Reviewerly/exHarmony huggingface dataset:
Board 372: Remote Engaged Student Learning through Hands-on Internet of Things

Abstract Remote learning environments have become integral to higher educational institutions even when most courses, particularly in STEM, have returned to face-to-face or hybrid instruction. Although the impetus for this project was the COVID-19 pandemic which forced institutions of higher education everywhere to move to an online remote teaching and learning format, and as such negatively affected STEM fields which require hands-on labs and access to instrumentation, remote learning still remains part of most courses today. We describe continuing efforts to create learning environments and materials to support remote hands-on engaged student learning off-campus at two Hispanic Serving Institutions (HSIs) to enable and enhance student learning beyond the institutional walls. The approach utilizes Internet of Things (IoT) kits as remote learning tools that are provided to students to allow hands-on learning experiences at students' own chosen environment and time. Problem- and Project-based Learning approaches are utilized to achieve student learning outcomes, and equip the students with the problem-solving skills necessary in STEM fields. Team-based projects engage students from multiple disciplines and demographics to work on projects, make individual contributions, and integrate their work with that of their team members. General feedback from students has been positive. Assessment of student performance also demonstrates the effectiveness of IoT-based learning in engineering and computer science disciplines.
Deep Neural Network-Oriented Indicator Method for Inverse Scattering Problems Using Partial Data

We consider the inverse scattering problem to reconstruct an obstacle using partial far-field data due to one incident wave. A simple indicator function, which is negative inside the obstacle and positive outside of it, is constructed and then learned using a deep neural network (DNN). The method is easy to implement and effective as demonstrated by numerical examples. Rather than developing sophisticated network structures for the classical inverse operators, we reformulate the inverse problem as a suitable operator such that standard DNNs can learn it well. The idea of the DNN-oriented indicator method can be generalized to treat other partial data inverse problems.
A comparative study between zero-knowledge proof (ZKP) and ring signatures targeting the legal and regulatory implications with the general data protection regulation (GDPR) and general data protection regulation (GDPR)

Privacy protection ensures that individuals have control over personal data, preventing abuse and preserving trust in the use of online services. In the “Digital Era”, where the collection, storage and processing of personal information have become ubiquitous, data privacy emerges as a relevant topic. In this sense, laws were created, such as the General Data Protection Law (LGPD) in Brazil and the General Data Protection Regulation (GDPR) in Europe, to control privacy and the processing of personal data. The article presents a comparative analysis of 2 (two) data privacy mechanisms, the Zero-Knowledge Proof (ZKP) and Ring Signatures, used in Blockchain, aiming at the legal and regulatory implications with the LGPD and GDPR. The comparative study between ZKP and Ring Signatures highlights the flexibility of ZKP in various contexts, including voting and secure authentication systems, while Ring Signatures offer significant advantages in terms of scalability and efficiency in systems where subscriber anonymity is considered fundamental. Furthermore, the legal and regulatory implications of the ZKP are discussed, mainly in relation to LGPD and GDPR. Finally, the article concludes that the comparative analysis offers insights into applications, challenges and legal and regulatory implications, particularly in relation to data privacy and compliance with regulations such as LGPD and GDPR.
Integrating joint latent class mixed models and Bayesian network for uncovering clinical subgroups of COVID-19 patients

When modelling the dynamics of biomarkers in biomedical studies, it is essential to identify homogeneous clusters of patients and analyse them from a precision medicine perspective. This need has emerged as crucial and urgent during the COVID-19 pandemic: early understanding of symptoms and patient heterogeneity has significant implications for prevention, early diagnosis, effective management, and treatment. Additionally, biomarker progression may be associated with clinically relevant time-toevent data. Therefore, statistical models are necessary to gain insight into complex disease mechanisms by properly accounting for unobservable heterogeneity in patients while jointly modelling longitudinal and time-to-event data. In this study, we leverage the key features of Latent Class modelling and Bayesian Network approaches and propose a unified framework to (a) uncover homogeneous subgroups of patients concerning their longitudinal and survival data and (b) describe patient subgroups within a multivariate framework.
Implementation of NOA@PAT

Due to the publication of the NAMUR Open Architecture (NOA), there are new oppurtunities to digitalize the existing process industry. The acquisition and direct forwarding to the target address is especially for the operation of process analytical technologies (PAT) of interest to improve efficiency and avaliability. In this article, the deeper benefit of NOA for PAT is outlined by presenting the advantages of standardized information models for PAT vitality data. Furthermore, the successful implementation of a NOA use case for PAT is shown in terms of the field wiring, the generation of the information model and the IT/OT transfer. At last, open aspects to achieve a comprehensive NOA implementation in existing installations are mentioned.
An Interactive Application for Hand Muscle Fatigue Relief Based on Gesture Recognition

