Reviewerly/exHarmony huggingface dataset:
Using the Price Sensitivity Meter to Measure the Value of Transformative Robot Sound

Transformative robot sound can improve perceptions of robots, but its implementation will likely require more hardware and cost. Does the addition of transformative sound yield an increase in value to offset this cost? Using the van Westendorp Price Sensitivity Meter, a questionnaire from marketing research, n = 97 participants measured acceptable price points for a robot with (and without) transformative sound. Results showed similar perceptual improvements as past studies, as well as a significant increase in perceived value, when transformative sound was included. These increases in social and value perceptions of robots confirm the utility of adding transformative sound to robots. This work benefits the broader human-robot interaction research community by sharing more ways to understand and validate the incorporation of transformative robot sound and other robot features.
Developing a Social Conversational Robot for the Hospital waiting room

Possible applications for Social Robots in health-care settings, that could have a tremendous social impact in helping alleviating staff workload, are those of a patient-facing role such as robot receptionist, providing assistance to patients and visitors. Examples of functions that such robots would need to be able to execute are greeting visitors, reception check-in/out of patients, answering common questions they may have, showing them where to sit, helping them locate missing objects, providing directions to facilities, guiding them to different locations, etc. In this paper we describe current progress towards developing a multimodal conversational AI system integrated in a Social Conversational Robot (an ARI robot) that will act as a receptionist in a hospital waiting room. We present the developed architecture of the system and report on an initial experimental validation study carried out in laboratory conditions with the ARI robot.
Preliminary Investigation of Collision Risk Assessment with Vision for Selecting Targets Paid Attention to by Mobile Robot

Vision plays an important role in motion planning for mobile robots which coexist with humans. Because a method predicting a pedestrian path with a camera has a trade-off relationship between the calculation speed and accuracy, such a path prediction method is not good at instantaneously detecting multiple people at a distance. In this study, we thus present a method with visual recognition and prediction of transition of human action states to assess the risk of collision for selecting the avoidance target. The proposed system calculates the risk assessment score based on recognition of human body direction, human walking patterns with an object, and face orientation as well as prediction of transition of human action states. First, we investigated the validation of each recognition model, and we confirmed that the proposed system can recognize and predict human actions with high accuracy ahead of 3 m. Then, we compared the risk assessment score with video interviews to ask a human whom a mobile robot should pay attention to, and we found that the proposed system could capture the features of human states that people pay attention to when avoiding collision with other people from vision.
Fusion of depth, color, and thermal images towards digital twins and safe human interaction with a robot in an industrial environment

Accurate detection of the human body and its limbs in real-time is one of the challenges toward human-robot collaboration (HRC) technology in the factory of the future. In this work, a new algorithm has been developed to fuse thermal, depth, and color information. Starting with the calibration of the sensors to each other, proceeding with data fusion and detection of human body parts, and ending with pose estimation. The proposed approach has been tested in various HRC scenarios. It has shown a significant decrease in errors and noise in industrial environments. Furthermore, it produces not only a higher positioning accuracy of the head and hands compared to the state of art algorithms (e.g., OpenPose), but it is also faster. Hence, such an algorithm could be joined with digital twins for safe and efficient human-robot collaboration.
An IMM-BP based Algorithm for Tracking Maneuvering Underwater Targets by Multistatic Marine Robot Networks

A mobile and distributed multistatic active sonar network formed by a collection of autonomous marine robots is a significant type of tool for underwater surveillance applications such as antisubmarine warfare. Developing a tracker that fuses the sonar data collected by the robots to accurately estimate the locations and velocities of many underwater maneuvering targets is a crucial issue for realizing such a robotic surveillance network. This paper investigates this issue and an IMM-BP tracking strategy. The strategy adopts the interacting multiple model (IMM) method to cope with the problem of accurate tracking of highly maneuvering targets and employs a belief propagation (BP)-based tracking method to implement the Bayesian multisensor-multitarget tracking. And a new algorithm implementing the integration of the IMM method with the BP-based tracker is developed and presented in this paper. The main features of the algorithm include the extraction of measurement-target association information and its utilization in the model probability update step, and using a track score-based strategy and predicted measurements in the track management step to improve the performance of track confirmation and maintenance. The performance of the developed algorithm is validated with numerical simulations. The results show that the developed algorithm could accurately estimate the number and states of multiple maneuvering targets in environments with clutter and conditions of missed measurements.
Generating 2-Gray codes for ballot sequences in constant amortized time

