Reviewerly/exHarmony huggingface dataset:
Combining convex hull and directed graph for fast and accurate ellipse detection

Detecting ellipses from images is a fundamental task in many computer vision applications. However, due to the complexity of real-world scenarios, it is still a challenge to detect ellipses accurately and efficiently. In this paper, we propose a novel method to tackle this problem based on the fast computation of convex hull and directed graph, which achieves promising results on both accuracy and efficiency. We use Depth-First-Search to extract branch-free curves after adaptive edge detection. Line segments are used to represent the curvature characteristic of the curves, followed by splitting at sharp corners and inflection points to attain smooth arcs. Then the convex hull is constructed, together with the distance, length, and direction constraints, to find co-elliptic arc pairs. Arcs and their connectivity are encoded into a sparse directed graph, and then ellipses are generated via a fast access of the adjacency list. Finally, salient ellipses are selected subject to strict verification and weighted clustering. Extensive experiments are conducted on eight real-world datasets (six publicly available and two built by ourselves), as well as five synthetic datasets. Our method achieves the overall highest F-measure with competitive speed compared to representative state-of-the-art methods.
Copyright in generative deep learning

Abstract Machine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created by human artists. These artworks are mainly based on generative deep learning (GDL) techniques, which have seen a formidable development and remarkable refinement in the very recent years. Given the inherent characteristics of these techniques, a series of novel legal problems arise. In this article, we consider a set of key questions in the area of GDL for the arts, including the following: is it possible to use copyrighted works as training set for generative models? How do we legally store their copies in order to perform the training process? Who (if someone) will own the copyright on the generated data? We try to answer these questions considering the law in force in both the United States and the European Union, and potential future alternatives. We then extend our analysis to code generation, which is an emerging area of GDL. Finally, we also formulate a set of practical guidelines for artists and developers working on deep learning generated art, as well as some policy suggestions for policymakers.
Do You Really Need to Know Where “That” Is? Enhancing Support for Referencing in Collaborative Mixed Reality Environments

Mixed Reality has been shown to enhance remote guidance and is especially well-suited for physical tasks. Conversations during these tasks are heavily anchored around task objects and their spatial relationships in the real world, making referencing - the ability to refer to an object in a way that is understood by others - a crucial process that warrants explicit support in collaborative Mixed Reality systems. This paper presents a 2x2 mixed factorial experiment that explores the effects of providing spatial information and system-generated guidance to task objects. It also investigates the effects of such guidance on the remote collaborator's need for spatial information. Our results show that guidance increases performance and communication efficiency while reducing the need for spatial information, especially in unfamiliar environments. Our results also demonstrate a reduced need for remote experts to be in immersive environments, making guidance more scalable, and expertise more accessible.
Medical image denoising using optimal thresholding of wavelet coefficients with selection of the best decomposition level and mother wavelet

Abstract Medical images have become omnipresent in diagnosis and therapy. However, they can be affected by various types of noise that reduce image quality and make the final diagnostic decision difficult. The main objective of this research is to effectively remove the noise while preserving the important image characteristics. This paper proposes a novel approach for image denoising based on discrete wavelet transform (DWT) with the selection of the best decomposition level and mother wavelet. Then, the thresholding function is carried out in the detail coefficients. Optimal thresholding is done using new optimization techniques such as the crow search algorithm and social spider optimization techniques. Finally, the inverse of DWT is applied to reconstruct the denoised image. The proposed method is evaluated using peak signal to noise ratio, mean square error, and the structural similarity index measure. The experimental results show the efficiency of the optimization‐based denoising method over standard methods. Interesting results are obtained with all kinds of noise, and improvements about 30 dB can be reached with the Rician noise.
Recall-based Machine Learning approach for early detection of Cervical Cancer

With frequent advancements in development of algorithms and need to incorporate them with clinically synthesized medical information is the paramount of modern-day bioinformatics. This aspect of computational study is of great healthcare significance as deduced results could be farfetched to generalize conclusions in regular medicine practice and diagnosis thus fastening up the process of detection. This paper tries to generalize cervical cancer detection approach with random forest regression technique. Unlike other papers which focus on accuracy and precision, this paper emphasizes on recall-based approach and beneficial tenets this approach over former ones. Four diagnostic tests used for early stage detection of Cervical cancer are Hinselmann's test, Schiller's test, Biopsy and Cytology. Each test is studied individually and analysis was made on the basis of confusion matrix, recall score and receiver operator curve (ROC). The basic aim during the entire development was to achieve higher recall scores with reduced false positive values.
Computing representations using hypervolume scalarizations