Prolonged use of a keyboard or mouse can lead to continuous bending and twisting of the wrist, which can lead to muscle fatigue and pain. Finger exercises can reduce the hand muscle fatigue effectively. However, the repetitive exercises will bring boring and tedious experience. In this paper, we developed a hand rehabilitation exercise system based on gesture recognition technology and gamification design, which provides a more enjoyable, personalized and effective rehabilitation experience. The hand fatigue relief system was designed with the fusion of game elements and gesture interaction, which can enhance users’ engagement and their rehabilitation motivation.
Integrated Profitability Evaluation for a Newsboy-Type Product in Own Brand Manufacturers

Effective inventory management depends on accurate estimates of product profitability to formulate ordering and manufacturing strategies. The achievable capacity index (ACI) is a simple yet efficient approach to measuring the profitability of newsboy-type products with normally distributed demand, wherein profitability is presented as the probability of achieving the target profit under the optimal ordering quantity. Unfortunately, the ACI is applicable only to retail stores with a single demand. In the current study, we addressed the issue of measuring the integrated profitability of newsboy-type products sold in multiple locations with independent demand levels, such as own-branding-and-manufacture (OBM) companies with multiple owned channels. We began by formulating profitability in accordance with multiple independent normal demands, and then developed an integrated ACI (IACI) to simplify expression. We also derived the statistical properties of the unbiased estimator to determine the true IACI in situations where demand patterns are unknown. Finally, we conducted hypothesis testing to determine whether the integrated profitability meets a stipulated minimum level. For convenience, we tabulated the critical values as a function of sample size, confidence level, the number of channels, and the stipulated minimum level. One can make decisions simply by estimating the IACI based on historical demand data from all channels and then looking up the critical value in the corresponding tables. Consequently, the proposed methods make it possible for OBM managers to address integrated profitability evaluation, which is effective in deciding the optimal timing to pull unprofitable items from the shelves by looking up generic tables. Furthermore, we also performed numerical and sensitivity analyses for a real-world case to illustrate the applicability and some managerial implications of the proposed scheme.
Errors in memory recall, ToT, SoT, FoK and BoP. Operational classification

The evocation of memory is a critical cognitive process that involves the activation of engrams, which in turn evoke concepts. During memory recall, errors often occur, some of which are well-known, such as the tip-of-the-tongue (ToT) phenomenon or slip of the tongue (SoT). A related variable is verbal taxonomy closely associated with ToT and SoT, as well as the anxiety variable, which has been linked to the occurrence of these phenomena. The results of this research identify errors unrelated to verbs, leading to the operationalization of previously unidentified phenomena called Bias of Perception (BoP) and feeling of Knowing (FoK). This paper explores these errors and their systematic occurrence. In summary, this document seeks to increase understanding of errors that occur during memory recall, their causes, and their implications for cognitive processes.
Radar High-Resolution Range Profile Rejection Based on Deep Multi-Modal Support Vector Data Description

Radar Automatic Target Recognition (RATR) based on high-resolution range profile (HRRP) has received intensive attention in recent years. In practice, RATR usually needs not only to recognize in-library samples but also to reject out-of-library samples. However, most rejection methods lack a specific and accurate description of the underlying distribution of HRRP, which limits the effectiveness of the rejection task. Therefore, this paper proposes a novel rejection method for HRRP, named Deep Multi-modal Support Vector Data Description (DMMSVDD). On the one hand, it forms a more compact rejection boundary with the Gaussian mixture model in consideration of the high-dimensional and multi-modal structure of HRRP. On the other hand, it captures the global temporal information and channel-dependent information with a dual attention module to gain more discriminative structured features, which are optimized jointly with the rejection boundary. In addition, a semi-supervised extension is proposed to refine the boundary with available out-of-library samples. Experimental results based on measured data show that the proposed methods demonstrate significant improvement in the HRRP rejection performance.
Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus

Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%.
Covert communication in power Internet of Things

Covert communication refers to achieving high-level secure transmission by protecting communication behaviour. This paper primarily centres on the examination of covert communication in the scene of the power Internet of Things (PIoT). Specifically, we explore the scenario where power users (PU) transmit information to the master station (MS) while being exposed to potential eavesdropping. In addition, we assume that Willie has noise uncertainty when detecting communication behaviour, which makes it difficult for Willie to accurately detect communication behaviour, and significantly affects the detection error probability (DEP) of Willie and the overall covert performance of the proposed model. It is worth noting that the simulation results indicate that the presence of noise uncertainty can enable MS and PU to successfully achieve covert transmission. By leveraging this uncertainty, MS and PU can effectively protect their communication behaviour and minimize the risk detected by potential Willie, which provides a solution for high-level secure communication in the field of PIoT and effectively protects the privacy of PU.
Automated identification of vehicles in very high-resolution UAV orthomosaics using YOLOv7 deep learning model

