Reviewerly/exHarmony huggingface dataset:
Evaluation of the Impact of the Introduction of Overseas High-Level Talents on the Development of Central Cities with Artificial Neural Network

For the central cities, the transformation and upgrading of the industrial structure, the continuous deepening of innovation capabilities, and the improvement of sustainable development capabilities, human resources are playing an increasingly important role. In the modern era, assembling an army of highly educated, high-quality, and highly talented personnel has become a practical demand of talent development strategy. In order to respond to national policies, keep up with the trend of the times, and give play to the comparative advantages of talent competition, the primary task of central cities is to do a good job in the introduction of overseas high-level talents. It is necessary for the central cities to establish a mechanism for the introduction of overseas high-level talents with the government as an important task and to formulate targeted talent introduction policies according to the local characteristics of the central cities. It is very important to evaluate the impact of the introduction of overseas high-level talents on the development of central cities so that the central cities can formulate talent introduction strategies according to the actual situation. This work uses an artificial neural network to evaluate the impact of the introduction of overseas high-level talents on the development of central cities. Aiming at the problem that the evaluation accuracy and computing efficiency of the deep learning-based method decrease due to the proliferation of neural network layers, and the improved residual network model is proposed. On the one hand, a multiscale feature fusion block is designed in the first layer of the network model, which can extract the multiscale feature information in the original special. On the other hand, the residual block is optimized and improved by using depthwise separable convolution to remove the computational burden on the network.
Twin-Field Quantum Key Distribution Protocol Based on Wavelength-Division-Multiplexing Technology

Quantum key distribution (QKD) generates information-theoretical secret keys between two parties based on the physical laws of quantum mechanics. Following the advancement in quantum communication networks, it becomes feasible and economical to combine QKD with classical optical communication through the same fiber using dense wavelength division multiplexing (DWDM) technology. This study proposes a detailed scheme of TF-QKD protocol with DWDM technology and analyzes its performance, considering the influence of quantum channel number and adjacent quantum crosstalk on the secret key rates. The simulation results show that the scheme further increases the secret key rate of TF-QKD and its variants. Therefore, this scheme provides a method for improving the secret key rate for practical quantum networks.
Neural stylist: Towards online styling service

Online stylist service enjoys huge economic potentials due to the trend of transformation of the fashion industry to digitalisation. Existing works either predict the fashion compatibility from the overall aspect or evaluate the compatibility with type-conditional representations. The prediction is hard to interpret due to the abstractive forecast. This paper proposes a visual and semantic representation model for explainable evaluation and recommendation. The model considers fashion compatibility from different factors, such as colour, material and style, by leveraging low to high-level features from former to later layers of CNN. The colour correlation and the pairwise relationship of fashion items in the same outfit are considered during the prediction stage. Instead of just predicting an outfit as compatible or incompatible, the model can classify an outfit as three precise evaluation levels: Good, Normal and Bad. The detailed compatible level is more consistent with the fashion sense of our human brain as Good or Bad outfits may have specific characteristics while Normal outfits tend to be ordinary. Additionally, the model can diagnose and recommend substitutions of the problematic fashion items from overall compatibility or colour-specific aspects by tracking the prediction matrices’ backpropagation gradients during the recommendation stage. Experiments in terms of outfit compatibility prediction and fill in the blank are conducted to evaluate the prediction ability of the proposed model. In contrast, fashion substitution recommendation experiments are conducted to assess the compatibility diagnosis and recommendation ability. Quantitative and qualitative results show that the model enables online stylist services with excellent explainability and generalisation on fashion prediction and recommendation.
‘DIGITAL’ OVERVIEW AT THE PROFILES OF PRE-SERVICE TEACHERS: DIGITAL AWARENESS, COMPETENCE AND FLUENCY

