Reviewerly/exHarmony huggingface dataset:
Post-hoc Saliency Methods Fail to Capture Latent Feature Importance in Time Series Data

Saliency methods provide visual explainability for deep image processing models by highlighting informative regions in the input images based on feature-wise (pixels) importance scores. These methods have been adopted to the time series domain, aiming to highlight important temporal regions in a sequence. This paper identifies, for the first time, the systematic failure of such methods in the time series domain when underlying patterns (e.g., dominant frequency or trend) are based on latent information rather than temporal regions. The latent feature importance postulation is highly relevant for the medical domain as many medical signals, such as EEG signals or sensor data for gate analysis, are commonly assumed to be related to the frequency domain. To the best of our knowledge, no existing post-hoc explainability method can highlight influential latent information for a classification problem. Hence, in this paper, we frame and analyze the problem of latent feature saliency detection. We first assess the explainability quality of multiple state-of-the-art saliency methods (Integrated Gradients, DeepLift, Kernel SHAP, Lime) on top of various classification methods (LSTM, CNN, LSTM and CNN trained via saliency-guided training) using simulated time series data with underlying temporal or latent space patterns. In conclusion, we identify that Integrated Gradients and DeepLift, if redesigned, could be potential candidates for latent saliency scores.
Test‐driven development, engagement in activity, and maintainability: An empirical study

Abstract The software engineering community aims to achieve and maintain high‐efficient software engineering practical activities. One of the techniques used for this purpose is Test‐Driven Development (TDD), which is a cyclic and test‐centered development method positively related to maintainability, which may vary depending on the context and application. Previous research has indicated that TDD's benefits on maintainability may be due to the improved focus on coding and testing activities. However, limited research has investigated the role of engagement in software quality. Therefore, this study investigates the relationship between TDD methods, engagement level in development activities, and maintainability. The primary research questions addressed in this study are does following TDD methods improve the engagement level in development activities? And does a higher engagement level leads to better maintainability? The study employs a statistical analysis technique, regression analysis to explore the research questions. The results show that TDD behaviours improve the engagement level, and engaging in development activities has a significant positive impact on maintainability. Additionally, the study finds that the positive impact of focussing on testing, such as writing more test cases, is more pronounced compared to coding activities. Our study adds to current software engineering literature that not only personal expertise but the engagement level in the development process are associated with software quality and calls for the emphasis on developer's engagement.
The Pedagogy of Gear Touchers: Unearthing Modes of Teaching Within and Through DIY Venues

Background: Within the body of literature on do-it-yourself (DIY) music scenes, researchers have routinely placed an emphasis on the role of material space in shaping the sociocultural and musical practices of punk music and other related genres. Scholars have also examined the teaching and learning processes of these musical subcultures under the banner of “punk pedagogy” scholarship. However, investigations into the intersection between these two strands of research need to occur because theories of punk pedagogy have largely overlooked the role of physical space within the educative practices of DIY music. Research into the thematically related space of the maker movement amplifies this need, because maker education scholars have repeatedly shown the multiple ways that materials and space shape how individuals learn through DIY production. Research Questions: I use this paper to attend to the following questions: How do DIY music venues shape the pedagogical practices of DIY music scenes? And in what ways do those pedagogies align with the ideological and ethical aims of these communities? By focusing on learning within DIY venues, I consider multiple forms of musical production outside of the context of a specific genre (i.e., punk). This study therefore provides insight into the mechanisms through which individuals learn and how those mechanisms relate to the physical affordances of these spaces. Research Design: To address these questions, I conducted a year-long comparative case study into two intertwined music series centered on noise music (an experimental subgenre within DIY music’s broad umbrella) and located in two separate DIY venues. Although each of the 13 events in this series included both a workshop and a concert, I focus my analysis on the concert portion of the series to explore a common site of interaction within DIY scenes. Through open and iterative qualitative analyses of field notes generated from observations of concerts in the series and interviews with featured artists and audience members, I provide a nuanced understanding of learning within DIY music venues and the role that both material space and technologies play in shaping that process. Conclusions: Drawing on this analysis, I contend that the stageless design of DIY venues provides a physical affordance that allows “gear toucher conversations” to occur. These conversations involve audience members engaging performers in discussions about the music technologies they use mere seconds after they finish performing, thus linking this pedagogical moment to the material attributes of the venue. However, these conversations reinscribe masculine notions of technology and undermine DIY music’s egalitarian politics, a finding that mirrors critical research into maker education. This work therefore calls on both researchers and practitioners to contend with the pedagogies of place and the educative processes that emerge out of situated technologies to further the liberatory praxes that DIY production can produce.
Designing a Digital Mini Stage Augmented Reality Catalog Application Using the Loading Package Model Feature

