Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Charging Coordination Scheme for Smart Power Grids Using a Blockchain

With the rapid emergence of smart grids, charging coordination is considered the intrinsic actor that merges energy storage units (ESUs) into the grid in addition to its substantial role in boosting the resiliency and efficiency of the grid. However, it suffers from several challenges beginning with dependency on the energy service provider (ESP) as a single entity to manage the charging process, which makes the grid susceptible to several types of attacks such as a single point of failure or a denial-of-service attack (DoS). In addition, to schedule charging, the ESUs should submit charging requests including time to complete charging (TCC) and battery state of charge (SoC), which may disclose serious information relevant to the consumers. The analysis of this data could reveal the daily activities of those consumers. In this paper, we propose a privacy-preservation charging coordination scheme using a blockchain. The blockchain achieves decentralization and transparency to defeat the security issues related to centralized architectures. The privacy preservation will be fulfilled using a verifiable aggregation mechanism integrated with an aggregated signing technique to identify the untrusted aggregator and assure the data source and the identity of the sender. Security and performance evaluations are performed, including off-chain and on-chain experiments and simulations, to assess the security and efficiency of the scheme.
Plaintext-related image encryption scheme without additional plaintext based on 2DCS

In the latest research on image compression and encryption, more and more encryption schemes associate key generation mechanism with plaintext to resist various attacks. This paper presents an image encryption scheme related to plaintext based on 2D compressive sensing(2DCS). It is a plaintext correlation compression and encryption scheme with no additional information. The chaotic sequence generated by Henon map randomly selects some positions in the plaintext image. Double random-phase encoding(DRPE) is applied to the pixels to establish the relationship between the plaintext and the key. The encryption process is divided into three parts. The first part is to map out these key pixels and perform double negative positive transformation(DNPT). In the second part, the plaintext image is sampled, quantified and reconstructed by 2DCS. DNA calculation and diffusion were performed on the reconstructed vector. In the third part, the key pixel matrix and diffusion matrix of DNPT are inversely reconstructed to obtain the cipher image. The proposed encryption mechanism can avoid the transmission of additional information related to the plaintext in the channel, which greatly saves the transmission cost and key storage space. The effectiveness and feasibility of the scheme are verified by various security analyses.
The role of knowledge-based resources in Agile Software Development contexts

The software value chain is knowledge-based since it is highly dependant on people. Consequently, a lack of practice in managing knowledge as a resource may jeopardise its application in software development. Knowledge-Based Resources (KBRs) relate to employees’ intangible knowledge that is deemed to be valuable to a company’s competitive advantage. In this study, we apply a grounded theory approach to examine the role of KBRs in Agile Software Development (ASD). To this aim, we collected data from 18 practitioners from five companies. We develop the Knowledge-Push theory, which explains how KBRs boost the need for change in ASD. Our results show that the practitioners who participated in the study utilise, as primary strategies, task planning, resource management, and social collaboration. These strategies are implemented through the team environment and settings and incorporate an ability to codify and transmit knowledge. However, this process of codification is non-systematic, which consequently introduces inefficiency in the domain of knowledge resource utilisation, resulting in potential knowledge waste. This inefficiency can generate negative implications for software development, including meaningless searches in databases, frustration because of recurrent problems, the unnecessary redesign of solutions, and a lack of awareness of knowledge sources.
Federated Clustering and Semi-Supervised learning: A new partnership for personalized Human Activity Recognition

Federated Learning (FL) is currently studied by several research groups as a promising paradigm for sensor-based Human Activity Recognition (HAR) to mitigate the privacy and scalability issues of classic centralized approaches. However, in the HAR domain, data is non-independently and identically distributed (non-IID), and personalization is one of the major challenges. Federated Clustering has been recently proposed to mitigate this issue by creating specialized global models for groups of similar users. While this approach significantly improves personalization, it assumes that labeled data are available on each client. In this work, we propose SS-FedCLAR, a novel HAR framework that combines Federated Clustering and Semi-Supervised learning. In SS-FedCLAR , each client uses a combination of active learning and label propagation to provide pseudo labels to a large amount of unlabeled data, which is then used to collaboratively train a Federated Clustering model. We evaluated SS-FedCLAR on two well-known public datasets, showing that it outperforms existing semi-supervised FL solutions while reaching recognition rates similar to fully-supervised FL approaches.
Data monetization: insights from a technology-enabled literature review and research agenda

