Reviewerly/exHarmony huggingface dataset:
Innovation, financial permeation, and income inequality: From a dynamic perspective in China

Innovation is a driving force of wealth distribution. To explore its time-varying effect on income inequality, we propose a nonparametric model using the local linear dummy variable estimation (LLDVE) method. Based on a province-level panel data from China spanning from 2006 to 2020, we find that innovation initially reduces income disparity until 2009, then exacerbates it from 2013 to 2016, and alleviates inequality again over 2018–2020. We further verify that financial permeation serves as a catalyst in the inequitable income distribution driven by innovation. However, this moderating effect reverses in the relationship between green innovation and income inequality. This suggests that we should enhance the financial service towards all aspects of innovation beyond its support of green innovation.
Anonymizing eye-tracking stimuli with stable diffusion

Casual users nowadays can create almost arbitrary image content by providing textual prompts to generative machine-learning models. These models rapidly improve image quality with each new generation, providing means to create photos, paintings in different styles, and even videos. One feature of such models is the ability to take an image as input and adjust content according to a prompt. A visual obfuscation of content can be achieved for static images and videos by slightly changing persons, text, and other objects. The potential of this technique can be applied in eye-tracking experiments for post-hoc dissemination of analysis results and visualization. In this work, we discuss how the technique could serve to anonymize stimuli (e.g., for double-blind reviews, remove product placements, etc.) and protect the privacy of people visible in the stimuli. We further investigate how the application of this anonymization process influences visual saliency and the depiction of stimuli in visualization techniques. Our results show that slight image transformations do not drastically change the saliency of a scene but obfuscate objects and faces while keeping important image structures for context.
Maximum Output Discrepancy Computation for Convolutional Neural Network Compression

Network compression methods minimize the number of network parameters and computation costs while maintaining desired network performance. However, the safety assurance of many compression methods is based on a large amount of experimental data, whereas unforeseen incidents beyond the experiment data may result in unsafe consequences. In this work, we developed a discrepancy computation method for two convolutional neural networks by giving a concrete value to characterize the maximum output difference between the two networks after compression. Using Imagestar-based reachability analysis, we propose a novel method to merge the two networks to compute the difference. We illustrate reachability computation for each layer in the merged network, such as the convolution, max pooling, fully connected, and ReLU layers. We apply our method to a numerical example to prove its correctness. Furthermore, we implement our developed methods on the VGG16 model with the Quantization Aware Training (QAT) compression method; the results show that our approach can efficiently compute the accurate maximum output discrepancy between the original neural network and the compressed neural network.
Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports

PURPOSE Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an off-the-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro F 1 scores. RESULTS We found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the site and laterality tasks. Versions of the Path-BigBird model performed best on the two most difficult tasks: subsite (micro F 1 score of 72.53, macro F 1 score of 35.76) and histology (micro F 1 score of 80.96, macro F 1 score of 37.94). The largest performance gains over the HiSAN model were for histology, for which a Path-BigBird model increased the micro F 1 score by 1.44 points and the macro F 1 score by 3.55 points. Overall, the results suggest that a Path-BigBird model with a vocabulary derived from well-curated and deidentified data is the best-performing model. CONCLUSION The Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.
Anomaly Detection of Sensor Measurements During a Turbo-Machine Prototype Testing - An Integrated ML Ops, Continual Learning Architecture

Abstract Building a reliable Machine Learning infrastructure in the Energy domain is a complex task; indeed, it requires a full data integration, continual learning, continual prediction, and the integration of human feedback. Continual learning is a challenging task due to the risk of machine learning to forget former data while learning from new ones (catastrophic forgetting). We present a Machine Learning Operations (MLOPS) architecture able to perform continual learning every day on more than 500 models, to perform inference on new data using such models and to take human feedback and data shift into account. More in detail, we continuously train a recurrent Deep Neural Network to build a virtual sensor from other signals and we compare the prediction versus the real signal to raise (in case) an anomaly. Furthermore, Kullback-Leibler (KL) divergence is used to estimate the overlap between the input distributions available at training time and the distributions seen at test time to estimate the confidence level of the prediction. Finally, we integrate human feedback to tune model retraining. The tool has been applied on a set of about 500 sensors on a three-months long test campaign. Results report 100% recall and 94% accuracy. Moreover, using a recurrent neural network, the system is self-explainable (XAI) by design: indeed, the user can compare the predicted vs real signal to understand the performance of the model. We propose a data intensive MLOPS architecture integrating continual learning, anomaly detection and user feedback. The architecture is based on a standard cloud, it is event-driven and integrates retraining based on GPU capabilities.
Augmented LT codes over binary extension fields with increased ratio of full‐degree columns

