Reviewerly/exHarmony huggingface dataset:
Picturing It!: The Effect of Image Styles on User Perceptions of Personas

Though photographs of real people are typically used to portray personas, there is little research into the potential advantages or disadvantages of using such images, relative to other image styles. We conducted an experiment with 149 participants, testing the effects of six different image styles on user perceptions and personality traits that are attributed to personas by the participants. Results show that perceptions of clarity, completeness, consistency, credibility, and empathy for a persona increase with picture realism. Personas with more realistic pictures are also perceived as more agreeable, open, and emotionally stable, with higher confidence in these assessments. We also find evidence of the uncanny valley effect, with realistic cartoon personas experiencing a decrease in the user perception scores.
Resource-Aware Collaborative Allocation for CPU-FPGA Cloud Environments

Cloud Warehouses have been exploiting CPU-FPGA environments to accelerate multi-tenant applications to achieve scalability and maximize resource utilization. In this scenario, kernels are sent to CPU and FPGA concurrently, considering available resources and workload characteristics, which are highly variant. Therefore, we propose a multi-objective optimization strategy to improve resource provisioning in CPU-FPGA environments. It is based on the Genetic Multidimensional Knapsack solution and can be tuned to minimize makespan or energy. Our strategy provides similar results as the optimal Exhaustive Search, but with feasible execution time, while presenting 77% energy savings with 39% lower makespan than the commonly-used First-Fit strategy.
Using Virtual Reality to Support Acting in Motion Capture with Differently Scaled Characters

Motion capture is a well-established technology for capturing actors' movements and performances within the entertainment industry. Many actors, however, witness the poor acting conditions associated with such recordings. Instead of detailed sets, costumes and props, they are forced to play in empty spaces wearing tight suits. Often, their co-actors will be imaginary, replaced by placeholder props, or they would be out of scale with their virtual counterparts. These problems do not only affect acting, they also cause an abundance of laborious post-processing clean-up work. To solve these challenges, we propose using a combination of virtual reality and motion capture technology to bring differently proportioned virtual characters into a shared collaborative virtual environment. A within-subjects user study with trained actors showed that our proposed platform enhances their feelings of body ownership and immersion. This in turn changed actors' performances which narrowed the gap between virtual performances and final intended animations.
Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.
Exploring Web Search Engines to Find Architectural Knowledge

Software engineers need relevant and up-to-date architectural knowledge (AK), in order to make well-founded design decisions.However, finding such AK is quite challenging.One pragmatic approach is to search for AK on the web using traditional search engines (e.g.Google); this is common practice among software engineers.Still, we know very little about what AK is retrieved, from where, and how useful it is.In this paper, we conduct an empirical study with 53 software engineers, who used Google to make design decisions using the Attribute-Driven-Design method.Based on how the subjects assessed the nature and relevance of the retrieved results, we determined how effective web search engines are to find relevant architectural information.Moreover, we identified the different sources of AK on the web and their associated AK concepts.
Echo State Speech Recognition

We propose automatic speech recognition (ASR) models inspired by echo state network (ESN) [1], in which a subset of recurrent neural networks (RNN) layers in the models are randomly initialized and untrained. Our study focuses on RNN-T and Conformer models, and we show that model quality does not drop even when the decoder is fully randomized. Furthermore, such models can be trained more efficiently as the decoders do not require to be updated. By contrast, randomizing encoders hurts model quality, indicating that optimizing encoders and learn proper representations for acoustic inputs are more vital for speech recognition. Overall, we challenge the common practice of training ASR models for all components, and demonstrate that ESN-based models can perform equally well but enable more efficient training and storage than fully-trainable counterparts.
The Complex Path to Quantum Resistance

There is a new technology on the horizon that will forever change the information security and privacy industry landscape. Quantum computing, together with quantum communication, will have many beneficial applications but will also be capable of breaking many of today's most popular cryptographic techniques that help ensure data protection?in particular, confidentiality and integrity of sensitive information. These techniques are ubiquitously embedded in today's digital fabric and implemented by many industries such as finance, health care, utilities, and the broader information communication technology (ICT) community. It is therefore imperative for ICT executives to prepare for the transition from quantum-vulnerable to quantum-resistant technologies.
Learning to Rank for Active Learning: A Listwise Approach

Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data-hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. The learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks.
Data as Delight: Eating data

The HCI community has a rich history of finding new ways to engage people with data beyond the screen. With our work, we aim to expand the scope of how interaction design can engage people, arguing that “eating data” has the potential to allow people to experience “data as delight”. With reference to prior work and our design research findings, we discuss the advantages and the challenges of this approach to integrating data and food. We then identify four themes to guide the design of engagements with data through food: food form, food commensality, food ephemerality, and emotional response to food. Within these design themes, we articulate twelve insights for interaction designers to use when working on serving data as delight.
Quantum algorithm for Feynman loop integrals