The adaptation of the teachers to the digital age, the integration of their digital skills into the learning and teaching processes, their digital awareness, competencies and fluency constitute an important problem which is within the scope of lifelong learning.The aim of the present study is to examine the digital profiles of pre-service teachers within the framework of universal education principles and online learning theories during the pandemic period. To serve for this purpose, 'digital' profiles of pre-service teachers, who prepare individuals for the digital society, are going to be investigated. Furthermore, their digital awareness, competence and fluency levels are going to be highlighted, and the concepts are going to be discussed in the light of current developments that are under the effects of the pandemic. Thus, the level of relationship between the concepts are going to be underlined to offer solutions to current digital problems. As for the procedure, the study used relational screening model on 539 pre-service teachers. According to the results, digital awareness, competence and fluency levels of pre-service teachers were high. There is a positive, moderate and significant relationship between digital awareness and digital fluency; similarly, there is a positive, high level and significant relationship between digital fluency and digital competence. At this point, digital awareness and competence explained 62% of the variation in digital fluency. Via the present study, it is possible to note down that the concepts of digital awareness, digital competence and digital fluency might be explained thoroughly. Hence, the present study is expected to contribute to the literature and all stakeholders related to education. Keywords: digital awareness, digital education, digital competence, digital fluency, the relational survey model
Audio–visual annotation graphs for guiding lens-based scene exploration

We introduce a novel approach for guiding users in the exploration of annotated 2D models using interactive visualization lenses. Information on the interesting areas of the model is encoded in an annotation graph generated at authoring time. Each graph node contains an annotation, in the form of a visual and audio markup of the area of interest, as well as the optimal lens parameters that should be used to explore the annotated area and a scalar representing the annotation importance. Directed graph edges are used, instead, to represent preferred ordering relations in the presentation of annotations, by having each node point to the set of nodes that should be seen before presenting its associated annotation. A scalar associated to each edge determines the strength of this constraint. At run-time, users explore the scene with the lens, and the graph is exploited to select the annotations that have to be presented at a given time. The selection is based on the current view and lens parameters, the graph content and structure, and the navigation history. The best annotation under the lens is presented by playing the associated audio clip and showing the visual markup in overlay. When the user releases control, requests guidance, opts for automatic touring, or when no available annotations are under the lens, the system guides the user towards the next best annotation using glyphs, and potentially moves the lens towards it if the user remains inactive. This approach supports the seamless blending of an automatic tour of the data with interactive lens-based exploration. The approach is tested and discussed in the context of the exploration of multi-layer relightable models.
A fair consensus model in blockchain based on computational reputation

Fair selection of community members' transactions in blockchain-based technologies, especially in finance, is essential and vital. Therefore, in this paper, a novel consensus model for fair selection of community members' transactions through reputation models is presented as fair reputation-based consensus model (FRCM), so that all transactions of community members have the same weight and transactions are selected regardless of the transaction fee or the assets or computing power but based on the reputation of the trustees who are responsible for placing the transactions in the block. For this purpose, a FRCM prototype has been designed and implemented based on the proposed algorithms. Experimental and theoretical evaluations and simulation results show that the FRCM can select the transactions of community members fairly while reducing the average response time effectively. In addition, comparative analysis with other consensus mechanisms proved that FRCM model has lower consensus latency than other mechanisms, while improving throughput and scalability.
Evolving Deep Neural Networks with Cultural Algorithms for Real-Time Industrial Applications

The goal of this paper is to investigate the applicability of evolutionary algorithms to the design of real-time industrial controllers. Present-day “deep learning” (DL) is firmly established as a useful tool for addressing many practical problems. This has spurred the development of neural architecture search (NAS) methods in order to automate the model search activity. CATNeuro is a NAS algorithm based on the graph evolution concept devised by Neuroevolution of Augmenting Topologies (NEAT) but propelled by cultural algorithm (CA) as the evolutionary driver. The CA is a network-based, stochastic optimization framework inspired by problem solving in human cultures. Knowledge distribution (KD) across the network of graph models is a key to problem solving success in CAT systems. Two alternative mechanisms for KD across the network are employed. One supports cooperation (CATNeuro) in the network and the other competition (WM). To test the viability of each configuration prior to use in the industrial setting, they were applied to the design of a real-time controller for a two-dimensional fighting game. While both were able to beat the AI program that came with the fighting game, the cooperative method performed statistically better. As a result, it was used to track the motion of a trailer (in lateral and vertical directions) using a camera mounted on the tractor vehicle towing the trailer. In this second real-time application (trailer motion), the CATNeuro configuration was compared to the original NEAT (elitist) method of evolution. CATNeuro is found to perform statistically better than NEAT in many aspects of the design including model training loss, model parameter size, and overall model structure consistency. In both scenarios, the performance improvements were attributed to the increased model diversity due to the interaction of CA knowledge sources both cooperatively and competitively.
A similarity-guided segmentation model for garbage detection under road scene

