve the problem of retrieving pictures under different posture, size and light conditions correctly. On three benchmark datasets, Market-1501, DukeMTMC-reID, and CUHK03, the proposed FSN achieves 94.9&#x0025;,89.1&#x0025;,67.7&#x0025; in rank-1 and 90.1&#x0025;,86.3&#x0025;,65.7&#x0025; in mAP respectively, surpassing most of state-of-the-art methods by a large margin.
Robust First Quality Factor Estimation For Double Compressed and Resized Images

The ability to identify tampering in JPEG images has become increasingly significant due to the high usage of JPEG as a compression technology. In doubly compressed and resized (DCR) images, this paper revisits the state-of-the-art resampling factor estimate and estimation of primary quality factor. As a result of the recent findings in the literature, the proposed technique improves existing approaches for analyzing double JPEG compression in the presence of resizing in between two compressions. By utilizing the period of Welch Power spectral density (PSD) of reverse resized image to estimate primary quality factor which is an important forensic clue of processing history of an image. Empirically find the nonlinear relationship between the first quality factor and location of the most prominent peak, which is the period of DC histogram of the first compression by utilizing Welch PSD analysis. Experimental validation of the proposed method is carried out on several JPEG images, and comparing with existing state-of-the-art methods reveals that the proposed method performs better.
An Improved AKAZE Algorithm for Feature Matching of Moving Objects

Aiming at the problems of fast feature extraction and low matching accuracy of moving target detection features for moving targets in the existing image processing feature matching, an improved method is proposed based on the AKAZE algorithm. Using the nonlinear method of the AKAZE algorithm to construct the calculated feature points in the scale space, obtain the displacement of the same feature point between adjacent frames, and filter out the unsuitable feature points according to the rules of the improved algorithm. The experimental results show that although the improved algorithm has the disadvantage of increasing the matching time, by reducing the number of mismatched point pairs, the accuracy of feature matching can be improved relatively, and it is suitable for the capture of accurate objects in a small range.
Character relationship extraction based on BERT bidirectional-GRU neural network-attention

With the rapid development of information technology, there are many kinds of data. Due to the variability of unstructured data, the extraction of entity relationship is no longer based on rule. Therefore, this paper combines the pre-training model BERT with the two-way GRU neural network model, and adds an attention mechanism to complete the extraction of 12 groups of character relationships in this paper. The detailed extraction results are shown in Table 1. The F1 value of character relationship as &#x201C;parent&#x201D; is the highest, reaching 0.95; The F1 values of character relationships of &#x201C;SELF&#x201D;, &#x201C;BROTHERS and SISTERS&#x201D; and &#x201C;CLASSMATES&#x201D; all reached more than 0.9. The results show that the proposed model has certain advantages and high stability compared with other relationship extraction models.