We present a simple algorithm that generates a cyclic 2-Gray code for ballot sequences. The algorithm generates each ballot sequence in constant amortized time using a linear amount of space. This is the first known cyclic 2-Gray code for ballot sequences that achieves this time bound. In addition, the algorithm can be easily modified to output ballot sequences in binary reflected Gray code order in constant amortized time per string using a linear amount of space.
Meta learning for domain generalization

One of the fundamental issues of deep neural networks is their inability to generalize to diverse test environments, often yielding unreliable predictions in novel environments not seen during training. This sensitivity is undesirable, and can even be fatal in some safety-critical applications such as medical imaging. Domain generalization comprises of a class of techniques that aim to train generalizable models that can perform reliably in unseen test environments. In this chapter, we will look at some approaches that use meta learning for training domain-generalizable deep networks.
Multiple image super-resolution from the BGU SWIR CubeSat satellite

The BGU CubeSat satellite is from a class of low-cost, compact satellites. Its dimensions are 10×10×30 cm. It is equipped with a low resolution 256×320 pixels short wave infrared (SWIR) camera at the 1.55-1.7mm wavelength band. Images are transmitted in bursts of tens of images at a time with few pixel shifts from the first image to the last. Each image burst is suitable for Multiple Image Super Resolution (MISR) enhancements. MISR can construct a high-resolution (HR) image from several low-resolution (LR) images yielding an image that can resolve more details that are crucial for research in remote sensing. In this research, we verify the applicability of SOTA deep learning MISR models that were developed following the publication of the PROBA-V MISR satellite dataset at the visible red and near IR. Our SWIR multiple images differ from PROBA-V by the spectral band and by the method of collecting multiple images of the exact location. Our imagery data is acquired by a burst of very close temporal images. PROBA-V revisits the satellite at a period smaller than 30 days, assuming the soil dryness is about the same. We compare the results of Single Image Super-Resolution (SISR) and MISR techniques to "off-the-shelf" products. The quality of the super-resolved images is compared by nonreference metrics suitable for remote sensing applications and by experts' visual inspection. Unlike remarkable achievements by the GAN technique that can achieve very appealing results that are not always faithful to the original ground truth, the super-resolved images should preserve the original details as much as possible for further scientific remote sensing analysis.
Analysis of the Impact of the Traditional Literature Environment Based on Big Data Technology on Overseas Literature

To study the influence of conventional literature on foreign literature driven by big data, this essay begins with surveys and interviews. Chinese big data-driven corpora are distinct from other Chinese corpora, as is widely known. Its main objective is to categorize professional corpora that are unknown and fall within the category of professional corpora. In order to provide a straightforward and useful domain partitioning model for corpus texts, this research makes use of text clustering and big data-driven methodologies. We can easily determine the domain of the aligned text, making it easier to do machine translation research in the future. The research findings demonstrate that the accuracy rate of the approach suggested in this article is essentially above 89.79%, demonstrating the viability of the way of automatically building a corpus suggested in this paper in the experiment.
Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm for Global Optimization

Dragonfly algorithm (DA) is a recently proposed optimization algorithm based on swarm intelligence, which has been successfully applied in function optimization, feature selection, parameter adjustment, etc. However, it fails to take individual optimal position into consideration but only relies on population optimal position and 5 behaviours to update individual position, leading to low accuracy, slow convergence, and local optima. To overcome these drawbacks, Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm (TPDA) is proposed. Tent chaotic map is used to initialize the population, making individuals distributed more uniformly in search space to improve population diversity and search efficiency. Population is classified according to individual fitness value, and different position update methods are adopted for different types of individuals to guide the search process and improve the ability of TPDA to jump out of local optima, thus realizing a balance between exploration and exploitation. The efficiency of TPDA has been validated by tests on 18 basic unconstrained benchmark functions. A comparative performance analysis between TPDA, Particle Swarm Optimization (PSO), DA, and Adaptive Learning Factor and Differential Evolution-Based Dragonfly Algorithm (ADDA) has been carried out. Experimental and statistical results demonstrate that TPDA gives significantly better performances compared with PSO, DA, and ADDA on the average and standard deviation in all 18 functions. The global optimization capability of TPDA on high-dimensional functions and the comparison of the time complexity of TPDA and other swarm intelligence algorithms is also verified in the paper. The results indicate that TPDA is able to perform better on optimizing functions without consuming more computational time.
Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism

