Reviewerly/exHarmony huggingface dataset:
Imagedata: A Python library to handle medical image data in NumPy array subclass Series

Imagedata is a python library to read and write medical image data into Series objects.In particular, imagedata will read, sort and write DICOM ® 3D and 4D series based on defined attributes.As far as possible, imagedata will handle geometry information between the medical image data formats like DICOM, NIfTI (Cox et al., 2004) and ITK (Yoo et al., 2002). Imagedata provides a Series class inheriting thenumpy.ndarray class (Harris et al., 2020), adding DICOM data structures.Plugins provide functions to import and export DICOM and other data formats.The DICOM plugin can read complete series, sorting the data as requested into multidimensional arrays.Input and output data can be accessed on various locations, including local files, DICOM servers and XNAT servers (Marcus et al., 2007).The Series class enables NumPy and derived libraries (like SciPy (Virtanen et al., 2020)) to work on medical images, simplifying input and output.A feature is the conversion between different image formats.E.g., a pipeline based on a clinical DICOM series can be converted to NIfTI, processed by some NIfTI-based tool (e.g.FSL (Smith et al., 2004)).Finally, the result can be converted back to DICOM and stored as a new series in PACS (Picture Archive and Communication system).A viewer is included, allowing the display of a stack of images, including modifying window width and centre, and scrolling through 3D and 4D image stacks.A region of interest (ROI) can be drawn, resulting in a mask as a NumPy ndarray, or as an outline.
3D Skeleton and Two Streams Approach to Person Re-identification Using Optimized Region Matching

Person re-identification (Re-ID) is a challenging and arduous task due to non-overlapping views, complex background, and uncontrollable occlusion in video surveillance. An existing method for capturing pedestrian local region information is to divide person regions into horizontal stripes, which may lead to invalid features and erroneous learning. To solve this problem, this paper proposes a 3D skeleton and a two-stream approach to person Re-ID. The first stream of the method uses the 3D skeleton for background filtering and region segmentation. The second stream uses Siamese net to extract the global descriptor. The features of the two streams are fused to preserve the integrity of the person. An optimized region matching method for metric learning is designed. Extensive comparing experiments were conducted with state-of-the-art Re-ID methods on the Market-1501, CUHK03, and DukeMTMC-reID datasets. Experimental results show that the proposed method outperforms the existing methods in recognition accuracy.
Do Immersive Displays Influence Exhibition Attendees’ Satisfaction?: A Stimulus-Organism-Response Approach

One of the fastest-growing trends in the exhibition industry is the utilization of immersive technology displays which provide exhibition attendees with enhanced interactive and dynamic experiences. However, little is known about the relationship between immersive technology displays and exhibition attendees’ satisfaction. This study aimed to examine the relationship between exhibitors’ immersive displays and exhibition attendees’ satisfaction in relation to the Stimulus-Organism-Response (S-O-R) framework. Additionally, the study categorized immersive displays and compared them with exhibition attendees’ socio-demographics. An online questionnaire survey was used for data collection, and a series of statistical analyses were carried out. The results revealed that 75% of respondents reported positive experiences with immersive displays, and some attendees were more satisfied with some of the immersive displays. Implications of the study are discussed.
On the utilization of non‐quality assessed literature in software engineering research

Abstract Quality of research and knowledge sources can be a critical factor to judge the quality of the research citing them. The prevailing online dissemination of research via blogs, websites, experience reports, and white papers can be helpful as it minimizes potential publication biases. In this paper, we have (i) identified the body of software engineering (SE) literature that is non‐indexed by Web‐of‐Science but has been used in published SE systematic literature review (SLR) studies (as primary studies); (ii) proposed a checklist‐based method for quality assessment of non‐indexed and grey literature; (iii) empirically evaluated the effectiveness of the proposed method; (iv) searched primary studies retrieved from the SLRs in DBLP and Google Scholar (GS) to verify whether DBLP or Google Scholar can act as viable options to search for non‐indexed Web‐of‐Science (non‐WOS‐indexed) literature. Besides proposing our synthesized checklist method, we found that (i) SE literature reviews use significantly non‐indexed Web‐of‐Science sources; (ii) DBLP only covers a portion of the non‐WOS‐indexed reference, whereas GS has a more complete coverage of the non‐indexed reference in comparison. Preliminary evaluation of the proposed quality assessment point system‐based and checklist‐based methods shows it to be a viable way to assess quality of the non‐indexed and grey literature.
Optimizing Multi-Objective Federated Learning on Non-IID Data with Improved NSGA-III and Hierarchical Clustering

