Reviewerly/exHarmony huggingface dataset:
Augmented Reality Guidance for Configuring an Anesthesia Machine to Serve as a Ventilator for COVID-19 Patients

When there is a shortage of ventilators in a hospital, an anesthesia machine can be used as a ventilator.However, using an anesthesia machine as a ventilator requires that it be set up in a way that would not be familiar to medical personnel who normally work with ventilators.To teach medical staff how to do this, we developed a smartphone augmented reality app that allows a user to interact with a life-size virtual anesthesia machine, and leads them through the necessary steps.This makes it possible for the user to practice the setup procedures in a way that preserves the 3D spatial layout of the tasks without requiring access to the physical machine.
Transitive Transfer Sparse Coding for Distant Domain

The transfer learning between the source and target domain has already achieved significant success in machine learning areas. However, the existing methods can not achieve satisfactory result when solving the two distant domains transfer learning problem. In the worst case, it could lead to the negative transfer. In this paper, we propose a novel framework called transitive transfer sparse coding (TTSC) to solve the two distant domains transfer learning problem. On the one hand, as an extension of the sparse coding, the TTSC framework constructs a robust and high-level dictionary across three different domains and simultaneously obtains three good feature sparse representations. On the other hand, TTSC utilizes the intermediate domain as a strong bridge to transfer valuable knowledge between the source domain and target domain. Empirical studies validated that the TTSC framework significantly could outperform state-of-the-art methods.
Game Theoretical Model for Cybersecurity Risk Assessment of Industrial Control Systems

Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) use advanced computing, sensors, control systems, and communication networks to monitor and control industrial processes and distributed assets. The increased connectivity of these systems to corporate networks has exposed them to new security threats and made them a prime target for cyber-attacks with the potential of causing catastrophic economic, social, and environmental damage. Recent intensified sophisticated attacks on these systems have stressed the importance of methodologies and tools to assess the security risks of Industrial Control Systems (ICS). In this paper, we propose a novel game theory model and Monte Carlo simulations to assess the cybersecurity risks of an exemplary industrial control system under realistic assumptions. We present five game enrollments where attacker and defender agents make different preferences and we analyze the final outcome of the game. Results show that a balanced defense with uniform budget spending is the best strategy against a look-ahead attacker.
Dual Metric Discriminator for Open Set Video Domain Adaptation

Existing video domain adaptation methods focus on addressing closed set problems. However, it is nearly impossible to guarantee different domains share exactly the same set of categories in realistic scenarios. Hence, open set video domain adaptation (OSVDA) problem, which involves unknown categories, has achieved increasingly close attention. In this paper, we propose a seminal framework, which involves spatial and temporal information to address OSVDA problem. Besides, we design a novel discrimination module, i.e., Dual Metric Discriminator (DMD), to separate known and unknown categories based on implicit and explicit similarity metrics. We conduct comprehensive experiments on several benchmarks and achieve state-of-the-art performance with 40.4%, 33.7%, and 79.2% accuracy on UCF to HMDB, HMDB to UCF, and Kinetics to UCF scenarios respectively.
Ordinal Depth Classification Using Region-based Self-attention

Depth perception is essential for scene understanding, autonomous navigation and augmented reality. Depth estimation from a single 2D image is challenging due to the lack of reliable cues, e.g. stereo correspondences and motions. Modern approaches exploit multi-scale feature extraction to provide more powerful representations for deep networks. However, these studies only use simple addition or concatenation to combine the extracted multi-scale features. This paper proposes a novel region-based self-attention (rSA) unit for effective feature fusions. The rSA recalibrates the multi-scale responses by explicitly modelling the dependency between channels in separate image regions. We discretize continuous depths to formulate an ordinal depth classification problem in which the relative order between categories is preserved. The experiments are performed on a dataset of 4410 RGB-D images, captured in outdoor environments at the University of Wollongong's campus. The proposed module improves the models on small-sized datasets by 22% to 40%.
Near-Optimal Resampling in Particle Filters Using the Ising Energy Model

