Miracle-dz/UniFAR-FTUs huggingface dataset:
Crevice Identification: A Survey on Surface Monitoring

Nowadays, it is crucial to continuously monitor and provide real-time analysis to reduce road-related accidents. Road cracks and practices occur on the daily basis on the road. As a result, a review of the numerous method and techniques for crack detection, recognition, and identification was undertaken. In this analysis, 20 to 25 articles were examined that which are published in the last 10 years, from 2012 to 2022. Based on the research, it was found that the Deep CNN is the most optimum method for crack categorization. The motivation of this review is that none of the models are combined into a single model, so a comprehensive list of all these models may be helpful to anyone conducting the study in this area. After reviewing the articles and datasets of the problem, a dataset was proposed that might be used in the field for more research. Finally, it was observed that using deep learning and computer vision or combining both technologies will create intelligent road crack detection is a promising research area.
Effect of the Nurse Work Environment on Older Hispanic Surgical Patient Readmissions

Background Readmissions following hospitalization for common surgical procedures are prevalent among older adults and are disproportionally experienced by Hispanic patients. One potential explanation for these disparities is that Hispanic patients may receive care in hospitals with lower-quality nursing care. Objectives The objective of this study was to evaluate the relationship between the hospital-level work environment of nurses and hospital readmissions among older Hispanic patients. Methods Using linked data sources from 2014 to 2016, we conducted a cross-sectional analysis of 522 hospitals and 732,035 general, orthopedic, and vascular surgical patients (80,978 Hispanic patients and 651,057 non-Hispanic White patients) in four states. Multivariable logistic regression models were employed to determine the relationship between the work environment and older Hispanic patient readmissions at multiple time periods (7, 30, and 90 days). Results In final adjusted models that included an interaction between work environment and ethnicity, an increase in the quality of the work environment resulted in a decrease in the odds of readmission that was greater for older Hispanic surgical patients at all time periods. Specifically, an increase in three of the five work environment subscales (Nurse Participation in Hospital Affairs, Nursing Foundations for Quality of Care, and Staffing and Resource Adequacy) was associated with a reduction in the odds of readmission that was greater for Hispanic patients than their non-Hispanic White counterparts. Discussion System-level investments in the work environment may reduce Hispanic patient readmission disparities. This study’s findings may be used to inform the development of targeted interventions to prevent hospital readmissions for Hispanic patients.
The potential of ion microprobe analysis in detecting geochemical variations across individual foraminifera tests

Trace element chemistry variations within individual foraminifera tests may provide valuable information on past ocean conditions. However, the combination of thin test walls and the presence of surface contaminants, not held within the carbonate structure, provide a stringent test to microanalytical methods. We analysed two tests of the benthic foraminifera Ammonia batavus by ion microprobe and compared two sets of analytical conditions. Using apertures to restrict the size of the analysed area (to ca. 10 μm diameter) reduced the effects of sample contamination. Sr concentrations were unaffected by any contamination encountered and Mg could be determined provided checks were made for silicate contamination using Al, Si and K intensities. Measured Sr concentrations (1.012–1.379 mmol mol−1) were in reasonable agreement with previously reported values. Mg concentrations (0.691–1.412 mmol mol−1) were significantly lower than values reported previously for calcitic benthic species. Significant variations in Sr concentrations occurred within individual test chambers and may reflect the juxtapositioning of calcite of different ages as the test is deposited. Significant variations in Mg and Sr concentrations also occurred between test chambers.
Racgap1 knockdown results in cells with multiple cilia due to cytokinesis failure

Most mammalian cells have a single primary cilium that acts as a signalling hub in mediating cellular functions. However, little is known about the mechanisms that result in aberrant supernumerary primary cilia per cell. In this study, we re‐analysed a previously published whole‐genome siRNA‐based reverse genetic screen for genes mediating ciliogenesis to identify knockdowns that permit multi‐ciliation. We identified siRNA knockdowns that caused significant formation of supernumerary cilia, validated candidate hits in different cell‐lines and confirmed that RACGAP1, a component of the centralspindlin complex, was the strongest candidate hit at the whole‐genome level. Following loss of RACGAP1, mother centrioles were specified correctly prior to ciliogenesis and the cilia appeared normal. Live cell imaging revealed that increased cilia incidence was caused by cytokinesis failure which led to the formation of multinucleate cells with supernumerary cilia. This suggests that the signalling mechanisms for ciliogenesis are unable to identify supernumerary centrosomes and therefore allow ciliation of duplicated centrosomes as if they were in a new diploid daughter cell. These results, demonstrating that aberrant ciliogenesis is de‐coupled from cell cycle regulation, have functional implications in diseases marked by centrosomal amplification.
Information-Theoretic Neural Decoding Reproduces Several Laws of Human Behavior

