Reviewerly/exHarmony huggingface dataset:
A survey on the model-centered approaches to conceptual modeling of IoT systems

Internet of Things (IoT) is a system of connected objects, entities, devices, and components which share and transfer data over a network. Many papers are published on the topic of conceptual models in the IoT context, but it is difficult to assess the current status of the conceptual modeling approaches and methods for IoT systems. This paper presents an overview of the state of the art as well as discusses fundamental concepts, challenges and current research gaps with potential future agenda for conceptual modeling of IoT. Search facilities in the selected online repositories were used to identify the most relevant papers. The primary results were scanned and papers were selected according to the inclusion/exclusion criteria. Selected papers were assessed to extract data for the defined attributes. This paper confirms that there is a large body of research related to modeling of IoT systems. However, the results show that there is a lack of commonly agreed approaches and supporting formal methods for conceptual modeling of IoT systems. On the other hand, recent studies that apply model-based or model-driven development principles that use ontology or metamodel based approaches are promising due to systematic use of models as the primary means of a development process enabling for the dissemination of the methods further to the emerging fields such as smart cities, factories, transportation, hospitals, healthcare, hospitality and tourism, etc.
Convergence of parameter estimation of a Gaussian mixture model minimizing the Gini index of dissimilarity

AbstractThe Gaussian mixture model (GMM) is a probabilistic model that represents the behavior of a data set as a linear combination of K Gaussian densities. The most used method to estimate the parameters of a GMM is the maximum likelihood, giving rise to the EM-algorithm. Another alternative is minimizing the Gini index of dissimilarity between the empirical distribution of the observed data and the parametric distribution GMM, deriving in an iterative algorithm called GID-algorithm. In this work, we prove its convergence with the help of the χ2 divergence.KEYWORDS: Gini index of dissimilarityGaussian mixture modelIterative algorithmχ2 divergence Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingA.L. López Lobato acknowledges the support from the Mexican National Council for Science and Technology (CONACyT) through a National scholarship to Ph.D. studies at the Faculty of Mathematics in the University of Veracruz.
Deep learning-based segmentation of brain parenchyma and ventricular system in CT scans in the presence of anomalies

The automatic segmentation of brain parenchyma and cerebrospinal fluid-filled spaces such as the ventricular system is the first step for quantitative and qualitative analysis of brain CT data. For clinical practice and especially for diagnostics, it is crucial that such a method is robust to anatomical variability and pathological changes such as (hemorrhagic or neoplastic) lesions and chronic defects. This study investigates the increase in overall robustness of a deep learning algorithm that is gained by adding hemorrhage training data to an otherwise normal training cohort.A 2D U-Net is trained on subjects with normal appearing brain anatomy. In a second experiment the training data includes additional subjects with brain hemorrhage on image data of the RSNA Brain CT Hemorrhage Challenge with custom reference segmentations. The resulting networks are evaluated on normal and hemorrhage test casesseparately, and on an independent test set of patients with brain tumors of the publicly available GLIS-RT dataset.Adding data with hemorrhage to the training set significantly improves the segmentation performance over an algorithm trained exclusively on normally appearing data, not only in the hemorrhage test set but also in the tumor test set. The performance on normally appearing data is stable. Overall, the improved algorithm achieves median Dice scores of 0.98 (parenchyma), 0.91 (left ventricle), 0.90 (right ventricle), 0.81 (third ventricle), and 0.80 (fourth ventricle) on the hemorrhage test set. On the tumor test set, the median Dice scores are 0.96 (parenchyma), 0.90 (left ventricle), 0.90 (right ventricle), 0.75 (third ventricle), and 0.73 (fourth ventricle).Training on an extended data set that includes pathologies is crucial and significantly increases the overall robustness of a segmentation algorithm for brain parenchyma and ventricular system in CT data, also for anomalies completely unseen during training. Extension of the training set to include other diseases may further improve the generalizability of the algorithm.
ANALYSIS OF INEFFECTIVENESS OF INDONESIAN SENTENCES IN STUDENTS' THESIS WRITING (A CASE STUDY OF SOCIOLOGY STUDENTS' THESES AT MUHAMMADIYAH UNIVERSITY OF MAKASSAR)