Resampling increasing the variance of the tracking algorithm in Particle Filtering (PF). Instead of utilizing resampling procedures that rely on asymptotic convergence properties, we show that intelligently selecting and replicating a set of samples can better represent the posterior approximation and improve the overall performance of the PF. To this end, we formulate the resampling procedure as an integer program that minimizes an upper bound on the Kullback-Leibler divergence (KLD) between the resampled distribution and the posterior approximation. We then transform the problem into an Ising energy minimization problem, which we are able to efficiently solve. Applying our novel paradigm to a challenging sequential importance resampling (SIR) simulation shows faster convergence over the number of resampled particles and a 35% improvement in the median KLD for a fixed number of particles.
Attribute-based quality assessment for demographic estimation in face videos

Most existing works regarding facial demographic estimation are focused on still image datasets, although nowadays the need to analyze video content in real applications is increasing. We propose to tackle gender, age and ethnicity estimation in the context of video scenarios. Our main contribution is to use an attribute-specific quality assessment procedure to select most relevant frames from a video sequence for each of the three demographic modalities. Selected frames are classified with fine-tuned MobileNet models and a final video prediction is obtained with a majority voting strategy. Our validation on three different datasets and our comparison with state-of-the-art models, show the effectiveness of the proposed demographic classifiers and the quality pipeline, which allows to reduce both: the number of frames to be classified and the processing time in practical applications; and improves the soft biometrics prediction accuracy.
J-POP: Japanese Puzzles as Optimization Problems

Japanese puzzle games such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sudoku</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Futoshiki</i> are familiar recreational pursuits, but they also present an interesting computational challenge. A number of algorithms exist for the automated solution of such puzzles, but, until now, these have not been compared in a unified way. Here, we present an integrated framework for the study of combinatorial black-box optimization, using Japanese puzzles as the test-bed. Importantly, our platform is extendable, allowing for the easy addition of both puzzles and solvers. We compare the performance of a number of optimization algorithms on five different puzzle games, and identify a subset of puzzle instances that could provide a challenging benchmark set for future algorithms.
Real-time Monocular Depth Estimation with Extremely Light-Weight Neural Network

Obstacle avoidance and environment sensing are crucial applications in autonomous driving and robotics. Among all types of sensors, RGB camera is widely used in these applications as it can offer rich visual contents with relatively low-cost, and using a single image to perform depth estimation has become one of the main focuses in resent research works. However, prior works usually rely on highly complicated computation and power-consuming GPU to achieve such task; therefore, we focus on developing a real-time light-weight system for depth prediction in this paper. Based on the well-known encoder-decoder architecture, we propose a supervised learning-based CNN with detachable decoders that produce depth predictions with different scales. We also formulate a novel log-depth loss function that computes the difference of predicted depth map and ground truth depth map in log space, so as to increase the prediction accuracy for nearby locations. To train our model efficiently, we generate depth map and semantic segmentation with complex teacher models. Via a series of ablation studies and experiments, it is validated that our model can efficiently performs real-time depth prediction with only 0.32M parameters, with the best trained model outperforms previous works on KITTI dataset for various evaluation matrices.
Virtual reality documentaries and the illusion of presence

I argue that virtual reality (VR) documentaries mandate that users employ a fictional attitude toward their presence in a virtual environment (VE) for the purpose of engaging with nonfictional content. The most salient feature of VR is that VR users typically feel as though their bodies were present in a VE. This paper explores presence in VR as a perceptual illusion facilitated by certain technological features. Drawing on Kendall Walton’s concept of fiction, I argue that the illusion of presence in VR also requires a fictional attitude that VR users employ when imagining themselves in a VE. In the case of VR documentaries, while users’ attitude in regards to the feeling of presence is best characterized as make-belief, they nevertheless employ an attitude of belief in regards to the content of the documentary and accept this content as nonfictional.
Questions of type selection of lighting devices for underwater telecommunication television systems

Abstract The problems of studying the underwater part of the water area through the lens of ecology exist all over the world. Difficulties associated with the study of the ecological state of the hydrosphere pose for modern engineers the task of developing specialized autonomous underwater robotic systems, as well as using telecommunication means to transfer information from them to the shore or ship. Due to the fact that visibility under water has a limited range, that is, it is possible to observe underwater objects only from a close distance, it is proposed to use underwater telecommunication television systems that function in conjunction with lighting devices. The article presents the results of an experiment carried out in a marine aquatic environment, on the basis of which the expediency of using light searchlights and laser devices in the near and far zones in underwater search systems is substantiated.
SANet++: Enhanced Scale Aggregation with Densely Connected Feature Fusion for Crowd Counting