The utilization of remote sensing products for vehicle detection through deep learning has gained immense popularity, especially due to the advancement of unmanned aerial vehicles (UAVs). UAVs offer millimeter-level spatial resolution at low flight altitudes, which surpasses traditional airborne platforms. Detecting vehicles from very high-resolution UAV data is crucial in numerous applications, including parking lot and highway management, traffic monitoring, search and rescue missions, and military operations. Obtaining UAV data at desired periods allows the detection and tracking of target objects even several times during a day. Despite challenges such as diverse vehicle characteristics, traffic congestion, and hardware limitations, the detection task must be executed swiftly and accurately. This study successfully achieved automated detection and instance segmentation of parked and moving vehicles across a large university campus by employing the robust learning capabilities of the You Only Look Once version 7 (YOLOv7) deep learning algorithm. The generation of an ultrahigh-resolution orthomosaic of the university campus was accomplished through photogrammetric processing, employing 20-megapixel aerial images obtained from RGB UAV flights with polygonal nadir-view and bundle-grid oblique-view imaging geometries. The vehicle dataset was created by cropping image patches containing vehicle objects from the orthomosaic and manually labeling the boundaries of the vehicle targets using the LabelMe annotation tool. After expanding the dataset by applying data augmentation, the YOLOv7 algorithm was trained and tested using the transfer learning approach. The accuracy metric of precision, recall, and mAP@0.50 scores for the bounding boxes and masks of vehicles were estimated as 99.79, 97.54, and 99.46%, respectively. In addition, the robustness of the trained algorithm was also tested on a short video and (>80%) prediction scores were achieved.
Fusion of standard and ordinal dropout techniques to regularise deep models

Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ignored. In this paper, we propose the fusion of standard dropout and a new dropout methodology for ordinal classification regularising deep neural networks to avoid overfitting and improve generalisation, but taking into account the extra information of the ordinal task, which is exploited to improve performance. The correlation between the outputs of every neuron and the target labels is used to guide the dropout process: the higher the neuron is correlated with the expected labels, the lower its probability of being dropped. Given that randomness also plays a crucial role in the regularisation process, a balancing factor (β) is also added to the training process to determine the influence of the ordinality with respect to a constant probability, providing a hybrid ordinal regularisation method. An extensive battery of experiments shows that the new hybrid ordinal dropout methodology perform better than standard dropout, obtaining improved results in most evaluation metrics, including not only ordinal metrics but also nominal ones.
Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism

Sparse histogram methods can be useful for returning differentially private counts of items in large or infinite histograms, large group-by queries, and more generally, releasing a set of statistics with sufficient item counts. We consider the Gaussian version of the sparse histogram mechanism and study the exact epsilon, delta differential privacy guarantees satisfied by this mechanism. We compare these exact epsilon, delta parameters to the simpler overestimates used in prior work to quantify the impact of their looser privacy bounds.
Cyberbullying of women on Twitter

Over 40% of American adults experience online harassment. As the nation struggles with questions of internet safety vs. freedom of speech, one must consider the impact of online abuse on victims. This paper builds on prior research of cyber harassment by extending the investigation to relational and societal-level effects of online harassment of women and potential moderating effect of support. Results revealed individual, relational and social impacts of cyberbullying. We discuss implications for addressing online harassment.
Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

Abstract This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals
Ensemble WGAN (EWG): Advancing Image Synthesis and Deepfake Detection with Heterogeneous Discriminator Approach

In the present time, deepfakes pose a big threat to the security of our society. Concerns regarding these fake images being used for malevolent reasons on social networking sites have increased. As a solution it, this paper proposed a new model called EWG (Ensemble WGAN) which helps to detect deepfake using its unique ensemble architecture. The EWG model is an expansion of the WGAN architecture that improves deepfake detection and GAN training issues. It employs a voting ensemble of three unique discriminators and a single generator. The approach works with generator weights updated by the best discriminator on each epoch. The model dynamically selects the best discriminator based on a unique diverse loss function that combines adversarial loss and the SSIM metric, boosting diversified performance. Leveraging the “Indian Actor Images Dataset” and “5-Celebrity Faces,” the EWG model achieves remarkable deepfake detection accuracy of 98.480% and 96.417%, with computation times of 1813.251 and 2197.011 seconds. Furthermore, it mitigates GAN training challenges like mode collapses, gradient penalties, and convergence and provides superior image quality, surpassing basic WGAN and other state-of-the-art methods. The EWG model demonstrates its dependability and potential for countering deepfakes and improving GAN capabilities.
Governance of Permissionless Blockchain Networks

A permissionless blockchain network is a system of physically distributed computers running a copy of a shared ledger and using the same software rules that enable all network participants to “read, submit, and validate transactions” (Beck, Müller-Bloch, and King, 2018, p. 1022). A permissionless system’s accessibility stands in contrast to that of permissioned systems, in which a central authority pre-selects validators and potentially restricts viewing and submission rights (Krause, Natarajan, and Gradstein, 2017; Beck, Müller-Bloch, and King, 2018).
The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate.