Reviewerly/exHarmony huggingface dataset:
Efficient Joint Gradient Based Attack Against SOR Defense for 3D Point Cloud Classification

Deep learning based classifiers on 3D point cloud data have been shown vulnerable to adversarial examples, while a defense strategy named Statistical Outlier Removal (SOR) is widely adopted to defend adversarial examples successfully, by discarding outlier points in the point cloud.
SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning

Abstract Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin (H&amp;E)-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that estimate the underlying distribution of the tumor cell marker pan-cytokeratin (panCK). To build a dataset suitable for learning this task, we developed a serial staining protocol which allows IF and H&amp;E images from the same tissue to be spatially registered. We show that deep learning-extracted morphological feature representations of histological images can guide representative sample selection, which improved SHIFT generalizability in a small but heterogenous set of human pancreatic cancer samples. With validation in larger cohorts, SHIFT could serve as an efficient preliminary, auxiliary, or substitute for panCK IF by delivering virtual panCK IF images for a fraction of the cost and in a fraction of the time required by traditional IF.
Attentive capsule network for click-through rate and conversion rate prediction in online advertising

Estimating Click-through Rate (CTR) and Conversion Rate (CVR) are two essential user response prediction tasks in computing advertising and recommendation systems. The mainstream methods map sparse, high-dimensional categorical features (e.g., user id, item id) into low-dimensional representations with neural networks. Although they have achieved significant advancement in recent years, how to capture user’s diverse interests effectively from past behaviors is still challenging. Recently some works try using attention-based methods to learn the representation from user behavior history adaptively. However, it is insufficient to capture the diversity of user’s interests. As a step forward to improve this goal, we propose a method named Attentive Capsule Network (ACN). It uses Transformers for feature interaction and leverages capsule networks to capture multiple interests from user behavior history. To precisely obtain sequence representation related to the current advertisement, we further design a modified dynamic routing algorithm integrating with an attention mechanism. Experimental results on real-world datasets demonstrate the effectiveness of our proposed ACN with significant improvement over state-of-the-art approaches. Moreover, it also offers good explainability when extracting diverse interest points of users from behavior history.
Hybrid Dynamic-static Context-aware Attention Network for Action Assessment in Long Videos

The objective of action quality assessment is to score sports videos. However, most existing works focus only on video dynamic information (i.e., motion information) but ignore the specific postures that an athlete is performing in a video, which is important for action assessment in long videos. In this work, we present a novel hybrid dynAmic-static Context-aware attenTION NETwork (ACTION-NET) for action assessment in long videos. To learn more discriminative representations for videos, we not only learn the video dynamic information but also focus on the static postures of the detected athletes in specific frames, which represent the action quality at certain moments, along with the help of the proposed hybrid dynamic-static architecture. Moreover, we leverage a context-aware attention module consisting of a temporal instance-wise graph convolutional network unit and an attention unit for both streams to extract more robust stream features, where the former is for exploring the relations between instances and the latter for assigning a proper weight to each instance. Finally, we combine the features of the two streams to regress the final video score, supervised by ground-truth scores given by experts. Additionally, we have collected and annotated the new Rhythmic Gymnastics dataset, which contains videos of four different types of gymnastics routines, for evaluation of action quality assessment in long videos. Extensive experimental results validate the efficacy of our proposed method, which outperforms related approaches.
Learning Modality-Invariant Latent Representations for Generalized Zero-shot Learning

Recently, feature generating methods have been successfully applied to zero-shot learning (ZSL). However, most previous approaches only generate visual representations for zero-shot recognition. In fact, typical ZSL is a classic multi-modal learning protocol which consists of a visual space and a semantic space. In this paper, therefore, we present a new method which can simultaneously generate both visual representations and semantic representations so that the essential multi-modal information associated with unseen classes can be captured. Specifically, we address the most challenging issue in such a paradigm, i.e., how to handle the domain shift and thus guarantee that the learned representations are modality-invariant. To this end, we propose two strategies: 1) leveraging the mutual information between the latent visual representations and the semantic representations; 2) maximizing the entropy of the joint distribution of the two latent representations. By leveraging the two strategies, we argue that the two modalities can be well aligned. At last, extensive experiments on five widely used datasets verify that the proposed method is able to significantly outperform previous the state-of-the-arts.
Deep Unsupervised Hybrid-similarity Hadamard Hashing