In this article, we use the hypervolume indicator as a scalarizing function for biobjective combinatorial optimization problems. In particular, we describe a generic solution approach that determines the nondominated set of a biobjective optimization problem by solving a sequence of hypervolume scalarizations with appropriate choices of the reference point. Moreover, this solution technique can also provide a compact representation of the efficient set that is a (1−1/e)-approximation to the optimal representation in terms of the hypervolume in an a priori manner. We illustrate these concepts for a particular variant of the biobjective knapsack problem and for a biobjective shortest path problem. Numerical results are presented for the former problem.
Speech-Language Pre-Training for End-to-End Spoken Language Understanding

End-to-end (E2E) spoken language understanding (SLU) can infer semantics directly from speech signal without cascading an automatic speech recognizer (ASR) with a natural language understanding (NLU) module. However, paired utterance recordings and corresponding semantics may not always be available or sufficient to train an E2E SLU model in a real production environment. In this paper, we propose to unify a well-optimized E2E ASR encoder (speech) and a pre-trained language model encoder (language) into a transformer decoder. The unified speech-language pre-trained model (SLP) is continually enhanced on limited labeled data from a target domain by using a conditional masked language model (MLM) objective, and thus can effectively generate a sequence of intent, slot type, and slot value for given input speech in the inference. The experimental results on two public corpora show that our approach to E2E SLU is superior to the conventional cascaded method. It also outperforms the present state-of-the-art approaches to E2E SLU with much less paired data.
Evaluating Object Manipulation Interaction Techniques in Mixed Reality: Tangible User Interfaces and Gesture

Tangible user interfaces (TUIs) have been widely studied in computer, virtual reality and augmented reality systems and are known to improve user experience in these mediums. However, there have been few evaluations of TUIs in wearable mixed reality (MR). In this study, we present the results from a comparative study evaluating three object manipulation techniques in wearable MR: (1) Space-multiplexed identical-formed TUI (i.e., a physical cube that acted as a dynamic tangible proxy with identical real and virtual forms); (2) Time-multiplexed TUI (i.e., a tangible controller that was used to manipulate virtual content); (3) Hand gesture (i.e., reaching, pinching and moving the hand to manipulate virtual content). The interaction techniques were compared with a user study with 42 participants. Results revealed that the tangible cube and the controller interaction methods were comparative to each other while both being superior to the hand gesture interaction method in terms of user experience, performance, and presence. We also present suggestions for interaction design for MR based on our findings.
Learning Emotional-Blinded Face Representations

We propose two face representations that are blind to facial expressions associated to emotional responses. This work is in part motivated by new international regulations for personal data protection, which enforce data controllers to protect any kind of sensitive information involved in automatic processes. The advances in Affective Computing have contributed to improve human-machine interfaces but, at the same time, the capacity to monitorize emotional responses triggers potential risks for humans, both in terms of fairness and privacy. We propose two different methods to learn these expression-blinded facial features. We show that it is possible to eliminate information related to emotion recognition tasks, while the performance of subject verification, gender recognition, and ethnicity classification are just slightly affected. We also present an application to train fairer classifiers in a case study of attractiveness classification with respect to a protected facial expression attribute. The results demonstrate that it is possible to reduce emotional information in the face representation while retaining competitive performance in other face-based artificial intelligence tasks.
Refining Time Delay Estimate of Complex Signal Using Polynomial Interpolation in Time Domain

This paper deals with time delay estimation of a complex random signal carried out in the time domain where its discrete-time finite observation is being processed. The paper observes polynomial interpolation methods applied to a sampled cross-correlation function (CCF) in the neighborhood of its maximum. This kind of approximation compromises the fast computation of the delay with the high accuracy of resultant estimate. The second and third order polynomial approximation schemes are considered for three approaches based on absolute value of correlation samples or their complex originals. The latter is shown to be obligatory forced with the condition that time delay estimate has to be real-valued despite the fact that complex signals are processed. The numerical simulation performed for first-order autoregressive model random process allows estimating the dependency of the bias, variance and total error of the estimates on signal-to-noise ratio. The comparative analysis conducted for the methods shows that second-order approximation applied to absolute value of CCF produce the accuracy that can be treated as satisfactory for general purpose.
Time-Domain Speaker Verification Using Temporal Convolutional Networks

Recently, speaker verification systems using deep neural networks have been widely studied. Many of them utilize hand-crafted features such as mel-filterbank energies, mel-frequency cepstral coefficients, and magnitude spectrograms, which are not designed specifically for the speaker verification task and may not be optimal. Recent releases of the large datasets such as VoxCeleb enable us to extract the task-specific features in a data-driven way. In this paper, we propose a speaker verification system that takes the time-domain raw waveforms as inputs, which adopts a learnable encoder and temporal convolutional networks (TCNs) that have shown impressive performance in speech separation. Moreover, we have applied the squeeze and excitation networks after each TCN block to apply channel-wise attention. Our experiments on the VoxCeleb1 dataset demonstrate that the speaker verification system utilizing the proposed feature extraction model outperforms previously proposed time-domain speaker verification systems.
Automatic Detection and Segmentation of Liver Tumors in Multi- phase CT Images by Phase Attention Mask R-CNN

In computer-aided diagnosis of liver tumors, tumor detection and segmentation are essential pretreatment steps. In this study, we proposed a phase attention mask R-CNN based method for simultaneous detection and segmentation of liver tumors in multi-phase CT images. Each feature of the triple phase image is selectively extracted by the attention network for each scale. The segmentation accuracy (Dice value) is about 0.60 ~ 0.66 for single-phase CT images, and the accuracy can be improved to about 0.77 by the proposed method using attention network with multi-phase CT images.
Credit Card Fraud Prevention Using Blockchain

With the advancement of online payments in various products and services, the likelihood of credit card fraud has risen compared to the decades-long history of credit cards. When blockchain systems' immutability meets smart contracts, third-party removal and decentralization could be met as a high level of security. Proposed blockchain with fraud detection technology will assist to mitigate fraudulent credit card transactions due to its intermediate parties. Authors propose a solution (B-Box.com) where credit card transactions are modeled on a blockchain so that the credit card processing can be decentralized and verifiable with an accredited set of computing nodes. This solution reduces fraud due to ambiguous contracts with the use of a smart contract between the bank and the customer. Also, this project includes a scaling mechanism to blockchain because the current projects have a lack of scalability. Moreover, the solution introduces a proactive anomaly detection to detect fraudulent credit card transactions, in which the system will resist frauds before the fraudulent transaction enters the blockchain. So the proposed solution will make transparency between the banks and the end-users and at the same time prevents frauds before it happens which helps the banks to save millions in otherwise lost due to fraud.
Bayesian Transformer Language Models for Speech Recognition

State-of-the-art neural language models (LMs) represented by Transformers are highly complex. Their use of fixed, deterministic parameter estimates fail to account for model uncertainty and lead to over-fitting and poor generalization when given limited training data. In order to address these issues, this paper proposes a full Bayesian learning framework for Transformer LM estimation. Efficient variational inference based approaches are used to estimate the latent parameter posterior distributions associated with different parts of the Transformer model architecture including multi-head self-attention, feed forward and embedding layers. Statistically significant word error rate (WER) reductions up to 0.5% absolute (3.18% relative) and consistent perplexity gains were obtained over the baseline Transformer LMs on state-of-the-art Switchboard corpus trained LF-MMI factored TDNN systems with i-Vector speaker adaptation. Performance improvements were also obtained on a cross domain LM adaptation task requiring porting a Transformer LM trained on the Switchboard and Fisher data to a low-resource DementiaBank elderly speech corpus.
Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification

RGB-Infrared person re-identification (RGB-IR Re-ID) aims to matching persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in the surveillance system under poor light conditions. Facing great challenges in complex variances including conventional single-modality and additional inter-modality discrepancies, most of the existing RGB-IR Re-ID methods propose to impose constraints in image level, feature level or a hybrid of both. Despite better performance of hybrid constraints, they are usually implemented with heavy network architecture. As a matter of fact, previous efforts contribute more as pioneering works in new cross-modal Re-ID area while leaving large space for improvement. This can be mainly attributed to: (a) lack of abundant person image pairs from different modalities for training, and (b) scarcity of salient modality-invariant features especially on coarse representations for effective matching. To address these issues, a novel Multi-Scale Part-Aware Cascading framework (MSPAC) is formulated by aggregating multi-scale fine-grained features from part to global in a cascading manner, which results in a unified representation containing rich and enhanced semantic features. Furthermore, a marginal exponential center (MeCen) loss is introduced to jointly eliminate mixed variances from intra- and inter-modal examples. Cross-modality correlations can thus be efficiently explored on salient features for distinctive modality-invariant feature learning. Extensive experiments are conducted to demonstrate that the proposed method outperforms all the state-of-the-art by a large margin.
The Technology Is Enemy for Me at the Moment: How Smart Home Technologies Assert Control Beyond Intent

