Reviewerly/exHarmony huggingface dataset:
SMOTE based class-specific extreme learning machine for imbalanced learning

Imbalanced learning is one of the substantial challenging problems in the field of data mining. The datasets that have skewed class distribution pose hindrance to conventional learning methods. Conventional learning methods give the same importance to all the samples. This leads to biased accuracy, which favors the majority classes. Several classifiers have been designed to tackle the class imbalance problems. Weighted kernel-based SMOTE (WKSMOTE) is a recently proposed method, which employs the minority oversampling in kernel space to tackle the class imbalance problem. Motivated by WKSMOTE, this work proposes a novel SMOTE based class-specific extreme learning machine (SMOTE-CSELM), a variant of class-specific extreme learning machine (CS-ELM), which exploits the benefit of both the minority oversampling and the class-specific regularization. For minority oversampling, this work uses synthetic minority oversampling technique (SMOTE). It increases the significance of the minority class samples for determining the decision region of the classifiers. The proposed method has comparable computational complexity than the weighted extreme learning machine (WELM) for imbalanced learning. The extensive experimental results evaluated on the real-world benchmark datasets demonstrate the efficacy of our proposed method.
Flexible and Efficient Blockchain-Based ABE Scheme With Multi-Authority for Medical on Demand in Telemedicine System

Telemedicine offers a medical-on-demand (MoD) service from a distance. This technology is designed to overcome distance barriers and improve the process of accessing medical services in distant rural communities. With the development of cloud computing, the MoD services in the telemedicine system are provided by the Cloud Service Provider (CSP). This CSP connects the patient and the medical staff in different places with both convenience and fidelity. Meanwhile, the outsourcing healthcare data on public cloud platforms bring some new challenges on the security. Although attribute-based encryption (ABE) algorithm realizes flexible and fine-grained access control, a large number of patients subscribe or unsubscribe the different medical services frequently in the cloud, which takes a huge cost for membership management. In this paper, an ABE scheme is presented to achieve the dynamic authentication and authorization with higher flexibility and efficiency for the MoD services in telemedicine system. On the one hand, when the patient alters his ordered service, it requires no updating on the parameters for those whose statuses remain unchanged. We construct an independent-update key policy ABE scheme in the distributed telemedicine system that aims to updates patient's keys separately, and there are multiple authorities to manage this system altogether which is more similar to the real situation. On the other hand, by using blockchain and distributed database technologies, the private healthcare data stored in public cloud is protected in integrity, which avoids the misdiagnosis accident from the inaccurate electronic health records distorted by a malicious user or authority from the inner cloud. Finally, we analyze the collusion attack in multiple authorities and formally prove the security of this protocol in a standard model. After comparing and simulating, the results of this work show a better performance.
Developing an Innovative Method for Visual Perception Evaluation in a Physical-Based Virtual Environment

With the developments in virtual reality technologies, significant researches have been conducted for human response on indoor luminous environment using head-mounted display to replace those in real environment. However, the limited resolution and luminance values offered by the devices might affect the perceived appearance and high-order impressions in the simulated virtual environment. In this study, a simulated 3-dimensional virtual office was compared against a real one. Both settings presented similar physical and luminous conditions to twenty participants (N = 20). The study investigated subjective and objective visual responses and participants' interaction with the virtual environment based on measurements of perceived presence. Subjective assessments included questions on luminous environment appearance (brightness, colour-temperature, distribution) and high-order perceptions (pleasantness, interest, spaciousness, excitement and complexity). Objective assessments measured contrast-sensitivity and colour-discrimination tasks to assess visual performance across the two representation environments. Results showed no significant differences between the two environments based on the studied parameters, indicating a high level of perceptual accuracy of appearance and high-order perceptions. Minor physical symptoms related to the headset use and high level of perceived presence were found, indicating the proposed methodology's capability to provide realistic immersive environments. Although attributes regarding scene quality: colours, details, and contrast were perceived significantly different to the real environment, objective tasks showed that similar contrast and colour appearance can be produced in the virtual environment with minor impact on fine-details due to limited resolution. Virtual reality maybe a promising alternative representation medium to investigate visual perceptions as the overall appearance of the scene can still be correctly acquired.
A reproducible survey on word embeddings and ontology-based methods for word similarity: Linear combinations outperform the state of the art

