Reviewerly/exHarmony huggingface dataset:
Multi-Domain Adversarial Feature Generalization for Person Re-Identification

With the assistance of sophisticated training methods applied to single labeled datasets, the performance of fully-supervised person re-identification (Person Re-ID) has been improved significantly in recent years. However, these models trained on a single dataset usually suffer from considerable performance degradation when applied to videos of a different camera network. To make Person Re-ID systems more practical and scalable, several cross-dataset domain adaptation methods have been proposed, which achieve high performance without the labeled data from the target domain. However, these approaches still require the unlabeled data of the target domain during the training process, making them impractical. A practical Person Re-ID system pre-trained on other datasets should start running immediately after deployment on a new site without having to wait until sufficient images or videos are collected and the pre-trained model is tuned. To serve this purpose, in this paper, we reformulate person re-identification as a multi-dataset domain generalization problem. We propose a multi-dataset feature generalization network (MMFA-AAE), which is capable of learning a universal domain-invariant feature representation from multiple labeled datasets and generalizing it to `unseen' camera systems. The network is based on an adversarial auto-encoder to learn a generalized domain-invariant latent feature representation with the Maximum Mean Discrepancy (MMD) measure to align the distributions across multiple domains. Extensive experiments demonstrate the effectiveness of the proposed method. Our MMFA-AAE approach not only outperforms most of the domain generalization Person Re-ID methods, but also surpasses many state-of-the-art supervised methods and unsupervised domain adaptation methods by a large margin.
A theoretical model for pattern discovery in visual analytics

The word 'pattern' frequently appears in the visualisation and visual analytics literature, but what do we mean when we talk about patterns? We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole. Our theoretical model describes how patterns are made by relationships existing between data elements. Knowing the types of these relationships, it is possible to predict what kinds of patterns may exist. We demonstrate how our model underpins and refines the established fundamental principles of visualisation. The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns, once discovered, are explicitly involved in further data analysis.
Support vector machine and artificial neural network modelling of orogenic gold prospectivity mapping in the Swayze greenstone belt, Ontario, Canada

Exploration for new mineral deposits has become increasingly difficult as new discoveries are being found under progressively deeper cover. To better understand and predict orogenic gold mineralization in the Archean Swayze greenstone belt, the essential ingredients of a mineral system are considered: 1) the source of gold and transport fluid ligands, 2) fluid pathways, 3) traps, and 4) the processes responsible for gold precipitation. The aim of this study is to use a mineral systems approach to help generate exploration targeting models using a spatial statistical method - weights of evidence (WofE) and data-driven machine learning tools, namely radial basis function neural networks (RBFNN) and support vector machine (SVM). The mineral prospectivity maps generated using the RBFNN and SVM machine learning methods were trained using the K-Fold cross-validation approach whereby 10 subsets of the data were used to train and test the model performance. The mean area under receiver operator curve after 10-fold cross-validations were 91% and 94% for the RBFNN models, and the SVM models obtained accuracies of 91% and 87%. Feature importance estimations obtained from both methods indicate that D2 and D3 high-strain zones, lithological contacts and D2 folds (i.e., synclines and anticlines) were found to be important predictor layers for targeting potential prospective zones of gold mineralization. The machine learning algorithms used in this study are novel and pragmatic methods that use the full potential of geoscience datasets in mapping orogenic gold prospectivity.
Deep mutual learning for visual object tracking

Existing deep trackers use deep convolutional neural networks to extract powerful features or directly predict the position of the target. For most deep trackers, it is hard to improve their performance by replacing the original backbone with a more powerfully heavyweight network directly. In this paper, we propose a novel mutual-learning-based training methodology for visual object tracking. By re-training the backbone network with this novel methodology, we can improve the tracking performance simply and effectively. We demonstrate this novel training methodology with two mainstream tracking approaches: correlation-filter-based approach and tracking-by-detection-based approach. First, we reformulate a correlation-filter-based tracker as a fully convolutional network and design an end-to-end tracking framework. With this framework, we can enhance the backbone network in a mutual learning way. Second, we integrate our training methodology into a typical tracking-by-detection-based tracker, and then we improve the tracking performance with a simple offline training process. Extensive experiments on the OTB2013, OTB2015, VOT2017 and LaSOT benchmarks demonstrate that the tracking performance can be improved effectively by using the proposed mutual-learning-based training methodology.
Towards Emotion-aware Recommender Systems: an Affective Coherence Model based on Emotion-driven Behaviors