The development of computer vision technology provides a possible path for realizing intelligent control of road sweepers to reduce energy waste in urban street cleaning work. For garbage segmentation of seven categories under road scene, we introduce an efficient deep-learning-based method. Our model follows a lightweight structure with a feature pyramid attention (FPA) module employed in the decoder to enhance feature integration at multi-levels. Besides, a similarity guidance (SG) module is added to the decoder branches, which calculates the cosine distance between learned prototypes and feature maps to guide the segmentation results from a metric learning perspective. Our model has less than 3 M parameters and can run at over 65 FPS in an RTX 2070 GPU. Experimental results demonstrate that our method can yield competitive results in terms of speed and accuracy trade-off, with overall mean intersection-over-union (mIoU) reaching 0.87 and 0.67, respectively, on two garbage data sets we built. Besides, our model can perform acceptable category-balanced segmentation from less than 20 annotated images per category by introducing the SG module.
Design of Vocal Music Teaching System Platform for Music Majors Based on Artificial Intelligence

The cyberspace consisting of information technology, artificial intelligence (AI), communication systems, computer systems, automatic control systems, digital devices, and the applications, services, and data they carry is advancing in an unprecedented way. It is no longer a dream to assist the development of pedagogical expert systems to solve the current problems of insufficient teachers and single teaching tools in music pedagogy reform and to realize the sharing of teaching resources. Artificial intelligence system under music education and education not only breaks the traditional music education model but also promotes the development of music education. This paper introduces the principle of artificial intelligence and analyzes the problems faced by university music education, and the implementation method of vocal education system based on artificial intelligence for music majors is also specifically studied in this paper. This mainly involves the design of the system architecture, such as the system architecture, which is designed to meet the requirements of the whole system. A survey of 100 students found that 82% of students preferred computer-based education to traditional music education. An artificial intelligence-based vocal teaching system for music majors is a system with good applications, where teachers keep track of students’ learning through reasoning and analysis, enabling students to learn on their own. Learning music requires constant practice, expanding horizons, and accepting new ways of teaching, and the combination of music and the Internet is the latest option to use resources wisely, optimize them, and provide comprehensive resources. Although a variety of music education aids have emerged, few are actually suitable for music classroom education, so it is important to study and implement AI-based speech education systems for music majors.
The more the better? Comparing two SQD-based learning designs in a teacher training on augmented and virtual reality

Abstract The purpose of this study was to investigate whether a learning design based entirely on the micro level strategies of the Synthesis of Qualitative Data (SQD) model is better suited to promote teachers augmented and virtual reality-related Will, Skill, and Tool (WST) compared to a learning design based less on the SQD model. To this end, we first developed two learning designs that were randomly distributed across two teacher professional development courses. In one course ( n = 23), teachers learned according to our developed and fully SQD-based Tell–Show–Enact–Do (TSED) learning design; in the other course, teachers ( n = 22) followed a Tell–Show–Enact (TSE) learning design that was less SQD-based. The results of the quasi-experimental field study show that the developed fully SQD-based TSED learning design is better able to promote the elements WST in teachers regarding the integration of augmented and virtual reality in the classroom. The results of the study have implications for theory and practice. For example, the developed TSED learning design can serve as a blueprint for other teacher educators, and the empirical findings support the micro level strategies recommended in the SQD model. Additional findings are discussed.
DSGMFFN: Deepest semantically guided multi-scale feature fusion network for automated lesion segmentation in ABUS images

Automated breast ultrasound (ABUS) imaging technology has been widely used in clinical diagnosis. Accurate lesion segmentation in ABUS images is essential in computer-aided diagnosis (CAD) systems. Although deep learning-based approaches have been widely employed in medical image analysis, the large variety of lesions and the imaging interference make ABUS lesion segmentation challenging.In this paper, we propose a novel deepest semantically guided multi-scale feature fusion network (DSGMFFN) for lesion segmentation in 2D ABUS slices. In order to cope with the large variety of lesions, a deepest semantically guided decoder (DSGNet) and a multi-scale feature fusion model (MFFM) are designed, where the deepest semantics is fully utilized to guide the decoding and feature fusion. That is, the deepest information is given the highest weight in the feature fusion process, and participates in every decoding stage. Aiming at the challenge of imaging interference, a novel mixed attention mechanism is developed, integrating spatial self-attention and channel self-attention to obtain the correlation among pixels and channels to highlight the lesion region.The proposed DSGMFFN is evaluated on 3742 slices of 170 ABUS volumes. The experimental result indicates that DSGMFFN achieves 84.54% and 73.24% in Dice similarity coefficient (DSC) and intersection over union (IoU), respectively.The proposed method shows better performance than the state-of-the-art methods in ABUS lesion segmentation. Incorrect segmentation caused by lesion variety and imaging interference in ABUS images can be alleviated.
3D face reconstruction and verification using multi-view RGB-D data