The street vendor business is one that is growing rapidly in this day and age. Some of these businesses are personal in nature, while others are not. The interest in this research stems from observations made with street vendors, where the service for ordering booths so far only uses photo media without the ability to explore in greater detail, where the service is not optimal if the manual method is still used, and thus it is necessary to develop new services to be able to display products in 3D and explore thoroughly. The author's goal in conducting this research is to develop applications so that partners can increase consumer traffic through digital catalogs using augmented reality (AR) technology and make it easier for buyers to interactively visualize booth models. The results of this study are augmented reality applications for interactive visualization of booth models. The booth is displayed in three dimensions using a 3D developer application. Users can explore booth models i teractively from all sides. This application is a portable digital catalog that provides a more intuitive user experience.
The Influence of Gender and Skin Colour on the Watchlist Imbalance Effect in Facial Identification Scenarios

Nowadays it is well known that artificial intelligence can be biased. In biometric recognition, this is a very sensitive topic since biased algorithms often discriminate against specific demographic groups. This can have severe consequences when searching criminal databases or blacklists. In this context, the watchlist imbalance effect might induce additional performance differentials based on the demographic composition of the target database. In this work, we utilise a fairly distributed subset of the FairFace database to evaluate the watchlist imbalance effect when combining the demographic attributes gender and skin colour. The results show that the skin colour has a huge impact on the differential performance to the disadvantage of dark skin tones.
On the Applicability of Prototypical Part Learning in Medical Images: Breast Masses Classification Using ProtoPNet

Deep learning models have become state-of-the-art in many areas, ranging from computer vision to agriculture research. However, concerns have been raised with respect to the transparency of their decisions, especially in the image domain. In this regard, Explainable Artificial Intelligence has been gaining popularity in recent years. The ProtoPNet model, which breaks down an image into prototypes and uses evidence gathered from the prototypes to classify an image, represents an appealing approach. Still, questions regarding its effectiveness arise when the application domain changes from real-world natural images to gray-scale medical images. This work explores the applicability of prototypical part learning in medical imaging by experimenting with ProtoPNet on a breast masses classification task. The two considered aspects were the classification capabilities and the validity of explanations. We looked for the optimal model’s hyperparameter configuration via a random search. We trained the model in a five-fold CV supervised framework, with mammogram images cropped around the lesions and ground-truth labels of benign/malignant masses. Then, we compared the performance metrics of ProtoPNet to that of the corresponding base architecture, which was ResNet18, trained under the same framework. In addition, an experienced radiologist provided a clinical viewpoint on the quality of the learned prototypes, the patch activations, and the global explanations. We achieved a Recall of 0.769 and an area under the receiver operating characteristic curve of 0.719 in our experiments. Even though our findings are non-optimal for entering the clinical practice yet, the radiologist found ProtoPNet’s explanations very intuitive, reporting a high level of satisfaction. Therefore, we believe that prototypical part learning offers a reasonable and promising trade-off between classification performance and the quality of the related explanation.
Toward more accurate diagnosis of multiple sclerosis: Automated lesion segmentation in brain magnetic resonance image using modified <scp>U‐Net</scp> model

Abstract Early diagnosis of multiple sclerosis (MS) through the delineation of lesions in the brain magnetic resonance imaging is important in preventing the deteriorating condition of MS. This study aims to develop a modified U‐Net model for automating lesions segmentation in MS more accurately. The proposed modified U‐Net uses residual dense blocks to replace the standard convolutional stacks and incorporates three axes (axial, sagittal, and coronal) of 2D slice images as input. Furthermore, a custom fusion method is also introduced for merging the predicted lesions from different axes. The model was implemented on ISBI2015 and OpenMS data sets. On ISBI2015, the proposed model achieves the best overall score of 93.090% and DSC of 0.857 on the OpenMS data set.
Can’t see the forest for the trees

