Reviewerly/exHarmony huggingface dataset:
Binary Noise Guidance Learning for Remote Sensing Image-to-Image Translation

Image-to-image translation (I2IT) is an important visual task that aims to learn a mapping of images from one domain to another while preserving the representation of the content. The phenomenon known as mode collapse makes this task challenging. Most existing methods usually learn the relationship between the data and latent distributions to train more robust latent models. However, these methods often ignore the structural information among latent variables, leading to patterns in the data being obscured during the process. In addition, the inflexibility of data modes caused by ignoring the latent mapping of two domains is also one of the factors affecting the performance of existing methods. To make the data schema stable, this paper develops a novel binary noise guidance learning (BnGLGAN) framework for image translation to solve these problems. Specifically, to eliminate uncertainty of domain distribution, a noise prior inference learning (NPIL) module is designed to infer an estimated distribution from a certain domain. In addition, to improve the authenticity of reconstructed images, a distribution-guided noise reconstruction learning (DgNRL) module is introduced to reconstruct the noise from the source domain, which can provide source semantic information to guide the GAN’s generation. Extensive experiments fully prove the efficiency of our proposed framework and its advantages over comparable methods.
Robust Face Mask Detection by a Socially Assistive Robot Using Deep Learning

Wearing masks in indoor and outdoor public places has been mandatory in a number of countries during the COVID-19 pandemic. Correctly wearing a face mask can reduce the transmission of the virus through respiratory droplets. In this paper, a novel two-step deep learning (DL) method based on our extended ResNet-50 is presented. It can detect and classify whether face masks are missing, are worn correctly or incorrectly, or the face is covered by other means (e.g., a hand or hair). Our DL method utilizes transfer learning with pretrained ResNet-50 weights to reduce training time and increase detection accuracy. Training and validation are achieved using the MaskedFace-Net, MAsked FAces (MAFA), and CelebA datasets. The trained model has been incorporated onto a socially assistive robot for robust and autonomous detection by a robot using lower-resolution images from the onboard camera. The results show a classification accuracy of 84.13% for the classification of no mask, correctly masked, and incorrectly masked faces in various real-world poses and occlusion scenarios using the robot.
Development of an Assessment Scale for Measurement of Usability and User Experience Characteristics of Bing Chat Conversational AI

After the introduction of the ChatGPT conversational artificial intelligence (CAI) tool in November 2022, there has been a rapidly growing interest in the use of such tools in higher education. While the educational uses of some other information technology (IT) tools (including collaboration and communication tools, learning management systems, chatbots, and videoconferencing tools) have been frequently evaluated regarding technology acceptance and usability attributes of those technologies, similar evaluations of CAI tools and services like ChatGPT, Bing Chat, and Bard have only recently started to appear in the scholarly literature. In our study, we present a newly developed set of assessment scales that are related to the usability and user experiences of CAI tools when used by university students, as well as the results of evaluation of these assessment scales specifically regarding the CAI Bing Chat tool (i.e., Microsoft Copilot). The following scales were developed and evaluated using a convenience sample (N = 126) of higher education students: Perceived Usefulness, General Usability, Learnability, System Reliability, Visual Design and Navigation, Information Quality, Information Display, Cognitive Involvement, Design Appeal, Trust, Personification, Risk Perception, and Intention to Use. For most of the aforementioned scales, internal consistency (Cronbach alpha) was in the range from satisfactory to good, which implies their potential usefulness for further studies of related attributes of CAI tools. A stepwise linear regression revealed that the most influential predictors of Intention to Use Bing Chat (or ChatGPT) in the future were the usability variable Perceived Usefulness and two user experience variables—Trust and Design Appeal. Also, our study revealed that students’ perceptions of various specific usability and user experience characteristics of Bing Chat were predominantly positive. The evaluated assessment scales could be beneficial in further research that would include other CAI tools like ChatGPT/GPT-4 and Bard.
Measuring the Risk of Vulnerabilities Exploitation

