Reviewerly/exHarmony huggingface dataset:
This looks More Like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation

Current machine learning models have shown high efficiency in solving a wide variety of real-world problems. However, their black box character poses a major challenge for the comprehensibility and traceability of the underlying decision-making strategies. As a remedy, numerous post-hoc and self-explanation methods have been developed to interpret the models’ behavior. Those methods, in addition, enable the identification of artifacts that, inherent in the training data, can be erroneously learned by the model as class-relevant features. In this work, we provide a detailed case study of a representative for the state-of-the-art self-explaining network, ProtoPNet, in the presence of a spectrum of artifacts. Accordingly, we identify the main drawbacks of ProtoPNet, especially its coarse and spatially imprecise explanations. We address these limitations by introducing Prototypical Relevance Propagation (PRP), a novel method for generating more precise model-aware explanations. Furthermore, in order to obtain a clean, artifact-free dataset, we propose to use multi-view clustering strategies for segregating the artifact images using the PRP explanations, thereby suppressing the potential artifact learning in the models.
General iterative algorithm for phase-extraction from fringe patterns with random phase-shifts, intensity harmonics and non-uniform phase-shift distribution

Advanced iterative algorithm (AIA) is a flexible and effective phase-shifting algorithm (PSA) which can extract phase from fringe patterns with random unknown phase-shifts, making it attractive in the scenarios where phase-shifts are unknown or not accurate. However, accuracy of AIA degrades when intensity harmonics and/or phase-shift non-uniformity are presented. To solve this problem, multiple PSAs have been proposed, but they restrict their fringe model in one way or another, and thus sacrifice the immunity to certain error source(s). In this paper, a general iterative algorithm (GIA) which adopts a most general fringe model is proposed. In GIA, the many unknowns in the fringe pattern model are divided into three groups including: (i) the fringe amplitudes, (ii) the phase and (iii) the phase-shifts related parameters, and alternatively optimized through univariate search technique group by group to improve accuracy and convergence. The Levenberg-Marquart method is used for the optimization of each group of unknowns due to its excellent accuracy and robustness. GIA is shown to have better accuracies than all of its relevant competitors through both a large number of simulations as well as real experiments with a Fizeau interferometer.
Progressive anatomically constrained deep neural network for 3D deformable medical image registration

The 3D deformable image registration is one of the most challenging tasks in medical image analysis. Due to the large and complex deformation in 3D medical images, many deep neural network based methods have been proposed to improve the image similarity after registration, among which recursive cascading network structure is one of the state-of-the-art. However, most existing works rely on the pixel-level image similarities to achieve anatomical rationality and overlook the global-level resemblance between the two structures. Therefore, the resulting registration is not quite clinically valuable. To this end, in this work, we propose a Progressive Anatomically Constrained deep neural Network (PACN) to incorporate the anatomical priors into a progressive cascading registration network to improve the anatomical plausibility as well as the pixel-level similarity of the registration results. Specifically, an Anatomical Constraint Encoder (ACE) network is proposed to encode the global context of the anatomical segmentations and attached to the dense registration network to form a registration unit. Repeated such units forming a cascading framework progressively warps the moving image toward the fixed one, with the output warped image of one unit as the input of the next unit. In this design, the global anatomical priors along with the pixel-level local information are used to guide the model learning process to produce high quality deformation field. Based on this, we explore two frameworks to investigate their registration effectiveness, one attaches the anatomical constraint encoder (ACE) to every dense registration sub-network and the other one attaches ACE only to the last dense registration unit. We test the two frameworks on benchmarks of three liver image datasets SLIVER, LiTS and LSPIG, and one brain dataset LPBA. Our two frameworks have achieved significantly better results in terms of average Dice score than the state-of-the-art baseline method on three liver datasets and comparable on LPBA when both tested with up to three cascades.
Deep Semisupervised Multiview Learning With Increasing Views