Abstract This letter proposes an augmented scheme of LT codes to improve the decoding success rate. The method involves substituting the 1‐s in full‐degree columns of the generator matrix of conventional LT codes with binary extension field elements and simultaneously increasing the ratio of full‐degree columns in the ideal soliton distribution. For non‐full‐degree columns, we retain the nonzero elements as 1‐s in the conventional LT codes generator matrix to preserve computational efficiency. Compared to conventional LT codes, the proposed method enhances the linear independence of the generator matrix, leading to a higher decoding success rate with minimal data packets. Experimental results demonstrate the effectiveness of the method at improving the performance of LT codes, with close to 100% decoding success rate achieved with around 5% data redundancy.
Implementation and Analysis of Shallow Parsing Techniques in Khasi Language

In the realm of natural language processing (NLP), after part-of-speech (POS) tagging, the subsequent crucial step is shallow parsing. In this endeavour, the authors have undertaken the development of a shallow parser for the Khasi language. The work explores an array of techniques from both traditional machine learning (ML) and modern deep learning (DL) methodologies. They have employed a variety of ML algorithms, including decision trees, logistic regression, support vector machines, random forest, and multinomial naive bayes. Additionally, they have harnessed the power of DL with models such as the vanilla recurrent neural network, long short-term memory network, gated recurrent units, and bidirectional LSTM, all geared towards achieving the shallow parsing objective. The crux of the effort lies in the meticulous comparative analysis of these techniques. The chapter delves into a comprehensive discussion of their individual performances.
Language Resource Acquisition for Low-Resource Languages in Digital Discourses

This chapter explores the acquisition of language resources for low-resource languages in digital discourse, focusing on code-mixed content in social media posts and comments. Comprehensive language resources enhance our understanding of language usage patterns in online discussions. The methodology includes data collection, annotation, and automated tools, resulting in a practical framework. Challenges addressed in the chapter include data diversity, annotation complexity, linguistic variability, limited annotated data, and ethical considerations. Solutions proposed range from robust data preprocessing techniques to contextual analysis approaches, ensuring acquired quality and ethical use of resources. The chapter also discusses future directions, paving the way for further research and technology development in natural language processing and sociolinguistics. This chapter equips researchers and practitioners with essential knowledge and tools for advancing linguistic analysis and technological applications in the evolving digital landscape.
Testing high-dimensional multinomials with applications to text analysis

Abstract Motivated by applications in text mining and discrete distribution inference, we test for equality of probability mass functions of K groups of high-dimensional multinomial distributions. Special cases of this problem include global testing for topic models, two-sample testing in authorship attribution, and closeness testing for discrete distributions. A test statistic, which is shown to have an asymptotic standard normal distribution under the null hypothesis, is proposed. This parameter-free limiting null distribution holds true without requiring identical multinomial parameters within each group or equal group sizes. The optimal detection boundary for this testing problem is established, and the proposed test is shown to achieve this optimal detection boundary across the entire parameter space of interest. The proposed method is demonstrated in simulation studies and applied to analyse two real-world datasets to examine, respectively, variation among customer reviews of Amazon movies and the diversity of statistical paper abstracts.
LFC-UNet: learned lossless medical image fast compression with U-Net

In the field of medicine, the rapid advancement of medical technology has significantly increased the speed of medical image generation, compelling us to seek efficient methods for image compression. Neural networks, owing to their outstanding image estimation capabilities, have provided new avenues for lossless compression. In recent years, learning-based lossless image compression methods, combining neural network predictions with residuals, have achieved performance comparable to traditional non-learning algorithms. However, existing methods have not taken into account that residuals often concentrate excessively, hindering the neural network's ability to learn accurate residual probability estimation. To address this issue, this study employs a weighted cross-entropy method to handle the imbalance in residual categories. In terms of network architecture, we introduce skip connections from U-Net to better capture image features, thereby obtaining accurate probability estimates. Furthermore, our framework boasts excellent encoding speed, as the model is able to acquire all residuals and residual probabilities in a single inference pass. The experimental results demonstrate that the proposed method achieves state-of-the-art performance on medical datasets while also offering the fastest processing speed. As illustrated by an instance using head CT data, our approach achieves a compression efficiency of 2.30 bits per pixel, with a processing time of only 0.320 seconds per image.
The impact of instructional support via generative learning strategies on the perception of visual authenticity, learning outcomes, and satisfaction in AR-based learning