With the rapid development of the aviation industry, it is particularly important to ensure the safe flight of aircraft. How to find potential hazards in the process of aircraft flight has always been one of the important topics of civil aviation research. At present, the Quick Access Recorder (QAR) is the most widely used equipment to store the data recorded on aircraft. QAR data contain a lot of valuable and unexplored information, which records the true status of the aircraft in detail. Therefore, finding abnormal data from QAR data lays an important foundation for obtaining the cause of abnormality and providing a guarantee for flight. In this paper, in order to discover the abnormal information in the QAR data, we applied a VAE-LSTM model with a multihead self-attention mechanism. Compared to the VAE and LSTM models alone, our model performs much better in anomaly detection and prediction, detecting all types of anomalies. We conducted extensive experiments on real-world QAR data sets to prove the efficiency and accuracy of our proposed neural network model. The experimental results proved that our proposed model can outperform state-of-the-art models under different experimental settings.
Transfer Learning via Representation Learning

The remarkable performance boost of artificial intelligence (AI) algorithms is a result of re-emergence of deep neural networks that have been applied in a diverse set of applications. The success of deep learning stems from relaxing the need for the non-trivial task of feature-engineering. However, this remarkable success is conditioned on manually annotating a large amount of data points to generate suitable training datasets to supervise training of these networks. Since manual data annotation is time-consuming and expensive in many applications, learning in data-scarce regimes has been a major recent area of research focus in machine learning (ML) and AI. Transferring and reusing knowledge from a related learning problem is a core strategy for addressing challenges of learning in data-scarce regimens. Transfer learning is not a new field in ML and several great survey exist on this topic [63, 95, 98, 105, 120]. However, these existing survey are meant to be general and extensively survey many works in the area. In this chapter, we survey a very specific subset of works in this area. Our goal is to explore a framework that unifies a broad range of knowledge transfer problems as learning cross-problems relations and similarities using an representation learning. By representation learning, we mean representing the data in the input space in a latent embedding space. The latent embedding space is meant as an intermediate space to explore relationships between several ML problems. We review the recently developed algorithms that use this strategy to address several primary transfer learning settings in five primary area of: (i) online and offline multitask learning, (ii) lifelong learning and continual learning, (iii) low-shot learning, including, few-shot learning and zero-shot learning, (iv) domain adaptation, and (v) collective/distributed learning. We discuss existing challenges and future potential research directions.
Cross-Silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems

In many cooperative systems (i.e. autonomous vehicles, robotics, hospital networks), data are privately and heterogeneously distributed among devices with various computational constraints, and no party has a global view of data or device distribution. Federated Neural Architecture Search (FedNAS) was previously proposed to adapt Neural Architecture Search (NAS) into Federated Learning (FL) to provide both privacy and model performance to such uninspectable and heterogeneous systems. However, these approaches mostly apply to scenarios where parties share the same data attributes and comparable computation resources. In this chapter, we present Self-supervised Vertical Federated Neural Architecture Search (SS-VFNAS) for automating FL where participants have heterogeneous data and resource constraints, a common cross-silo scenario. SS-VFNAS not only simultaneously optimizes all parties’ model architecture and parameters for the best global performance under a vertical FL (VFL) framework using only a small set of aligned and labeled data, but also preserves each party’s local optimal model architecture under a self-supervised NAS framework. We demonstrate that SS-VFNAS is a promising framework of superior performance, communication efficiency and privacy, and is capable of generating high-performance and highly-transferable heterogeneous architectures with only limited overlapping samples, providing practical solutions for designing collaborative systems with both limited data and resource constraints.
Imaging through curved glass: windshield optical impact on automotive cameras

