Reviewerly/exHarmony huggingface dataset:
Directed Acyclic Graph Neural Network for Human Motion Prediction

Human motion prediction is essential in human-robot interaction. Current research mostly considers the joint dependencies but ignores the bone dependencies and their relationship in the human skeleton, thus limiting the prediction accuracy. To address this issue, we represent the human skeleton as a directed acyclic graph with joints as vertexes and bones as directed edges. Then, we propose a novel directed acyclic graph neural network (DA-GNN) that follows the encoder-decoder structure. The encoder is stacked by multiple encoder blocks, each of which includes a directed acyclic graph computational operator (DA-GCO) to update joint and bone attributes based on the relationship between joint and bone dependencies in the observed human states, and a temporal update operator (TUO) to update the temporal dynamics of joints and bones in the same observation. After progressively implementing the above update process, the encoder outputs the final update result, fed into the decoder. The decoder includes a directed acyclic graph-based gated recurrent unit (DAG-GRU) and a multi-layered perceptron (MLP) to predict future human states sequentially. To the best of our knowledge, this is the first time to introduce the relationship between bone and joint dependencies in human motion prediction. Our experimental evaluations on two datasets, CMU Mocap and Human 3.6m, prove that DA-GNN outperforms current models. Finally, we showcase the efficacy of DA-GNN in a realistic HRI scenario.
Deep learning based affective computing

Purpose Decision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments. Design/methodology/approach The present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer. Findings The result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making. Originality/value The study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.
The relationship between vection, cybersickness and head movements elicited by illusory motion in virtual reality

Cybersickness is an unpleasant side effect of Virtual Reality and is often detrimental to a user's experience. It shows a complex relationship to vection (illusory self-motion) as well as postural instability. Three experiments were conducted presenting both expanding and rotating colourful optimised Fraser Wilcox illusions as well as grey-scaled controlled versions of the illusions. Cybersickness and vection were reported and head movements in medio-lateral and anterior-posterior direction were recorded. The experiments found that perceived visual motion (illusory motion) is sufficient to elicit vection in the absence of any stimulated visual motion. The strength of motion perceived in the illusions was related to the experience of cybersickness and vection, with illusions that were perceived as moving more eliciting stronger experiences of both. Surprisingly, rotating illusions were continuously perceived as moving more compared to expanding motion illusions, which could be related to missing stereoscopic motion-in-depth cues. Head movements were unrelated to any stimuli properties, suggesting that the motion signal elicited by the illusions might not have been strong enough to cause postural instability. Finally, dizziness has been identified as the possible link between cybersickness, vection and head movements supporting sensory conflict as well postural instability theories of cybersickness.
Combinatorial BLAS 2.0: Scaling Combinatorial Algorithms on Distributed-Memory Systems

Combinatorial algorithms such as those that arise in graph analysis, modeling of discrete systems, bioinformatics, and chemistry, are often hard to parallelize. The Combinatorial BLAS library implements key computational primitives for rapid development of combinatorial algorithms in distributed-memory systems. During the decade since its first introduction, the Combinatorial BLAS library has evolved and expanded significantly. This article details many of the key technical features of Combinatorial BLAS version 2.0, such as communication avoidance, hierarchical parallelism via in-node multithreading, accelerator support via GPU kernels, generalized semiring support, implementations of key data structures and functions, and scalable distributed I/O operations for human-readable files. Our article also presents several rules of thumb for choosing the right data structures and functions in Combinatorial BLAS 2.0, under various common application scenarios.
R-GAN: Exploring Human-like Way for Reasonable Text-to-Image Synthesis via Generative Adversarial Networks

Despite recent significant progress on generative models, context-rich text-to-image synthesis depicting multiple complex objects is still non-trivial. The main challenges lie in the ambiguous semantic of a complex description and the intricate scene of an image with various objects, different positional relationship and diverse appearances. To address these challenges, we propose R-GAN, which can generate reasonable images according to the given text in a human-like way. Specifically, just like humans will first find and settle the essential elements to create a simple sketch, we first capture a monolithic-structural text representation by building a scene graph to find the essential semantic elements. Then, based on this representation, we design a bounding box generator to estimate the layout with position and size of target objects, and a following shape generator, which draws a fine-detailed shape for each object. Different from previous work only generating coarse shapes blindly, we introduce a coarse-to-fine shape generator based on a shape knowledge base. At last, to finish the final image synthesis, we propose a multi-modal geometry-aware spatially-adaptive generator conditioned on the monolithic-structural text representation and the geometry-aware map of the shapes. Extensive experiments on the real-world dataset MSCOCO show the superiority of our method in terms of both quantitative and qualitative metrics.
Multi-Task Based Deep Learning Approach for Open-Set Wireless Signal Identification in ISM Band

