Reviewerly/exHarmony huggingface dataset:
I = Robot: An Investigation of How Perspective Switching Can Support People’s Acceptance of AI-Powered Social Robots

Digital companions and social robots empowered to have social capabilities by artificial intelligence (AI) are increasingly finding their way into our everyday lives. Even though these technologies benefit individuals seeking companionship and social interaction, adopting AI-powered social robots and companions remains a challenge. There is little known about effective approaches to overcoming barriers between humans and such systems that prevent their acceptance in the field. By applying the approach of perspective switching, we are examining whether taking the perspective of an AI-powered social robot can improve users’ perceptions of social robots and increase relevant antecedents of technology acceptance. Our study demonstrates that instruction-based perspective switching is a promising approach to influence the perceived usefulness, perceived enjoyment, and perceived sociability towards an AI-powered social robot and thus proves to be a useful means to support robot acceptance in society.
Personality-aware Natural Language Generation for Task-oriented Dialogue using Reinforcement Learning

A task-oriented dialogue system capable of expressing personality can improve user engagement and satisfaction. To realize such a system, this paper presents a method of building a personality-aware natural language generation (NLG) module in task-oriented dialogue using reinforcement learning (RL). This method handles both the expression of personality and system intent. During the RL process, a positive reward is given when the generated utterance correctly expresses the assigned personality and conveys its system intent simultaneously. In our experiments on the MultiWOZ dataset, we fine-tuned a personality-aware NLG module for two personality traits (extraversion and neural perception sensitivity). We experimented with data from MultiWOZ and a user simulator to confirm its effectiveness in terms of the ability to express personality and task performance.
Boundary Conditions for Human Gaze Estimation on A Social Robot using State-of-the-Art Models

Appearance-based methods are a promising solution for gaze estimation, as they eliminate the need for additional devices and calibration. This makes them particularly well-suited for human-robot interaction (HRI) research. However, until recently their performance was under par compared to traditional eye-trackers. Recent breakthroughs have been made with the release of two large-scale datasets with a wide range of gaze directions (Gaze360 and ETH-XGaze) and the accompanying state-of-the-art deep neural networks (L2CS and ETH). In this paper, we systematically evaluate the performance of these two appearance-based models on a social robot. In our setup, we vary the distance from the robot (1-3m) and camera resolution (640 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> 480 and 3840 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> 2160) and analyze the performance in terms of accuracy and precision. We find that the L2CS model trained on the Gaze360 dataset combined with a 4K camera achieves the best performance on the 2 m and 3 m distances. We show that a simple offset correction on pitch and yaw can further increase the accuracy and precision by 18.6% and 9.6% respectively. We conclude that for a range up to 3 m appearance-based gaze estimation models provide a promising approach for application in HRI research.
A Virtual Reality System for Predictive Display Functionality in a Telexistence-Controlled SEED-Noid Humanoid Robot with Evaluation of VR Sickness

Telexistence technology enables the manipulation of a remote robot using sensory feedback and leader-follower control, giving the operator the impression of controlling their own body. However, the robot’s operability declines significantly due to increasing communication time delays. In order to address this issue, the "Predictive Display" technique has been proposed and developed. This technique displays both the real robot’s movements with time delay and the computer-generated (CG) robot’s movements without time delay. In the Telexistence robot operation system, a predictive display system can be constructed using a virtual reality (VR) system. In this study, we have developed a VR system for Telexistence operation with THK’s SEED-Noid humanoid robot. Unity is utilized, which enables the predictive display function for the stereo camera mounted on the SEED-Noid. We developed and evaluated a display mode without neck joint angle restrictions to reduce VR sickness and workload using the developed VR system. Additionally, we developed a display mode with a limited neck joint angle for comparison purposes.
Autonomous UAV Navigation in Complex Environments using Human Feedback

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) has real-life applications in remote sensing, wildlife surveillance, search and rescue operations. A popular training paradigm to learn optimal actions for navigating such complex, dynamic, and uncertain environments is Reinforcement Learning (RL), where the optimal decisions are learnt over time through a reward-feedback received from the environment. However, manually constructing a feedback function that can help guide the UAV to accomplish the desired objective is often very hard. Preference-based Reinforcement Learning (PbRL) is an emerging sub-field of RL where the manual construction of reward function is replaced with human feedback. In this setting, a human is presented with a pair of trajectories followed by the RL agent to elicit the subject’s preference for one over the other. A PbRL algorithm would then compute an optimal sequence of actions using just the set of preferences collected over different trajectories. In this work, we consider PbRL for UAV navigation and follow an ensemble approach to enhance navigation performance. We demonstrate the efficacy of the proposed algorithm through experiments on a range of complex environments and tasks. Ours is the first work that uses human preferences to solve the UAV navigation problem to the best of our knowledge.
Recognizing football game events: Handball based on Computer Vision