Decision making is the cognitive process of identifying and choosing alternatives based on preferences, beliefs, and degree of importance given by the decision maker to objects or actions. For instance, choosing which movie to watch is a simple, small-sized decision-making process. Recommender systems help people to make this kind of choices, usually by computing a short list of suggestions that reduces the space of possible options. These systems are strongly based on the knowledge of user preferences but, in order to fully support people, they should be grounded on a holistic view of the user behavior, that includes also how emotions, mood, and personality traits influence her choosing patterns. In this work, we investigate how to include emotional aspects in the recommendation process. We suggest that the affective state of the user, defined by a set of emotions (e.g., joy, surprise), constitutes part of choosing situation that should be taken into account when modeling user preferences. The main contribution of the paper is a general emotion-aware computational model based on affective user profiles in which each preference, such as a 5-star rating on a movie, is associated with the affective state felt by the user at the time when that preference was collected. The model estimates whether an unseen item is suitable for the current affective state of the user, by computing an affective coherence score that takes into account both the affective user profile and not-affective item features. The approach has been implemented into an Emotion-aware Music Recommender System, whose effectiveness has been assessed by performing in-vitro experiments on two benchmark datasets. The main outcome is that our system showed improved accuracy of recommendations compared to baselines which include no affective information in the recommendation model.
DADA: Driver Attention Prediction in Driving Accident Scenarios

Driver attention prediction is becoming an essential research problem in human-like driving systems. This work makes an attempt to predict the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u> river <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> ttention in <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u> riving <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> ccident scenarios (DADA). However, challenges tread on the heels of that because of the dynamic traffic scene, intricate and imbalanced accident categories. In this work, we design a semantic context induced attentive fusion network (SCAFNet). We first segment the RGB video frames into the images with different semantic regions (i.e., semantic images), where each region denotes one semantic category of the scene (e.g., road, trees, etc.), and learn the spatio-temporal features of RGB frames and semantic images in two parallel paths simultaneously. Then, the learned features are fused by an attentive fusion network to find the semantic-induced scene variation in driver attention prediction. The contributions are three folds. 1) With the semantic images, we introduce their semantic context features and verify the manifest promotion effect for helping the driver attention prediction, where the semantic context features are modeled by a graph convolution network (GCN) on semantic images; 2) We fuse the semantic context features of semantic images and the features of RGB frames in an attentive strategy, and the fused details are transferred over frames by a convolutional LSTM module to obtain the attention map of each video frame with the consideration of historical scene variation in driving situations; 3) The superiority of the proposed method is evaluated on our previously collected dataset (named as DADA-2000) and two other challenging datasets with state-of-the-art methods.
Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification

Applying curriculum learning requires both a range of difficulty in data and a method for determining the difficulty of examples. In many tasks, however, satisfying these requirements can be a formidable challenge.In this paper, we contend that histopathology image classification is a compelling use case for curriculum learning. Based on the nature of histopathology images, a range of difficulty inherently exists among examples, and, since medical datasets are often labeled by multiple annotators, annotator agreement can be used as a natural proxy for the difficulty of a given example. Hence, we propose a simple curriculum learning method that trains on progressively-harder images as determined by annotator agreement. We evaluate our hypothesis on the challenging and clinically-important task of colorectal polyp classification. Whereas vanilla training achieves an AUC of 83.7% for this task, a model trained with our proposed curriculum learning approach achieves an AUC of 88.2%, an improvement of 4.5%. Our work aims to inspire researchers to think more creatively and rigorously when choosing contexts for applying curriculum learning.
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertainties.
The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose

The availability of a large labeled dataset is a key requirement for applying deep learning methods to solve various computer vision tasks. In the context of understanding human activities, existing public datasets, while large in size, are often limited to a single RGB camera and provide only per-frame or per-clip action annotations. To enable richer analysis and understanding of human activities, we introduce IKEA ASM-a three million frame, multi-view, furniture assembly video dataset that includes depth, atomic actions, object segmentation, and human poses. Additionally, we benchmark prominent methods for video action recognition, object segmentation and human pose estimation tasks on this challenging dataset. The dataset enables the development of holistic methods, which integrate multi-modal and multi-view data to better perform on these tasks.
A discrete shuffled frog-leaping algorithm based on heuristic information for traveling salesman problem

A discrete shuffled frog-leaping algorithm based on heuristic information is proposed and the traveling salesman problem is used as a test problem. First, a new individual generation operator utilizing the nearest neighbor information is designed. Then, four improved searching strategies are designed to improve the algorithm performance: (1) the opposite roulette selection that makes the candidate solutions participate in the evolution alternately and keeps the population diversity; (2) establishment of an independent elite set which helps to strengthen the exploring ability, balance the searching ability among subgroups and reduce the population assimilation speed; (3) mutation of the local optimal solution in each subgroup which is beneficial to jump out of the local extremum in the search space; and (4) enhancement of the local search on some best solutions in the shuffled population which aims to strengthen the exploiting ability and improve the searching precision. These defined operators make a good balance between exploration and exploitation. Finally, a large number of TSP instances in the open data set TSPLIB are selected to validate the effectiveness of the new individual generation operator and four improved strategies. Comparison results with three classical and six state-of-the-art algorithms indicate that the proposed algorithm can obtain solutions with a higher accuracy and show a better stability.
A KNN quantum cuckoo search algorithm applied to the multidimensional knapsack problem

Optimization algorithms and particularly metaheuristics are constantly improved with the goal of reducing execution times, increasing the quality of solutions, and addressing larger target instances. Hybridizing techniques are one of these methods particularly interesting for the broad scope of problems to which they can be adapted. In this work, we assessed a hybrid algorithm that uses the k-nearest neighbor technique to improve the results of a quantum cuckoo search algorithm for resource allocation. The k-nearest neighbor technique is used to direct the movement of solutions. Numerical experiments were performed to obtain insights from the contribution of the k-nearest neighbor technique in the final result of solutions. The well-known multidimensional knapsack problem was addressed in order to validate our procedure; a comparison is made with state-of-the-art algorithms. Our results show that our hybrid algorithm consistently produces better results in most of the analyzed instances.
Single-Image Real-Time Rain Removal Based on Depth-Guided Non-Local Features

Rain is a common weather phenomenon that affects environmental monitoring and surveillance systems. According to an established rain model (Garg and Nayar, 2007), the scene visibility in the rain varies with the depth from the camera, where objects faraway are visually blocked more by the fog than by the rain streaks. However, existing datasets and methods for rain removal ignore these physical properties, thus limiting the rain removal efficiency on real photos. In this work, we analyze the visual effects of rain subject to scene depth and formulate a rain imaging model that collectively considers rain streaks and fog. Also, we prepare a dataset called RainCityscapes on real outdoor photos. Furthermore, we design a novel real-time end-to-end deep neural network, for which we train to learn the depth-guided non-local features and to regress a residual map to produce a rain-free output image. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to show its superiority over others.
Deep convolutional neural network architecture design as a bi-level optimization problem