Crowd counting has gained considerable attention recently but remains challenging mainly due to large scale variations. In this paper, we present SANet++ with a novel architecture to generate high-quality density maps and further perform accurate counting. SANet++ obtains enhanced multi-scale representation with densely connected feature fusion between branches. Our approach avoids information redundancy while exploits complementary features at different scales. In addition, we introduce a novel Bulk loss which incorporates the spatial correlation within a whole patch. This global structural supervision enforces the network to learn the interactions between pixels without limitations on region size. Our SANet++ outperforms state-of-the-art crowd counting approaches according to extensive experiments conducted on three major datasets.
Training a Bank of Wiener Models with a Novel Quadratic Mutual Information Cost Function

This paper presents a novel training methodology to adapt parameters of a bank of Wiener models (BWMs), i.e., a bank of linear filters followed by a static memoryless nonlinearity, using full pdf information of the projected outputs and the desired signal. BWMs also share the same architecture with the first layer of a time-delay neural networks (TDNN) with a single hidden layer, which is often trained with backpropagation. To optimize BWMs, we develop a novel cost function called the empirical embedding of quadratic mutual information (E-QMI) that is metric-driven and efficient in characterizing the statistical dependency. We demonstrate experimentally that by applying this cost function to the proposed model, our method is comparable with state-of-the-art neural network architectures for regressions tasks without using backpropagation of the error.
Representation Learning with LDA Models for Entity Disambiguation in Specific Domains

Entity disambiguation is extremely important in knowledge construction. The word representation model ignores the influence of the ordering between words on the sentence or text information. Thus, we propose a domain entity disambiguation method that fuses the doc2vec and LDA topic models. In this study, the doc2vec document is used to indicate that the model obtains the vector form of the entity reference item and the candidate entity from the domain corpus and knowledge base, respectively. Moreover, the context similarity and category referential similarity calculations are performed based on the knowledge base of the upper and lower relation domains that are constructed. The LDA topic model and doc2vec model are used to obtain word expressions with different meanings of polysemic words. We use the k-means algorithm to cluster the word vectors under different topics to obtain the topic domain keywords of the text, and perform the similarity calculations under the domain keywords of the different topics. Finally, the similarities of the three feature types are merged and the candidate entity with the highest similarity degree is used as the final target entity. The experimental results demonstrate that the proposed method outperforms the existing model, which proves its feasibility and effectiveness.
Person Recognition with HGR Maximal Correlation on Multimodal Data

Multimodal person recognition is a common task in video analysis and public surveillance, where information from multiple modalities, such as images and audio extracted from videos, are used to jointly determine the identity of a person. Previous person recognition techniques either use only uni-modal data or only consider shared representations between different input modalities, while leaving the extraction of their relationship with identity information to downstream tasks. Furthermore, real-world data often contain noise, which makes recognition more challenging practical situations. In our work, we propose a novel correlation-based multimodal person recognition framework that is relatively simple but can efficaciously learn supervised information in multimodal data fusion and resist noise. Specifically, our framework learns a discriminative embeddings of persons by joint learning visual features and audio features while maximizing HGR maximal correlation among multimodal input and persons' identities. Experiments are done on a subset of Voxceleb2. Compared with state-of-the-art methods, the proposed method demonstrates an improvement of accuracy and robustness to noise.
Social Media Emotion Analysis in Indonesian Using Fine-Tuning BERT Model

Social media is the leading platform were users' express opinions and emotions. Emotion Analysis aims to identify emotions: happy, sad, angry, fear, disgust, shame, and guilt. InaMoodMeter framework takes the unstructured status of a Facebook user. It processes it to extract emotion from teenage users for self-assessment and to observe emotion related to online customer satisfaction. We also created an Indonesian Dataset for Emotion Analysis from Facebook. The dataset can be utilized as a valuable benchmark for emotion classification in Indonesian. With this dataset, many state-of-the-art approaches are evaluated. We also experimented on the ISEAR Indonesian translated dataset. This research consists of two stages: obtaining training data to build the dataset and performing classification. Our proposed method can achieve the highest accuracy of 79% with the Fine-Tuning Bert Model.
Producing Liveness