The aim of this study is to describe the forms of sentence ineffectiveness in the theses of students from the Faculty of Education at Muhammadiyah University of Makassar. This research falls under the category of qualitative descriptive study. The data for this study are obtained from students' theses using the observation method through note-taking technique. The results of the research reveal five forms of sentence ineffectiveness, namely (1) lack of fluency, (2) inaccuracy, (3) lack of clarity, (4) inefficiency, and (5) lack of parallelism
Multimodel ensemble projection of photovoltaic power potential in China by the 2060s

China's demand for solar energy has been growing rapidly to meet energy transformation targets. However, the potential of solar energy is affected by weather conditions and is expected to change under climate warming. Here, we project the photovoltaic (PV) power potential over China under low and high emission scenarios by the 2060s, taking advantage of meteorological variables from 24 CMIP6 models and 4 PV models with varied formats. The ensemble mean of these models yields an average PV power of 277.2 KWh m−2 yr−1 during 2004–2014, with a decreasing tendency from the west to east. By 2054–2064, the national average PV power potential is projected to increase by 2.29% under a low emission scenario but decrease by 0.43% under a high emission scenario. The emission control in the former scenario significantly enhances surface solar radiation and promotes PV power in the east. On the contrary, strong warming causes inhibitions to PV power generation under the high emission scenario. Extreme warming events on average decrease the PV power potential by 0.28% under the low emission scenario and 0.44% under the high emission scenario, doubling and tripling the present-day loss, respectively. Our projections reveal large benefits of controlling emissions for the future solar energy in China due to both the clean atmosphere and the moderate warming. 为了实现能源转型的目标, 中国对太阳能的需求一直在极速增长.然而, 太阳能发电潜力受到天气条件的影响并预期在气候变暖背景下发生改变.本文中, 我们利用第六次国际耦合模式比较计划 (CMIP6) 24个气候模式的气象变量以及4个不同形式的光伏模型, 预估了在2060年代低排放或高排放情况下中国的光伏发电潜力.多模式集合平均光伏功率在2004-2014年为277.2 KWh m−2 yr−1, 并呈现出从西到东的下降趋势.到2054-2064年, 在低排放情景下, 全国平均光伏发电潜力将增加2.29%, 而在高排放情景下则减少0.43%.低排放情景的排放控制大大增强了地表太阳辐射, 促进了东部的光伏发电.相反, 在高排放情景下, 强烈的变暖对光伏发电产生了抑制作用.极端暖事件使光伏发电潜力在低排放情景下降低0.28%, 而高排放情景下降低0.44%, 分别相当于当代损失量的两倍和三倍.我们的预估表明排放控制带来的清洁空气和适度变暖对中国未来的太阳能利用是有益的.
ARDA‐UNIT recurrent dense self‐attention block with adaptive feature fusion for unpaired (unsupervised) image‐to‐image translation

Abstract One of the most challenging topics in artificial intelligence is image‐to‐image translation, the purpose of which is generating images close to those in the target domain while preserving the important features of the images in the source domain. In this direction, various types of generative adversarial networks have been developed. ARDA‐UNIT, presented in this paper, seeks to meet the main challenges of these networks, that is, producing a high‐quality image in a reasonable amount of time, and transferring content between two images with different structures. The proposed recurrent dense self‐attention block, applied in ARDA‐UNIT's generator latent space, simultaneously increases its generating capability and decreases the training parameters. ARDA‐UNIT has a feature extraction module which feeds both the generator and the discriminator. This module uses a new adaptive feature fusion method which combines multi‐scale features in such a way that the characteristics of each scale are preserved. The module also uses a pre‐trained CNN that reduces the training parameters. Moreover, a feature similarity loss is introduced that guides the model to change the structure of the source domain in accordance with that in the target domain. Experiments performed on different datasets using FID, KID and IS evaluation criteria have shown that the model reduces computational loads, transfers structures well, and achieves better qualities.
FPIseg: Iterative segmentation network based on feature pyramid for few‐shot segmentation