Federated learning (FL) can tackle the problem of data silos of asymmetric information and privacy leakage; however, it still has shortcomings, such as data heterogeneity, high communication cost and uneven distribution of performance. To overcome these issues and achieve parameter optimization of FL on non-Independent Identically Distributed (non-IID) data, a multi-objective FL parameter optimization method based on hierarchical clustering and the third-generation non-dominated sorted genetic algorithm III (NSGA-III) algorithm is proposed, which aims to simultaneously minimize the global model error rate, global model accuracy distribution variance and communication cost. The introduction of a hierarchical clustering algorithm on non-IID data can accelerate convergence so that FL can employ an evolutionary algorithm with a low FL client participation ratio, reducing the overall communication cost of the NSGA-III algorithm. Meanwhile, the NSGA-III algorithm, with fast greedy initialization and a strategy of discarding low-quality individuals (named NSGA-III-FD), is proposed to improve the convergence efficiency and the quality of Pareto-optimal solutions. Under two non-IID data settings, the CNN experiments on both MNIST and CIFAR-10 datasets show that our approach can obtain better Pareto-optimal solutions than classical evolutionary algorithms, and the selected solutions with an optimized model can achieve better multi-objective equilibrium than the standard federated averaging (FedAvg) algorithm and the Clustering-based FedAvg algorithm.
A Novel recipes recommendation system Based on Knowledge-Graph

With the improvement of living standards, the demand for personalized recipes is getting more and more attention. Therefore, this paper designs and implements a beneficial recipe recommendation system based on dietary knowledge mapping. The front-end of the system uses Vue.js to build the user interface, and the back-end uses Spring MVC framework to implement. And the recommendation technology based on the knowledge graph is studied, and the recipe recommendation method is improved by using knowledge graph. Firstly, we crawled recipe knowledge through crawlers and built a dietary knowledge graph integrating multi-domain information by using the rich semantics of knowledge graph. Secondly, the knowledge graph is combined with the collaborative filtering algorithm implemented by Mahout to improve the effectiveness of recommendations. Finally, an intelligent Q&#x0026;A module is developed for the system based on the knowledge graph to provide accurate and effective recipes for people and patients with dietary choice difficulties.
Mineral-specific Quantitative Element Mapping Applied to Visualization of Geochemical Variation in Glauconitic Clasts

The geochemical composition of glauconitic clasts is a provenance marker to distinguish the origin of gravity flows in sedimentary basins. Their geochemical variation is visible in major and trace elements composition, where the former can be visualized by element mapping of glauconitic clasts. By applying automated quantitative mineralogy on a Scanning Electron Microscope equipped with the ZEISS Mineralogic™ software platform, we developed a new way to visualize the element distribution in selected minerals, while masking out the other minerals simultaneously. This software applies energy dispersive spectroscopy spectrum deconvolution for each analysis point, therefore quantitative concentrations (wt%) of each element are determined for each pixel and visualized in the false-coloured element map with reproducible results for individual grains and zonations in these grains. The investigated glauconitic clasts were collected from eleven drill cores, covering a 1400 m-depth interval, and crosscutting four different gravity flow members. The clasts show three different trends: first, cores of glauconitic clasts show a variation of compositions that are mainly dependent on the conditions during their formation. This is most visible for Si and Al. Secondly, the composition of the glauconitic clasts changes with depth, their cores become first more, than less Fe-rich and more K-rich. This is probably an alteration effect, depending on temperature, and therefore mineral stability. And, thirdly, most glauconitic clasts are zoned, where rims are richer in Al and poorer in Fe and K. The visualization of the chemical variation in the glauconitic clasts proofs to be a useful tool to separate these conflicting element exchange processes.
Multi-feature Co-learning for Image Inpainting

Image inpainting has achieved great advances by simultaneously leveraging image structure and texture features. However, due to lack of effective multi-feature fusion techniques, existing image inpainting methods still show limited improvement. In this paper, we design a deep multi-feature co-learning network for image inpainting, which includes Soft-gating Dual Feature Fusion (SDFF) and Bilateral Propagation Feature Aggregation (BPFA) modules. To be specific, we first use two branches to learn structure features and texture features separately. Then the proposed SDFF module integrates structure features into texture features, and meanwhile uses texture features as an auxiliary in generating structure features. Such a co-learning strategy makes the structure and texture features more consistent. Next, the proposed BPFA module enhances the connection from local feature to overall consistency by co-learning contextual attention, channel-wise information and feature space, which can further refine the generated structures and textures. Finally, extensive experiments are performed on benchmark datasets, including CelebA, Places2, and Paris StreetView. Experimental results demonstrate the superiority of the proposed method over the state-of-the-art. The source codes are available at https://github.com/GZHU-DVL/MFCL-Inpainting.
Research on Aspect-Level Sentiment Analysis Based on Text Comments