Modeling the vulnerabilities lifecycle and exploitation frequency are at the core of security of networks evaluation. Pareto, Weibull, and log-normal models have been widely used to model the exploit and patch availability dates, the time to compromise a system, the time between compromises, and the exploitation volumes. Random samples (systematic and simple random sampling) of the time from publication to update of cybervulnerabilities disclosed in 2021 and in 2022 are analyzed to evaluate the goodness-of-fit of the traditional Pareto and log-normal laws. As censoring and thinning almost surely occur, other heavy-tailed distributions in the domain of attraction of extreme value or geo-extreme value laws are investigated as suitable alternatives. Goodness-of-fit tests, the Akaike information criterion (AIC), and the Vuong test, support the statistical choice of log-logistic, a geo-max stable law in the domain of attraction of the Fréchet model of maxima, with hyperexponential and general extreme value fittings as runners-up. Evidence that the data come from a mixture of differently stretched populations affects vulnerabilities scoring systems, specifically the common vulnerabilities scoring system (CVSS).
A Comparative Sentiment Analysis of Airline Customer Reviews Using Bidirectional Encoder Representations from Transformers (BERT) and Its Variants

The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model’s consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models’ application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role.
Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring

Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP) compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models’ precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.
Innovative Approach for the Determination of a DC Motor’s and Drive’s Parameters Using Evolutionary Methods and Different Measured Current and Angular Speed Responses

The determination is presented of seven parameters of a DC motor’s drive. The determination was based on a comparison between the measured and simulated current and speed responses. For the parameters’ determination, different evolutionary methods were used and compared to each other. The mathematical model presenting the DC drives model was written using two coupled differential equations, which were solved using the Runge–Kutta first-, second-, third- and fourth-order methods. The approach allows determining the parameters of controlled drives in such a way that the controller is taken into account with the measured voltage. Between the tested evolutionary methods, which were Differential Evolution with three strategies, Teaching-Learning Based Optimization and Artificial Bee Colony, the Differential Evolution (DE/rand/1/exp) can be suggested as the most appropriate for the presented problem. Measurements with different sampling times were used, and it was found out that at least some measuring points should be at the speed-up interval. Different lengths of the measured signal were tested, and it is sufficient to use a signal consisting of the drive’s acceleration and a short part of the stationary operation. The analysis showed that the procedure has good repeatability. The biggest deviation of calculated parameters considering 10 repeated measurements was 6% in case of the La calculation. The deviations of all the other parameters’ calculations were less than 2%.
Locality-Constraint Discriminative Nonnegative Representation for Pattern Classification

Representation-based classification methods (RBCM) have recently garnered notable attention in the field of pattern classification. Diverging from conventional methods reliant on ℓ1 or ℓ2-norms, the nonnegative representation-based classifier (NRC) enforces a nonnegative constraint on the representation vector, thus enhancing the representation capabilities of positively correlated samples. While NRC has achieved substantial success, it falls short in fully harnessing the discriminative information associated with the training samples and neglects the locality constraint inherent in the sample relationships, thereby limiting its classification power. In response to these limitations, we introduce the locality-constraint discriminative nonnegative representation (LDNR) method. LDNR extends the NRC framework through the incorporation of a competitive representation term. Recognizing the pivotal role played by the estimated samples in the classification process, we include estimated samples that involve discriminative information in this term, establishing a robust connection between representation and classification. Additionally, we assign distinct local weights to different estimated samples, augmenting the representation capacity of homogeneous samples and, ultimately, elevating the performance of the classification model. To validate the effectiveness of LDNR, extensive comparative experiments are conducted on various pattern classification datasets. The findings demonstrate the competitiveness of our proposed method.
RGGC-UNet: Accurate Deep Learning Framework for Signet Ring Cell Semantic Segmentation in Pathological Images