Abstract Few‐shot segmentation (FSS) enables rapid adaptation to the segmentation task of unseen‐classes object based on a few labelled support samples. Currently, the focal point of research in the FSS field is to align features between support and query images, aiming to improve the segmentation performance. However, most existing FSS methods implement such support/query alignment by solely leveraging middle‐level feature for generalization, ignoring the category semantic information contained in high‐level feature, while pooling operation inevitably lose spatial information of the feature. To alleviate these issues, the authors propose the Iterative Segmentation Network Based on Feature Pyramid (FPIseg), which mainly consists of three modules: Feature Pyramid Fusion Module (FPFM), Region Feature Enhancement Module (RFEM), and Iterative Optimization Segmentation Module (IOSM). Firstly, FPFM fully utilizes the foreground information from the support image to implement support/query alignment under multi‐scale, multi‐level semantic backgrounds. Secondly, RFEM enhances the foreground detail information of aligned feature to improve generalization ability. Finally, ISOM iteratively segments the query image to optimize the prediction result and improve segmentation performance. Extensive experiments on the PASCAL‐5 i and COCO‐20 i datasets show that FPIseg achieves considerable segmentation performance under both 1‐shot and 5‐shot settings.
Preserving Voter Privacy and Security in Online Voting through Diffie-Hellman Encryption

This research paper presents an advanced online voting system, incorporating the Diffie-Hellman encryption algorithm, to ensure voter privacy and address key challenges in digital elections. The system offers additional features such as anonymous authentication, verifiable decryption, and secure ballot transmission. Through a rigorous evaluation process, the proposed approach received an overall evaluation score of 3.55 out of 5, indicating its effectiveness in upholding voter privacy and system efficiency. Specific evaluation criteria revealed accuracy with a score of 3.57 out of 5, efficiency at 3.53 out of 5, reliability at 3.54 out of 5, timeliness at 3.50 out of 5, and a commendable security rating of 3.59 out of 5. These results highlight the system's potential in providing a secure and user-friendly online voting platform, encouraging voter participation and reinforcing the democratic principles of transparency and integrity.
TTN‐FCN: A Tangut character classification framework by tree tensor network and fully connected neural network

Abstract The classification of Tangut characters plays a significant role in Western Xia research, and yet it is still a great challenge to recognize Tangut characters accurately due to the less inter‐class similarity and smaller intra‐class variation of Tangut character images. The main reason is that Tangut characters possess an extremely intricate construct despite some other character recognition methods emerging. For this reason, the authors propose a novel framework for Tangut character classification, named tree tensor network‐fully connected neural network (TTN‐FCN), in which TTN is embedded to fully connect neural network. Firstly, Tangut images are encoded into quantum product states without entanglement in pre‐processing. Then the TTN is adopted to contract quantum product states to intermediate low dimensional quantum states. Finally, low dimensional quantum states are input to the FCN network to perform classification tasks. The Model is evaluated on the Tangut character dataset that is constructed from Tangut character‐related documents by scanning and consists of 30,293 Tangut character images with 6077 categories. Experimental results show that TTN‐FCN has a faster convergence speed and achieves classification precision (AC) of 99.98% and loss of 0.688% with the max batch size 2042, which outperforms 30 compared networks. Moreover, the proposed model can also be generalized to other character recognition, which enhances its potential for cultural relic research and development.
Zero‐shot temporal event localisation: Label‐free, training‐free, domain‐free