A bstract We present a novel benchmark application of a quantum algorithm to Feynman loop integrals. The two on-shell states of a Feynman propagator are identified with the two states of a qubit and a quantum algorithm is used to unfold the causal singular configurations of multiloop Feynman diagrams. To identify such configurations, we exploit Grover’s algorithm for querying multiple solutions over unstructured datasets, which presents a quadratic speed-up over classical algorithms when the number of solutions is much smaller than the number of possible configurations. A suitable modification is introduced to deal with topologies in which the number of causal states to be identified is nearly half of the total number of states. The output of the quantum algorithm in IBM Quantum and QUTE Testbed simulators is used to bootstrap the causal representation in the loop-tree duality of representative multiloop topologies. The algorithm may also find application and interest in graph theory to solve problems involving directed acyclic graphs.
An End-to-End Speech Accent Recognition Method Based on Hybrid CTC/Attention Transformer ASR

This paper proposes a novel accent recognition system in the framework of a transformer-based end-to-end speech recognition system. To incorporate the pronunciation and linguistic knowledge into the network, we first pre-train an ASR model in a hybrid CTC/attention manner. Then, focusing on accent recognition, we extend the output token list by inserting accent labels to the transcripts and finetune the network parameters with an accented speech dataset. Our work is evaluated on the Interspeech 2020 Accented English Speech Recognition Challenge. Experiments show that our method achieves an accuracy of 72.39% on the test set and 80.98% on the development set, outperforming the baseline system by a very large margin. Our submitted system ranked second in the accent recognition task in the challenge.
Multi-Initialization Meta-Learning with Domain Adaptation

Recently, meta learning providing multiple initializations has drawn much attention due to its capability of handling multi-modal tasks drawn from diverse distributions. However, because of the difference of class distribution between meta-training and meta-test domain, the domain shift occurs in multi-modal meta-learning setting. To improve the performance on multi-modal tasks, we propose multi-initialization meta-learning with domain adaptation (MIML-DA) to tackle such domain shift. MIML-DA consists of a modulation network and a novel meta separation network (MSN), where the modulation network is to encode tasks into common and private modulation vectors, and then MSN uses these vectors separately to update the cross-domain meta-learner via a double-gradient descent process. In addition, the regularization using inequality measure is considered to improve the generalization ability of the meta-learner. Extensive experiments demonstrate the effectiveness of our MIML-DA method to new multi-modal tasks.
Implementing an augmented reality and animated infographics application for presentations: effect on audience engagement and efficacy of communication

Abstract Presentations are the most successful and popular form of communication in business. However, the formats of presentations in business have not changed much for past few decades. The emergent and disruptive technologies such as Augmented Reality and Animated Infographics have provided potential for enhancing communications in businesses to increase engagement and therefore increasing the effectiveness of such communications. This paper focuses on the impact and effectiveness of using interactive AR in business presentations. The paper presents the design and development of our AR presentation application. Followed by a presentation of an empirical study into the usability and effectiveness of using Augmented Reality and Animated Infographics in business presentation and compares the results against the traditional slideware slides presentation approach. The results of the experiment with 94 participants are presented and analysed. The results demonstrate that the AR approach out performs the traditional methods in terms of usability, audience engagement and effectiveness of communication.
How Long Does It Take for a Voice to Become Familiar? Speech Intelligibility and Voice Recognition Are Differentially Sensitive to Voice Training

When people listen to speech in noisy places, they can understand more words spoken by someone familiar, such as a friend or partner, than someone unfamiliar. Yet we know little about how voice familiarity develops over time. We exposed participants ( N = 50) to three voices for different lengths of time (speaking 88, 166, or 478 sentences during familiarization and training). These previously heard voices were recognizable and more intelligible when presented with a competing talker than novel voices—even the voice previously heard for the shortest duration. However, recognition and intelligibility improved at different rates with longer exposures. Whereas recognition was similar for all previously heard voices, intelligibility was best for the voice that had been heard most extensively. The speech-intelligibility benefit for the most extensively heard voice (10%–15%) is as large as that reported for voices that are naturally very familiar (friends and spouses)—demonstrating that the intelligibility of a voice can be improved substantially after only an hour of training.
Addressing the Class Imbalance Problem in Medical Image Segmentation via Accelerated Tversky Loss Function

Image segmentation in the medical domain has gained a lot of research interest in recent years with the advancements in deep learning algorithms and related technologies. Medical image datasets are often imbalanced and to handle the imbalance problem, deep learning models are equipped with modified loss functions to effectively penalize the training weights for false predictions and conduct unbiased learning. Recent works have introduced various loss functions suitable for certain scenarios of segmentation. In this paper, we have explored the existing loss functions that are widely used for medical image segmentation, following which an accelerated Tversky loss (ATL) function is proposed that uses log cosh function to better optimize the gradients. The no-new U-Net (nn-Unet) model is adopted as the base model to validate the behaviour of the loss functions by using the standard benchmark segmentation performance metrics. To establish the robustness and effectiveness of the loss functions, multiple datasets are adopted, where ATL function illustrated better performance with faster convergence and better mask generation.
A Custom Word Embedding Model for Clustering of Maintenance Records