Sentiment analysis is the processing of textual data and giving positive or negative opinions to sentences. In the ABSA dataset, most sentences contain one aspect of sentiment polarity, or sentences of one aspect have multiple identical sentiment polarities, which weakens the sentiment polarity of the ABSA dataset. Therefore, this paper uses the SemEval 14 Restaurant Review dataset, in which each document is symmetrically divided into individual sentences, and two versions of the datasets ATSA and ACSA are created. ATSA: Aspect Term Sentiment Analysis Dataset. ACSA: Aspect Category Sentiment Analysis Dataset. In order to symmetrically simulate the complex relationship between aspect contexts and accurately extract the polarity of emotional features, this paper combines the latest development trend of NLP, combines capsule network and BRET, and proposes the baseline model CapsNet-BERT. The experimental results verify the effectiveness of the model.
A Modelling Method for Describing and Facilitating the Reuse of SysML Models during Design Process

Abstract MBSE and SysML are increasingly finding their applications in industry as well as in academia. The reuse of the information described in SysML models depends, among others, on the modelling methods, management of dependencies between elements and on the needed remodelling effort. In this paper, a modelling method is presented that address the reuse of SysML models and descriptions as well as reuse of model variants in SysML. A case example is presented to explain the modelling methods and the gained experience is summarised in the form of general recommendations for further use.
Innovation of Artificial Intelligence and Digital Media in Environmental Art Design

With the current upsurge of innovation and entrepreneurship in China, this paper objectively analyzes the importance of designers’ professional quality and scientific awareness in response to the need for the integration and development of innovative skills and artistic spirit in environmental design. This paper emphasizes that the level of intelligent operation of science and technology should be improved in design, the rigor of intelligent utilization in creative expression should be improved, and the problem of evaluation criteria of artistic design value should be solved based on professional development. This paper gives the design scheme of intelligent monitoring module based on Lora technology and designs the hardware circuit of the intelligent monitoring module with MSP430F2272 as the core. It includes a microcontroller circuit, a Lora module interface circuit, an input signal detection module, a control output module, and the like. This paper focuses on the content, representation, and implementation of the associated configuration table, and compares four analog input signals with variable thresholds. The sensor detects 800Lux, and the error is 500Lux, which is higher than errmax, so it outputs -10 brightening every time to generate analog input logic. The 4 digital inputs are combined with the analog input logic to generate output control signals by associating operations.
Coin classification using Xilinx System Generator

Computer vision systems usually require a large amount of arithmetic processing to get the required information. Field-Programmable Gate Arrays (FPGAs) can be programmed to easily modify their internal logic functions to achieve high-speed hardware processing and parallel operations, making them a more convenient solution for high-performance embedded vision systems. The power consumption of FPGA-based embedded vision systems is much lower than that of traditional Central Processing Unit (CPU)-and Graphics Processing Unit (GPU)-based vision systems, and System Generator, jointly introduced by MathWorks and Xilinx, reduces the need to implement algorithms for image processing using hardware description languages (HDLs). In this paper, by using Xilinx System Generator (XSG) in a FPGA to implement some algorithms for coin classification, including coin image interference background removal, binarization, etc. This coin classification scheme is verified and validated by using XSG in Matlab/Simulink.
Enabling Distributed Teams – A Process Model for Early and Continuous Method Validation

Abstract Neglecting challenges of distributed collaboration can lead to significant efficiency and effectiveness losses in agile, distributed development teams. The EDiT method provides support for improving distributed collaboration of development teams. To ensure acceptance, applicability, and contribution to success in industrial development practice, it is necessary to validate the EDiT method. The goal of this contribution is the development of a process model for early and incremental validation of the EDiT method in the field finally leading to a validation of the EDiT method itself.
Application of Blockchain Technology in the Supply Chain Finance

The emergence and development of blockchain technology has injected vigorous vitality into innovations in the fields of digital finance and supply chain finance. Supply chain finance connects the transaction process between core enterprises and upstream and downstream enterprises, providing flexible financial products and services for finance. Since the demand side of funds is mostly enterprises, financial institutions face challenges in credit identification, transaction supervision, risk control and other business aspects of supply chain finance. The application innovation of blockchain technology in the field of supply chain finance will help solve the information asymmetry and credit crisis in the financial transaction chain. After introducing the basic theory of blockchain technology, this paper analyzes and researches the application of blockchain technology in supply chain finance, which not only provides a process-oriented reference for better integration of blockchain technology and finance field, but also provides inspiration for subsequent in-depth practical application in this field.
An Efficient Reference-Point Based k Neighbors Algorithm for Imbalanced Data