Abstract Temporal event localisation (TEL) has recently attracted increasing attention due to the rapid development of video platforms. Existing methods are based on either fully/weakly supervised or unsupervised learning, and thus they rely on expensive data annotation and time‐consuming training. Moreover, these models, which are trained on specific domain data, limit the model generalisation to data distribution shifts. To cope with these difficulties, the authors propose a zero‐shot TEL method that can operate without training data or annotations. Leveraging large‐scale vision and language pre‐trained models, for example, CLIP, we solve the two key problems: (1) how to find the relevant region where the event is likely to occur; (2) how to determine event duration after we find the relevant region. Query guided optimisation for local frame relevance relying on the query‐to‐frame relationship is proposed to find the most relevant frame region where the event is most likely to occur. Proposal generation method relying on the frame‐to‐frame relationship is proposed to determine the event duration. The authors also propose a greedy event sampling strategy to predict multiple durations with high reliability for the given event. The authors’ methodology is unique, offering a label‐free, training‐free, and domain‐free approach. It enables the application of TEL purely at the testing stage. The practical results show it achieves competitive performance on the standard Charades‐STA and ActivityCaptions datasets.
Determining the proper number of proposals for individual images

Abstract The region proposal network is indispensable to two‐stage object detection methods. It generates a fixed number of proposals that are to be classified and regressed by detection heads to produce detection boxes. However, the fixed number of proposals may be too large when an image contains only a few objects but too small when it contains much more objects. Considering this, the authors explored determining a proper number of proposals according to the number of objects in an image to reduce the computational cost while improving the detection accuracy. Since the number of ground truth objects is unknown at the inference stage, the authors designed a simple but effective module to predict the number of foreground regions, which will be substituted for the number of objects for determining the proposal number. Experimental results of various two‐stage detection methods on different datasets, including MS‐COCO, PASCAL VOC, and CrowdHuman showed that equipping the designed module increased the detection accuracy while decreasing the FLOPs of the detection head. For example, experimental results on the PASCAL VOC dataset showed that applying the designed module to Libra R‐CNN and Grid R‐CNN increased over 1.5 AP 50 while decreasing the FLOPs of detection heads from 28.6 G to nearly 9.0 G.
Deep Bregman divergence for self-supervised representations learning

Neural Bregman divergence measures the divergence of data points using convex neural networks, which is beyond Euclidean distance and capable of capturing divergence over distributions. The non-Euclidean geometry is not well explored in deep representation learning and remains a challenging endeavor for self-supervised representation learning. In this paper, we propose deep Bregman divergences for self-supervised pretext task learning, where we aim to enhance self-supervised embedding representation by training additional networks based on functional Bregman divergences. Our framework can capture the divergence of embedding distributions and improve the quality of learned representation using an arbitrary Bregman divergence over data embedding. Specifically, we develop a novel self-supervised architecture and a new divergence loss that measures the asymmetric distance of arbitrary Bergman divergences of neural networks. We show that the combination of self-supervised contrastive learning and our proposed method outperforms the baseline as well as most established methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets. Moreover, the learned representations generalize well when transferred to other datasets and tasks.
A new dynamic DNA-coding model for gray-scale image encryption

Abstract To protect the images and provide a more secure cipher image, DNA encoding is crucial in image encryption. Applying a single, easily detectable coding rule to the image during DNA encoding has no impact on the encryption model's security level. Therefore, using various coding rules while applying encryption to the image, dynamic DNA-coding techniques have emerged to strengthen and improve the encryption of the image and its security. This study integrates a dynamic DNA-coding method with an encryption model. The model is applied to gray-scale images, where using a predetermined coding rule, every two bits are DNA-encoded in the image. The proposed model generates the key by sending the image and its metadata to hash functions. Following that, the hyperchaotic system constructs three chaotic sequences using the key, and the Lorenz–Liu chaotic system generates a sequence of coding rules. Then, the image is passed to Arnold Transform, where the resulted image is diffused by applying five chaotic maps. Last, using the coding rules, it is DNA-encoded, provided with the chaotic sequences to DNA, and DNA-decoded. Twelve metrics were used to assess the proposed model on ten widely used images. Results show a promising improvement in performance, since it enhanced the security of the model.
Visual privacy behaviour recognition for social robots based on an improved generative adversarial network