Football or Soccer is one of the most popular games in the world. “Handball” event in the game is one of the most controversial and important decisions by a single referee. This paper proposes a method to define a “handball event” in a football game by recognizing the hand, ball, and their interaction from a single camera. We trained a model to identify hands and balls using detectron2, then used the vertices of two objects to find out the overlapping situation between these two objects to determine the handball event in a football game. The train and test result of detection was satisfactory with 96% for hand and 100% for the ball respectively.
Using Multivariate Normal Distribution and Poisson Distribution for Spawn Items in Battle Royale Games

Nowadays, computer games have gained immense popularity, captivating gamers who can easily spend hours engrossed in their laptops. However, not every game has the ability to keep players emotionally engaged. To achieve this, a game requires a unique storyline and well-developed characters, each with their own distinct traits. One genre that tends to be highly addictive is the "survival of the fittest" concept, where a player's success depends on their chosen gameplay strategy. This article explores the utilization of multivariate normal distribution and Poisson distribution for spawning items in "Battle Royale" games. The term "Battle Royale" refers to a specific genre of video games that has gained significant traction in recent years. These games typically involve a large number of players competing simultaneously within a single game session, often ranging from tens to hundreds of participants. The primary objective for players in battle royale games is to survive and outlast their opponents, creating an intense and competitive gaming experience.
Enhancing Customs Fraud Detection: A Comparative Study of Methods for Performance Measurement and Feature Improvement

This article presents a comprehensive investigation into the augmentation of customs fraud detection capabilities through the integration of Random Forests, XGBoost, and Ensemble Stacking techniques. In response to the growing intricacies of fraudulent activities in cross-border trade, this study explores advanced machine learning methods as vital solutions. Leveraging a meticulously preprocessed dataset of customs import data, Random Forests and XGBoost models were extensively trained to pinpoint fraudulent transactions with precision.Performance metrics including accuracy, precision, recall, and F1-score were systematically employed to evaluate the models. Moreover, graphical representations, encompassing confusion matrices, feature importance plots, and log-loss curves, provided insightful visualizations for performance comparison. The analysis highlights the distinctive strengths and weaknesses of Random Forests and XGBoost models in customs fraud detection, further accentuated through Ensemble Stacking. This work not only emphasizes their competence in flagging fraudulent transactions but also elucidates the hierarchical importance of individual features.The results obtained pave the way for fortified fraud detection systems, reinforcing the security and integrity of cross-border trade operations in response to evolving threats.
Guidelines for a Human-Robot Interaction Specification Language

Designing novel application development environments (ADEs) is a growing area of systems research within the human-robot interaction (HRI) community. This research involves the design of a novel system, the ADE, to afford end users and application designers the ability to develop robot applications. Researchers then usually validate their ADEs in the form of user studies or a series of case studies. In this paper, we highlight a problem with the typical approach to conducting ADE research within HRI—there is currently little standardization in how these systems are designed, developed, and validated, leading to difficulty in sharing resources between different research groups and the inability to compare similar ADEs to each other. We argue that a standardized formal representation embedded within an Interaction Specification Language (ISL) can lead to more streamlined development and validation of ADEs for HRI. Furthermore, we discuss several desired characteristics that an ISL should embody.
Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction

Learning fine-grained movements is a challenging topic in robotics, particularly in the context of robotic hands. One specific instance of this challenge is the acquisition of fingerspelling sign language in robots. In this paper, we propose an approach for learning dexterous motor imitation from video examples without additional information. To achieve this, we first build a URDF model of a robotic hand with a single actuator for each joint. We then leverage pre-trained deep vision models to extract the 3D pose of the hand from RGB videos. Next, using state-of-the-art reinforcement learning algorithms for motion imitation (namely, proximal policy optimization and soft actor-critic), we train a policy to reproduce the movement extracted from the demonstrations. We identify the optimal set of hyperparameters for imitation based on a reference motion. Finally, we demonstrate the generalizability of our approach by testing it on six different tasks, corresponding to fingerspelled letters. Our results show that our approach is able to successfully imitate these fine-grained movements without additional information, highlighting its potential for real-world applications in robotics.
VAFOR: Proactive Voice Assistant for Object Retrieval in the Physical World