Abstract Human response times conform to several regularities including the Hick-Hyman law, the power law of practice, speed-accuracy trade-offs, and the Stroop effect. Each of these has been thoroughly modeled in isolation, but no account describes these phenomena as predictions of a unified framework. We provide such a framework and show that the phenomena arise as decoding times in a simple neural rate code with an entropy stopping threshold. Whereas traditional information-theoretic encoding systems exploit task statistics to optimize encoding strategies, we move this optimization to the decoder, treating it as a Bayesian ideal observer that can track transmission statistics as prior information during decoding. Our approach allays prominent concerns that applying information-theoretic perspectives to modeling brain and behavior requires complex encoding schemes that are incommensurate with neural encoding.
Eye tracking in human-computer interaction and usability research : Ready to deliver the promises

This section considers the application of eye movements to user interfaces—both for analyzing interfaces, measuring usability, and gaining insight into human performance—and as an actual control medium within a human-computer dialogue. The two areas have generally been reported separately; but this book seeks to tie them together. For usability analysis, the user’s eye movements while using the system are recorded and later analyzed retrospectively, but the eye movements do not affect the interface in real time. As a direct control medium, the eye movements are obtained and used in real time as an input to the user-computer dialogue. They might be the sole input, typically for disabled users or hands-busy applications, or they might be used as one of several inputs, combining with mouse, keyboard, sensors, or other devices.
Everything is Infinite: Children’s Beliefs About Endless Space, Time, and Number

Abstract How do children form beliefs about the infinity of space, time, and number? We asked whether children held similar beliefs about infinity across domains, and whether beliefs in infinity for domains like space and time might be scaffolded upon numerical knowledge (e.g., knowledge successors within the count list). To test these questions, 112 U.S. children (aged 4;0–7;11) completed an interview regarding their beliefs about infinite space, time, and number. We also measured their knowledge of counting, and other factors that might impact performance on linguistic assessments of infinity belief (e.g., working memory, ability to respond to hypothetical questions). We found that beliefs about infinity were very high across all three domains, suggesting that infinity beliefs may arise early in development for space, time, and number. Second, we found that—across all three domains—children were more likely to believe that it is always possible to add a unit than to believe that the domain is endless. Finally, we found that understanding the rules underlying counting predicted children’s belief that it is always possible to add 1 to any number, but did not predict any of the other elements of infinity belief.
Twenty-Five Year Record of Changes in Plant Cover on Tundra of Northeastern Alaska

Abstract Northern Alaska has warmed over recent decades and satellite data indicate that vegetation productivity has increased. To document vegetation changes in the Arctic National Wildlife Refuge, we monitored plant cover at 27 plots between 1984 and 2009. These are among the oldest permanently marked and continuously monitored vegetation plots in the Arctic. We quantified percent cover of all plant species by line-point intercept sampling and assessed change over time for seven plant growth forms. Cover of bryophytes and deciduous shrubs showed slight decreasing trends. Evergreen shrubs, horsetails, and depth of thawed soil above permafrost had no trends. For lichens, graminoids, and forbs, trends varied by plant community type. Overall, vegetation in the plots changed little over the study period, in contrast to results from other studies in northern Alaska. A few plots had dramatic changes, however, which we attributed to subsidence from melting ground ice or to floodplain dynamics. Our results demonstrate that vegetation change on the Arctic Refuge coastal plain over the past quarter century has been spatially heterogeneous and facilitated by disturbance. The findings highlight the need for greater work linking plotlevel and regional remote sensing measurements of change.
A comparative analysis of the development of performance-based management systems in Dutch and Norwegian local government

The purpose of this paper is to compare performance management in Dutch and Norwegian municipalities. The analysis of performance budgets and annual reports from nine municipalities from each country shows that performance management changed during the 1990s. Contingent factors such as fiscal stress, opportunity for change, organizational size (uncertainty), and characteristics of the policy fields (ambiguity) were analyzed to explain this pattern. The results indicated two important implications for public management and for contingency theory: the Nordic, incremental and consensual model may give substantial opportunity for change; and the conventional wisdom in organizational control requires further development regarding political control. © 2002 Information Age
Revolutionizing Banana Grading with ResNeXt and SVM: An Automated Approach

