Reviewerly/exHarmony huggingface dataset:
Egocentric Distance Perception: A Comparative Study Investigating Differences Between Real and Virtual Environments

Virtual reality systems are a popular tool in behavioral sciences. The participants’ behavior is, however, a response to cognitively processed stimuli. Consequently, researchers must ensure that virtually perceived stimuli resemble those present in the real world to ensure the ecological validity of collected findings. Our article provides a literature review relating to distance perception in virtual reality. Furthermore, we present a new study that compares verbal distance estimates within real and virtual environments. The virtual space—a replica of a real outdoor area—was displayed using a state-of-the-art head-mounted display. Investigated distances ranged from 8 to 13 m. Overall, the results show no significant difference between egocentric distance estimates in real and virtual environments. However, a more in-depth analysis suggests that the order in which participants were exposed to the two environments may affect the outcome. Furthermore, the study suggests that a rising experience of immersion leads to an alignment of the estimated virtual distances with the real ones. The results also show that the discrepancy between estimates of real and virtual distances increases with the incongruity between virtual and actual eye heights, demonstrating the importance of an accurately set virtual eye height.
Again-VC: A One-Shot Voice Conversion Using Activation Guidance and Adaptive Instance Normalization

Recently, voice conversion (VC) has been widely studied. Many VC systems use disentangle-based learning techniques to separate the speaker and the linguistic content information from a speech signal. Subsequently, they convert the voice by changing the speaker information to that of the target speaker. To prevent the speaker information from leaking into the content embeddings, previous works either reduce the dimension or quantize the content embedding as a strong information bottleneck. These mechanisms somehow hurt the synthesis quality. In this work, we propose AGAIN-VC, an innovative VC system using Activation Guidance and Adaptive Instance Normalization. AGAIN-VC is an auto-encoder-based model, comprising of a single encoder and a decoder. With a proper activation as an information bottleneck on content embeddings, the trade-off between the synthesis quality and the speaker similarity of the converted speech is improved drastically. This one-shot VC system obtains the best performance regardless of the subjective or objective evaluations.
Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition

Although text recognition has significantly evolved over the years, state-of the-art (SOTA) models still struggle in the wild scenarios due to complex backgrounds, varying fonts, uncontrolled illuminations, distortions and other artifacts. This is because such models solely depend on visual information for text recognition, thus lacking semantic reasoning capabilities. In this paper, we argue that semantic information offers a complementary role in addition to visual only. More specifically, we additionally utilize semantic information by proposing a multi-stage multi-scale attentional decoder that performs joint visual-semantic reasoning. Our novelty lies in the intuition that for text recognition, prediction should be refined in a stage-wise manner. Therefore our key contribution is in designing a stage-wise unrolling attentional decoder where non-differentiability, invoked by discretely predicted character labels, needs to be bypassed for end-to-end training. While the first stage predicts using visual features, subsequent stages refine on-top of it using joint visual-semantic information. Additionally, we introduce multi-scale 2D attention along with dense and residual connections between different stages to deal with varying scales of character sizes, for better performance and faster convergence during training. Experimental results show our approach to outperform existing SOTA methods by a considerable margin.
Evolutionary bagging for ensemble learning

Ensemble learning has gained success in machine learning with major advantages over other learning methods. Bagging is a prominent ensemble learning method that creates subgroups of data, known as bags, that are trained by individual machine learning methods such as decision trees. Random forest is a prominent example of bagging with additional features in the learning process. Evolutionary algorithms have been prominent for optimisation problems and also been used for machine learning. Evolutionary algorithms are gradient-free methods that work with a population of candidate solutions that maintain diversity for creating new solutions. In conventional bagged ensemble learning, the bags are created once and the content, in terms of the training examples, are fixed over the learning process. In our paper, we propose evolutionary bagged ensemble learning, where we utilise evolutionary algorithms to evolve the content of the bags in order to iteratively enhance the ensemble by providing diversity in the bags. The results show that our evolutionary ensemble bagging method outperforms conventional ensemble methods (bagging and random forests) for several benchmark datasets under certain constraints. We find that evolutionary bagging can inherently sustain a diverse set of bags without reduction in performance accuracy.
Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction

