Reviewerly/exHarmony huggingface dataset:
Towards Ontology-Based Requirements Engineering for IoT-Supported Well-Being, Aging and Health

Ontologies serve as a one of the formal means to represent and model knowledge in computer science, electrical engineering, system engineering and other related disciplines. Ontologies within requirements engineering may be used for formal representation of system requirements. In the Internet of Things, ontologies may be used to represent sensor knowledge and describe acquired data semantics. Designing an ontology comprehensive enough with an appropriate level of knowledge expressiveness, serving multiple purposes, from system requirements specifications to modeling knowledge based on data from IoT sensors, is one of the great challenges. This paper proposes an approach towards ontology-based requirements engineering for well-being, aging and health supported by the Internet of Things. Such an ontology design does not aim at creating a new ontology, but extending the appropriate one already existing, SAREF4EHAW, in order align with the well-being, aging and health concepts and structure the knowledge within the domain. Other contributions include a conceptual formulation for Well-Being, Aging and Health and a related taxonomy, as well as a concept of One Well-Being, Aging and Health. New attributes and relations have been proposed for the new ontology extension, along with the updated list of use cases and particular ontological requirements not covered by the original ontology. Future work envisions full specification of the new ontology extension, as well as structuring system requirements and sensor measurement parameters to follow description logic.
FastGeodis: Fast Generalised Geodesic Distance Transform

Geodesic and Euclidean distance transforms have been widely used in a number of applications where distance from a set of reference points is computed.Methods from recent years have shown effectiveness in applying the Geodesic distance transform to interactively annotate 3D medical imaging data (Criminisi et al., 2008;Wang et al., 2018).The Geodesic distance transform enables providing segmentation labels, i.e., voxel-wise labels, for different objects of interests.Despite existing methods for efficient computation of the Geodesic distance transform on GPU and CPU devices (Criminisi et al., 2008(Criminisi et al., , 2009;;Toivanen, 1996;Weber et al., 2008), an open-source implementation of such methods on the GPU does not exist.On the contrary, efficient methods for the computation of the Euclidean distance transform (Felzenszwalb & Huttenlocher, 2012) have open-source implementations (Abadi et al., 2015;Seung-Lab, 2018).Existing libraries, e.g., Wang (2020), provide C++ implementations of the Geodesic distance transform; however, they lack efficient utilisation of the underlying hardware and hence result in significant computation time, especially when applying them on 3D medical imaging volumes.The FastGeodis package provides an efficient implementation for computing Geodesic and Euclidean distance transforms (or a mixture of both), targeting efficient utilisation of CPU and GPU hardware.In particular, it implements the paralellisable raster scan method from Criminisi et al. (2009), where elements in a row (2D) or plane (3D) can be computed with parallel threads.This package is able to handle 2D as well as 3D data, where it achieves up to a 20x speedup on a CPU and up to a 74x speedup on a GPU as compared to an existing open-source library (Wang, 2020) that uses a non-parallelisable single-thread CPU implementation.The performance speedups reported here were evaluated using 3D volume data on an Nvidia GeForce Titan X (12 GB) with a 6-Core Intel Xeon E5-1650 CPU.Further in-depth comparison of performance improvements is discussed in the FastGeodis documentation.
DeepJoin

We introduce DeepJoin, an automated approach to generate high-resolution repairs for fractured shapes using deep neural networks. Existing approaches to perform automated shape repair operate exclusively on symmetric objects, require a complete proxy shape, or predict restoration shapes using low-resolution voxels which are too coarse for physical repair. We generate a high-resolution restoration shape by inferring a corresponding complete shape and a break surface from an input fractured shape. We present a novel implicit shape representation for fractured shape repair that combines the occupancy function, signed distance function, and normal field. We demonstrate repairs using our approach for synthetically fractured objects from ShapeNet, 3D scans from the Google Scanned Objects dataset, objects in the style of ancient Greek pottery from the QP Cultural Heritage dataset, and real fractured objects. We outperform six baseline approaches in terms of chamfer distance and normal consistency. Unlike existing approaches and restorations generated using subtraction, DeepJoin restorations do not exhibit surface artifacts and join closely to the fractured region of the fractured shape. Our code is available at: https://github.com/Terascale-All-sensing-Research-Studio/DeepJoin.
SV-PBFT: An Efficient and Stable Blockchain PBFT Improved Consensus Algorithm for Vehicle-to-Vehicle Energy Transactions