The development of computer graphics and technologies leads to the emergence of different methods for presenting the three-dimensional information in the virtual world. In this paper we present an algorithm for 3D model reconstruction of a human face. A key aspect is that the face is captured from multiple views by a stationary low cost sensor providing both color and depth data. The different views are then registered into a common model. A verification method for assessing the accuracy of the reconstructed face is also presented.
Recommendation of English Reading in Vocational Colleges Using Linear Regression Training Model

High-quality vocational education is a vital basis for China’s efforts to develop high-quality teachers and technical and skilled workers. The increasing development of contemporary vocational education is given higher importance in the new era. We proposed the stick to moral education while carefully integrating it with the requirements of changes in technology and modernization, continuous improvements in the quality of college vocational education, and the development of more conceptual and skilled talented individuals with both political integrity and the capacity to build modernization. Using artificial intelligence technology to challenge these difficulties in the context of social informatization can play important role in enhancing the teaching quality of high vocational education. With the advent of the Internet era, students and parents have an increasingly strong demand for online independent vocational English reading materials, although there is some English learning software that provides online vocational English reading materials in the education market. However, most of this software only changes the reading form of paper vocational English reading materials but fails to completely meet the real demand of students for vocational English reading materials, so how to carry out personalized recommendation reading according to the real reading situation and reading preferences of students is a vital problem to be resolved. In this background, this paper studies the personalized recommendation technology, in the use of recommendation algorithms on the based association rules to obtain higher vocational English reading content recommended the strongest association rules, on the based combination of user-based collaborative clarifying recommendation algorithm. The linear regression model combined with the users reading interest and personalized recommendation of higher vocational English reading process is optimized, improving the accuracy of English reading recommendation in vocational colleges.
Data Extraction of Circular-Shaped and Grid-like Chart Images

Chart data extraction is a crucial research field in recovering information from chart images. With the recent rise in image processing and computer vision algorithms, researchers presented various approaches to tackle this problem. Nevertheless, most of them use different datasets, often not publicly available to the research community. Therefore, the main focus of this research was to create a chart data extraction algorithm for circular-shaped and grid-like chart types, which will accelerate research in this field and allow uniform result comparison. A large-scale dataset is provided containing 120,000 chart images organized into 20 categories, with corresponding ground truth for each image. Through the undertaken extensive research and to the best of our knowledge, no other author reports the chart data extraction of the sunburst diagrams, heatmaps, and waffle charts. In this research, a new, fully automatic low-level algorithm is also presented that uses a raster image as input and generates an object-oriented structure of the chart of that image. The main novelty of the proposed approach is in chart processing on binary images instead of commonly used pixel counting techniques. The experiments were performed with a synthetic dataset and with real-world chart images. The obtained results demonstrate two things: First, a low-level bottom-up approach can be shared among different chart types. Second, the proposed algorithm achieves superior results on a synthetic dataset. The achieved average data extraction accuracy on the synthetic dataset can be considered state-of-the-art within multiple error rate groups.
A single image dehazing method based on decomposition strategy

Outdoor haze has adverse impact on outdoor image quality, including contrast loss and poor visibility. In this paper, a novel dehazing algorithm based on the decomposition strategy is proposed. It combines the advantages of the two-dimensional variational mode decomposition (2DVMD) algorithm and dark channel prior. The original hazy image is adaptively decomposed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm. The low-frequency image is dehazed by using the improved dark channel prior, and then fused with the high-frequency image. Furthermore, we optimize the atmospheric light and transmittance estimation method to obtain a defogging effect with richer details and stronger contrast. The proposed algorithm is compared with the existing advanced algorithms. Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.