Hashing has become increasingly important for large-scale image retrieval. Recently, deep supervised hashing has shown promising performance, yet little work has been done under the more realistic unsupervised setting. The most challenging problem in unsupervised hashing methods is the lack of supervised information. Besides, existing methods fail to distinguish image pairs with different similarity degrees, which leads to a suboptimal construction of similarity matrix. In this paper, we propose a simple yet effective unsupervised hashing method, dubbed Deep Unsupervised Hybrid-similarity Hadamard Hashing (DU3H), which tackles these issues in an end-to-end deep hashing framework. DU3H employs orthogonal Hadamard codes to provide auxiliary supervised information in unsupervised setting, which can maximally satisfy the independence and balance properties of hash codes. Moreover, DU3H utilizes both highly and normally confident image pairs to jointly construct a hybrid-similarity matrix, which can magnify the impacts of different pairs to better preserve the semantic relations between images. Extensive experiments conducted on three widely used benchmarks validate the superiority of DU3H.
Instance-Aware Self-supervised Learning for Nuclei Segmentation

Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology. The annotating of nuclei instances, requiring experienced pathologists to manually draw the contours, is extremely laborious and expensive, which often results in the deficiency of annotated data. The deep learning based segmentation approaches, which highly rely on the quantity of training data, are difficult to fully demonstrate their capacity in this area. In this paper, we propose a novel self-supervised learning framework to deeply exploit the capacity of widely-used convolutional neural networks (CNNs) on the nuclei instance segmentation task. The proposed approach involves two sub-tasks (i.e., scale-wise triplet learning and count ranking), which enable neural networks to implicitly leverage the prior-knowledge of nuclei size and quantity, and accordingly mine the instance-aware feature representations from the raw data. Experimental results on the publicly available MoNuSeg dataset show that the proposed self-supervised learning approach can remarkably boost the segmentation accuracy of nuclei instance—a new state-of-the-art average Aggregated Jaccard Index (AJI) of 70.63%, is achieved by our self-supervised ResUNet-101. To our best knowledge, this is the first work focusing on the self-supervised learning for instance segmentation.
Gray2ColorNet: Transfer More Colors from Reference Image

Image colorization is an effective approach to provide plausible colors for grayscale images, which can achieve better and pleasing visual qualities. Although exemplar based colorization approaches provide promising results, they are relied on semantic colors or global colors only from the reference images. For the former situation, when the correspondence between the input grayscale image and reference image is not established, the colors of the reference image cannot be transferred to the input grayscale image successfully. With the later circumstance, because only global colors are considered, it is hard to produce a color image whose objects have the same color as the reference image when they are semantically related. Thus, an end-to-end colorization network Gray2ColorNet is proposed in this work, where an attention gating mechanism based color fusion network is designed to accomplish the colorization tasks. Relied on the proposed method, the semantic colors and global color distribution from the reference image are fused effectively, which are transferred to the final color images along with the prior knowledge of colors contained in the training data. The experimental results demonstrate the superior colorization performances of the proposed method compared to other state-of-the-art approaches.
A Structured Graph Attention Network for Vehicle Re-Identification

Vehicle re-identification aims to identify the same vehicle across different surveillance cameras and plays an important role in public security. Existing approaches mainly focus on exploring informative regions or learning an appropriate distance metric. However, they not only neglect the inherent structured relationship between discriminative regions within an image, but also ignore the extrinsic structured relationship among images. The inherent and extrinsic structured relationships are crucial to learning effective vehicle representation. In this paper, we propose a Structured Graph ATtention network (SGAT) to fully exploit these relationships and allow the message propagation to update the features of graph nodes. SGAT creates two graphs for one probe image. One is an inherent structured graph based on the geometric relationship between the landmarks that can use features of their neighbors to enhance themselves. The other is an extrinsic structured graph guided by the attribute similarity to update image representations. Experimental results on two public vehicle re-identification datasets including VeRi-776 and VehicleID have shown that our proposed method achieves significant improvements over the state-of-the-art methods.
Preaching-inspired swarm intelligence algorithm and its applications