&lt;p&gt;Aiming at the problems of difficult charging, long waiting time and energy loss of IoEV (Internet of electric vehicles) during peak charging period, we design a V2V (vehicle to vehicle) energy trading model to simulate the energy trading process. Considering the problems of malicious node attacks and privacy protection, we combine the V2V energy transaction model with a federated chain and explore a distributed ledger to record the V2V energy transaction process. In addition, we design a credibility mechanism to initialize nodes according to their comprehensive strength and select master nodes based on their behavioral performance of participating in consensus, which ensures the reliability of the consensus. Based on this, we propose a more efficient and promising consensus algorithm SV-PBFT (shapley value-PBFT), which simplifies the consensus process, reduces the communication overhead, and improves the consensus efficiency. The SV-PBFT consensus algorithm is used to replace the traditional consensus algorithm in V2V energy trading. The proposed SV-PBFT algorithm is validated by extensive simulation experiments, and numerical results are provided to confirm the good performance of SV-PBFT in V2V energy trading models.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Decision support for efficient XAI services - A morphological analysis, business model archetypes, and a decision tree

Abstract The black-box nature of Artificial Intelligence (AI) models and their associated explainability limitations create a major adoption barrier. Explainable Artificial Intelligence (XAI) aims to make AI models more transparent to address this challenge. Researchers and practitioners apply XAI services to explore relationships in data, improve AI methods, justify AI decisions, and control AI technologies with the goals to improve knowledge about AI and address user needs. The market volume of XAI services has grown significantly. As a result, trustworthiness, reliability, transferability, fairness, and accessibility are required capabilities of XAI for a range of relevant stakeholders, including managers, regulators, users of XAI models, developers, and consumers. We contribute to theory and practice by deducing XAI archetypes and developing a user-centric decision support framework to identify the XAI services most suitable for the requirements of relevant stakeholders. Our decision tree is founded on a literature-based morphological box and a classification of real-world XAI services. Finally, we discussed archetypical business models of XAI services and exemplary use cases.
Research on model design and operation mechanism of enterprise blockchain digital system

Abstract Emerging technologies such as blockchain have accelerated the digitization of a variety of industries, improved the operational efficiency of enterprises, and promoted in-depth integration of digital technology with the real economy. Blockchain has characteristics that include distributed storage, peer-to-peer transmission, strong confidentiality, and easy traceability. This article introduces blockchain into an enterprise’s information management system with the aim of breaking the enterprise’s digital barriers by using technologies such as distributed ledgers, smart contracts, and asymmetric encryption, thus improving the security and applicability of the enterprise data assets. This article explores the characteristics and security of three types of blockchain in depth, designs the model framework of the blockchain digital system (BDS) based on industry needs, and analyzes the functions and the operating mechanisms of each level of the system in detail. Finally, based on the characteristics of public blockchain, consortium blockchain, and private blockchain, three typical application scenarios in which the BDS can be used are selected, and the article discusses how E-retail supply chains, virtual power plants, and carbon trading platforms can realize digital management using the BDS, thus providing a practical basis for construction and application of the BDS.
RJ-RRT: Improved RRT for Path Planning in Narrow Passages

As a representative of sampling-based planning algorithms, rapidly exploring random tree (RRT), is extensively welcomed in solving robot path planning problems due to its wide application range and easy addition of nonholonomic constraints. However, it is still challenging for RRT to plan the path for configuration space with narrow passages. As a variant algorithm of RRT, rapid random discovery vine (RRV) gives a better solution, but when configuration space contains more obstacles instead of narrow passages, RRV performs slightly worse than RRT. In order to solve these problems, this paper re-examines the role of sampling points in RRT. Firstly, according to the state of the random tree expanding towards the current sampling point, a greedy sampling space reduction strategy is proposed, which decreases the redundant expansion of the random tree in space by dynamically changing the sampling space. Secondly, a new narrow passage judgment method is proposed according to the environment around of sampling point. After the narrow passage is identified, the narrow passage is explored by generating multiple subtrees inside the passage. The subtrees can be merged into the main tree that expands in a larger area by subsequent sampling. These improvements further enhance the value of sampling points. Compared with the existing RRT algorithms, the adaptability for different environments is improved, and the planning time and memory usage are saved.
Histogram equalization using a selective filter