In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovoltaics, enhance organizational dependability, and maximize the utilization factor of the PV systems for something such as an energy management system (EMS) of microgrids. This paper proposes an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, the solar energy output may be projected using prior sun illumination and meteorological data. The feature selection and prediction modules use an AUTO encoder-based Neural Network to improve the process of energy prediction (AUTO-NN). Restricted Boltzmann Machines (RBM) can be used during a set of regulations for development-based feature extraction. The proposed model’s result is evaluated using various constraints. As a result, the proposed AUTO-NN achieved 58.72% of RMSE (Root Mean Square Error), 62.72% of nRMSE (Normalized Root Mean Square Error), 48.04% of MaxAE (Maximum Absolute Error), 48.66% of (Mean Absolute Error), and 46.76% of (Mean Absolute Percentage Error).
Demand forecasting in restaurants using machine learning and statistical analysis

In this paper, demand forecasting in restaurants using machine learning is proposed. Many researches have been proposed on demand forecasting technology using POS data. However, in order to make demand forecasts at a real store, it is necessary to establish a store-specific demand forecasting model in consideration of various factors such as the store location, the weather, events, etc. Therefore, we constructed a demand forecasting model that functionally combines the above mentioned data using machine learning. In this paper, the demand forecasting model using machine learning and the verification result of the model using real store data is discussed.
Supporting Coping with Parkinson's Disease Through Self Tracking

Self-tracking can help people understand their medical condition and the factors that influence their symptoms. However, it is unclear how tracking technologies should be tailored to help people cope with the progression of a degenerative disease. To understand how smartphone apps and other tracking technologies can support people in coping with an incurable illness, we interviewed both people with Parkinson's Disease (n=17) and care partners (n=6) who help people with Parkinson's manage their lives. We describe how symptom trackers can help people identify and solve problems to improve their quality of life, the role symptom trackers can play in helping people combat their own tendencies towards avoidance and denial, and the complex role of care partners in defining and tracking ambiguous symptoms. Our findings yield insights that can guide the design of tracking technologies to help people with Parkinson's Disease accept and plan for their condition.
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity

We study finite sample expressivity, i.e., memorization power of ReLU networks. Recent results require $N$ hidden nodes to memorize/interpolate arbitrary $N$ data points. In contrast, by exploiting depth, we show that 3-layer ReLU networks with $\Omega(\sqrt{N})$ hidden nodes can perfectly memorize most datasets with $N$ points. We also prove that width $\Theta(\sqrt{N})$ is necessary and sufficient for memorizing $N$ data points, proving tight bounds on memorization capacity. The sufficiency result can be extended to deeper networks; we show that an $L$-layer network with $W$ parameters in the hidden layers can memorize $N$ data points if $W = \Omega(N)$. Combined with a recent upper bound $O(WL\log W)$ on VC dimension, our construction is nearly tight for any fixed $L$. Subsequently, we analyze memorization capacity of residual networks under a general position assumption; we prove results that substantially reduce the known requirement of $N$ hidden nodes. Finally, we study the dynamics of stochastic gradient descent (SGD), and show that when initialized near a memorizing global minimum of the empirical risk, SGD quickly finds a nearby point with much smaller empirical risk.
Unsupervised Image Matching and Object Discovery as Optimization

Learning with complete or partial supervision is power- ful but relies on ever-growing human annotation efforts. As a way to mitigate this serious problem, as well as to serve specific applications, unsupervised learning has emerged as an important field of research. In computer vision, unsu- pervised learning comes in various guises. We focus here on the unsupervised discovery and matching of object cate- gories among images in a collection, following the work of Cho et al. [12]. We show that the original approach can be reformulated and solved as a proper optimization problem. Experiments on several benchmarks establish the merit of our approach.
Deep Head Pose Estimation Using Synthetic Images and Partial Adversarial Domain Adaption for Continuous Label Spaces