Swarm intelligence algorithms have been widely used in both research and engineering fields, but they face the problems of low accuracy and premature convergence, which limit their further applications. Inspired by the preachers' social behaviors, a novel meta-heuristic swarm intelligence algorithm, Preaching Optimization Algorithm, is proposed in this paper. Its convergence accuracy is effectively improved by improving the initial range of offspring individuals. Meanwhile, by introducing the combined weight including individual fitness and position relationship between individuals, the diversity of individuals is improved, thus reducing the possibility of algorithm premature convergence. In this paper, the parameter sensitivity of the Preaching Optimization Algorithm is analyzed firstly. Secondly, the proposed algorithm is evaluated by comparing it with the other meta-heuristic algorithms on CEC'17 benchmark functions. The results indicate the proposed algorithm has strong competitiveness both accuracy and robustness in solving optimization problems. Finally, the Preaching Optimization Algorithm is used to solve the typical problems in engineering and image threshold segmentation, which further verifies the excellent optimization performance of the proposed algorithm. In this paper, the Preaching Optimization Algorithm is explained in detail and compared with other existing methods to evaluate its comprehensive performance.
Dungeons &amp; Replicants: Automated Game Balancing via Deep Player Behavior Modeling

Balancing the options available to players in a way that ensures rich variety and viability is a vital factor for the success of any video game, and particularly competitive multiplayer games. Traditionally, this balancing act requires extensive periods of expert analysis, play testing and debates. While automated gameplay is able to predict outcomes of parameter changes, current approaches mainly rely on heuristic or optimal strategies to generate agent behavior. In this paper, we demonstrate the use of deep player behavior models to represent a player population (n = 213) of the massively multiplayer online role-playing game Aion, which are used, in turn, to generate individual agent behaviors. Results demonstrate significant balance differences in opposing enemy encounters and show how these can be regulated. Moreover, the analytic methods proposed are applied to identify the balance relationships between classes when fighting against each other, reflecting the original developers' design.
Fractional Fourier Transform based Riesz fractional derivative approach for edge detection and its application in image enhancement

Edge detection plays a key role in detecting the boundaries of an object in the image to improve the quality of image edges. The edge detection techniques based on integer order derivatives are more prevalent in literature. But these techniques have the limitation of providing thicker edges and being sensitive to noise. Therefore, in order to deal with this issue, an edge detection technique based on Riesz fractional derivative (RFD) in Fractional Fourier Transform (FrFT) domain is proposed in this paper. The RFD mask used for edge detection is obtained by using various interpolation methods. The selection of mask size is done on the basis of Figure of Merit (FOM) and Edge Preservation Index (EPI). The edges obtained with proposed approach in FrFT domain are further used for image enhancement. The effectiveness of proposed approach is confirmed using test images from different standard datasets by considering performance metrics such as FOM, EPI, Information Entropy, Average Gradient, Edge Intensity, etc. Further validation of the proposed approach is done by considering various uncontrolled features such as Poisson noise, different illumination conditions, JPEG compression artifacts, etc. Experimental results show that proposed technique outperforms the existing techniques by providing improvement in various performance parameters.
Application Experiences Using IoT Devices in Education

The Internet of Things (IoT) is becoming a regular part of our lives. The devices can be used in many sectors, such as education and in the learning process. The article describes the possibilities of using commonly available devices such as smart wristbands (watches) and eye tracking technology, i.e., using existing technical solutions and methods that rely on the application of sensors while maintaining non-invasiveness. By comparing the data from these devices, we observed how the students’ attention affects their results. We looked for a correlation between eye tracking, heart rate, and student attention and how it all impacts their learning outcomes. We evaluate the obtained data in order to determine whether there is a degree of dependence between concentration and heart rate of students.
AAR

Current wearable AR devices create an isolated experience with a limited field of view, vergence-accommodation conflicts, and difficulty communicating the virtual environment to observers. To address these issues and enable new ways to visualize, manipulate, and share virtual content, we introduce Augmented Augmented Reality (AAR) by combining a wearable AR display with a wearable spatial augmented reality projector. To explore this idea, a system is constructed to combine a head-mounted actuated pico projector with a Hololens AR headset. Projector calibration uses a modified structure from motion pipeline to reconstruct the geometric structure of the pan-tilt actuator axes and offsets. A toolkit encapsulates a set of high-level functionality to manage content placement relative to each augmented display and the physical environment. Demonstrations showcase ways to utilize the projected and head-mounted displays together, such as expanding field of view, distributing content across depth surfaces, and enabling bystander collaboration.
A simulated annealing algorithm with a dual perturbation method for clustering