The kNNs are very simple, effective and online mul-ticategory learning algorithms, which makes them widely applica-ble to various fields. However, almost all kNNs have the problem of low accuracy in imbalanced data. In this paper, an interesting point is observed that the nearest neighbors might not always be the best samples for prediction, and a reference-point based k Neighbors algorithm (RPkN) is proposed. The proposed algorithm uses some reference-points to measure the distance between two data points rather than directly computing the distance. This makes it able to find better neighbors than their nearest ones, and the problem of low accuracy in imbalanced data sets could be solved. Therefore, the proposed method can achieve higher aver-age accuracy than the existing exact kNNs. In addition, it avoids the direct computation of distances between each pair of data points without using any tree structure, and this makes the RPkN decrease the time complexity to O(nlogn). The time complexity to classify a new data point is O(logn). The time complexity is lower than almost all the existing exact kNNs. The low time complexity and not relying on tree structures makes it very suitable to be paralleled for large-scale data processing when compared with other fast exact kNNs using tree structure.
Masked Face Recognition using Deep Learning based on Unmasked Area

Researches related to face recognition find new problems, especially due to current lifestyle change by wearing a face mask. A masked face means that features or information contained in the face are missing. Hence, some modifications should be made to the existing face recognition system in this new situation. On the other hand, some studies indicate that face recognition using partial faces still gives high accuracy, especially on the top-half face (part of the face that contains the forehead to before the nose). Therefore, the proposed training technique uses only partial faces for masked face recognition. The used dataset in this research is our own dataset consisting of 125 subjects. This dataset contains videos taken of people's faces with different devices and backgrounds. Some methodologies from previous studies were used in this study, namely YOLOv4 as a face detection method, pre-trained VGGFace model as a feature extraction method, and artificial neural network as a classification method. Using the same training technique as the previous research, our masked face recognition system achieved a test accuracy of 79.58%. When using the proposed training technique, the obtained test accuracy was 99.53%. It shows that the performance is much better when using a more appropriate training technique in the case of masked face recognition.
An improved gravitational search algorithm combining with centripetal force

Although GSA has strong search ability, it is still easy to fall into local optimum and accuracy is often not high enough. Inspired by the concept of centripetal force, we propose a centripetal force based gravity search algorithm, named CF-GSA. It controls the parameters in different stages, which balances the exploration and exploitation capabilities of particles. We performed comparative experiments on 23 benchmark functions, and analyzed the performance of this algorithm. In experiments, it can effectively prevent falling into local optimun and achieve higher accuracy and better convergence.
Implemented Parallel Annealing in Scalable Fully Coupled Annealing Processing System

There has been progress in annealing processors for solving combinatorial optimization problems. There are two types of annealing processors: the "sparsely coupled" type, which implements only some spin-to-spin couplings, and the "fully coupled" type, which implements all spin-to-spin couplings. The latter has the advantage of having a large number of problems to be solved per spin, but had been said to have the disadvantage of poor scalability due to the complicated coupling. To overcome this, we first created a scalable Fully coupled annealing processing system (SFCAPS) by using multi-chip operation and implemented it on an FPGA board. This is to reduce the amount of coupling per chip by performing the multi-chip operation, and to improve the scalability by facilitating the connection between the chips. However, in the architecture, multi-chip operation could not be fully used and most of the chips on the board were idle for a large amount of time. Therefore, for this study, we implemented a parallel annealing method called multi-spin threading on this architecture to reduce the number of idle chips and demonstrated that the architecture can operate more efficiently. As a result, the architecture became more efficient, which raised the parallelism of the architecture. This has resulted in an average 5% improvement in the accuracy of the annealing processor's solution, demonstrating that it is easier to achieve a high accuracy solution.
A multi‐task learning based approach for efficient breast cancer detection and classification

Abstract Automatic segmentation and classification of breast tumours in ultrasound images using deep learning approaches can help early detect breast cancer. Such predictive modelling can potentially significantly improve the survival chances of the involved patients. Most of the typical deep convolutional neural network (CNN) based approaches consider segmentation and classification tasks separately. But this loses important supervisory information to help achieve better model training. This work proposes the integrated learning of both of these tasks in an end‐to‐end manner, using a multi‐task learning based approach. More specifically, a convolutional encoder‐decoder based architecture is coupled with a residual CNN for performing segmentation and classification together. The level‐wise feature maps from both the encoder and decoder parts of the segmentation network are utilized for classification in the proposed approach. From experimental analysis on a publicly available breast ultrasound image (BUSI) dataset, it has been observed that the proposed approach can achieve impressive performances, both with respect to tumour segmentation and classification. A mean test set AUC of 0.97 and a mean dice score of 0.74 is achieved, establishing a new state‐of‐the‐art performance on the BUSI dataset. From the impressive experimental observations, it can be concluded that learning to perform both segmentation and classification simultaneously can have a very high positive impact on the overall quality of the predictive model. Such observations suggest that the proposed approach can be beneficial in providing real‐time decision support to the involved diagnostic radiologists, which can help improve the survival chances of the corresponding patients.