In this article, we study two challenging problems in semisupervised cross-view learning. On the one hand, most existing methods assume that the samples in all views have a pairwise relationship, that is, it is necessary to capture or establish the correspondence of different views at the sample level. Such an assumption is easily isolated even in the semisupervised setting wherein only a few samples have labels that could be used to establish the correspondence. On the other hand, almost all existing multiview methods, including semisupervised ones, usually train a model using a fixed dataset, which cannot handle the data of increasing views. In practice, the view number will increase when new sensors are deployed. To address the above two challenges, we propose a novel method that employs multiple independent semisupervised view-specific networks (ISVNs) to learn representation for multiple views in a view-decoupling fashion. The advantages of our method are two-fold. Thanks to our specifically designed autoencoder and pseudolabel learning paradigm, our method shows an effective way to utilize both the labeled and unlabeled data while relaxing the data assumption of the pairwise relationship, that is, correspondence. Furthermore, with our view decoupling strategy, the proposed ISVNs could be separately trained, thus efficiently handling the data of increasing views without retraining the entire model. To the best of our knowledge, our ISVN could be one of the first attempts to make handling increasing views in the semisupervised setting possible, as well as an effective solution to the noncorresponding problem. To verify the effectiveness and efficiency of our method, we conduct comprehensive experiments by comparing 13 state-of-the-art approaches on four multiview datasets in terms of retrieval and classification.
A Multi-Schematic Classifier-Independent Oversampling Approach for Imbalanced Datasets

Labelled imbalanced data, used for classification problems, have an unequal distribution of samples over the classes.Traditional classification models, such as random forest, gradient boosting, face a problem when dealing with imbalanced datasets.Over 85 oversampling algorithms, mostly extensions of the SMOTE algorithm, have been built over the past two decades, to solve the problem of imbalanced datasets.However, it has been evident from previous studies that different oversampling algorithms have different degrees of efficiency with different classifiers.With numerous algorithms available, it is difficult to decide on an oversampling algorithm for a chosen classifier.Here, we overcome this problem with a multi-schematic and classifier-independent oversampling approach, referred to as ProWRAS (Proximity Weighted Random Affine Shadowsampling).ProWRAS integrates the Localized Random Affine Shadowsampling (LoRAS) algorithm and the Proximity Weighted Synthetic oversampling (ProWSyn) algorithm.By controlling the variance of the synthetic samples, as well as a proximity-weighted clustering system of the minority class data, the ProWRAS algorithm improves performance, compared to algorithms that generate synthetic samples through modelling high dimensional convex spaces of the minority class.ProWRAS is multi-schematic by employing four oversampling schemes, each of which has its unique way to model the variance of the generated data.The proximity weighted clustering approach of ProWRAS allows one to generate low variance synthetic samples only in borderline clusters to avoid overlap with the majority class.Most importantly, the performance of ProWRAS with proper choice of oversampling schemes, is independent of the classifier used.We have benchmarked our newly developed ProWRAS algorithm against five stateof-the-art oversampling models and four different classifiers on 20 publicly available datasets.Our results show that ProWRAS outperforms other oversampling algorithms in a statistically significant way, in terms of both F1-score and κ-score.Moreover, we have introduced a novel measure for classifier independence I-score, and showed quantitatively that ProWRAS performs better, independent of the classifier used.Thus, ProWRAS is highly effective for homogeneous tabular data where convex modelling of the data space can be done.In practice, ProWRAS customizes synthetic sample generation according to a classifier of choice and thereby reduces benchmarking efforts.
On Gradient Coding with Partial Recovery

We consider a generalization of the recently proposed gradient coding framework where a large dataset is divided across <tex>$n$</tex> workers and each worker transmits to a master node one or more linear combinations of the gradients over the data subsets assigned to it. Unlike the conventional framework which requires the master node to recover the sum of the gradients over all the data subsets in the presence of <tex>$s$</tex> straggler workers, we relax the goal of the master node to computing the sum of at least some &#x03B1; fraction of the gradients. The broad goal of our work is to study the optimal computation and communication load per worker for this approximate gradient coding framework. We begin by deriving a lower bound on the computation load of any feasible scheme and also propose a strategy which achieves this lower bound, albeit at the cost of high communication load and a number of data partitions which can be polynomial in the number of workers n. We then restrict attention to schemes which utilize a number of data partitions equal to <tex>$n$</tex> and propose schemes based on cyclic assignment which have a lower communication load. When each worker transmits a single linear combination, we also prove lower bounds on the computation load of any scheme using <tex>$n$</tex> data partitions. A full version of this paper is accessible at: https://arxiv.org/abs/2102.10163
FPDA: Fault-Tolerant and Privacy-Enhanced Data Aggregation Scheme in Fog-Assisted Smart Grid