Data is one of the most valuable resources in the world. The new data economy has led to several data use cases and one of them is monetization. Given how recent the concept of data monetization is, this study aims to investigate it, better understand its meaning, implications and issues in the academic literature and provide guidance for practitioners as well as direction for further research. A systematic literature review is conducted on English literature from 2013 to 2022. 54 articles were identified from where a topic and sub-topic categorization and a conceptual framework is developed. The paper makes contributions in three areas: (1) providing a holistic understanding of data monetization models through the extension of a framework from the literature, (2) categorizing of the key topics and trends in data monetization, and (3) using a systematic approach to identify managerial implications and a future research agenda.
NeRFFaceEditing: Disentangled Face Editing in Neural Radiance Fields

Recent methods for synthesizing 3D-aware face images have achieved rapid development thanks to neural radiance fields, allowing for high quality and fast inference speed. However, existing solutions for editing facial geometry and appearance independently usually require retraining and are not optimized for the recent work of generation, thus tending to lag behind the generation process. To address these issues, we introduce NeRFFaceEditing, which enables editing and decoupling geometry and appearance in the pretrained tri-plane-based neural radiance field while retaining its high quality and fast inference speed. Our key idea for disentanglement is to use the statistics of the tri-plane to represent the high-level appearance of its corresponding facial volume. Moreover, we leverage a generated 3D-continuous semantic mask as an intermediary for geometry editing. We devise a geometry decoder (whose output is unchanged when the appearance changes) and an appearance decoder. The geometry decoder aligns the original facial volume with the semantic mask volume. We also enhance the disentanglement by explicitly regularizing rendered images with the same appearance but different geometry to be similar in terms of color distribution for each facial component separately. Our method allows users to edit via semantic masks with decoupled control of geometry and appearance. Both qualitative and quantitative evaluations show the superior geometry and appearance control abilities of our method compared to existing and alternative solutions.
DC-COX: Data collaboration Cox proportional hazards model for privacy-preserving survival analysis on multiple parties

The demand for the privacy-preserving survival analysis of medical data integrated from multiple institutions or countries has been increased. However, sharing the original medical data is difficult because of privacy concerns, and even if it could be achieved, we have to pay huge costs for cross-institutional or cross-border communications. To tackle these difficulties of privacy-preserving survival analysis on multiple parties, this study proposes a novel data collaboration Cox proportional hazards (DC-COX) model based on a data collaboration framework for horizontally and vertically partitioned data. By integrating dimensionality-reduced intermediate representations instead of the original data, DC-COX obtains a privacy-preserving survival analysis without iterative cross-institutional communications or huge computational costs. DC-COX enables each local party to obtain an approximation of the maximum likelihood model parameter, the corresponding statistic, such as the p-value, and survival curves for subgroups. Based on a bootstrap technique, we introduce a dimensionality reduction method to improve the efficiency of DC-COX. Numerical experiments demonstrate that DC-COX can compute a model parameter and the corresponding statistics with higher performance than the local party analysis. Particularly, DC-COX demonstrates outstanding performance in essential feature selection based on the p-value compared with the existing methods including the federated learning-based method.
Pre-training language model incorporating domain-specific heterogeneous knowledge into a unified representation

Existing technologies expand BERT from different perspectives, e.g. designing different pre-training tasks, different semantic granularities, and different model architectures. Few models consider expanding BERT from different text formats. In this paper, we propose a heterogeneous knowledge language model (HKLM), a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text. To capture the corresponding relations among these multi-format knowledge, our approach uses masked language model objective to learn word knowledge, uses triple classification objective and title matching objective to learn entity knowledge and topic knowledge respectively. To obtain the aforementioned multi-format text, we construct a corpus in the tourism domain and conduct experiments on 5 tourism NLP datasets. The results show that our approach outperforms the pre-training of plain text using only 1/4 of the data. We further pre-train the domain-agnostic HKLM and achieve performance gains on the XNLI dataset.
Mass detection in automated 3-D breast ultrasound using a patch Bi-ConvLSTM network

Breast cancer mortality can be significantly reduced by early detection of its symptoms. The 3-D Automated Breast Ultrasound (ABUS) has been widely used for breast screening due to its high sensitivity and reproducibility. The large number of ABUS slices, and high variation in size and shape of the masses, make the manual evaluation a challenging and time-consuming process. To assist the radiologists, we propose a convolutional BiLSTM network to classify the slices based on the presence of a mass. Because of its patch-based architecture, this model produces the approximate location of masses as a heat map. The prepared dataset consists of 60 volumes belonging to 43 patients. The precision, recall, accuracy, F1-score, and AUC of the proposed model for slice classification were 84%, 84%, 93%, 84%, and 97%, respectively. Based on the FROC analysis, the proposed detector obtained a sensitivity of 82% with two false positives per volume.
The mediating role of E‐learning motivation on the relationship between technology access and satisfaction with E‐learning