The global pandemic has driven musicians online. We report an ethnographic account of how two traditional folk clubs with little previous interest in digital platforms transitioned to online experiences. They followed very different approaches: one adapted their existing singaround format to video conferencing while the other evolved a weekly community-produced, pre-recorded show that could be watched together. However, despite their successes, participants ultimately remained unable to 'sing in chorus' due to network constraints. We draw on theories of liveness from performance studies to explain our findings, arguing that HCI might orientate itself to online liveness as being co-produced through rich participatory structures that dissolve traditional distinctions between live and recorded and performer and audience. We discuss how participants appropriated existing platforms to achieve this, but these in turn shaped their practices in unforeseen ways. We draw out implications for the design and deployment of future live performance platforms.
Optimization of SMOTE for imbalanced data based on AdaRBFNN and hybrid metaheuristics

Oversampling ratio N and the minority class’ nearest neighboring number k are key hyperparameters of synthetic minority oversampling technique (SMOTE) to reconstruct the class distribution of dataset. No optimal default value exists there. Therefore, it is of necessity to discuss the influence of the output dataset on the classification performance when SMOTE adopts various hyperparameter combinations. In this paper, we propose a hyperparameter optimization algorithm for imbalanced data. By iterating to find reasonable N and k for SMOTE, so as to build a balanced and high-quality dataset. As a result, a model with outstanding performance and strong generalization ability is trained, thus effectively solving imbalanced classification. The proposed algorithm is based on the hybridization of simulated annealing mechanism (SA) and particle swarm optimization algorithm (PSO). In the optimization, Cohen’s Kappa is used to construct the fitness function, and AdaRBFNN, a new classifier, is integrated by multiple trained RBF neural networks based on AdaBoost algorithm. Kappa of each generation is calculated according to the classification results, so as to evaluate the quality of candidate solution. Experiments are conducted on seven groups of KEEL datasets. Results show that the proposed algorithm delivers excellent performance and can significantly improve the classification accuracy of the minority class.
Comprehensive Analysis of Object Detection And Tracking Methodologies From Surveillance Videos

With an exponential advancement in technology, it's not uncommon that there is a rise in cameras and surveillance systems over the last few decades. As the world moves towards automated era monitoring and surveillance tasks dependency with humans is decreasing. On the contrary to human capabilities which are susceptible to errors and inconsistency, automated systems seem to be more promising and convenient and hence paves a way for a research trend comprising object detection and tracking in computer vision. Object detection and tracking in surveillance videos refers to accurately identifying the relative position/orientation of an object of interest and its trajectory of motion across the video sequence. Though having access to very high definition robust surveillance systems, practical environmental conditions such as occlusion, motion blur, cluttered background, illumination changes poses significant challenges in object detection and tracking. The intent of this research is to shed a light on recent innovations and developments that have enabled tackling the challenges to some extent. This research encloses brief understanding of recent object detection and tracking methodologies that are gaining popularity. In this paper, conventional as well as soft computing methodologies are covered. This paper aims to provide tentative guidance to young researchers to quickly grasp the field and can help enlighten improvements and innovation in existing methodologies.
Web Table Classification Based on Visual Features

Tables on the web constitute a valuable data source for many applications, like factual search and knowledge base augmentation. However, as genuine tables containing relational knowledge only account for a small proportion of tables on the web, reliable genuine web table classification is a crucial first step of table extraction. Previous works usually rely on explicit feature construction from the HTML code. In contrast, we propose an approach for web table classification by exploiting the full visual appearance of a table, which works purely by applying a convolutional neural network on the rendered image of the web table. Since these visual features can be extracted automatically, our approach circumvents the need for explicit feature construction. A new hand labeled gold standard dataset containing HTML source code and images for 13,112 tables was generated for this task. Transfer learning techniques are applied to well known VGG16 and ResNet50 architectures. The evaluation of CNN image classification with fine tuned ResNet50 (F1 93.29%) shows that this approach achieves results comparable to previous solutions using explicitly defined HTML code based features. By combining visual and explicit features, an F-measure of 93.70% can be achieved by Random Forest classification, which beats current state of the art methods.