The data aggregation approach in smart grid (SG) is an effective solution to make data available while keeping privacy preserving at the same time. The fault tolerance means decryption can still be carried out successfully even if some smart meters (SMs) are breaking down. It is a challenging issue to design an efficient, fault-tolerant data aggregation scheme with no help of centralized trusted authority (TA) or key update after each fault recovery. Recently, a fault-tolerant data aggregation scheme FESDA (Saleem <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.,</i> 2020) was presented. However, we identify a serious and inherent vulnerability in its fault tolerance. Specifically, given an equivalent ciphertext derived from each SM’s private key aiming at resiting faults, the control center can abuse it to obtain any SM’s reading. An effective attack is launched with both theoretical proof and experimentative verification. Furthermore, to fix it and solve the challenging issue, we first design an extended Shamir’s threshold secret-sharing scheme (tSSS) with master secret security and reusability, allowing SMs to reconstruct subsequent multiple secrets without leaking their original secret shares. Then, if some SMs fail to submit data successfully, the fog node (FN) starts extra request–response interactivities among itself and a limited number of SMs. Finally, a privacy-enhanced aggregation of normal reports can also be achieved. Extensive experiments demonstrate that the majority of computation costs and communication overload to acquire fault tolerance are offload on FNs, while SMs are computationally economical.
Mutual Support of Data Modalities in the Task of Sign Language Recognition

This paper presents a method for automatic sign language recognition that was utilized in the CVPR 2021 ChaLearn Challenge (RGB track). Our method is composed of several approaches combined in an ensemble scheme to perform isolated sign-gesture recognition. We combine modalities of video sample frames processed by a 3D ConvNet (I3D), with body-pose information in the form of joint locations processed by a Transformer, hand region images transformed into a semantic space, and linguistically defined locations of hands. Although the individual models perform sub-par (60% to 93% accuracy on validation data), the weighted ensemble results in 95.46% accuracy.
Search Methods in Motion Planning for Mobile Robots

Mobile robots are used in a variety of applications ranging from domestic robotics to autonomous vehicles. In several applications, it is important to conduct motion planning for robots to find optimal paths with respect to parameters such as space and time. Classical search methods in the literature can be adapted to guide robotic paths and plan the motion of mobile robots. In this work, we explore three methods, namely the deterministic depth-first search and breadth-first search, and the heuristic A* search. We adapt these to the context of mobile robots navigating a maze and accordingly present the deployed algorithms and corresponding experiments with comparisons. These findings are useful in applications where mobile robots are utilized since the optimization of their paths is beneficial in motion planning. We explain applications of this study in areas such as human-robot collaboration (HRC) and unmanned aerial vehicles (UAVs) for motion planning in robotic paths.
Approximating median absolute deviation with bounded error

The median absolute deviation (MAD) is a statistic measuring the variability of a set of quantitative elements. It is known to be more robust to outliers than the standard deviation (SD), and thereby widely used in outlier detection. Computing the exact MAD however is costly, e.g., by calling an algorithm of finding median twice, with space cost O ( n ) over n elements in a set. In this paper, we propose the first fully mergeable approximate MAD algorithm, OP-MAD, with one-pass scan of the data. Remarkably, by calling the proposed algorithm at most twice, namely TP-MAD, it guarantees to return an (ϵ, 1)-accurate MAD, i.e., the error relative to the exact MAD is bounded by the desired ϵ or 1. The space complexity is reduced to O ( m ) while the time complexity is O ( n + m log m ), where m is the size of the sketch used to compress data, related to the desired error bound ϵ. To get a more accurate MAD, i.e., with smaller ϵ, the sketch size m will be larger, a trade-off between effectiveness and efficiency. In practice, we often have the sketch size m ≪ n , leading to constant space cost O (1) and linear time cost O ( n ). The extensive experiments over various datasets demonstrate the superiority of our solution, e.g., 160000× less memory and 18x faster than the aforesaid exact method in datasets pareto and norm . Finally, we further implement and evaluate the parallelizable TP-MAD in Apache Spark, and the fully mergeable OP-MAD in Structured Streaming.
How divergent is your data?

We present DivExplorer, a tool that enables users to explore datasets and find subgroups of data for which a classifier behaves in an anomalous manner. These subgroups, denoted as divergent subgroups, may exhibit, for example, higher-than-normal false positive or negative rates. DivExplorer can be used to analyze and debug classifiers. If the data has ethical or social implications, DivExplorer can be also used to identify bias in classifiers.
SAND in action

Subsequence anomaly detection in long data series is a significant problem. While the demand for real-time analytics and decision making increases, anomaly detection methods have to operate over streams and handle drifts in data distribution. Nevertheless, existing approaches either require prior domain knowledge or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. Moreover, subsequence anomaly detection methods usually require access to the entire dataset and are not able to learn and detect anomalies in streaming settings. To address these limitations, we propose SAND, a novel online system suitable for domain-agnostic anomaly detection. SAND relies on a novel steaming methodology to incrementally update a model that adapts to distribution drifts and omits obsolete data. We demonstrate our system over different streaming scenarios and compare SAND with other subsequence anomaly detection methods.
Regularization of Deep Neural Network With Batch Contrastive Loss