In this paper, we present a proactive robotic voice assistant with a perceive-reason-act loop that carries out pick-and-place operations based on verbal commands. Unlike existing systems, our robot can retrieve a target object not only when the target is explicitly spelled out, but also given an indirect command that implicitly reflects the human intention or emotion. For instance, when the verbal command is “I had a busy day, so I didn’t have much to eat.”, the target object would be something that can help with hunger. To successfully estimate the target object from indirect commands, our framework consists of separate modules for the complete perceive-reason-act loop as follows. First, for perception, it runs an object detector on the robot’s onboard computer to detect all objects in the surroundings and records a verbal command from a microphone. Second, for reasoning, a list of available objects as well as a transcription of the verbal command are integrated into a prompt for a Large Language Model (LLM) in order to identify the target object in the command. Finally, for action, a TurtleBot3 with a 5 DOF robotic arm finds the target object and brings it to the human. Our experiments show that with a properly designed prompt, the robot can identify the correct target object from implicit commands with at most 97% accuracy. In addition, it is shown that the technique of fine-tuning a language model based on the proposed prompt designing process amplifies the performance of the smallest language model by a factor of five. Our data and code are available at https://github.com/bekatan/vafor
Real-time Detection and Tracking of Surgical Instrument Based on YOLOv5 and DeepSORT<sup>*</sup>

To enable the operator to control the surgical assistant robot without a separate joystick, we aim to develop an interface using artificial intelligence that responds to the movement of surgical instruments in the laparoscopic screen. In this study, we propose a method for detecting and tracking surgical tools using the YOLOv5 (YOU ONLY LOOK ONCE v5) and DeepSORT (Deep Learning based Simple Online and Real-time Tracking) algorithms. The proposed approach employs the YOLOv5 algorithm to detect surgical tools in real-time, and the DeepSORT algorithm to track the detected tools across multiple frames. The YOLOv5 model requires minimal computation, which enables the rapid detection of surgical instruments. Furthermore, the DeepSORT algorithm can precisely track object movements in complex environments. The proposed method’s tracking stability was assessed using the average pixel error performance metric test. To evaluate the method’s performance, we used a 2-degree-of-ffeedom remote center motion experimental setup and employed the PID control algorithm to control the laparoscopic camera’s movements.
Optimal Robot Path Planning In a Collaborative Human-Robot Team with Intermittent Human Availability

This paper presents a solution for the problem of optimal planning for a robot in a collaborative humanrobot team, where the human supervisor is intermittently available to assist the robot in completing tasks more quickly. Specifically, we address the challenge of computing the fastest path between two configurations in an environment with time constraints on how long the robot can wait for assistance. To solve this problem, we propose a novel approach that utilizes the concepts of budget and critical departure times, which enables us to obtain optimal solution while scaling to larger problem instances than existing methods. We demonstrate the effectiveness of our approach by comparing it with several baseline algorithms on a city road network and analyzing the quality of the obtained solutions. Our work contributes to the field of robot planning by addressing a critical issue of incorporating human assistance and environmental restrictions, which has significant implications for real-world applications.
Costmap-based Local Motion Planning using Deep Reinforcement Learning

Local motion planning is an essential component of autonomous robot navigation systems as it involves generating collision-free trajectories for the robot in real-time, given its current position, the map of the environment and a goal. Considering an a priori goal path, computed by a global planner or as the output of a mission planning approach, this paper proposes a Two-Stream Deep Reinforcement Learning strategy for local motion planning that takes as inputs a local costmap representing the robot’s surrounding obstacles and a local costmap representing the nearest goal path. The proposed approach uses a Double Dueling Deep Q-Network and a new reward model to avoid obstacles while trying to maintain the lateral error between the robot and the goal path close to zero. Our approach enables the robot to navigate through complex environments, including cluttered spaces and narrow passages, while avoiding collisions with obstacles. Evaluation of the proposed approach was carried out in an in-house simulation environment, in five scenarios. Double and Double Dueling architectures were evaluated; the presented results show that the proposed strategy can correctly follow the desired goal path and, when needed, avoid obstacles ahead and recover back to following the goal path.
From UX to Instructional Design: Supporting Rural Teachers in Transforming Students’ Reading Interests Into Academic Success