Head pose estimation aims at predicting an accurate pose from an image. Current approaches rely on supervised deep learning, which typically requires large amounts of labeled data. Manual or sensor-based annotations of head poses are prone to errors. A solution is to generate synthetic training data by rendering 3D face models. However, the differences (domain gap) between rendered (source-domain) and real-world (target-domain) images can cause low performance. Advances in visual domain adaptation allow reducing the influence of domain differences using adversarial neural networks, which match the feature spaces between domains by enforcing domain-invariant features. While previous work on visual domain adaptation generally assumes discrete and shared label spaces, these assumptions are both invalid for pose estimation tasks. We are the first to present domain adaptation for head pose estimation with a focus on partially shared and continuous label spaces. More precisely, we adapt the predominant weighting approaches to continuous label spaces by applying a weighted resampling of the source domain during training. To evaluate our approach, we revise and extend existing datasets resulting in a new benchmark for visual domain adaption. Our experiments show that our method improves the accuracy of head pose estimation for real-world images despite using only labels from synthetic images.
Evaluating driver cognitive distraction by eye tracking: From simulator to driving

Driver cognitive distraction, a critical factor for road safety, is challenging for researchers to evaluate, especially under real conditions. This paper introduces a novel method for simulating involuntary eye movement by combining the vestibulo-ocular reflex model and the optokinetic response. The difference between the predicted and observed eye movements is then assumed to be a measure of the level of cognitive distraction. When this hypothesis was validated under two sets of conditions, in a driving simulator and in a naturalistic situation, our algorithm was able to capture the cognitive distraction event in the naturalistic case. In addition, we also review and discuss the eye-movement sensor, which has a marked effect on the results of the evaluation, and the potential of using eye-movement sensors to evaluate cognitive distraction in drivers.
A histopathological image dataset for grading breast invasive ductal carcinomas

Breast cancer is a common cancer in women, and one of the major causes of death among women around the world. Invasive ductal carcinoma (IDC) is the most widespread type of breast cancer with about 80% of all diagnosed cases. Early accurate diagnosis plays an important role in choosing the right treatment plan and improving survival rate among the patients. In recent years, efforts have been made to predict and detect all types of cancers by employing artificial intelligence. An appropriate dataset is the first essential step to achieve such a goal. This paper introduces a histopathological microscopy image dataset of 922 images related to 124 patients with IDC. The dataset has been published and is accessible through the web at: http://databiox.com. The distinctive feature of this dataset as compared to similar ones is that it contains an equal number of specimens from each of three grades of IDC, which leads to approximately 50 specimens for each grade.
Min-Max Average Pooling Based Filter for Impulse Noise Removal

Image corruption is a common phenomenon which occurs due to electromagnetic interference, and electric signal instabilities in a system. In this letter, a novel multi procedure Min-Max Average Pooling based Filter is proposed for removal of salt, and pepper noise that betide during transmission. The first procedure functions as a pre-processing step that activates for images with low noise corruption. In latter procedure, the noisy image is divided into two instances, and passed through multiple layers of max, and min pooling which allow restoration of intensity transitions in an image. The final procedure recombines the parallel processed images from the previous procedures, and performs average pooling to remove all residual noise. Experimental results were obtained using MATLAB software, and show that the proposed filter significantly improves edges over exiting literature. Moreover, Peak Signal to Noise Ratio was improved by 1.2 dB in de-noising of medical images corrupted by medium to high noise densities.
End-to-End Neural Transformer Based Spoken Language Understanding

Spoken language understanding (SLU) refers to the process of inferring the semantic information from audio signals. While the neural transformers consistently deliver the best performance among the state-of-the-art neural architectures in field of natural language processing (NLP), their merits in a closely related field, i.e., spoken language understanding (SLU) have not beed investigated. In this paper, we introduce an end-to-end neural transformer-based SLU model that can predict the variable-length domain, intent, and slots vectors embedded in an audio signal with no intermediate token prediction architecture. This new architecture leverages the self-attention mechanism by which the audio signal is transformed to various sub-subspaces allowing to extract the semantic context implied by an utterance. Our end-to-end transformer SLU predicts the domains, intents and slots in the Fluent Speech Commands dataset with accuracy equal to 98.1 \%, 99.6 \%, and 99.6 \%, respectively and outperforms the SLU models that leverage a combination of recurrent and convolutional neural networks by 1.4 \% while the size of our model is 25\% smaller than that of these architectures. Additionally, due to independent sub-space projections in the self-attention layer, the model is highly parallelizable which makes it a good candidate for on-device SLU.
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups.
Understanding Global Feature Contributions With Additive Importance Measures