Maintenance records of industrial equipment contain rich descriptive information in free-text format, such as involved parts, failure mechanisms, operating conditions, etc. Our objective is to leverage this unstructured textual information to identify groups of similar maintenance jobs. In this article, we use a natural language based approach and propose a novel custom word embedding model, which utilizes two sources of information, first, maintenance records collected from in-field operations and second, industrial taxonomy, to effectively identify clusters. The advantages of our model include combined use of semantic and taxonomic sources of information for clustering, one step/simultaneous training, which enables knowledge sharing between the two information sources and reduces hyperparameters, and no dependence on third-party data. We demonstrate the efficacy of our model for cluster identification using a real-world dataset. The results show that simultaneous incorporation of semantic and taxonomic information enables accurate extraction of contextual insights for improving maintenance decision-making and equipment reliability.
ZORRO: Valid, Sparse, and Stable Explanations in Graph Neural Networks

With the ever-increasing popularity and applications of graph neural networks, several proposals have been made to explain and understand the decisions of a graph neural network. Explanations for graph neural networks differ in principle from other input settings. It is important to attribute the decision to input features and other related instances connected by the graph structure. We find that the previous explanation generation approaches that maximize the mutual information between the label distribution produced by the model and the explanation to be restrictive. Specifically, existing approaches do not enforce explanations to be valid, sparse, or robust to input perturbations. In this paper, we lay down some of the fundamental principles that an explanation method for graph neural networks should follow and introduce a metric <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RDT-Fidelity</i> as a measure of the explanation's effectiveness. We propose a novel approach Zorro based on the principles from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rate-distortion theory</i> that uses a simple combinatorial procedure to optimize for RDT-Fidelity. Extensive experiments on real and synthetic datasets reveal that Zorro produces sparser, stable, and more faithful explanations than existing graph neural network explanation approaches.
Unrolling of Deep Graph Total Variation for Image Denoising

While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set. In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design—one that utilizes interpretable analytical low-pass graph filters and employs 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN. Specifically, to construct a suitable similarity graph for graph spectral filtering, we first adopt a CNN to learn feature representations per pixel, and then compute feature distances to establish edge weights. Given a constructed graph, we next formulate a convex optimization problem for denoising using a graph total variation (GTV) prior. Via a l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> graph Laplacian reformulation, we interpret its solution in an iterative procedure as a graph low-pass filter and derive its frequency response. For fast filter implementation, we realize this response using a Lanczos approximation. Experimental results show that in the case of statistical mistmatch, our algorithm outperformed DnCNN by up to 3dB in PSNR.
Personalized sentiment classification of customer reviews via an interactive attributes attention model

Incorporating extra attributes of customer reviews, such as user and product information, to align text representations to each attribute has improved the sentiment polarity classification performance. Existing works only treated such attributes separately thus ignored the interactive information between these attributes. In this paper, we proposed an interactive attributes attention model that considered all attributes to be relevant and investigated the interactive relationships in and across separate features to improve the sentiment classification performance for customer reviews. In addition to the local text encoder, three more interactive attribute encoders, including user–product, user–text, and product–text encoders, are applied to extract implicit information to align attribute features to text representations with a bilinear interaction instead of self-attention. To better integrate different information, a multiloss objective function is used to further improve the performance. The comparative experiments on the IMDB, Yelp, and Amazon datasets show that the proposed model achieves significant improvements in the effects of the bilinear interactions in and across attributes and local text features.
Graph-Adaptive Incremental Learning Using an Ensemble of Gaussian Process Experts

Graph-guided semi-supervised learning (SSL) is a major task emerging in a gamut of network science applications. However, most SSL approaches rely on deterministic similarity metrics for prediction, thus providing only point estimates of the sought function. To allow for uncertainty quantification, which is of utmost importance in safety-critical applications, this work tackles the SSL task in a Gaussian process (GP) based Bayesian framework to propagate the distribution of nonparametric function estimates. Specifically, an incremental learning scenario is considered, where prediction of the desired value of a new node per iteration is followed by processing the corresponding nodal observation. Capitalizing on random features for scalability, an ensemble of GP experts is employed, each associated with a unique kernel from a known dictionary, to choose the fitted kernel combination in a graph- and data-adaptive fashion, thus bypassing the need for offline model training. Experiments with synthetic and real data showcase the merits of the proposed approach.
Frontalization and adaptive exponential ensemble rule for deep-learning-based facial expression recognition system

Automatic facial expression recognition (FER) is an important technique in human–computer interfaces and surveillance systems. It classifies the input facial image into one of the basic expressions (anger, sadness, surprise, happiness, disgust, fear, and neutral). There are two types of FER algorithms: feature-based and convolutional neural network (CNN)-based algorithms. The CNN is a powerful classifier, however, without proper auxiliary techniques, its performance may be limited. In this study, we improve the CNN-based FER system by utilizing face frontalization and the hierarchical architecture. The frontalization algorithm aligns the face by in-plane or out-of-plane, rotation, landmark point matching, and removing background noise. The proposed adaptive exponentially weighted average ensemble rule can determine the optimal weight according to the accuracy of classifiers to improve robustness. Experiments on several popular databases are performed and the results show that the proposed system has a very high accuracy and outperforms state-of-the-art FER systems.