To examine the e-learning motivation mediating role on the relationship between technology access and satisfaction with e-learning among nursing students during COVID-19 pandemic.This is a cross-sectional, descriptive design.A convenience sample of students (N = 139) from public and private nursing colleges was recruited in this study. Data were collected using students' satisfaction, motivation and access to technology tools.Nursing students in the study were somewhat satisfied with e-learning, had access to the required technology for e-learning and had moderate levels of e-learning motivation. The multiple regression analysis showed that technology access and e-learning motivation influenced and explained 30.4% of the variance in satisfaction with e-learning. Previous experience with e-learning was associated with satisfaction with e-learning. A series of regression analyses revealed that e-learning motivation partially mediated the relationship between technology access and satisfaction with e-learning.
Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems

When we face real problems using computational resources, we understand that it is common to find combinatorial problems in binary domains. Moreover, we have to take into account a large number of possible candidate solutions, since these can be numerous and make it complicated for classical algorithmic techniques to address them. When this happens, in most cases, it becomes a problem due to the high resource cost they generate, so it is of utmost importance to solve these problems efficiently. To cope with this problem, we can apply other methods, such as metaheuristics. There are some metaheuristics that allow operation in discrete search spaces; however, in the case of continuous swarm intelligence metaheuristics, it is necessary to adapt them to operate in discrete domains. To perform this adaptation, it is necessary to use a binary scheme to take advantage of the original moves of the metaheuristics designed for continuous problems. In this work, we propose to hybridize the whale optimization algorithm metaheuristic with the Q-learning reinforcement learning technique, which we call (the QBWOA). By using this technique, we are able to realize an smart and fully online binarization scheme selector, the results have been statistically promising thanks to the respective tables and graphs.
Interconversion of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> and Greenberger-Horne-Zeilinger states for Ising-coupled qubits with transverse global control

Interconversions of $W$ and Greenberger-Horne-Zeilinger states in various physical systems are lately attracting considerable attention. We address this problem in the fairly general physical setting of qubit arrays with long-ranged (all-to-all) Ising-type qubit-qubit interaction, which are simultaneously acted upon by transverse Zeeman-type global control fields. Motivated in part by a recent Lie-algebraic result that implies state-to-state controllability of such a system for an arbitrary pair of states that are invariant with respect to qubit permutations, we present a detailed investigation of the state-interconversion problem in the three-qubit case. The envisioned interconversion protocol has the form of a pulse sequence that consists of two instantaneous (delta-shaped) control pulses, each of them corresponding to a global qubit rotation, and an Ising-interaction pulse of finite duration between them. Its construction relies heavily on the use of the (four-dimensional) permutation-invariant subspace (symmetric sector) of the three-qubit Hilbert space. In order to demonstrate the viability of the proposed state-interconversion scheme, we provide a detailed analysis of the robustness of the underlying pulse sequence to systematic errors, i.e. deviations from the optimal values of its five characteristic parameters.
Medical image enhancement strategy based on morphologically processing of residuals using a special kernel

Abstract Medical imaging is playing a pivotal role in the domain of medical diagnosis. But the major problems related to medical images is that most of the images (of different modalities) are suffered from noise and other quality‐related problems such as poor contrast, blurring, and difficulties in extracting appropriate information. Therefore, it is necessary to construct some techniques that could improve medical images in such a way that it will be ideal for diagnostic applications. Medical image enhancement is the process of increasing the contrast quality of intensity variations and improves the visual representation of medical images. To provide suitable interpretation and clearer image for the observers with reduced noise levels, this article proposes a novel competent medical image enhancement method based on morphologically processing of residuals using a special kernel. First, it combines linear low pass filtering with nonlinear technique that allows for selection of essential regions where edges will get well preserved. The selection of those regions is based on morphological processing of linear filter residuals and then aims to find significant regions specified by edges of high amplitude and appropriate size. The reconstructed regions are combined with the output of low pass filtering to recovers the original shape of edges. In addition, the method allows to control the contrast to avoid blurring while preserving significant image information. In the end, a special kernel is convolved with an image to obtain the sharper image. Experiments on the different types of medical images are performed and the results are compared using different standard evaluation metrics. The quantitative results obtained through proposed method for all the metrics are optimal compared to other competing methods. It is observed that the results on various test images affirm that the proposed method generates excellent image visual quality and significantly enhances the contrast of all images which helps with better diagnosis and treatment.
Why do volunteer content moderators quit? Burnout, conflict, and harmful behaviors