Abstract Augmented reality (AR) presents significant opportunities for creating authentic learning environments by accurately mirroring real-world objects, contexts, and tasks. The visual fidelity of AR content, seamlessly integrated into the real world, contributes to its perceived authenticity. Despite acknowledging AR’s positive impact on learning, scant research explores specific learning strategies within an AR context, and there’s a lack of studies linking perceived visual authenticity to these strategies. This study addresses these gaps by surveying learners using AR technology to study the human cardiovascular system, exploring perceived visual authenticity, learning outcomes, and satisfaction. Learners used either (1) AR with the self-explanation learning strategy, (2) AR with the drawing learning strategy, or (3) AR only. Analysis of variance and correlation was used for data analysis. Results indicated no significant differences in perceived visual authenticity and satisfaction among the learning strategy groups. However, groups employing learning strategies showed superior learning outcomes compared to the AR-only group. Crucially, the self-explanation learning strategy significantly enhanced knowledge gain compared to drawing and AR-only groups, indicating that self-explanation, together with the visual input from the AR-learning environment, fosters a more coherent mental representation. This increased learning efficacy was achieved while maintaining a consistent perception of visual authenticity and satisfaction with the learning material. These findings expand the current landscape of AR research by moving beyond media comparison studies.
Logical generation of groups

A group G is called logically generated by a subset S, if every element of G can be defined by a first order formula with parameters from S. We consider the case where G is a direct product of finite nilpotent groups with mutually coprime orders and we show that logical and algebraic generations are equivalent in G. We also prove that in the case when G is a free non-abelian group, if S logically generates G then either it generates G algebraically or 〈S〉 is not a free factor of G.
Photonic Reservoir Computing System for Pattern Recognition Based on an Array of Four Distributed Feedback Lasers

Reservoir computing (RC), especially photonic RC based on a single semiconductor laser with a feedback loop, as a method of machine learning, shows excellent performance in time series prediction and classification tasks. The faster the processing speed, the shorter the feedback loop should be employed. However, the performance of the system is not ideal enough due to the limited reservoir nodes caused by the short feedback loop. To overcome this drawback, it is necessary and challenging to develop integrated photonic RC. In this paper, we propose an integrated neuromorphic photonic time-delayed RC scheme based on an array of four distributed feedback lasers (F-DFBs) with optical feedback and injection. Here, we investigate the feasibility of using larger laser arrays and shorter external feedback cavities to provide the RC system with more virtual nodes at the same processing speed, enabling it to handle more complex tasks. Additionally, we compare the performance of the systems with a single DFB (the S-DFB RC) and the F-DFBs RC through simulations and experiments involving iris recognition tasks. Furthermore, the minimum symbol error rate values can reach 0.052 in experiments (and 0 in simulations).
Risk assessment of aircraft anti-skid braking system based on improved fuzzy FMEA

A risk assessment method based on improved fuzzy FMEA is proposed to solve the problem of frequent failures caused by complex working environment of aircraft anti-skid braking system. On the basis of analyzing the application of FMEA, the fuzzy language of fault evaluation is established according to fuzzy set theory, and the standardized evaluation matrix of failure mode is established. is established according to fuzzy set theory, and the standardized evaluation matrix of failure mode is constructed by quantitatively describing expert experience through triangular fuzzy numbers; Then the grey relational-TOPSIS model is nested to calculate the comprehensive closeness degree of various failure modes, and the grey relational-TOPSIS model is nested to calculate the comprehensive closeness degree of various failure modes. Then the grey relational-TOPSIS model is nested to calculate the comprehensive closeness degree of various failure modes, and the final risk assessment conclusion is obtained. The research results show that the improved fuzzy FMEA is better than the The research results show that the improved fuzzy FMEA is better than the FMEA in evaluating risk of aircraft anti-skid braking system,and has good practical application value.
Fair and green hyperparameter optimization via multi-objective and multiple information source Bayesian optimization