Various deep learning and machine learning techniques were developed and used for classifying fruit grade or ripeness. However, these methods often require a large amount of training data and computational costs to produce reliable models. In this study, in order to address this issue, we propose a method that combines a ResNeXt-50 convolutional neural network with a support vector machine and optimizes it using an artificial bee colony as a metaheuristic optimizer in order to automate empirical tuning while finding an optimal solution. This approach achieved excellent results with 100% accuracy and a fast-training time of only 0.01 seconds, demonstrating the effectiveness of combining a convolutional neural network as a feature extractor with a support vector machine as a classifier in reducing the training data and computational costs of the model. Results suggest that this method could be a helpful tool for accurately and efficiently classifying fruit grade and ripeness.
Clustering Deliberation Sequences Through Regulatory Triggers in Collaborative Learning

Recent advances in Learning Analytics (LA) and Artificial Intelligence (AI) have enabled us to gain a better understanding of socially shared regulation (SSRL), which is in collaborative learning. Although recent progress in studying SSRL with LA and AI has provided holistic insights into the temporal and cyclical processes of SSRL, few studies have investigated SSRL processes at a granular level. To address these limitations, we utilise AI techniques to explore the sequences of group-level deliberation as a process and its pattern through cognitive and emotional regulation triggering events in the context of face-to-face collaborative learning. This study involved ten triads of secondary students ($\mathrm{N}=30$) working on a collaborative learning task and receiving regulation-triggering events during their learning. Results from Agglomerative Hierarchical Clustering (AHC) identified two distinct types of deliberation sequences with different approaches to regulation and collaboration practices: 1) the plan and implementation approach (PIA) focused on analysing, discussing, and collaborating; and 2) the trials and failures approach (TFA) focused on random idea testing. Interestingly, we found that most groups maintain the same approach in response to triggering events, emphasizing the importance of supporting learners to recognize and react to the emerging needs of regulation.
Permuted layer-based CNN for Emotion Detection with Multi-Modality Physiological Signals

Human emotion detection is a challenging task. Emotions are expressed via facial expressions, body/face temperature, EEG recordings, voice recordings. In this paper, a novel CNN based model is reported to detect human emotion using multimodal human emotion representation data. The model is based on the concepts of one-dimensional convolution on the permuted-ensembled feature space representing EEG signals. The model addresses the issues with localized one-dimensional convolution over high-dimensional feature spaces and attempts to improve the classification performance. Results indicate improved detection accuracy over different emotions as compared to other popular machine learning approaches.
Preliminary Exploration of the Effectiveness of Music Listening and Music Recommender for Studying in Naturalistic Settings

Listening to music is a common behavior when people study or work. However, effects of music listening on studying are still disputed in previous studies. To explore the associations between music characteristics and learning performance and engagement and to develop a music recommender for studying in naturalistic settings, we conducted a two-month field experiment with 51 undergraduate and graduate students. A mobile application based on the experience sampling method was designed and implemented to ubiquitously collect participants' learning status and music listening traces. Statistical tests and machine learning were adopted respectively for uncovering the associations between music listening on learning and constructing a music recommendation model. Results first indicated that learners' music preferences and several musical features were positively correlated with self-reported learning performance and concentration. Furthermore, machine learning modeling demonstrated promising results for developing a music recommender for studying in naturalistic settings. Findings are expected to contribute to research on learning with background music and learning-oriented music recommendation.
Power and Performance Costs of Radiation-Hardened ML Inference Models Running on Edge Devices

Integrating Machine Learning (ML) inference models into edge computing devices has introduced several challenges related to improving power efficiency, performance, and reliability. As the susceptibility of these models to radiation-induced soft errors is a significant concern, applying lightweight mitigation techniques is key, mostly due to power and memory constraints inherent to edge devices. In this regard, assessing the potential power and performance penalties associated with deploying soft error mitigation techniques on customized ML inference models running in such resource-constrained devices is crucial. This paper, therefore, investigates the performance and power consumption implications of applying software-based mitigation techniques on ML inference models optimized for edge devices. The experiments demonstrate that implementing RAT technique reduced up to 3.2 x the susceptibility to the occurrence of soft errors caused by radiation with low performance and power consumption costs w.r. t. a P-TMR technique.
Hoax Detection in Social Media using Bidirectional Long Short-Term Memory (Bi-LSTM) and 1 Dimensional-Convolutional Neural Network (1D-CNN) Methods

Technology development facilitates the dissemination of information and news from various media, especially on social media. The news spread on social media is not necessarily the truth, and it could cause many fake news or hoaxes to spread and mislead the public. The number of hoaxes circulating on social media confuses readers in understanding the truth of information. Several models for detecting hoaxes have been built using a Deep Learning approach to process unstructured data such as text and images. In this study, three models were created using a deep learning approach to detect hoaxes on social media Twitter. The three deep learning methods used are Bidirectional Long Short-Term Memory (Bi-LSTM), 1 Dimensional-Convolutional Neural Network (1D-CNN), and Hybrid Bi-LSTM-1DCNN. Term Frequency - Inverse Document Frequency (TF-IDF) is used as feature extraction and Global Vectors (GloVe) as feature expansion. Several scenarios were applied to compare the methods to achieve the best model with the best accuracy results. 1D-CNN method gets the highest accuracy of 96.51%, followed by Bi-LSTM with an accuracy of 96.09%, and Hybrid Bi-LSTM-1D-CNN with an accuracy of 95.94%.
Design and Performance Analysis of Defected Grounded Hexagonal Patch Antenna at 5.17 GHz