Semantic segmentation of Signet Ring Cells (SRC) plays a pivotal role in the diagnosis of SRC carcinoma based on pathological images. Deep learning-based methods have demonstrated significant promise in computer-aided diagnosis over the past decade. However, many existing approaches rely heavily on stacking layers, leading to repetitive computational tasks and unnecessarily large neural networks. Moreover, the lack of available ground truth data for SRCs hampers the advancement of segmentation techniques for these cells. In response, this paper introduces an efficient and accurate deep learning framework (RGGC-UNet), which is a UNet framework including our proposed residual ghost block with ghost coordinate attention, featuring an encoder-decoder structure tailored for the semantic segmentation of SRCs. We designed a novel encoder using the residual ghost block with proposed ghost coordinate attention. Benefiting from the utilization of ghost block and ghost coordinate attention in the encoder, the computational overhead of our model is effectively minimized. For practical application in pathological diagnosis, we have enriched the DigestPath 2019 dataset with fully annotated mask labels of SRCs. Experimental outcomes underscore that our proposed model significantly surpasses other leading-edge models in segmentation accuracy while ensuring computational efficiency.
Cross Attention Multi Scale CNN-Transformer Hybrid Encoder Is General Medical Image Learner

Medical image segmentation plays a crucial role in medical artificial intelligence. Recent advancements in computer vision have introduced multiscale ViT (Vision Transformer), revealing its robustness and superior feature extraction capabilities. However, the independent processing of data patches by ViT often leads to insufficient attention to fine details. In medical image segmentation tasks like organ and tumor segmentation, precise boundary delineation is of utmost importance. To address this challenge, this study proposes two novel CNN-Transformer feature fusion modules: SFM (Shallow Fusion Module) and DFM (Deep Fusion Module). These modules effectively integrate high-level and low-level semantic information from the feature pyramid while maintaining network efficiency. To expedite network convergence, the Deep Supervise method is introduced during the training phase. Additionally, extensive ablation experiments and comparative studies are conducted on well-known public datasets, namely Synapse and ACDC, to evaluate the effectiveness of the proposed approach. The experimental results not only demonstrate the efficacy of the proposed modules and training method but also showcase the superiority of our architecture compared to previous methods. The code and trained models will be available soon.
A Comprehensive Multi-modal Domain Adaptative Aid Framework for Brain Tumor Diagnosis

Accurate segmentation and grading of brain tumors from multi-modal magnetic resonance imaging (MRI) play a vital role in the diagnosis and treatment of brain tumors. The gene expression in glioma also influences the selection of treatment strategies and assessment of patient survival, such as the gene mutation status of isocitrate dehydrogenase (IDH), the co-deletion status of 1p/19q, and the value of Ki67. However, obtaining medical image annotations is both time-consuming and expensive, and it is challenging to perform tasks such as brain tumor segmentation, grading, and genotype prediction directly using label-deprived multi-modal MRI. We proposed a comprehensive multi-modal domain adaptative aid (CMDA) framework building on hospital datasets from multiple centers to address this issue, which can effectively relieve distributional differences between labeled source datasets and unlabeled target datasets. Specifically, a comprehensive diagnostic module is proposed to simultaneously accomplish the tasks of brain tumor segmentation, grading, genotyping, and glioma subtype classification. Furthermore, to learn the data distribution between labeled public datasets and unlabeled local hospital datasets, we consider the semantic segmentation results as the output capturing the similarity between different data sources, and we employ adversarial learning to facilitate the network in learning domain knowledge. Experimental results show that our end-to-end CMDA framework outperforms other methods based on direct transfer learning and other state-of-the-art unsupervised methods.
Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development

A backpropagation neural network (BPNN) was used to predict salinity levels in the Chott Djerid shallow aquifers. A set of 51 water samples was collected from the Chott Djerid plio-quaternary aquifers for geochemical analysis. Major elements and nitrates were ascertained by using high performance liquid-ion chromatography. The BPNN was trained on a dataset of 51 water samples with variable geochemical parameters. Our results indicated a high accuracy when applying a model with 13 inputs, 1 hidden layer (6 neurons) and 1 output (TDS in mg/L). The collected data were split into 80% for training the model and 20% for testing and cross validation. The result was evaluated using various statistical performance criteria (i.e., MSE, RMSE, R2, SSE, SD, Accuracy, Sensitivity, specificity, and Kappa test); it showed that BPNN model properly predicted the salinity of the Chott Djerid plio-quaterny water samples (RMSE = 0.0402; R2 = 0.9721 and SSE = 0.0146). The BPNN was able to capture the complex relationship between salinity levels and other aquifer parameters. The potential application of BPNNs for predicting salinity levels in shallow aquifers was crucial in supporting decision-makers for water management; it provided valuable insights into the salinity fluctuation of the studied shallow aquifers.
Interplay Between Neural Computational Energy and Multimodal Processing in Robot-Robot Interaction