Abstract Many popular modern image processing software packages implement a naïve form of histogram equalization. This implementation is known to produce histograms that are not truly uniform. While exact histogram equalization techniques exist, these may produce undesirable artifacts in some scenarios. In this paper we consider the link between the established continuous theory for global histogram equalization and its discrete implementation, and we formulate a novel histogram equalization technique that builds upon and considerably improves the naïve approach. We show that we can linearly interpolate the cumulative distribution of a low-bit image by approximately dequantizing its intensities using a selective box filter. This helps to distribute the intensities more evenly. The proposed algorithm is subsequently evaluated and compared with existing works in the literature. We find that the method is capable of producing an equalized histogram that has a high entropy, while distances between similar intensities are preserved. The described approach has implications on several related image processing problems, e.g., edge detection.
Malaria Parasite Identification from Red Blood Cell Images Using Transfer Learning Models

Malaria is a dangerous viral disease caused by Plasmodium protozoan parasites that are spread by the bite of an infected female Anopheles mosquito. This pandemic disease's fast and precise identification is essential for effective treatment. The most reliable method for diagnosing malaria is a microscopic examination of a thick and thin blood smear, which looks for the parasite and counts the number of infected cells. The ability to wholly or partially automate the identification of the disease using the information in medical images highlights the critical role that computer-aided diagnosis plays in modern medicine, in which machine learning and deep learning play a critical role. In this study, we have presented an in-depth overview of the techniques and methods used to diagnose the malaria parasite through blood slides automatically. One of the techniques is using transfer learning models to detect the malaria parasite. We have compared the performance of transfer learning models on identifying infected malaria cells by feeding the models a large dataset of uninfected and parasite cell images. The results show that the DensNet models have the edge over the other models, with DenseNet-201 achieving the highest accuracy and F1 score of 0.9339 and 0.9321, respectively. Also, DenseNet-169 outperformed the other models with 0.9594 in precision, and finally, Densenet-121 had the highest recall with 0.9490.
Adapting multilingual speech representation model for a new, underresourced language through multilingual fine-tuning and continued pretraining

In recent years, neural models learned through self-supervised pretraining on large scale multilingual text or speech data have exhibited promising results for underresourced languages, especially when a relatively large amount of data from related language(s) is available. While the technology has a potential for facilitating tasks carried out in language documentation projects, such as speech transcription, pretraining a multilingual model from scratch for every new language would be highly impractical. We investigate the possibility for adapting an existing multilingual wav2vec 2.0 model for a new language, focusing on actual fieldwork data from a critically endangered tongue: Ainu. Specifically, we (i) examine the feasibility of leveraging data from similar languages also in fine-tuning; (ii) verify whether the model's performance can be improved by further pretraining on target language data. Our results show that continued pretraining is the most effective method to adapt a wav2vec 2.0 model for a new language and leads to considerable reduction in error rates. Furthermore, we find that if a model pretrained on a related speech variety or an unrelated language with similar phonological characteristics is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance when there is very little labeled data in the target language.
Design and Testing of Cooperative Motion Controller for UAV-UGV System

Unmanned ground vehicles (UGVs) and quadrotor unmanned aerial vehicles (UAVs) can work together to solve challenges like intelligent transportation, thanks to their excellent performance complements in perception, loading, and endurance.This study presents a UAV-UGV system cooperative control mechanism.To achieve collaborative trajectory tracking, the leader-follower strategy based on a centralized control structure is firstly established in conjunction with the application scenario.The fuzzy robust controller is created to control the quadrotor UAV and improve attitude stability.Meanwhile, the UGV's controller uses the pure pursuit algorithm and a proportional integral derivative (PID) controller.In order to evaluate the cooperative control strategy and algorithm, the UAV-UGV experimental platform is set up based on the QDrone and QCar, and the experimental results show the viability of the suggested plan.
A Novel Ensemble Weight-Assisted Yolov5-Based Deep Learning Technique for the Localization and Detection of Malaria Parasites

The traditional way of diagnosing malaria takes time, as physicians have to check about 5000 cells to produce the final report. The accuracy of the final report also depends on the physician’s expertise. In the event of a malaria epidemic, a shortage of qualified physicians can become a problem. In the manual method, the parasites are identified by visual identification; this technique can be automated with the use of new algorithms. There are numerous publicly available image datasets containing the intricate structure of parasites, and deep learning algorithms can recognize these complicated patterns in the images. This study aims to identify and localize malaria parasites in the photograph of blood cells using the YOLOv5 model. In this research, a publicly available malaria trophozoite dataset is utilized which contains 1182 data samples. YOLOv5, with the novel technique of weight ensemble and traditional transfer learning, is trained using this dataset, and the results were compared with the other object detection models—for instance, Faster RCNN, SSD net, and the hybrid model. It was observed that YOLOv5 with the ensemble weights yields better results in terms of precision, recall, and mAP values: 0.76, 0.78, and 0.79, respectively. The mAP score closer to 1 signifies a higher confidence in localizing the parasites. This study is the first implementation of ensemble YOLOv5 in the malaria parasite detection field. The proposed ensemble model can detect the presence of malaria parasites and localize them with bounding boxes better than previously used models.
Mask Wearing Detection Based on YOLOv5 Target Detection Algorithm under COVID-19