Wireless signal identification plays an important role in effectively implementing spectrum monitoring and management. However, in ISM (Industrial, Science and Medical) band, it becomes a challenging task due to the heterogeneity of variously emerging wireless techniques, and part of the potential unknown spectral occupants may even hinder the feasibility of wireless signal identification. To overcome such difficulties, we focus on open-set recognition (OSR) in this paper and present a multi-task learning architecture based on deep neural network for identifying known and unknown spectral occupants. A novel structured extension of the counterfactual GAN (CountGAN) architecture is proposed and we introduce a multi-tasking architecture to take advantage of the modulation-domain information from the captured signal, thus improving the representation of individual wireless signals and further enhancing model robustness and adaptability to open-set scenarios. In particular, Circle-loss in metric learning and extreme value theory are also applied to make tighter and clearer decision boundaries for signal identification, and further enhance the optimization of the decision boundaries between known and unknown classes. Numerical results indicate that the proposed framework consistently outperforms state-of-the-art OSR algorithms and several baselines for wireless signal identification task, both in terms of convergence performance and classification accuracy.
SEM-EDX hyperspectral data analysis for the study of soil aggregates

Scanning electron microscopy coupled with microanalysis (SEM-EDX) is an important analytical tool for the morphological and chemical characterization of different types of materials. In many applications, SEM-EDX elemental maps are usually used and processed as images, thus flattening and reducing the spectroscopic information contained in EDX hyperspectral data cubes. The exploitation of the full hyperspectral dataset could be indeed very useful for the study of complex matrices like soil. In order to maximize the information attainable by SEM-EDX data cubes analysis, the software package "Datamuncher Gamma" was implemented and applied to study soil aggregates. By using this approach, different phases (silicates, aluminosilicates, Ca-carbonates, Ca-phosphates, organic matter, iron oxides) inside soil aggregates were successfully identified and segmented. The advantages of this method over the common ROI imaging approach are presented. Finally, this method was used to compare different aggregates in a Cr-polluted soil and understand their possible pedological history. The present method can be used for the analysis of every type of SEM-EDX data cubes, allowing its application to different types of samples and fields of study.
FabHydro: Printing Interactive Hydraulic Devices with an Affordable SLA 3D Printer

We introduce FabHydro, a set of rapid and low-cost methods to prototype interactive hydraulic devices based on an off-the-shelf 3D printer and flexible photosensitive resin. We first present printer settings and custom support structures to warrant the successful print of flexible and deformable objects. We then demonstrate two printing methods to seal the transmission fluid inside these deformable structures: the Submerged Printing process that seals the liquid resin without manual assembly, and the Printing with Plugs method that allows the use of different transmission fluids without modification to the printer. Following the printing methods, we report a design space with a range of 3D printable primitives, including the hydraulic generator, transmitter, and actuator. To demonstrate the feasibility of our approaches and the breadth of new designs that they enable, we showcase a set of examples from a printed robotic gripper that can be operated at a distance to a mobile phone stand that serves as a status reminder by repositioning the user’s phone. We conclude with a discussion of our approach’s limitations and possible future improvements.
Mobile AR: User Evaluation in a Cultural Heritage Context

The growing number of mobile augmented reality applications has been favoring its awareness and usage among diversified areas. Focusing on cultural heritage applications, this study presents an evaluation of a mobile augmented reality application tested at Conimbriga, an archaeological site. The prototype developed for this purpose, named DinofelisAR, allowed users to view, over 360 degrees, a majestic reconstruction of a Forum from the Roman Era superimposed over its current ruins. Thus, users were able to keep perceiving the present-day surroundings of a Roman city in ruins while, at the same time, had the possibility to explore the matching virtual model. The results presented, arising from 90 participants involved in this evaluation, praise the sense of opportunity for new augmented reality solutions targeted at cultural heritage sites.
Unsupervised Anomaly Detection Based on Deep Autoencoding and Clustering