Abstract Although social robots equipped with visual devices may leak user information, countermeasures for ensuring privacy are not readily available, making visual privacy protection problematic. In this article, a semi‐supervised learning algorithm is proposed for visual privacy behaviour recognition based on an improved generative adversarial network for social robots; it is called PBR‐GAN. A 9‐layer residual generator network enhances the data quality, and a 10‐layer discriminator network strengthens the feature extraction. A tailored objective function, loss function, and strategy are proposed to dynamically adjust the learning rate to guarantee high performance. A social robot platform and architecture for visual privacy recognition and protection are implemented. The recognition accuracy of the proposed PBR‐GAN is compared with Inception_v3, SS‐GAN, and SF‐GAN. The average recognition accuracy of the proposed PBR‐GAN is 85.91%, which is improved by 3.93%, 9.91%, and 1.73% compared with the performance of Inception_v3, SS‐GAN, and SF‐GAN respectively. Through a case study, seven situations are considered related to privacy at home, and develop training and test datasets with 8,720 and 1,280 images, respectively, are developed. The proposed PBR‐GAN recognises the designed visual privacy information with an average accuracy of 89.91%.
Usefulness of QoS in Multidimensional QoE Prediction for Haptic–Audiovisual Communications

This article investigates prediction of Quality of Experience (QoE) by comparing borrowing-from-neighbor situations and isolated ones. We demonstrate that joint utilization of multiple QoE measures enhances the accuracy of QoE prediction compared to that by a collection of individual QoE measures each regressed on Quality of Service (QoS) parameters, while the accuracy improvement with additional usage of QoS information in the former is limited. As an example of system that needs multidimensional QoE representation, the article gives haptic audiovisual interactive communications. We employ QoE and QoS data taken previously in an experiment, where 13 QoE measures (a five-point score each) and 12 QoS parameters (nonnegative continuous values each) are available at three average rates of load traffic. We build two kinds of Bayesian models for QoE prediction; one is a logistic regression model of a single QoE measure as the response variable and QoS parameters as predictors, which is a typical traditional method of discrete QoE prediction and isolated in a sense. The other is a structural equation model (SEM) with latent constructs (i.e., factors) of audiovisual quality, haptic quality, and user experience quality; the original SEM, which contains only QoE indicators of three constructs (audiovisual quality (AVQ), haptic quality (HQ), and user experience quality (UXQ)), was proposed in one of the author’s previous studies. This article extends the SEM to accommodate QoS parameters. We develop two kinds of new SEMs with QoS parameters: One has three extended constructs referred to as eAVQ, eHQ, and eUXQ, each of which has both QoE and QoS indicators, and the other has separate constructs for QoE and QoS, which lead to totally six constructs (AVqoe, Hqoe, UXqoe, AVqos, Hqos, and UXqos). We performed Markov chain Monte Carlo simulation of the Bayesian models with the JAGS software in an R environment. For comparison of QoE prediction accuracy, we adopt the 10-fold cross validation method and in part widely applicable information criterion. We then found that the three-construct models outperform the logistic regression models with respect to all subjective QoE measures and that the two kinds of the models are comparable as for the objective measure. The six-construct model exhibits almost the same accuracy as that of the three-construct one unless the number of QoE measures ( n qoe ) in the model is small. When the number n qoe is small, single-construct models may be a better choice. We have thus learned that multiple QoE measures should be utilized jointly (i.e., borrowing from neighbor) in QoE prediction rather than resorting to QoS information only.
ShedBox: enabling digital storytelling in men’s sheds

Men’s sheds are community spaces where older, retired men come together to conduct woodworking and other types of craftwork and DIY activities. There is little research on exploring how technology can help us understand shed members’ perceived values associated with men’s sheds. In order to investigate this further, we designed and deployed a mobile storytelling application called ShedBox for an Australian men’s shed for over six weeks. ShedBox allows shed members to share audio-visual stories. Our analysis of resulting 58 stories and follow-up interviews with 13 shed members unpacked three important themes: a sense of comradery, kinship and companionship present within the shed; material and emotional engagement with the space; and, health and wellbeing of shed members. Our results also highlight a strong masculine culture of the space and how the shed is a site of active learning and engagement for its members. Lastly, our conclusion presents suggestions regarding how such forms of storytelling technology can be leveraged in similar contexts, and also how they can be effectively designed to support different demography.