Abstract Random forests are currently one of the most popular algorithms for supervised machine learning tasks. By taking into account for many trees instead of a single one the resulting forest model is no longer easy to understand and also often denoted as a black box. The paper is dedicated to the interpretability of random forest models using tree-based explanations. Two different concepts, namely most representative trees and surrogate trees are analyzed regarding both their ability to explain the model and to be understandable by humans. For this purpose explanation trees are further extended to groves, i.e. small forests of few trees. The results of an application to three real world data sets underline the inherent trade of between both requirements. Using groves allows to control for the complexity of an explanation while simultaneously analyzing their explanatory power.
Replication of the Climbing Post on Mount Arjuno Using Augmented Reality (AR) with the Open Asset Import Library Method

Mount Arjuno is a 3,339meter high cone-shaped volcano located in East Java, Indonesia. At this time, information about climbers that is spread in online media makes it easy, there are many features that can be used to find climbing information on Mount Arjuno. Making this Arjuno augmented reality application uses the Rapid Application Development model with the main goal of producing high quality and quantity. Where this method places more emphasis on working on application software and user feedback in the planner. And testing for the tracking distance of the camera to the marker, to ensure the distance that can be traveled by the camera when scanning objects to targets. By creating an android application that uses the Marker Based Tracking method which can assist climbers in choosing hiking trails on Mount Arjuno so that climbers know the distance of the path traveled when climbing.
Heterogeneous multi-sensor fusion for PHD filter in decentralized sensor networks

This paper addresses distributed multi-sensor multi-object tracking based on probability hypothesis density (PHD) filter. Due to the scalar fusion weights, existing distributed fusion methods only work well in cases where the information confidence within multi-object densities (MODs) returned by local filters remains unchanged over the object state space. Therefore, severe performance degradation can be observed as, in practice, the information confidence of an MOD is usually space-varying and can be utterly different due to the limited sensor abilities and various environment impacts. We make three contributions towards addressing this problem. Firstly, we propose a heterogeneous fusion method for multi-object Poisson process (MPP) MODs. The resulting MOD has a parallelizable structure that allows multiple clusters of sub-densities to be fused independently based on different sets of heterogeneous fusion weights. Secondly, a density-based heterogeneous fusion weights design method is proposed, in which the fusion weights are determined according to the relative information confidence of MODs. Lastly, to enhance the adaptability and robustness of the proposed fusion method, we extend it to decentralized sensor networks in conjunction with consensus and flooding protocols, respectively. The implementation of the proposed fusion algorithms using Gaussian approximation is also presented. The efficacy of the proposed fusion methods is demonstrated in various numerical experiments, including a challenging scenario in which each sensor in a decentralized network has dynamic detection probability and space-varying detection accuracy. In the experiments, the proposed method significantly outperforms the state-of-the-art in distributed fusion. In fact, the Optimal Sub-Pattern Assignment (OSPA) distances returned by the proposed method are at least 38.8% less than other solutions.
Classical-to-quantum convolutional neural network transfer learning

Machine learning using quantum convolutional neural networks (QCNNs) has demonstrated success in both quantum and classical data classification. In previous studies, QCNNs attained a higher classification accuracy than their classical counterparts under the same training conditions in the few-parameter regime. However, the general performance of large-scale quantum models is difficult to examine because of the limited size of quantum circuits, which can be reliably implemented in the near future. We propose transfer learning as an effective strategy for utilizing small QCNNs in the noisy intermediate-scale quantum era to the full extent. In the classical-to-quantum transfer learning framework, a QCNN can solve complex classification problems without requiring a large-scale quantum circuit by utilizing a pre-trained classical convolutional neural network (CNN). We perform numerical simulations of QCNN models with various sets of quantum convolution and pooling operations for MNIST data classification under transfer learning, in which a classical CNN is trained with Fashion-MNIST data. The results show that transfer learning from classical to quantum CNN performs considerably better than purely classical transfer learning models under similar training conditions.
Recent developments in cervical cancer diagnosis using deep learning on whole slide images: An Overview of models, techniques, challenges and future directions