Moderating content on social media can lead to severe psychological distress. However, little is known about the type, severity, and consequences of distress experienced by volunteer content moderators (VCMs), who do this work voluntarily. We present results from a survey that investigated why Facebook Group and subreddit VCMs quit, and whether reasons for quitting are correlated with psychological distress, demographics, and/or community characteristics. We found that VCMs are likely to experience psychological distress that stems from struggles with other moderators, moderation team leads’ harmful behaviors, and having too little available time, and these experiences of distress relate to their reasons for quitting. While substantial research has focused on making the task of detecting and assessing toxic content easier or less distressing for moderation workers, our study shows that social interventions for VCM workers, for example, to support them in navigating interpersonal conflict with other moderators, may be necessary.
Profile likelihood-based parameter and predictive interval analysis guides model choice for ecological population dynamics

Calibrating mathematical models to describe ecological data provides important insight via parameter estimation that is not possible from analysing data alone. When we undertake a mathematical modelling study of ecological or biological data, we must deal with the trade-off between data availability and model complexity. Dealing with the nexus between data availability and model complexity is an ongoing challenge in mathematical modelling, particularly in mathematical biology and mathematical ecology where data collection is often not standardised, and more broad questions about model selection remain relatively open. Therefore, choosing an appropriate model almost always requires case-by-case consideration. In this work we present a straightforward approach to quantitatively explore this trade-off using a case study exploring mathematical models of coral reef regrowth after some ecological disturbance, such as damage caused by a tropical cyclone. In particular, we compare a simple single species ordinary differential equation (ODE) model approach with a more complicated two-species coupled ODE model. Univariate profile likelihood analysis suggests that the both models are practically identifiable. To provide additional insight we construct and compare approximate prediction intervals using a new parameter-wise prediction approximation, confirming both the simple and complex models perform similarly with regard to making predictions. Our approximate parameter-wise prediction interval analysis provides explicit information about how each parameter affects the predictions of each model. Comparing our approximate prediction intervals with a more rigorous and computationally expensive evaluation of the full likelihood shows that the new approximations are reasonable in this case. All algorithms and software to support this work are freely available as jupyter notebooks on GitHub so that they can be adapted to deal with any other ODE-based models.
SafeFac: Video-based smart safety monitoring for preventing industrial work accidents

This work presents SafeFac, an intelligent camera-based system for managing the safety of factory environments. In SafeFac a set of cameras installed on the assembly line are used to capture images of workers that approach the machinery under hazardous situations to alert system managers and halt the line if needed. Given a challenging set of practical application-level requirements such as multi-camera support and low response latency, SafeFac exploits a YOLOv3-based light-weight human object detection. To address the latency–accuracy tradeoff, SafeFac incorporates a set of algorithms as pre- and post-processing modules and a novel adaptive camera scheduling scheme. Our evaluation with a video dataset containing more that 113,000 frames from real assembly line activity shows that SafeFac achieves high precision (99.93%) and recall (96.44%), and SafeFac successfully satisfies such challenging requirements as a ready-for-deployment system for safe factory management.
Concurrence triangle induced genuine multipartite entanglement measure

We study the quantification of genuine multipartite entanglement (GME) for general multipartite states. A set of inequalities satisfied by the entanglement of $N$-partite pure states is derived by exploiting the restrictions on entanglement distributions, showing that the bipartite entanglement between each part and its remaining ones cannot exceed the sum of the other partners with their remaining ones. Then a series of triangles, named concurrence triangles, are established corresponding to these inequalities. Proper genuine multipartite entanglement measures are thus constructed by using the geometric mean area of these concurrence triangles, which are non-increasing under local operation and classical communication. The GME measures classify which parts are separable or entangled with the rest ones for non genuine entangled pure states. The GME measures for mixed states are given via the convex roof construction, and a witness to detect the GME of multipartite mixed states is presented by an approach based on state purifications. Detailed examples are given to illustrate the effectiveness of our GME measures.