A defected grounded hexagonal patch antenna tuned at 5.17 GHz is designed in this manuscript. This antenna has reflection coefficient of -39.41 dB at its resonant frequency. It provides a fractional bandwidth of 22.97% around 5.17 GHz and it radiates over wide frequency range of 4.66 GHz to 5.85 GHz. This antenna possesses 1.63 dBi realized gain at its resonant frequency. Fabrication of this antenna has performed in this work to validate the simulated results. The resonant frequency of the fabricated antenna is observed as 5.10 GHz and its return loss value is 13.20 dB at this frequency. Comparing simulated and measured result, it is observed that these results slightly differ due to fabrication error. This antenna can be used for aeronautical radio-navigation and wireless communications. Due to presence of defected ground plane, back lobe is present in far field pattern of this antenna. Therefore, a metal reflector has been placed at a certain distance from the ground plane in the back side of the antenna.
End-to-End Solution for Braille-To-Chinese

In the field of education, Teachers of Students with Visual Impairments need to read and review student’s Braille homework. Reading Braille consumes a lot of teachers' time and energy. We propose an end-to-end solution to translate Braille into Chinese. Our approach uses a deep learning method to detect and recognize Braille in images and translate them into Chinese and mathematical formulas. Finally, we reconstruct the translation results into the style of the input Braille image. Our approach is able to adapt to more complex Braille images and make the translation results easier to read and understand, significantly improving the speed of reading Braille for non-disabled.
A 24.25-30.5GHz Fully Integrated SiGe Phase Shifter/VGA/Power Amplifier in 0.13µm BiCMOS Technology for 5G Beamforming Applications

This paper presents a 24.25GHz to 30.5GHz wideband SiGe front-end module including a passive phase shifter (PS), a low impedance and low phase variation variable gain amplifier (VGA) and a linear power amplifier (PA) dedicated to beamforming architectures. The whole chip exhibits 33dB of maximum gain, 24dBm of saturation power (Psat) and 32.5% of maximum Power Added Efficiency (PAEmax) at 27GHz. Phase adjustment covers 360° with a minimum resolution of 5.6° and gain covers a 16dB range by 0.5dB steps. The circuit is implemented in a SiGe 130nm BiCMOS process and occupies 0.53 mm2 without pads.
Continuous and Autonomous Digital Twinning of Large-Scale Dynamic Indoor Environments

In the context of Industry 4.0, the interest in digitalizing manufacturing environments is steadily increasing. Hence, the need of a frequently updated digital twin of the facilities is also growing. In order to create a digital twin, specialized hardware is used to capture data with lidars and cameras. The data is then processed by a SLAM algorithm. However, the data acquisition process is typically done manually by multiple employees. This requires dedicated training and many working hours and hence is not feasible to do on a daily basis. In this paper, we present a solution to automate the data acquisition process, by combining an autonomous mobile robot and a scanning device. We show that the quality of the resulting point clouds matches the one of the manual scanning process and is hence ready to be deployed in real environments.
A Review of Privacy-preserving and Efficient Data Collection and Aggregation in Smart Grids

The smart grid fuses the traditional electrical power grid with smart technology to allow for a more distributed, bidirectional flow of communication, data, and electricity. Its advanced metering infrastructure (AMI) of smart meters (SMs) and sensors can efficiently collect data from end users and send it to utility companies. It also allows for near-instantaneous detection of problems in the grid so there can be a quick recourse to fix them. The strength of the SMs is that they can provide reports showing electrical usage to both consumers and utility companies, allowing users to act accordingly to decrease their bill while allowing utility companies to accurately manage billing by reviewing near real-time supply and demand. This paper will explore the specific issues that arise in the smart grid with respect to aggregating consumers’ data and sending it to utility companies and will also propose solutions to these issues. The two primary solutions in this paper focus on the use of homomorphic encryption and a lightweight data aggregation scheme, which decrease computational costs, improve scalability, and improve security —all while simultaneously preserving the customer’s privacy. Finally, a thorough comparison of the existing works is presented along with future research directions that will serve to further enhance the security levels of future AMI smart metering systems.