Human similarity and relatedness judgements between concepts underlie most of cognitive capabilities, such as categorisation, memory, decision-making and reasoning. For this reason, the proposal of methods for the estimation of the degree of similarity and relatedness between words and concepts has been a very active line of research in the fields of artificial intelligence, information retrieval and natural language processing among others. Main approaches proposed in the literature can be categorised in two large families as follows: (1) Ontology-based semantic similarity Measures (OM) and (2) distributional measures whose most recent and successful methods are based on Word Embedding (WE) models. However, the lack of a deep analysis of both families of methods slows down the advance of this line of research and its applications. This work introduces the largest, reproducible and detailed experimental survey of OM measures and WE models reported in the literature which is based on the evaluation of both families of methods on a same software platform, with the aim of elucidating what is the state of the problem. We show that WE models which combine distributional and ontology-based information get the best results, and in addition, we show for the first time that a simple average of two best performing WE models with other ontology-based measures or WE models is able to improve the state of the art by a large margin. In addition, we provide a very detailed reproducibility protocol together with a collection of software tools and datasets as supplementary material to allow the exact replication of our results.
Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction

We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to fuse multiple deep features encoded from input images. However, GRU based approaches are unable to consistently estimate 3D shapes given different permutations of the same set of input images as the recurrent unit is permutation variant. It is also unlikely to refine the 3D shape given more images due to the long-term memory loss of GRU. Commonly used pooling approaches are limited to capturing partial information, e.g., max/mean values, ignoring other valuable features. In this paper, we present a new feed-forward neural module, named AttSets, together with a dedicated training algorithm, named FASet, to attentively aggregate an arbitrarily sized deep feature set for multi-view 3D reconstruction. The AttSets module is permutation invariant, computationally efficient and flexible to implement, while the FASet algorithm enables the AttSets based network to be remarkably robust and generalize to an arbitrary number of input images. We thoroughly evaluate FASet and the properties of AttSets on multiple large public datasets. Extensive experiments show that AttSets together with FASet algorithm significantly outperforms existing aggregation approaches.
A Feature-Reduction Multi-View k-Means Clustering Algorithm

The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas. Since internet, social network, and big data grow rapidly, multi-view data become more important. For analyzing multi-view data, various multi-view k-means clustering algorithms have been studied. However, most of multi-view k-means clustering algorithms in the literature cannot give feature reduction during clustering procedures. In general, there often exist irrelevant feature components in multi-view data sets that may cause bad performance for these clustering algorithms. There also exists high feature dimension in multi-view data sets so it is necessary to consider reducing its dimension for clustering algorithms. In this paper, a learning mechanism for the multi-view k-means algorithm to automatically compute individual feature weight is constructed. It can reduce these irrelevant feature components in each view. A new multi-view k-means objective function is firstly proposed for constructing the learning mechanism for feature weights in multi-view clustering. A schema for eliminating irrelevant feature(s) with small weight(s) is then considered for feature reduction. Therefore, a new type of multi-view k-means, called a feature-reduction multi-view k-means (FRMVK), is proposed. The computational complexity of FRMVK is also analyzed. Numerical and real data sets are used to compare FRMVK with other feature-weighted multi-view k-means algorithms. Experimental results and comparisons actually demonstrate the effectiveness and usefulness of the proposed FRMVK clustering algorithm.
GANimation: One-Shot Anatomically Consistent Facial Animation

Recent advances in generative adversarial networks (GANs) have shown impressive results for the task of facial expression synthesis. The most successful architecture is StarGAN (Choi et al. in CVPR, 2018), that conditions GANs’ generation process with images of a specific domain, namely a set of images of people sharing the same expression. While effective, this approach can only generate a discrete number of expressions, determined by the content and granularity of the dataset. To address this limitation, in this paper, we introduce a novel GAN conditioning scheme based on action units (AU) annotations, which describes in a continuous manifold the anatomical facial movements defining a human expression. Our approach allows controlling the magnitude of activation of each AU and combining several of them. Additionally, we propose a weakly supervised strategy to train the model, that only requires images annotated with their activated AUs, and exploit a novel self-learned attention mechanism that makes our network robust to changing backgrounds, lighting conditions and occlusions. Extensive evaluation shows that our approach goes beyond competing conditional generators both in the capability to synthesize a much wider range of expressions ruled by anatomically feasible muscle movements, as in the capacity of dealing with images in the wild. The code of this work is publicly available at https://github.com/albertpumarola/GANimation.
Exploring Research in Blockchain for Healthcare and a Roadmap for the Future

