Reviewerly/exHarmony huggingface dataset:
Relevance aggregation for neural networks interpretability and knowledge discovery on tabular data

The lack of interpretability of neural networks is partially why they are not adopted in a wider variety of applications. Many works focus on explaining their predictions, but few take tabular data into consideration, which led to a small adoption even though this data is of high academic and business interest. We present relevance aggregation, an algorithm that combines the relevance computed from several samples as learned by a neural network and generates scores for each input feature. We also present two methods for visualizing the learned patterns, leading to a better model comprehension. The method was tested in synthetic and real-world datasets (breast cancer gene expression, online shopping behavior, and national high school exam) for classification and regression tasks. It correctly identified which features are the most important for the network’s predictions. The selected features can be distinct for each class. The rank of features scores also matches their contribution to the model’s performance. The results selected relevant features from the data, paving the way for knowledge discovery. The top-ranked features were consistently able to improve the performance of another independent classifier. For poorly trained neural networks, relevance aggregation helped identify incorrect rules or machine bias.
Adaptive Graph Adversarial Networks for Partial Domain Adaptation

This article tackles Partial Domain Adaptation (PDA) where the target label set is a subset of the source label set. A key challenging issue in PDA is to prevent negative transfer by isolating source-private classes. Since there is no label information for a target domain, PDA methods require to estimate a label commonness score between source and target domains. Existing approaches use either class-level or sample-level commonness to alleviate the negative transfer issue. However, class-level methods assign the same label commonness to all samples of the same class without considering each sample’s characteristics. Also, the recently introduced sample-level approaches show better performance but they still suffer from negative transfer due to non-trivial anomaly samples. To address these limitations, we propose Adaptive Graph Adversarial Networks (AGAN) consisting of two specialized modules. The adaptive class-relational graph module is designed to utilize the intra- and inter-domain structures through adaptive feature propagation. Complementarily, the sample-level commonness predictor computes a commonness score of each sample. Extensive experimental results on public PDA benchmark datasets demonstrate that our structure-aware method outperforms state-of-the-art methods.
Oracle-Supported Dynamic Exploit Generation for Smart Contracts

Despite the high stakes involved in smart contracts, they are often developed in an undisciplined manner, leaving the security and reliability of blockchain transactions at risk. In this article, we introduce ContraMaster—an oracle-supported dynamic exploit generation framework for smart contracts. Existing approaches mutate only single transactions; ContraMaster exceeds these by mutating the transaction sequences. ContraMaster uses data-flow, control-flow, and the dynamic contract state to guide its mutations. It then monitors the executions of target contract programs, and validates the results against a general-purpose semantic test oracle to discover vulnerabilities. Being a dynamic technique, it guarantees that each discovered vulnerability is a violation of the test oracle and is able to generate the attack script to exploit this vulnerability. In contrast to rule-based approaches, ContraMaster has not shown any false positives, and it easily generalizes to unknown types of vulnerabilities (e.g., logic errors). We evaluate ContraMaster on 218 vulnerable smart contracts. The experimental results confirm its practical applicability and advantages over the state-of-the-art techniques, and also reveal three new types of attacks.
Person re-identification with part prediction alignment

The key to success of person re-identification(re-id) is extracting the discriminative person features. Various part-level feature extraction methods are proposed to capture local person features for re-id. A prerequisite of part feature extraction is that each part should be well located. We believe that ID predictions in different parts of the same image should be consistent. Instead of using the external dataset and pose estimator for guiding, we propose a re-id model with Part Prediction Alignment (PPA), which aims at aligning the predicted distributions between each part. Due to the global feature and local feature contains different spacial information, we consider that the combination of two sides will further improve the detection effect. Therefore, in this paper we adopt the teacher–student training strategy based on PPA for global–local feature extraction, and the global feature extraction branch as a teacher to guide the training of local feature branch. Experimental results on Market-1501, DukeMTMC-reID and CUHK03 (including CUHK03_Detected and CUHK03_Labeled) datasets confirm the effectiveness of our proposal, we achieve Rank1 with 92.4%, 85.1%, 65.5%, 69.2% on Market-1501, DukeMTMC-reID, CUHK03_Detected and CUHK03_Labeled, respectively.
A complete hierarchy for the pure state marginal problem in quantum mechanics

Clarifying the relation between the whole and its parts is crucial for many problems in science. In quantum mechanics, this question manifests itself in the quantum marginal problem, which asks whether there is a global pure quantum state for some given marginals. This problem arises in many contexts, ranging from quantum chemistry to entanglement theory and quantum error correcting codes. In this paper, we prove a correspondence of the marginal problem to the separability problem. Based on this, we describe a sequence of semidefinite programs which can decide whether some given marginals are compatible with some pure global quantum state. As an application, we prove that the existence of multiparticle absolutely maximally entangled states for a given dimension is equivalent to the separability of an explicitly given two-party quantum state. Finally, we show that the existence of quantum codes with given parameters can also be interpreted as a marginal problem, hence, our complete hierarchy can also be used.
A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&amp;E stained human tissues