This design case records a design competition project in which the team adapted various user experience (UX) design methodologies to instructional design in support of China’s K-6 rural school teachers for the increasingly challenging teaching requirements on reading literacy. The solution landed on a mobile application design, YoungRead. The project is the winner of the University of Michigan Learning Levers Development Award and the Zell Lurie Institute Innovation and Entrepreneurship Award in 2020. This design case will describe the design process and how the adapted UX design model may be used in similar instructional design situations to facilitate under-resourced teachers in developing professional skills to bridge students’ reading interests and academic achievement.
V-CNN: A Versatile Light CNN Structure For Image Recognition On Resources Constrained Platforms

This paper introduces V-CNN, a versatile structure inspired from previously introduced light models such as NL-CNN. What makes the V-CNN structure efficient is the process of optimizing its hyper-parameters. Consequently, several aspects in proper design for building of efficient (validation accuracy near state of the art while keeping the complexity of the structure) V-CNN structures, are considered and detailed in this paper. While V-CNN includes as particular cases several previously defined models such as NL-CNN and XNL-CNN, it can be better tailored for efficient deployment given a specific dataset. A V-CNN model with 1.5 million parameters obtained 91.55% validation accuracy on CIFAR-10 dataset, surpassing the previous result of 90.60% using the NL-CNN. The V-CNN model offers the following advantages: i) using optimization hints described herein it allows maximal efficiency (good accuracy at low complexity) for a wide variety of datasets; ii) has a low number of layer primitives, thus making easier their specific design for deployment on various TinyML or EdgeAI platforms, including FPGAS.
A 12 Bit R-2R Digital-to-Analog Converter for Shuttling Operation in a Trapped-Ion Quantum Computer

This work presents a 12 bit, resistive R-2R ladder-based Digital-to-Analog Converter (DAC) intended to generate arbitrary waveforms for shuttling operation in a trapped-ion quantum computer (TIQC) using 171 Yb + ions. The DAC operates at a sampling frequency of 1 MHz and can replicate constant, sinusoidal, rectangular, and triangular signals within a full-scale (FS) range of ±0.9V and a resolution of 500 μV. To mitigate ion shaking in the trap, a low pass filter minimizes glitch errors at major output code transitions and a spectral noise density (SND) of 45.3nV/VHz has been measured at 100 kHz. Implemented in a 130 nm high-voltage (HV) bulk CMOS technology, the DAC consumes 1.1 m W from a 1.5 V power supply at room temperature (RT). This includes a reference buffer and a serial peripheral interface (SPI) to read in the DAC control words. The whole circuitry takes up an active area of 1200 x 700 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> excluding pads. Due to its small area, low power consumption and excellent noise performance, the DAC is ideally suited as the core building block for an ion shuttling controller IC.
FL2: Fuzzy Logic for Device Selection in Federated Learning

Federated Learning is an emerging paradigm in the field of Machine Learning which deals with distributed computing. Massively and geographically distributed clients participate in the training without using their private data and only send their locally trained model to the server. Since the clients may have different computational power, sizes of training sample labels with different distributions, it’s important to give different weights to each client based on these attributes. In this study, we proposed a method for giving different weights to clients using Fuzzy Logic. Results showed that the proposed approach performed better than the original Federated Averaging algorithm.
Comparative Study of Quantum to Classical Machine Learning Algorithms for Breast Cancer Classification

Current research paper presents a comparative analysis of classical and quantum machine learning algorithms for breast cancer diagnosis. The study focuses on two classical machine learning algorithms, XGBoost and SVM, along with two quantum models, Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC). The performance of the models is evaluated based on F1 score and recall. Results indicated that classic SVM and QSVC showed competitive results considering the other two algorithms regarding F1 score. This work contributes to the growing research on the application of classical and quantum ML algorithms in breast cancer classification.
Generation of Textual/Video Descriptions for Technological Products Based on Structured Data

With the recent development of deep learning and Natural Language Generation (NLG) techniques in particular, machines have been given the ability to write and speak, overcoming the limitations of traditional template-based approaches. However, implementing sophisticated models for practical usage is definitely demanding, especially in business contexts in which the final output quality has a direct impact on the economic performance of the service. Our work deals with proposing a specific framework for data-to-text and text-to-speech generation, mainly based on a pre-trained language model and popular NLP tasks, as Named Entity Recognition. The ultimate objective is to generate detailed product descriptions autonomously, starting from the product sheet, in order to promote automation and reduce human effort. Rather than simply reporting product features, the system independently creates a coherent structure by interpreting the information it receives. In this way, it is able to formulate reasonable considerations and motivate possible benefits while trying to remain consistent with the source information. The obtained results present the tendency to properly reproduce the semantic, lexical and general linguistic style of the given context.