Deep learning methods have been widely used in object detection in recent years as a result of advancements in artificial intelligence algorithms and hardware computing capacity.In light of the drawbacks of current manual testing mask wearing methods, this study offers a real-time detection method of mask wearing status based on the deep learning YOLOv5 algorithm to prevent COVID-19 and quicken the recovery of industrial production.The algorithm normalizes the original dataset, before connecting the data to the YOLOv5 network for iterative training, and saving the ideal weight data as a test set.The training and test results of the suggested approach are presented visually on a tensor board.With the help of cameras, this technique can collect faces, identify masked faces, and present prompts for mask use.According to experiment results, the suggested algorithm can match the requirements of real-world applications and has a high detection accuracy and good real-time performance.
Enhanced Single Image Dehazing Technique based on HSV Color Space

The clarity of images degrades significantly due to the impact of weather conditions such as fog and haze. Persistent particles scatter light, attenuating reflected light from the scene, and the dispersed atmospheric light will mix with the light received by the camera affecting image contrast in both outdoor and indoor images. Conventionally, the atmospheric scattering model (ATSM) is a model often used to recover hazy images. In ATSM, two unknown factors/parameters must be estimated: Airlight and scene transmission. The accuracy of these estimations has a significant influence on the dehazed image quality. This paper focuses on the first parameter. It introduces a new technique for estimating the airlight based on the HSV color space. The HSV color space is utilized to identify the haziest opaque area in the image. Consequently, the amount of airlight in the selected area is calculated. To assess the effectiveness of the suggested approach, the well-known dataset, RESIDE SOTS, has been used that contains two parts; namely, SOTS-indoor and SOTS-outdoor. Each of dataset includes 500 images. Experimental findings show that the suggested approach outperforms the existing techniques in terms of peak signal-to-noise-ratio and structural similarity index`.
The Use of Modern Digital Technologies in the Design and Technology VET in Ukraine

The objective of this paper is to substantiate the system of vocational education and training (VET) of fashion designers and technologists to perform computer modeling and design of the clothes. The use of the developed system of training specialists in fashion design, which provides for the use of CAD systems, contributes to the formation of readiness of future fashion designers to use specialized software in their professional activities. To increase the efficiency of VET, we used the following types of learning activities: development of creative collage, development of artistic sketches of clothes, design of templates for different sizes, use of programs for the 3D design of the clothes. A feature of this study is the selection of the software used in the design of the clothes, namely GraziaCAD and JULIVI CAD. The effectiveness of these types of tasks is confirmed by an experiment, in which 80 students from Kyiv National University of Technologies and Design took part (CG 44 students, EG 36 students). Comparing the results of incoming and outgoing control of the level of students’ readiness, a decrease in the number of students with a low level of readiness for the use of ICT in the professional activities was noted: from 22.7 % to 11.4 % in CG and from 22.2 % to 5.6 % in EG. At the same time, the percentage of students with a high level of readiness increased in both groups: from 29.5 % to 38.6 % in CG and from 25.0 % to 66.7 % in EG. Also, it should be noted that the biggest changes in the number of students with a high level of knowledge took place in EG. This, with 95% reliability according to Pearson's criterion, means that the experimental educational program led to a significant increase in the level of readiness for the use of ICT in the EG, in comparison with the CG. This confirms the effectiveness of the proposed educational program. The results of the study confirm the effectiveness of the generally accepted international practice of using the digital design in VET in the field of fashion design and technologies.
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale

Abstract We introduce Transformer Grammars (TGs), a novel class of Transformer language models that combine (i) the expressive power, scalability, and strong performance of Transformers and (ii) recursive syntactic compositions, which here are implemented through a special attention mask and deterministic transformation of the linearized tree. We find that TGs outperform various strong baselines on sentence-level language modeling perplexity, as well as on multiple syntax-sensitive language modeling evaluation metrics. Additionally, we find that the recursive syntactic composition bottleneck which represents each sentence as a single vector harms perplexity on document-level language modeling, providing evidence that a different kind of memory mechanism—one that is independent of composed syntactic representations—plays an important role in current successful models of long text.