Healthcare is a data-intensive domain, once a considerable volume of data is daily to monitoring patients, managing clinical research, producing medical records, and processing medical insurance claims. While the focus of applications of blockchain in practice has been to build distributed ledgers involving virtual tokens, the impetus of this emerging technology has now extended to the medical domain. With the increased popularity, it is crucial to study how this technology accompanied with a system for smart contracts can support and challenge the healthcare domain for all interrelated actors (patients, physicians, insurance companies, regulators) and involved assets (e.g., patients’ data, physician’s data, equipment’s and drug’s supply chain, etc.). The contributions of this paper are the following: (i) report the results of a systematic literature review conducted to identify, extract, evaluate and synthesize the studies on the symbiosis of blockchain in healthcare; (ii) summarize and categorize existing benefits/challenges on incorporating blockchain in healthcare domain; (iii) provide a framework that will facilitate new research activities; and (iv) establish the state of evidence with in-depth assessment.
Cultural coyote optimization algorithm applied to a heavy duty gas turbine operation

In the past decades, the quantity of researches regarding industrial gas turbines (GT) has increased exponentially in terms of number of publications and diversity of applications. The GTs offer high power output along with a high combined cycle efficiency and high fuel flexibility. As consequence, the energy efficiency, the pressure oscillations, the pollutant emissions and the fault diagnosis have become some of the recent concerns related to this type of equipment. In order to solve these GTs related problems and many other real-world engineering and industry 4.0 issues, a set of new technological approaches have been tested, such as the combination of Artificial Neural Networks (ANN) and metaheuristics for global optimization. In this paper, the recently proposed metaheuristic denoted Coyote Optimization Algorithm (COA) is applied to the operation optimization of a heavy duty gas turbine placed in Brazil and used in power generation. The global goal is to find the best valves setup to reduce the fuel consumption while coping with environmental and physical constraints from its operation. In order to treat it as an optimization problem, an integrated simulation model is implemented from original data-driven models and others previously proposed in literature. Moreover, a new version of the COA that links some concepts from Cultural Algorithms (CA) is proposed, which is validated under a set of benchmarks functions from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) 2017 and tested to the GT problem. The results show that the proposed Cultural Coyote Optimization Algorithm (CCOA) outperforms its counterpart for benchmark functions. Further, non-parametric statistical significance tests prove that the CCOA’s performance is competitive when compared to other state-of-the-art metaheuristics after a set of experiments for five case studies. In addition, the convergence analysis shows that the cultural mechanism employed in the CCOA has improved the COA balance between exploration and exploitation. As a result, the CCOA can improve the current GT operation significantly, reducing the fuel consumption up to 3.6% meanwhile all constraints are accomplished.
The Impact of AI on the Advertising Process: The Chinese Experience

Knowing that the demand for advertising in a growing e-commerce market cannot be sufficiently addressed in a traditional advertising operation model, advertisers and e-commerce platforms apply artificial intelligence (AI) technologies in advertising to improve efficiency and meet the market demand. From observations in the Chinese advertising market over the past five years, the authors gain insight into how AI technologies are applied in the advertising process and propose that the advertising process powered by AI technologies is composed of four steps: consumer insight discovery, ad creation, media planning and buying, and ad impact evaluation. This new advertising process, which is supported by a data-based platform with algorithms at its core, is tool based, synchronized, and highly efficient. AI has reorganized and upgraded the traditional advertising process and improved advertising efficiency; however, the new process is still born out of the traditional process and is not yet a reengineered one.
Depth Coefficients for Depth Completion

Depth completion involves estimating a dense depth image from sparse depth measurements, often guided by a color image. While linear upsampling is straight forward, it results in depth pixels being interpolated in empty space across discontinuities between objects. Current methods use deep networks to maintain gaps between objects. Nevertheless depth smearing remains a challenge. We propose a new representation for depth called Depth Coefficients (DC) to address this problem. It enables convolutions to more easily avoid inter-object depth mixing. We also show that the standard Mean Squared Error (MSE) loss function can promote depth mixing, and so we propose instead to use cross-entropy loss for DC. Both quantitative and qualitative evaluation are conducted on benchmarks, and we show that switching out sparse depth input and MSE loss functions with our DC representation and loss is a simple way to improve performance, reduce pixel depth mixing and can improve object detection.
CleanupSpec