Smart technology turns the home into an active agent, which shifts the power structure within the household. This paper examines how initiators of smart technology insert their vision of the good life into households, and how these technologies exert power over the residents. Through a thematic analysis of interviews with five households, we consider Foucault's theory on disciplinary power to examine how smart home technologies shape the experience of the home by shifting the flow of information and thereby reify power structures. Results indicate that the implementation of smart technology can affect access to shared spaces, constrain interactions, and predefine practices thereby establishing hierarchies within the household. We turn the discussion towards ethical challenges concerning control, whose problems the smart home is concerned with, and how the smart home embeds itself in the household. We conclude with design considerations and future work.
Intensity enhancement via GAN for multimodal face expression recognition

Face expression recognition (FER) on low expression intensity is not well studied in the literature. This paper investigates this problem and presents a novel Generative Adversarial Network (GAN) based multimodal approach to it. The method models the tasks of intensity enhancement and expression recognition jointly, ensuring that the synthesize faces not only present expression of high intensity, but also truly promote the performance of FER. The proposed model is flexible enough that faces can be expressed in various formats, such as RGB image, depth maps, 3D point-clouds, etc., so that complementarity of texture and geometry clues can be further exploited. Extensive experiments are conducted on the BU-3DFE, BU-4DFE, Oulu-CASIA and CK+ datasets. State-of-the-art FER performance is achieved for not only the circumstance of low expression intensities, but also the general FER scenarios, clearly validating the effectiveness of the proposed method.
Air-Writing with Sparse Network of Radars using Spatio-Temporal Learning

Hand gesture and motion sensing offer an intuitive and natural form of human-machine interface. Air-writing systems allow users to draw alpha-numerical or linguistic characters in the virtual board in air through hand gestures. Traditionally, radar-based air-writing systems have been based on a network of radars, at least three, to localize the hand target through trilateration algorithm followed by tracking to extract the drawn trajectory, which is then followed by recognition of the drawn character by either Long-Short Term Memory (LSTM) utilizing the sensed trajectory or Deep Convolutional Neural Network (DCNN) utilizing a reconstructed 2D image from the trajectory. However, the practical deployments of such systems are limited since the detection of the finger or hand target by all three radars cannot be guaranteed leading to failure of the trilateration algorithm. Further placement of three or more radars for the air-writing solution is neither always physically plausible nor cost-effective. Furthermore, these solutions do not exploit the full potentials of deep neural networks, which are generally capable of learning features implicitly. In this paper, we propose an air-writing system based on a network of sparse radars, i.e. strictly less than three, using 1D DCNN-LSTM-1D transposed DCNN architecture to reconstruct and classify the drawn character utilizing only the range information from each radar. The paper employs real data using one and two 60 GHz milli-meter wave radar sensors to demonstrate the success of the proposed air-writing solution.
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection &amp; Segmentation

In recent years, deep neural networks have achieved state-of-the-art performance in a variety of recognition and segmentation tasks in medical imaging including brain tumor segmentation. We investigate that segmenting a brain tumor is facing to the imbalanced data problem where the number of pixels belonging to the background class (non tumor pixel) is much larger than the number of pixels belonging to the foreground class (tumor pixel). To address this problem, we propose a multitask network which is formed as a cascaded structure. Our model consists of two targets, i.e., (i) effectively differentiate the brain tumor regions and (ii) estimate the brain tumor mask. The first objective is performed by our proposed contextual brain tumor detection network, which plays a role of an attention gate and focuses on the region around brain tumor only while ignoring the far neighbor background which is less correlated to the tumor. Different from other existing object detection networks which process every pixel, our contextual brain tumor detection network only processes contextual regions around ground-truth instances and this strategy aims at producing meaningful regions proposals. The second objective is built upon a 3D atrous residual network and under an encode-decode network in order to effectively segment both large and small objects (brain tumor). Our 3D atrous residual network is designed with a skip connection to enables the gradient from the deep layers to be directly propagated to shallow layers, thus, features of different depths are preserved and used for refining each other. In order to incorporate larger contextual information from volume MRI data, our network utilizes the 3D atrous convolution with various kernel sizes, which enlarges the receptive field of filters. Our proposed network has been evaluated on various datasets including BRATS2015, BRATS2017 and BRATS2018 datasets with both validation set and testing set. Our performance has been benchmarked by both region-based metrics and surface-based metrics. We also have conducted comparisons against state-of-the-art approaches. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Code and models will be publicly available.