The unsupervised anomaly detection task based on high-dimensional or multidimensional data occupies a very important position in the field of machine learning and industrial applications; especially in the aspect of network security, the anomaly detection of network data is particularly important. The key to anomaly detection is density estimation. Although the methods of dimension reduction and density estimation have made great progress in recent years, most dimension reduction methods are difficult to retain the key information of original data or multidimensional data. Recent studies have shown that the deep autoencoder (DAE) can solve this problem well. In order to improve the performance of unsupervised anomaly detection, we propose an anomaly detection scheme based on a deep autoencoder (DAE) and clustering methods. The deep autoencoder is trained to learn the compressed representation of the input data and then feed it to clustering approach. This scheme makes full use of the advantages of the deep autoencoder (DAE) to generate low-dimensional representation and reconstruction errors for the input high-dimensional or multidimensional data and uses them to reconstruct the input samples. The proposed scheme could eliminate redundant information contained in the data, improve performance of clustering methods in identifying abnormal samples, and reduce the amount of calculation. To verify the effectiveness of the proposed scheme, massive comparison experiments have been conducted with traditional dimension reduction algorithms and clustering methods. The results of experiments demonstrate that, in most cases, the proposed scheme outperforms the traditional dimension reduction algorithms with different clustering methods.
Analysing stable feature selection through an augmented marine predator algorithm based on <scp>opposition‐based</scp> learning

Abstract Retrieving the relevant information from the high‐dimensional dataset enhances the classification accuracy of a predictive model. This research critique has devised an improved marine predator algorithm based on opposition learning for stable feature selection to overcome the problem of high‐dimensionality. Marine predator algorithm is a population‐based meta‐heuristics optimization algorithm that works on the ‘survival‐of‐the‐fittest’ theory. Classical marine predator algorithm explores the search space merely in one direction, affecting its converging capacity while being responsible for stagnation at local minima. The proposed opposition‐based learning nuances enhance the exploration capacity of marine predator algorithm and productively converges the model to global optima. The proposed OBL‐based marine predator algorithm selects stable, substantial elements from six different high‐dimensional microarray datasets. The performance of the proposed method is investigated using five predominantly used classifiers. From the result, it is understood that the proposed approach outperforms other conventional feature selection techniques in terms of converging capability, classification accuracy, and stable feature selection.
Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia

Data scientists often collaborate with clients to analyze data to meet a client's needs. What does the end-to-end workflow of a data scientist's collaboration with clients look like throughout the lifetime of a project? To investigate this question, we interviewed ten data scientists (5 female, 4 male, 1 non-binary) in diverse roles across industry and academia. We discovered that they work with clients in a six-stage outer-loop workflow, which involves 1) laying groundwork by building trust before a project begins, 2) orienting to the constraints of the client's environment, 3) collaboratively framing the problem, 4) bridging the gap between data science and domain expertise, 5) the inner loop of technical data analysis work, 6) counseling to help clients emotionally cope with analysis results. This novel outer-loop workflow contributes to CSCW by expanding the notion of what collaboration means in data science beyond the widely-known inner-loop technical workflow stages of acquiring, cleaning, analyzing, modeling, and visualizing data. We conclude by discussing the implications of our findings for data science education, parallels to design work, and unmet needs for tool development.
GazeChat: Enhancing Virtual Conferences with Gaze-aware 3D Photos

Communication software such as Clubhouse and Zoom has evolved to be an integral part of many people's daily lives. However, due to network bandwidth constraints and concerns about privacy, cameras in video conferencing are often turned off by participants. This leads to a situation in which people can only see each others' profile images, which is essentially an audio-only experience. Even when switched on, video feeds do not provide accurate cues as to who is talking to whom. This paper introduces GazeChat, a remote communication system that visually represents users as gaze-aware 3D profile photos. This satisfies users' privacy needs while keeping online conversations engaging and efficient. GazeChat uses a single webcam to track whom any participant is looking at, then uses neural rendering to animate all participants' profile images so that participants appear to be looking at each other. We have conducted a remote user study (N=16) to evaluate GazeChat in three conditions: audio conferencing with profile photos, GazeChat, and video conferencing. Based on the results of our user study, we conclude that GazeChat maintains the feeling of presence while preserving more privacy and requiring lower bandwidth than video conferencing, provides a greater level of engagement than to audio conferencing, and helps people to better understand the structure of their conversation.
Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce

In e-commerce, the watchlist enables users to track items over time and has emerged as a primary feature, playing an important role in users' shopping journey. Watchlist items typically have multiple attributes whose values may change over time (e.g., price, quantity). Since many users accumulate dozens of items on their watchlist, and since shopping intents change over time, recommending the top watchlist items in a given context can be valuable. In this work, we study the watchlist functionality in e-commerce and introduce a novel watchlist recommendation task. Our goal is to prioritize which watchlist items the user should pay attention to next by predicting the next items the user will click. We cast this task as a specialized sequential recommendation task and discuss its characteristics. Our proposed recommendation model, Trans2D, is built on top of the Transformer architecture, where we further suggest a novel extended attention mechanism (Attention2D) that allows to learn complex item-item, attribute-attribute and item-attribute patterns from sequential-data with multiple item attributes. Using a large-scale watchlist dataset from eBay, we evaluate our proposed model, where we demonstrate its superiority compared to multiple state-of-the-art baselines, many of which are adapted for this task.
Profile measurement of non-Lambertian surfaces by integrating fringe projection profilometry with near-field photometric stereo

Fringe projection profilometry (FPP) performs poorly when measuring non-Lambertian surfaces, since the measured points are missing in specular regions. The existing solutions need to make a trade-off between precision and efficiency, because no extra information about specular regions is available. To tackle the specular reflection problem of FPP, this paper proposes a multi-sensor measurement method by integrating FPP with near-field photometric stereo (NFPS). Firstly, the point clouds in non-specular regions are measured by FPP. Then, a deep neural network is applied to estimate the normal map of the surface by NFPS. Finally, the intact point clouds are reconstructed by minimizing the depth gradient error calculated from the normal map. The proposed method can achieve high-precision measurement without any human intervention. In the experiment of measuring an aluminum alloy free-form surface, the deviation of the profile error between the proposed method and a coordinate measuring machine is only 6.9 µm.
Fast robotic pencil drawing based on image evolution by means of genetic algorithm

Even if pencil drawing is the basic method of human artistic expression, it remains at the forefront of scientific attention in the field of robotics, which focuses mainly on painting. Although various methods of artistic robotic drawing have been developed in the past, the results are not always satisfactory. The vast majority of existing systems focus only on sketches, and detailed pencil drawings are time-consuming. In this article, we present a novel general robotic system for creating realistic pencil drawings based on image evolution. We show that by procedural image generation approach using genetic algorithms, we can create realistic drawings with an element of machine creativity. Furthermore, we show that the image approximation using even simple line segments leads to aesthetic and fast drawings. Finally, we describe a hardware solution using an industrial robot, a software implementation, and preliminary experimental results.
Tweet Sentiment Analysis for Cryptocurrencies

Many traders believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of automated sentiment analysis for cryptocurrencies. For the study, we targeted one cryptocurrency (NEO) altcoin and collected related data. The data collection and cleaning were essential components of the study. First, the last five years of daily tweets with NEO hashtags were obtained from Twitter. The collected tweets were then filtered to contain or mention only NEO. We manually tagged a subset of the tweets with positive, negative, and neutral sentiment labels. We trained and tested a Random Forest classifier on the labeled data where the test set accuracy reached 77%. In the second phase of the study, we investigated whether the daily sentiment of the tweets was correlated with the NEO price. We found positive correlations between the number of tweets and the daily prices, and between the prices of different crypto coins. We share the data publicly.
Confidence‐driven weighted retraining for predicting safety‐critical failures in autonomous driving systems

Safe handling of hazardous driving situations is a task of high practical relevance for building reliable and trustworthy cyber-physical systems such as autonomous driving systems. This task necessitates an accurate prediction system of the vehicle's confidence to prevent potentially harmful system failures on the occurrence of unpredictable conditions that make it less safe to drive. In this paper, we discuss the challenges of adapting a misbehavior predictor with knowledge mined during the execution of the main system. Then, we present a framework for the continual learning of misbehavior predictors, which records in-field behavioral data to determine what data are appropriate for adaptation. Our framework guides adaptive retraining using a novel combination of in-field confidence metric selection and reconstruction error-based weighing. We evaluate our framework to improve a misbehavior predictor from the literature on the Udacity simulator for self-driving cars. Our results show that our framework can reduce the false positive rate by a large margin and can adapt to nominal behavior drifts while maintaining the original capability to predict failures up to several seconds in advance.