Understanding the inner workings of complex machine learning models is a long-standing problem and most recent research has focused on local interpretability. To assess the role of individual input features in a global sense, we explore the perspective of defining feature importance through the predictive power associated with each feature. We introduce two notions of predictive power (model-based and universal) and formalize this approach with a framework of additive importance measures, which unifies numerous methods in the literature. We then propose SAGE, a model-agnostic method that quantifies predictive power while accounting for feature interactions. Our experiments show that SAGE can be calculated efficiently and that it assigns more accurate importance values than other methods.
High-Dimensional Two-Photon Interference Effects in Spatial Modes

Two-photon interference is a fundamental quantum optics effect with numerous applications in quantum information science. Here, we study two-photon interference in multiple transverse-spatial modes along a single beam-path. Besides implementing the analogue of the Hong-Ou-Mandel interference using a two-dimensional spatial-mode splitter, we extend the scheme to observe coalescence and anti-coalescence in different three and four-dimensional spatial-mode multiports. The operation within spatial modes, along a single beam-path, lifts the requirement for interferometric stability and opens up new pathways of implementing linear optical networks for complex quantum information tasks.
Improving User Experience of Virtual Health Assistants: Scoping Review

Virtual assistants can be used to deliver innovative health programs that provide appealing, personalized, and convenient health advice and support at scale and low cost. Design characteristics that influence the look and feel of the virtual assistant, such as visual appearance or language features, may significantly influence users' experience and engagement with the assistant.This scoping review aims to provide an overview of the experimental research examining how design characteristics of virtual health assistants affect user experience, summarize research findings of experimental research examining how design characteristics of virtual health assistants affect user experience, and provide recommendations for the design of virtual health assistants if sufficient evidence exists.We searched 5 electronic databases (Web of Science, MEDLINE, Embase, PsycINFO, and ACM Digital Library) to identify the studies that used an experimental design to compare the effects of design characteristics between 2 or more versions of an interactive virtual health assistant on user experience among adults. Data were synthesized descriptively. Health domains, design characteristics, and outcomes were categorized, and descriptive statistics were used to summarize the body of research. Results for each study were categorized as positive, negative, or no effect, and a matrix of the design characteristics and outcome categories was constructed to summarize the findings.The database searches identified 6879 articles after the removal of duplicates. We included 48 articles representing 45 unique studies in the review. The most common health domains were mental health and physical activity. Studies most commonly examined design characteristics in the categories of visual design or conversational style and relational behavior and assessed outcomes in the categories of personality, satisfaction, relationship, or use intention. Over half of the design characteristics were examined by only 1 study. Results suggest that empathy and relational behavior and self-disclosure are related to more positive user experience. Results also suggest that if a human-like avatar is used, realistic rendering and medical attire may potentially be related to more positive user experience; however, more research is needed to confirm this.There is a growing body of scientific evidence examining the impact of virtual health assistants' design characteristics on user experience. Taken together, data suggest that the look and feel of a virtual health assistant does affect user experience. Virtual health assistants that show empathy, display nonverbal relational behaviors, and disclose personal information about themselves achieve better user experience. At present, the evidence base is broad, and the studies are typically small in scale and highly heterogeneous. Further research, particularly using longitudinal research designs with repeated user interactions, is needed to inform the optimal design of virtual health assistants.
Dual Path Learning for Domain Adaptation of Semantic Segmentation

Domain adaptation for semantic segmentation enables to alleviate the need for large-scale pixel-wise annotations. Recently, self-supervised learning (SSL) with a combination of image-to-image translation shows great effectiveness in adaptive segmentation. The most common practice is to perform SSL along with image translation to well align a single domain (the source or target). However, in this single-domain paradigm, unavoidable visual inconsistency raised by image translation may affect subsequent learning. In this paper, based on the observation that domain adaptation frameworks performed in the source and target domain are almost complementary in terms of image translation and SSL, we propose a novel dual path learning (DPL) framework to alleviate visual inconsistency. Concretely, DPL contains two complementary and interactive single-domain adaptation pipelines aligned in source and target domain respectively. The inference of DPL is extremely simple, only one segmentation model in the target domain is employed. Novel technologies such as dual path image translation and dual path adaptive segmentation are proposed to make two paths promote each other in an interactive manner. Experiments on GTA5→Cityscapes and SYNTHIA→Cityscapes scenarios demonstrate the superiority of our DPL model over the state-of-the-art methods. The code and models are available at: https://github.com/royee182/DPL.