Clustering is a powerful tool in exploratory data analysis that partitions a set of objects into clusters with the goal of maximizing the similarity of objects within each cluster. Due to the tendency of clustering algorithms to find suboptimal partitions of data, the approximation method Simulated Annealing (SA) has been used to search for near-optimal partitions. However, existing SA-based partitional clustering algorithms still settle to local optima. We propose a new SA-based clustering algorithm, the Simulated Annealing with Gaussian Mutation and Distortion Equalization algorithm (SAGMDE), which uses two perturbation methods to allow for both large and small perturbations in solutions. Our experiments on a diverse collection of data sets show that SAGMDE performs more consistently and yields better results than existing SA clustering algorithms in terms of cluster quality while maintaining a reasonable runtime. Finally, we use generative art as a visualization tool to compare various partitional clustering algorithms.
Machine learning in the prediction of medical inpatient length of stay

Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be difficult for medical patients. This review was conducted with the aim of identifying and assessing previous studies on the application of machine learning to the prediction of total hospital inpatient LOS for medical patients. A review of machine learning in the prediction of total hospital LOS for medical inpatients was conducted using the databases PubMed, EMBASE and Web of Science. Of the 673 publications returned by the initial search, 21 articles met inclusion criteria. Of these articles the most commonly represented medical specialty was cardiology. Studies were also identified that had specifically evaluated machine learning LOS prediction in patients with diabetes and tuberculosis. The performance of the machine learning models in the identified studies varied significantly depending on factors including differing input datasets and different LOS thresholds and outcome metrics. Common methodological shortcomings included a lack of reporting of patient demographics and lack of reporting of clinical details of included patients. The variable performance reported by the studies identified in this review supports the need for further research of the utility of machine learning in the prediction of total inpatient LOS in medical patients. Future studies should follow and report a more standardised methodology to better assess performance and to allow replication and validation. In particular, prospective validation studies and studies assessing the clinical impact of such machine learning models would be beneficial.
Multi-robot path planning based on improved artificial potential field and fuzzy inference system1

In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems.
Intelligent recommender system based on unsupervised machine learning and demographic attributes

Recommendation systems aim to predict users interests and recommend items most likely to interest them. In this paper, we propose a new intelligent recommender system that combines collaborative filtering (CF) with the popular unsupervised machine learning algorithm K-means clustering. Also, we use certain user demographic attributes such as the gender and age to create segmented user profiles, when items (movies) are clustered by genre attributes using K-means and users are classified based on the preference of items and the genres they prefer to watch. To recommend items to an active user, Collaborative Filtering approach then is applied to the cluster where the user belongs. Following the experimentation for well known movies, we show that the proposed system satisfies the predictability of the CF algorithm in GroupLens. In addition, our proposed system improves the performance and time response speed of the traditional collaborative Filtering technique and the Content-Based technique too.
Empowering Relational Network by Self-attention Augmented Conditional Random Fields for Group Activity Recognition

This paper presents a novel relational network for group activity recognition. The core of our network is to augment the conditional random fields (CRF), amenable to learning inter-dependency of correlated observations, with the newly devised temporal and spatial self-attention to learn the temporal evolution and spatial relational contexts of every actor in videos. Such a combination utilizes the global receptive fields of self-attention to construct a spatio-temporal graph topology to address the temporal dependency and non-local relationships of the actors. The network first uses the temporal self-attention along with the spatial self-attention, which considers multiple cliques with different scales of locality to account for the diversity of the actors’ relationships in group activities, to model the pairwise energy of CRF. Afterward, to accommodate the distinct characteristics of each video, a new mean-field inference algorithm with dynamic halting is also addressed. Finally, a bidirectional universal transformer encoder (UTE), which combines both of the forward and backward temporal context information, is used to aggregate the relational contexts and scene information for group activity recognition. Simulations show that the proposed approach surpasses the state-of-the-art methods on the widespread Volleyball and Collective Activity datasets.