Abstract It has been recently remarked that focusing only on accuracy in searching for optimal Machine Learning models amplifies biases contained in the data, leading to unfair predictions and decision supports. Recently, multi-objective hyperparameter optimization has been proposed to search for Machine Learning models which offer equally Pareto-efficient trade-offs between accuracy and fairness. Although these approaches proved to be more versatile than fairness-aware Machine Learning algorithms—which instead optimize accuracy constrained to some threshold on fairness—their carbon footprint could be dramatic, due to the large amount of energy required in the case of large datasets. We propose an approach named FanG-HPO: fair and green hyperparameter optimization (HPO), based on both multi-objective and multiple information source Bayesian optimization. FanG-HPO uses subsets of the large dataset to obtain cheap approximations (aka information sources) of both accuracy and fairness, and multi-objective Bayesian optimization to efficiently identify Pareto-efficient (accurate and fair) Machine Learning models. Experiments consider four benchmark (fairness) datasets and four Machine Learning algorithms, and provide an assessment of FanG-HPO against both fairness-aware Machine Learning approaches and two state-of-the-art Bayesian optimization tools addressing multi-objective and energy-aware optimization.
Inferior, Yet Transformative: the User Experience with Robotic Vacuum Cleaners

Abstract Robotic vacuum cleaners are a prime example of home automation and a rich source of information about how people experience it. On the basis of a 3-week diary study, this article compares householders’ user experience (UX) of robotic vacuum cleaners with their UX of three types of manual vacuum cleaner. The main finding is that robotic vacuum cleaners are inferior in use, yet transform vacuuming. While their inferiority is about their pragmatic qualities, their transformative power relates to their autonomy, agency and hedonic qualities. This contradictory UX involves that robotic vacuum cleaners perform worse or not at all on seven out of nine surfaces and, at the same time, appear to escalate cleanliness standards. The transformation of household chores such as vacuuming is ongoing and calls for further research on engineering and merging the pragmatic and hedonic aspects of UX.
Adaptive temperature scaling for Robust calibration of deep neural networks

Abstract In this paper, we study the post-hoc calibration of modern neural networks, a problem that has drawn a lot of attention in recent years. Despite the plethora of calibration methods proposed, there is no consensus yet on the inherent complexity of the task and, while some authors claim that simple functions solve the problem, others suggest that more expressive models are needed to capture misscalibration. As a first approach, we focus on the task of confidence scaling, specifically on post-hoc methods that generalize Temperature Scaling, which we refer to as the Adaptive Temperature Scaling family. We begin by demonstrating that while complex models like neural networks provide an advantage when there is ample data, they fail in scenarios where it is limited, notably common in fields like medical diagnosis. We then show how under this ideal data conditions the more expressive methods learn a relationship between the entropy of a prediction and its level of overconfidence, and based on this observation, we propose Entropy-based Temperature Scaling, a simple method that scales the confidence of a prediction according to this relationship. Results show that our method obtains state-of-the-art performance and is robust against data scarcity. Moreover, our proposed model enables a deeper understanding of the calibration process by the interpretation of the entropy as a measure of uncertainty in the network outputs.
Feasibility study of in-line particle image velocimetry

Abstract This article describes recent tests and developments of imaging and evaluation techniques for particle image velocimetry (PIV) that exploit the forward scattering of tracer particles by placing the camera in-line with the illuminating light source, such as a laser or a light emitting diode. These techniques have been in use for some time in microscopy and in the design of optical instruments in astronomy. However, they have not yet been used for macroscopic PIV flow measurements. This study highlights the most promising approaches of the various recording configurations and evaluation techniques.
Will virtual influencers overcome the uncanny valley? The moderating role of social cues

Abstract Companies are increasingly collaborating with virtual influencers (VIs) as endorsers for their social media campaigns, yet they often lack awareness of the risks and opportunities involved. VIs are AI‐generated, and visually presented as an interactive, real‐time animated entity in a digital environment. Grounding on the uncanny valley theory, this study investigates how the anthropomorphism of VIs influences consumer behavior. Through two experimental studies, we investigate the effect of a VI's level of anthropomorphism on consumer intentions, revealing the mediating role of uncanniness. Specifically, we find that highly anthropomorphized VIs may elicit a greater sense of uncanniness among consumers. Interestingly, the second study unveils that, in the presence of social cues, the influence of anthropomorphism on uncanniness is attenuated. From a theoretical perspective, these findings contribute to the existing literature on the uncanny valley theory and reveal the role of social cues in mitigating consumers' sense of uncanniness. Alongside this, it offers practical insights for practitioners navigating the complexities of managing VIs in digital marketing endeavors.