Neural networks have become deeper in recent years and this has improved its capacity to handle more complex tasks. However, deep neural network has more parameters and is easier to overfit, especially when training samples are insufficient. In this paper, we present a new regularization technique called batch contrastive regularization to improve generalization performance. The loss function is based on contrastive loss which enforces intra-class compactness and inter-class separability of batch samples. We explore three different contrastive losses: (1) the center contrastive loss which regularizes based on distances between data points and their corresponding class centroid, (2) the sample contrastive loss which is based on batch sample-pair distances, and (3) the multicenter loss which is similar to center contrastive loss except that the cluster centers are discovered from training. The proposed network has two heads, one for classification and the other for regularization. The regularization head is discarded during inference. We also introduce bag sampling to ensure that all classes in a batch are well represented. The performance of the proposed architecture is evaluated on the CIFAR-10 and CIFAR-100 datasets. Our experiments show that network regularized by batch contrastive loss display impressive generalization performance over a wide variety of classes, yielding more than 11% improvement for ResNet50 on CIFAR-100 when trained from scratch.
DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation

The rapid increment of morbidity of brain stroke in the last few years have been a driving force towards fast and accurate segmentation of stroke lesions from brain MRI images. With the recent development of deep-learning, computer-aided and segmentation methods of ischemic stroke lesions have been useful for clinicians in early diagnosis and treatment planning. However, most of these methods suffer from inaccurate and unreliable segmentation results because of their inability to capture sufficient contextual features from the MRI volumes. To meet these requirements, 3D convolutional neural networks have been proposed, which, however, suffer from huge computational requirements. To mitigate these problems, we propose a novel Dimension Fusion Edge-guided network (DFENet) that can meet both of these requirements by fusing the features of 2D and 3D CNNs. Unlike other methods, our proposed network uses a parallel partial decoder (PPD) module for aggregating and upsampling selected features, rich in important contextual information. Additionally, we use an edge-guidance and enhanced mixing loss for constantly supervising and improvising the learning process of the network. The proposed method is evaluated on publicly available Anatomical Tracings of Lesions After Stroke (ATLAS) dataset, resulting in mean DSC, IoU, Precision and Recall values of 0.5457, 0.4015, 0.6371, and 0.4969 respectively. The results, when compared to other state-of-the-art methods, outperforms them by a significant margin. Therefore, the proposed model is robust, accurate, superior to the existing methods, and can be relied upon for biomedical applications.
Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network

The detection of face tampering in videos created by artificial intelligence techniques (commonly known as the Deep Fakes) has become an important and challenging task in network security defense. In this paper, we propose a novel attention-based deep fake video detection method, which captures the sharp changes in terms of the facial features caused by the composite video. We utilize the convolutional long short-term memory to extract both spatial and temporal information of DeeFake videos. Meanwhile, we apply the attention mechanism to emphasize the specific facial area of each video frame. Finally, we design a decoder to further fusion multiple frames information for more accurate detection results. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework achieves superior performance.
Integration of Extended Reality and a High-Fidelity Simulator in Team-Based Simulations for Emergency Scenarios

Wearable devices such as smart glasses are considered promising assistive tools for information exchange in healthcare settings. We aimed to evaluate the usability and feasibility of smart glasses for team-based simulations constructed using a high-fidelity simulator. Two scenarios of patients with arrhythmia were developed to establish a procedure for interprofessional interactions via smart glasses using 15-h simulation training. Three to four participants formed a team and played the roles of remote supporter or bed-side trainee with smart glasses. Usability, attitudes towards the interprofessional health care team and learning satisfaction were assessed. Using a 5-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree), 31 participants reported that the smart glasses were easy to use (3.61 ± 0.95), that they felt confident during use (3.90 ± 0.87), and that that responded positively to long-term use (3.26 ± 0.89) and low levels of physical discomfort (1.96 ± 1.06). The learning satisfaction was high (4.65 ± 0.55), and most (84%) participants found the experience favorable. Key challenges included an unstable internet connection, poor resolution and display, and physical discomfort while using the smart glasses with accessories. We determined the feasibility and acceptability of smart glasses for interprofessional interactions within a team-based simulation. Participants responded favorably toward a smart glass-based simulation learning environment that would be applicable in clinical settings.