Speculation-based attacks affect hundreds of millions of computers. These attacks typically exploit caches to leak information, using speculative instructions to cause changes to the cache state. Hardware-based solutions that protect against such forms of attacks try to prevent any speculative changes to the cache sub-system by delaying them. For example, InvisiSpec, a recent work, splits the load into two operations: the first operation is speculative and obtains the value and the second operation is non-speculative and changes the state of the cache. Unfortunately, such a "Redo" based approach typically incurs slowdown due to the requirement of extra operations for correctly speculated loads, that form the large majority of loads.
Ensemble of Diverse Mappings

Near-term quantum computers do not have the ability to perform error correction. Such Noisy Intermediate Scale Quantum (NISQ) computers can produce incorrect output as the computation is subjected to errors. The applications on a NISQ machine try to infer the correct output by running the same program thousands of times and logging the output. If the error rates are low and the errors are not correlated, then the correct answer can be inferred as the one appearing with the highest frequency. Unfortunately, quantum computers are subjected to correlated errors, which can cause an incorrect answer to appear more frequently than the correct answer.
Unsupervised Video Interpolation Using Cycle Consistency

Learning to synthesize high frame rate videos via interpolation requires large quantities of high frame rate training videos, which, however, are scarce, especially at high resolutions. Here, we propose unsupervised techniques to synthesize high frame rate videos directly from low frame rate videos using cycle consistency. For a triplet of consecutive frames, we optimize models to minimize the discrepancy between the center frame and its cycle reconstruction, obtained by interpolating back from interpolated intermediate frames. This simple unsupervised constraint alone achieves results comparable with supervision using the ground truth intermediate frames. We further introduce a pseudo supervised loss term that enforces the interpolated frames to be consistent with predictions of a pre-trained interpolation model. The pseudo supervised loss term, used together with cycle consistency, can effectively adapt a pre-trained model to a new target domain. With no additional data and in a completely unsupervised fashion, our techniques significantly improve pre-trained models on new target domains, increasing PSNR values from 32.84dB to 33.05dB on the Slowflow and from 31.82dB to 32.53dB on the Sintel evaluation datasets.
Learning Single Camera Depth Estimation Using Dual-Pixels

Deep learning techniques have enabled rapid progress in monocular depth estimation, but their quality is limited by the ill-posed nature of the problem and the scarcity of high quality datasets. We estimate depth from a single cam-era by leveraging the dual-pixel auto-focus hardware that is increasingly common on modern camera sensors. Classic stereo algorithms and prior learning-based depth estimation techniques underperform when applied on this dual-pixel data, the former due to too-strong assumptions about RGB image matching, and the latter due to not leveraging the understanding of optics of dual-pixel image formation. To allow learning based methods to work well on dual-pixel imagery, we identify an inherent ambiguity in the depth estimated from dual-pixel cues, and develop an approach to estimate depth up to this ambiguity. Using our approach, existing monocular depth estimation techniques can be effectively applied to dual-pixel data, and much smaller models can be constructed that still infer high quality depth. To demonstrate this, we capture a large dataset of in-the-wild 5-viewpoint RGB images paired with corresponding dual-pixel data, and show how view supervision with this data can be used to learn depth up to the unknown ambiguities. On our new task, our model is 30% more accurate than any prior work on learning-based monocular or stereoscopic depth estimation.
FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On

Beyond current image-based virtual try-on systems that have attracted increasing attention, we move a step forward to developing a video virtual try-on system that precisely transfers clothes onto the person and generates visually realistic videos conditioned on arbitrary poses. Besides the challenges in image-based virtual try-on (e.g., clothes fidelity, image synthesis), video virtual try-on further requires spatiotemporal consistency. Directly adopting existing image-based approaches often fails to generate coherent video with natural and realistic textures. In this work, we propose Flow-navigated Warping Generative Adversarial Network (FW-GAN), a novel framework that learns to synthesize the video of virtual try-on based on a person image, the desired clothes image, and a series of target poses. FW-GAN aims to synthesize the coherent and natural video while manipulating the pose and clothes. It consists of: (i) a flow-guided fusion module that warps the past frames to assist synthesis, which is also adopted in the discriminator to help enhance the coherence and quality of the synthesized video; (ii) a warping net that is designed to warp clothes image for the refinement of clothes textures; (iii) a parsing constraint loss that alleviates the problem caused by the misalignment of segmentation maps from images with different poses and various clothes. Experiments on our newly collected dataset show that FW-GAN can synthesize high-quality video of virtual try-on and significantly outperforms other methods both qualitatively and quantitatively.