During the last decade, deep neural networks have shown a great performance in many machine learning tasks such as classification and clustering. One of the most successful networks is the CNN (Convolutional Neural Network), which has been applied in many application domains such as pattern recognition, medical diagnosis, and signal processing. Despite the very interesting performance of CNNs, their architecture design is still so far a major challenge for researchers and practitioners. Several works have been proposed in the literature with the aim to find optimized architectures such as ResNet and VGGNet. Unfortunately, most of these architectures are either manually defined by experts or automatically designed by greedy induction algorithms. Recent works suggest the use of Evolutionary Algorithms (EAs) thanks to their ability to escape locally-optimal architectures. Despite the fact that EAs have shown interesting performance, researchers in this direction have considered the design task as a single-level optimization problem; which represents the main research gap we tackle in this paper. The main contribution behind our work consists in the fact that CNN architecture design has a hierarchical nature and thus could be seen as a Bi-Level Optimization Problem (BLOP) where: (1) the upper level minimizes the network complexity defined by the number of blocks and the number of nodes per block; and (2) the lower level optimizes the convolution block 'graphs' topologies by maximizing the classification accuracy. Motivated by the originality of our observation with respect to the state of the art, we frame for the first time the CNN architecture design problem as a BLOP and then solve it using an adapted version of an existing efficient bi-level EA; through the definition of the solution encoding, the fitness function, and the variation operators at each level. The adapted EA is named BLOP-CNN and is assessed on the image classification task using the commonly employed CIFAR-10 and CIFAR-100 benchmark data sets. The analysis of our experimental results show the merits of our proposed method in providing the user with optimized architectures that outperform many recent and prominent architectures coming from the three different approaches, namely: manual design, reinforcement learning-based generation, and evolutionary optimization. Moreover, to show the applicability of our approach, we have conducted a case study on the detection of the COVID-19 using a set of benchmark chest X-ray and Computed Tomography (CT) images.
Music in Extended Realities

The intersection between music and Extended Reality (XR) has grown significantly over the past twenty years, amounting to an established area of research today. The use of XR technologies represents a fundamental paradigm shift for various musical contexts as they disrupt traditional notions of musical interaction by enabling performers and audiences to interact musically with virtual objects, agents, and environments. This article both surveys and expands upon the knowledge accumulated in existing research in this area to build a foundation for future works that bring together Music and XR. To this end, we created a freely available dataset of 260 publications in this space and conducted an in-depth analysis covering 199 works in the last decade. We conducted this analysis using a list of conceptual dimensions belonging to technical, artistic, perceptual and methodological domains. This review of the literature is complemented with a set of interviews with domain experts with the goal of establishing a definition for the emergent field of Musical XR, i.e., the field of music in Extended Realities. Based on the results of the conducted review, a research agenda for the field is proposed.
Enhancing problem-solving skills with smart personal assistant technology

Smart Personal Assistants (SPAs; such as Amazon's Alexa or Google's Assistant) let users interact with computers in a more natural and sophisticated way that was not possible before. Although there exists an increasing amount of research of SPA technology in education, empirical evidence of its ability to offer dynamic scaffolding to enhance students problem-solving skills is still scarce. To fill this gap, the aim of this paper is to find out whether interactions with scaffolding-based SPA technology enable students to internalize and apply problem-solving steps on their own in a 10th grade high school and a vocational business school class. Students in the experiment classes completed their assignments using Smart Personal Assistants, whereas students in the control classes completed the same assignments using traditional methods. This study used a mixed-method approach consisting of two field quasi-experiments and one post-experiment focus group discussion. The empirical results revealed that students in the experiment classes acquired significantly more problem-solving skills than those in the control classes (Study 1: p = 0.0396, study 2: p < 0.001), and also uncovered several changes in students' learning processes. The findings provide first empirical evidence for the value of using SPA technology on skill development in general, and on problem-solving skill development in particular.
Thinking strategically about blockchain adoption and risk mitigation

Blockchain technologies are quickly changing the competitive business landscape. Companies need to think strategically about how best to prepare for a future in which they might systematically enhance value and unleash new value using blockchain. In this article, we address how cooperating companies jointly create unique value with blockchain technology, the risks they face along the way, and how they can mitigate those risks. We briefly identify three different reasons a company might adopt blockchain: to enhance value creation, to strengthen existing value ecosystems, or to create new value ecosystems. Then, we identify three strategic risks of blockchain adoption as they relate to business issues, legal issues, and technological issues. Finally, we highlight four different strategic maneuvers for minimizing these risks. The strategic maneuvers include the all-industry approach, the walled-garden approach, the many-gardens approach, and the options approach. We provide prescriptive advice to managers on how to strategically think about blockchain adoptions, how to identify the underlying risks, and how to consider strategic approaches to mitigate their adoption risk.