The state-of-the-art position of cameras in forward-facing Advanced Driver Assistance Systems (ADAS) is behind the windshield, integrated within the rearview mirror holder. In this position, the quality of the windshield as an optical element directly impacts the quality of the camera image. With increasing camera resolution and narrow field of view optics required for large object detection distances, the optical impact of the windshield becomes increasingly important. We suggest a method based on computer graphics for evaluating the optical performance of windshields in front of ADAS cameras. Using a ray tracing framework, we produce quantitative simulations of the light transport through the windshield. To represent the geometry of the windshield, we fit ellipsoid models to measurements of its inner and outer surfaces produced using a chromatic white light sensor in a coordinate measuring machine. The ellipsoid fits enable accurate ray intersections with the windshield even for cameras positioned close to the windshield surface. Additionally, we investigate the windshield microgeometry using optical profilometry and find that the microstructure is smaller than 200 nm. Thus, the microgeometry can only cause a very slight diffractive blur, and we consider the ellipsoidal macrogeometry sufficient for evaluating the light transport. In simulation experiments, we evaluate the impact of the windshield on a forward-facing ADAS camera by computing the modulation transfer function degradation of the camera image. In our experiments, we vary camera aperture and resolution as well as distance and angle of the windshield to the camera. To validate our results, we reconstruct the angle variation experiment in the lab.
An Embedding Model for Knowledge Graph Completion Based on Graph Sub-Hop Convolutional Network

The research on knowledge graph completion based on representation learning is increasingly dependent on the node structural feature in the graph. However, a large number of nodes have few immediate neighbors, resulting in the node features unable to be fully expressed. Hence, multi-hop structure features are crucial to the representation learning of nodes. GCN (Graph Convolutional Network) is a graph embedding model that can introduce the multi-hop structure. However, the multi-hop information transmitted between GCN layers suffers a lot of losses. This would lead to the insufficient mining of the node structure features and semantic feature association among entities, further reducing the efficiency of graph knowledge completion. A gate-controlled graph sub-hop convolutional network model for knowledge graph completion is proposed to fill these research gaps. Firstly, a graph sub-hop convolutional network based on matrix representation is designed, which can transmit multi-hop neighbor features directly to the encoded node vector to avoid a large loss of features during multi-hop transmission. On this basis, the implicit multi-hop relations are explicitly embedded into the model based on the TransE. In the process of each hop convolution, aiming at the accumulation of noise redundancy caused by the increase of the receptive field, a sub-hop gate mechanism strategy is proposed to filter information. Finally, the linear model is used to decode the encoded nodes and then complete the knowledge graph. We carried out experimental comparison and analysis on WN18RR, FB15k-237, UMLS, and KINSHIP datasets. The results show that the embedding method based on the sub-hop structural information fusion can greatly improve the results of link prediction.
Impacts of Teaching towards Training Gesture Recognizers for Human-Robot Interaction

The use of hand-based gestures has been proposed as an intuitive way for people to communicate with robots. Typically the set of gestures is defined by the experimenter. However, existing works do not necessarily focus on gestures that are communicative, and it is unclear whether the selected gesture are actually intuitive to users. This paper investigates whether different people inherently use similar gestures to convey the same commands to robots, and how teaching of gestures when collecting demonstrations for training recognizers can improve resulting accuracy. We conducted this work in two stages. In Stage 1, we conducted an online user study (n=190) to investigate if people use similar gestures to communicate the same set of given commands to a robot when no guidance or training was given. Results revealed large variations in the gestures used among individuals With the absences of training. Training a gesture recognizer using this dataset resulted in an accuracy of around 20%. In response to this, Stage 2 involved proposing a common set of gestures for the commands. We taught these gestures through demonstrations and collected ~ 7500 videos of gestures from study participants to train another gesture recognition model. Initial results showed improved accuracy but a number of gestures had high confusion rates. Refining our gesture set and recognition model by removing those gestures, We achieved an final accuracy of 84.1 ± 2.4%. We integrated the gesture recognition model into the ROS framework and demonstrated a use case, where a person commands a robot to perform a pick and place task using the gesture set.
Head Pose for Object Deixis in VR-Based Human-Robot Interaction

Modern robotics heavily relies on machine learning and has a growing need for training data. Advances and commercialization of virtual reality (VR) present an opportunity to use VR as a tool to gather such data for human-robot interactions. We present the Robot Interaction in VR simulator, which allows human participants to interact with simulated robots and environments in real-time. We are particularly interested in spoken interactions between the human and robot, which can be combined with the robot’s sensory data for language grounding. To demonstrate the utility of the simulator, we describe a study which investigates whether a user’s head pose can serve as a proxy for gaze in a VR object selection task. Participants were asked to describe a series of known objects, providing approximate labels for the focus of attention. We demonstrate that using a concept of gaze derived from head pose can be used to effectively narrow the set of objects that are the target of participants’ attention and linguistic descriptions.