Reliable counting of glomeruli and evaluation of glomerulosclerosis in renal specimens are essential steps to assess morphological changes in kidney and identify individuals requiring treatment. Because microscopic identification of sclerosed glomeruli performed under the microscope is labor intensive, we developed a deep learning (DL) approach to identify and classify glomeruli as normal or sclerosed in digital whole slide images (WSIs). The segmentation and classification of glomeruli was performed by the U-Net model. Subsequently, glomerular classifications were refined based on glomerular histomorphometry. The U-Net model was trained using patches from Periodic Acid-Schiff (PAS) stained WSIs (n=31) from the AIDPATH - a multi-center dataset, and then tested on an independent set of WSIs (n=20) including PAS (n=6), and hematoxylin and eosin (H&E) stained WSIs (n=14) from four other institutions. The training and test WSIs were obtained from formalin fixed and paraffin embedded blocks with of human kidney specimens each presenting various proportions of normal and sclerosed glomeruli. In the PAS stained WSIs, normal and sclerosed glomeruli were respectively classified with the F1-score of 97.5% and 68.8%. In the H&E stained WSIs, the F1-scores of 90.8% and 78.1% were achieved. Regardless the tissue staining, the glomeruli in the test WSIs were classified with the F1-score of 94.5% (n=923, normal) and 76.8% for (n=261, sclerosed). These results demonstrate for the first time that a framework based on the U-Net model trained with glomerular patches from PAS stained WSIs can reliably segment and classify normal and sclerosed glomeruli in PAS and also H&E stained WSIs. Our approach yielded higher accuracy of glomerular classifications than some of the recently published methods. Additionally, our test set of images with ground truth is publicly available.
Hierarchical Federated Learning for Hybrid Data Partitioning Across Multitype Sensors

Emerging hardware technology enables the utilization of a large number of multitype sensors with diverse sensing capabilities for data collection and the training of AI models. Each sensor collects partial data samples on a type-specific feature space, which results in hybrid data partitioning across the local datasets and brings challenges to developing a novel communication-efficient and scalable training algorithm. We propose a hierarchical federated learning framework for such hybrid data partitioning with a multitier-partitioned neural network architecture. Specifically, we adopt a primal-dual transform to decompose the training problem on both the sample and feature space. Then, a stochastic coordinate gradient descent ascent algorithm is implemented with intratype and intertype over-the-air aggregation for the update of the primal variables and dual variables, respectively. The incorporation of over-the-air aggregation for signal transmission naturally harnesses the channel perturbations and interference for lower communication complexity and preserved privacy. Despite the influence of transmission noise and channel distortion, convergence analysis is provided for general objective functions, which illustrates the robust training performance of the proposed algorithm with a theoretical guarantee.
Teachers’ perceptions of exploiting immersive virtual field trips for learning in primary education

This study aimed to understand what role teachers consider the immersive virtual field trip (VFT) system to play in primary school classrooms. There were 19 teachers' perceptions of immersive VFTs for educational purposes explored. The interview transcripts were analyzed using the phenomenographic method. The results showed that, for usability issues, three categories: lack of interaction, language affordance, and hardware and network problems, were generated from the teachers' concerns. The teachers proposed three ideas: instructional scaffolding, microscopic scale, and localized content, for immersive VFT systems and content design. Regarding learning influences, the teachers considered that the learning by immersive VFTs may provide the benefits of: enriching experiences, increasing motivation, understanding abstract concepts, and fostering inquiry learning. The teachers suggested five instructional approaches: advance organizer, learning extension, learning evaluation, collaboration, and role playing for the implementation of immersive VFTs in classrooms. These findings could benefit future development of immersive VFT learning systems.
Digital platforms aren’t mere tools —they’re complex environments

Antero Garcia and T. Philip Nichols explore how classrooms and schools must reframe their conceptions of technology from a focus on tools that serve specific purposes to a focus on platforms and their ecologies. In doing so, they argue, educational stakeholders should attend to three different dimensions of how technology is integrated in schools: the social uses of digital technologies, the design decisions that were made about these products, and the material resources that help make them operate. This approach requires educators to ask complicated questions about what technology does in schools and how to teach with and about it.
Low Curvature Activations Reduce Overfitting in Adversarial Training