Integration of whole slide imaging (WSI) and deep learning technology has led to significant improvements in the screening and diagnosis of cervical cancer. WSI enables the examination of all cells on a slide simultaneously and deep learning algorithms can accurately label them as cancerous or non-cancerous. Although many studies have investigated the application of deep learning for diagnosing various diseases, there is a lack of research focusing on the evolution, limitations, and gaps of intelligent algorithms in conjunction with WSI for cervical cancer. This paper provides a comprehensive overview of the state-of-the-art deep learning algorithms used for the timely and precise analysis of cervical WSI images. A total of 115 relevant papers were reviewed, and 37 were selected after screening with specific inclusion and exclusion criteria. Methodological aspects including deep learning techniques, data sources, architectures, and classification techniques employed by the selected studies were analyzed. The review presents the most popular techniques and current trends in deep learning-based cervical classification systems, and categorizes the evolution of the domain based on deep learning techniques, citing an in-depth analysis of various models developed over time. The paper advocates for the implementation of transfer supervised learning when utilizing deep learning models such as ResNet, VGG19, and EfficientNet, and builds a solid foundation for applying relevant techniques in different fields. Although some progress has been made in developing novel models for the diagnosis of cervical cancer, substantial work remains to be done in creating standardized benchmark databases of WSI images for the research community. This paper serves as a comprehensive guide for understanding the fundamental concepts, benefits, and challenges related to various deep learning models on WSI, including their application for cervical system classification. Additionally, it provides valuable insights into future research directions in this area.
BYTEv2: Associating More Detection Boxes Under Occlusion for Improved Multi-person Tracking

Most of the multi-person trackers in literature assign only high-confident detections to already tracked targets so the full potential of available detections is not leveraged. Recently, a new association method named BYTE has been proposed that considers also low-confident detections in a second matching step. We think this idea further and additionally utilize heavy-occluded detections in the association which are usually discarded by non-maximum suppression. With our further development termed BYTEv2, the tracking performance under occlusion can be significantly improved. Aiming at a tracking framework that works well under camera motion and is still real-time capable, we built a fast and lightweight model for camera motion compensation based on ORB features. Moreover, a method to terminate inactive tracks under severe camera motion is proposed which further improves the performance. We validate our tracking components with extensive ablative experiments and achieve state-of-the-art results on the two popular benchmarks MOT17 and MOT20.
Towards an Accurate 3D Deformable Eye Model for Gaze Estimation

3D eye gaze estimation has emerged as an interesting and challenging task in recent years. As an attractive alternative to appearance-based models, 3D model-based gaze estimation methods are powerful because a general prior of eye anatomy or geometry has been integrated into the 3D model hence they adapt well under various head poses and illumination conditions. We present a method for constructing an anatomically accurate 3D deformable eye model from the IR images of eyes and demonstrate its application to 3D gaze estimation. The 3D eye model consists of a deformable basis capable of representing individual real-world eyeballs, corneas, irises and kappa angles. To validate the model’s accuracy, we combine it with a 3D face model (without eyeball) and perform image-based fitting to obtain eye basis coefficients The fitted eyeball is then used to compute 3D gaze direction. Evaluation results on multiple datasets show that the proposed method generalizes well across datasets and is robust under various head poses.
Region-Based Trajectory Analysis for Abnormal Behaviour Detection: A Trial Study for Suicide Detection and Prevention

We propose a region-based trajectory analysis method to detect abnormal activities in a scene. It provides a self-adapted, location-sensitive and interpretable trajectory analysis method for different scenarios. Our integrated pipeline consists of a pedestrian detection and tracking module to extract density, speed and direction features. In addition, it contains a grid-based feature extraction and clustering module that automatically generates a region map with corresponding feature importance. During testing, the pipeline analyses the segments that fall into the regions, but do not comply with the important features, and clusters trajectories together to detect abnormal behaviours. Our case study of a suicide hotspot proves the effectiveness of such an approach.
A Masked Face Classification Benchmark on Low-Resolution Surveillance Images

We propose a novel image dataset focused on tiny faces wearing face masks for mask classification purposes, dubbed Small Face MASK (SF-MASK), composed of a collection made from 20k low-resolution images exported from diverse and heterogeneous datasets, ranging from 7 x 7 to 64 x 64 pixel resolution. An accurate visualization of this collection, through counting grids, made it possible to highlight gaps in the variety of poses assumed by the heads of the pedestrians. In particular, faces filmed by very high cameras, in which the facial features appear strongly skewed, are absent. To address this structural deficiency, we produced a set of synthetic images which resulted in a satisfactory covering of the intra-class variance. Furthermore, a small subsample of 1701 images contains badly worn face masks, opening to multi-class classification challenges. Experiments on SF-MASK focus on face mask classification using several classifiers. Results show that the richness of SF-MASK (real + synthetic images) leads all of the tested classifiers to perform better than exploiting comparative face mask datasets, on a fixed 1077 images testing set. Dataset and evaluation code are publicly available here: https://github.com/HumaticsLAB/sf-mask