Multimodal learning is an active research area that is gaining importance in human-robot interaction. Despite the obvious benefit of levering multiple sensors for perceiving the world, its neural computational cost has not been addressed in robotics, especially in Robot-Robot Interaction (RRI). This study addresses the role of computational cost in multimodal processing by considering robot-robot interaction in a sequential multimodal memory recall task. In this setting, the learner (Nao) robot receives auditory-only, visual-only, or audio-visual information from an instructor (Pepper) robot and the environment regarding previously learned memory items. The goal of the learner robot is to perform the interactive task with as low as possible neural computational cost. The learner robot has two cognitive modules: a multimodal auto-associative network that stands for the perceptual-cognitive processing of the robot and an internal reward mechanism that monitors the changes in neural energy incurred for two consecutive steps by the processing of the attended stimuli. The reward computed is used to build an action policy for minimizing the neural energy consumption over the sequential memory recall task. The experimental results show that having access to both auditory and visual information is beneficial not only for better memory recall but also for minimizing the cost of neural computation.
Clear-Sky Detection Using Time-Averaged, Tilted-Plane Data

A method is presented to detect clear-sky periods for plane-of-array, time-averaged irradiance data that is based on the algorithm originally described by Reno and Hansen. We show this new method improves the state-of-the-art by providing accurate detection at longer data intervals, and by detecting clear periods in plane-of-array data, which is novel. We illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.
Empowering Intelligent Home Safety: Indoor Family Fall Detection with YOLOv5

As we all know, population aging is a significant challenge faced by Chinese society, and ensuring the health and safety of elderly individuals has become an urgent topic of concern. Within the context of family safety, elderly individuals often experience falls or fainting due to age-related physical decline or underlying medical conditions. In response to this phenomenon, this paper presents a method for enhancing the wellbeing of family members by utilizing the YOLOv5 model to detect falls. Moreover, due to the built-in capability of YOLOv5 to read video from webcam, this technology can also be integrated into loT devices, turning these devices into a part of smart homes. Considering the specific nature of the home environment, CAU CAFall is considered to be the most suitable dataset. Various variations of the YOLOv5 model are experi-mented on a CAUCAFall dataset and achieve promising results. The YOLOv5x model achieved a precision of 82.2%, while the YOLOv5s model, with improved running speed, achieved an precision of 79.6%. Finally, we explored and selected the most suitable YOLOv5 model for home fall detection considering comprehensive evaluation metrics, and it is YOLOv5s.
A Multi-Layer Topologically Reconfigurable Broadcast-and-Weight Photonic Neural Network

Broadcast-and-weight (BaW) photonic neural networks can process high-bandwidth signals with limited chip area, but they traditionally lack topological reconfigurability. We propose using a fully-connected recurrent BaW system as a topologically reconfigurable network and demonstrate a multi-layer feedforward network implemented on such a system.
Record Photon Information Efficiency with Optical Clock Transmission and Recovery of 12.5 bits/photon After 77 dB of Optical Path Loss

We experimentally demonstrate optical detection at 12.5 bits per incident photon, 9.4 dB higher than the theoretical limit of conventional coherent detection. The signal undergoes 77 dB of optical attenuation before quantum detection followed by optical clock and data recovery.
Characterizing the Performance of Distributed Edge Processing Resource Allocation in Dynamic Networked Environments

Distributed edge processing in resource-constrained networks relies on accurate understanding of available resources to enable effective deployment of complex workflows to execute analytics. This paper presents an initial analysis characterizing the impact of measurement error on application placement and performance of distributed analytics. We conduct experiments on a distributed edge processing cycle including resource monitoring, resource allocation, and analytics execution phases. To facilitate experimentation of situations with a broad range of monitoring error, we leveraged a machine learning-based model trained on empirically obtained performance metrics to predict analytics performance. We also introduce error into the resource monitoring steps, which result in diminished performance and different resource placement dynamics. Our initial results suggest that more accurate/frequent CPU metrics reporting should be prioritized in this system, rather than bandwidth metrics.