Adversarial training is one of the most effective defenses against adversarial attacks. Previous works suggest that overfitting is a dominant phenomenon in adversarial training leading to a large generalization gap between test and train accuracy in neural networks. In this work, we show that the observed generalization gap is closely related to the choice of the activation function. In particular, we show that using activation functions with low (exact or approximate) curvature values has a regularization effect that significantly reduces both the standard and robust generalization gaps in adversarial training. We observe this effect for both differentiable/smooth activations such as SiLU as well as non-differentiable/non-smooth activations such as LeakyReLU. In the latter case, the "approximate" curvature of the activation is low. Finally, we show that for activation functions with low curvature, the double descent phenomenon for adversarially trained models does not occur.
“Alexa, who am I?”: Voice Assistants and Hermeneutic Lemniscate as the Technologically Mediated Sense-Making

Abstract In this paper, I argue that AI-powered voice assistants, just as all technologies, actively mediate our interpretative structures, including values. I show this by explaining the productive role of technologies in the way people make sense of themselves and those around them. More specifically, I rely on the hermeneutics of Gadamer and the material hermeneutics of Ihde to develop a hermeneutic lemniscate as a principle of technologically mediated sense-making. The lemniscate principle links people, technologies and the sociocultural world in the joint production of meaning and explicates the feedback channels between the three counterparts. When people make sense of technologies, they necessarily engage their moral histories to comprehend new technologies and fit them in daily practices. As such, the lemniscate principle offers a chance to explore the moral dynamics taking place during technological appropriation. Using digital voice assistants as an example, I show how these AI-guided devices mediate our moral inclinations, decisions and even our values, while in parallel suggesting how to use and design them in an informed and critical way.
Interoceptive Interaction: An Embodied Metaphor Inspired Approach to Designing for Meditation

Meditation is a mind-body practice with considerable wellbeing benefits that can take different forms. Novices usually start with focused attention meditation that supports regulation of attention towards an inward focus or internal bodily sensations and away from external stimuli or distractors. Most meditation technologies employ metaphorical mappings of meditative states to visual or soundscape representations to support awareness of mind wandering and attention regulation, although the rationale for such mappings is seldom articulated. Moreover, such external modalities also take the focus attention away from the body. We advance the concept of interoceptive interaction and employed the embodied metaphor theory to explore the design of mappings to the interoceptive sense of thermoception. We illustrate this concept with WarmMind, an on-body interface integrating heat actuators for mapping meditation states. We report on an exploratory study with 10 participants comparing our novel thermal metaphors for mapping meditation states with comparable ones, albeit in aural modality, as provided by Muse meditation app. Findings indicate a tension between the highly discoverable soundscape's metaphors which however hinder attention regulation, and the ambiguous thermal metaphors experienced as coming from the body and supported attention regulation. We discuss the qualities of embodied metaphors underpinning this tension and propose an initial framework to inform the design of metaphorical mappings for meditation technologies.
Study on Pedestrian Detection Based on an Improved YOLOv4 Algorithm

Pedestrian detection, which is widely used in automatic driving and pedestrian analysis, has always been a hot research topic in the fields of artificial intelligence and computer vision. With the development of deep learning, pedestrian detectors are becoming more accurate and faster. However, most of them can't strike a balance between accuracy and speed well. Therefore, in this study, we propose a pedestrian detection model based on an improved YOLOv4 algorithm which concerns both detection accuracy and efficiency. The detection model combines a new type of SPP (Spatial Pyramid Pooling) network and K-means clustering algorithm with YOLOv4 model for easier feature extraction. Furthermore, Mish activation function is applied in the neck of the detection model, replacing Leaky ReLU activation function to improve the detection performance. Our pedestrian detector achieves excellent results on the Caltech pedestrian dataset: 84.7% AP at a real-time speed of 36.4 FPS on Titan XP.
“MedChemVR”: A Virtual Reality Game to Enhance Medicinal Chemistry Education

Medicinal chemistry (MC) is an indispensable component of the pharmacy curriculum. The pharmacists’ unique knowledge of a medicine’s chemistry enhances their understanding of the pharmacological activity, manufacturing, storage, use, supply, and handling of drugs. However, chemistry is a challenging subject for both teaching and learning. These challenges are typically caused by the inability of students to construct a mental image of the three-dimensional (3D) structure of a drug molecule from its two-dimensional presentations. This study explores a prototype virtual reality (VR) gamification option, as an educational tool developed to aid the learning process and to improve the delivery of the MC subject to students. The developed system is evaluated by a cohort of 41 students. The analysis of the results was encouraging and provided invaluable feedback for the future development of the proposed system.
Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision

Pseudo-LiDAR point cloud interpolation is a novel and challenging task in autonomous driving, which aims to address the frequency mismatching problem between a camera and a LiDAR. Previous works represent the 3D spatial motion relationship with a coarse 2D optical flow, and the quality of interpolated point clouds only depends on the supervision of depth maps. As a result, the generated point clouds suffer from inferior global distributions and local appearances. To solve the above problems, we propose a Pseudo-LiDAR point cloud interpolation network to generate temporally and spatially high-quality point cloud sequences. By exploiting the scene flow from point clouds, the proposed network is able to learn a more accurate representation of the 3D spatial motion relationship. For a more comprehensive perception of the distribution of a point cloud, we design a novel reconstruction loss function with the chamfer distance to supervise the generation of Pseudo-LiDAR point clouds in 3D space. In addition, we introduce a multi-modal deep aggregation module to facilitate the efficient fusion of texture and depth features. As the benefits of the improved motion representation, training loss function, and model structure, our approach gains significant improvements on the Pseudo-LiDAR point cloud interpolation task. The experimental results evaluated on KITTI dataset demonstrate the state-of-the-art quantitative and qualitative performance of the proposed network.
Quaternionic extended local binary pattern with adaptive structural pyramid pooling for color image representation

This paper proposes a novel feature representation method for color images, namely quaternionic extended local binary pattern (QxLBP) with adaptive structural pyramid pooling (ASPP). First, we propose a QxLBP operator to encode local neighboring information and complementary modulus and phase information in the quaternion domain of color images. In QxLBP, an extended quaternionic representation (EQR) is proposed which introduces an information term into the real part of a quaternion. The resulting EQR enables us to flexibly encode discriminative features and handle multichannel image data. Second, we propose ASPP as a multiresolution pooling way to aggregate local features. Unlike the traditional spatial pyramid pooling which is sensitive to image rotation and spatial changes, ASPP is structure-oriented pooling which can adaptively aggregate the encoded features into multiresolution histogram representations. Experiments on four benchmark image datasets demonstrate that the proposed method achieves the state-of-the-art performance for color texture classification and scene categorization.
Multi-stream slowFast graph convolutional networks for skeleton-based action recognition

Recently, many efforts have been made to model spatial–temporal features from human skeleton for action recognition by using graph convolutional networks (GCN). Skeleton sequence can precisely represent human pose with a small number of joints while there is still a lot of redundancies across the skeleton sequence in the term of temporal dependency. In order to improve the effectiveness of spatial–temporal feature extraction from skeleton sequence, a SlowFast graph convolution network (SF-GCN) is proposed by implementing the architecture of SlowFast network, which is consisted of the Fast and Slow pathway, in the GCN model. The Fast pathway is a temporal attention embedded lightweight GCN for extracting the feature of fast temporal changes from the skeleton sequence with a high frame rate and fast refreshing speed. The Slow pathway is a spatial attention embedded GCN for extracting the feature of slow temporal changes from the skeleton sequence with a low frame rate and slow refreshing speed. The features of two pathways are fused by using lateral connection and weighted by using channel attention. Based on the aforementioned design, SF-GCN can achieve superior ability of feature extraction while the computational cost significantly drops. In addition to the coordinate information of joints, five high order sequences including edge, the spatial difference and temporal difference of joints and edges are induced to enhance the representation of human action. Six SF-GCNs are implemented for extracting spatial–temporal feature from six kinds of sequences and fused for skeleton-based action recognition, which is called multi-stream SlowFast graph convolutional networks (MSSF-GCN). Extensive experiments are conducted to evaluate the proposed method on three skeleton-based action recognition databases including NTU RGB + D, NTU RGB + D 120, and Skeleton-Kinetics. The results show that the proposed method is effective for skeleton-based action recognition and can achieve the recognition accuracy with an obvious advantage in comparison with the state-of-the-art.
Deep graph cut network for weakly-supervised semantic segmentation

The scarcity of fully-annotated data becomes the biggest obstacle that prevents many deep learning approaches from widely applied. Weakly-supervised visual learning which can utilize inexact annotations is developed rapidly to remedy such a situation. In this paper, we study the weakly-supervised task achieving pixel-level semantic segmentation only with image-level labels as supervision. Different from other methods, our approach tries to transform the weakly-supervised visual learning problem into a semi-supervised visual learning problem and then utilizes semi-supervised learning methods to solve it. Utilizing this transformation, we can adopt effective semi-supervised methods to perform transductive learning with context information. In the semi-supervised learning module, we propose to use the graph cut algorithm to label more supervision from the activation seeds generated from a classification network. The generated labels can provide the segmentation model with effective supervision information; moreover, the graph cut module can benefit from features extracted by the segmentation model. Then, each of them updates and optimizes the other iteratively until convergence. Experiment results on PASCAL VOC and COCO benchmarks demonstrate the effectiveness of the proposed deep graph cut algorithm for weakly-supervised semantic segmentation.