Reviewerly/exHarmony huggingface dataset:
The Nexus between Cryptocurrencies and the Fear Index: Evidence from Bitcoin and Ethereum

This study examines the nexus between selected cryptocurrencies represented by Bitcoin and Ethereum and the expected market volatility denoted by the VIX index. We use daily data for the period of 06/01/2021 – 07/07/2023. Using Hong (2001) and Hong et al. (2009) Granger causality tests we find no causality in mean, however, we find strong bidirectional causality in variance for the variables at the 5% significance level across all lag lengths. At the time-varying causality analysis, we find BTC causes VIX in the 1st lag. The analysis findings have important implications for portfolio managers and potential investors. Although no direct causal relationship was found between Bitcoin and VIX, or Ethereum and VIX in terms of returns, the strong bidirectional causality in variance suggests that changes in volatility can affect related assets. This information can be utilized by portfolio managers to effectively assess mandates and manage portfolio risk.
Method for Individuals’ Speech Popularity Assessment Based on the National Terminological Information System

Readability of texts is an important parameter that determines the ease of the text perception by the reader. Therefore, texts should be presented in a precise, clear and understandable way. This article highlights the automation of the assessment of texts perception. In this respect, we propose a method for texts popularity assessment based on the National Terminological Information System. A popularity index is introduced and analyzed to assess the perception level of an individual’s speech by the target audience. The article also touches upon the issues of assessing the level of general and scientific fluency of the speech. It emphasizes that the popularity index of individuals’ speech (writing) can be regularly analyzed, improved over time, and the trends can be identified.
A Model-based Curriculum Learning Strategy for Training SegFormer

The use of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision opened up new tracks in this area. However, a significant drawback of these models is the large amount of data required to obtain competitive results. This critical issue limits their application in domains where large labeled data collections are unavailable. Some strategies have been proposed to use relatively limited labeled data sets to train CNN-based models. Curriculum learning is one of the currently available strategies to train deep learning models faster and with less data. However, to our knowledge, curriculum learning techniques have never been used at the model level to support ViT training for semantic segmentation. We propose a new curriculum learning technique tailored to ViT models to fill this gap. The results show the effectiveness of the proposed strategy in training ViT models from scratch to solve the semantic segmentation task.
Software Supply Chain Security [Guest Editors’ Introduction]

Today, the Ancient Mariner would rhyme “Code, code every where, Not any drop to trust.” This special issue of IEEE Security & Privacy highlights software supply chain security research and experiences of value to practitioners and researchers alike.
Game Theory Based Modelling of Unmanned aerial vehicles’ Onboard Software Integrity Attacks

Attacks on the integrity of Unmanned aerial vehicles (UAVs) onboard software are on the rise. Such attacks are very critical and can lead to man-made incidents. There are several approaches for modeling these attacks. This article explores the interaction between an attacker and a defender. The authors have developed a tree model for attacking firmware integrity. Using this attack-defense tree, a game-theoretic risk assessment scheme is formulated. The effectiveness of the proposed scheme was evaluated on the example of a firmware integrity attack and conclusions were shown confirming the viability of the proposed method.
Probabilistic decoders of Reed-Muller codes based on polynomial differentiation

Several probabilistic decoders of second-order Reed-Muller codes are considered. These decoders share a common construction idea based on the differentiation of polynomials. The properties of these decoders are considered. It is shown that it is possible to modify these decoders for the case of any odd Galois field of cardinality more than 3, but it is theoretically impossible to justify the correctness of such algorithms.
Using Artificial Intelligence Applications For E-Government Services As Iris Recognition

E-government is defined as the effective integrated use of all information and communication technologies to facilitate the daily administrative operations of government sectors. The revolution in digital communication technologies has put more pressure on many public sector institutions to transform their operations into the world of electronic business. This is what is known as e-government. E-government is defined as the effective integrated use of all information and communication technologies. People are recognised by the use of their fingerprints, faces, hand geometries, and iris prints, among other biometric characteristics. This research intends to use iris recognition to identify people through the image of the iris and apply a programme using Python and the deep learning method of a convolutional neural network (CNN) to match the printed iris images of people, where the images match the images of people and show the name of the person who owns this photo. This research also intends to use the deep learning method to match the printed iris images of people. This is a contemporary system that aids in the identification of individuals and replaces e-government systems with smart systems that incorporate artificial intelligence into their operations to deliver the best services possible to residents. How much does the Jordanian government want to employ biometric characteristics, such as the iris of the eye, which is regarded as part of the biometric features, in all of the electronic government services.
Coordinate Robot for Smart Search and Recognition by QR Code of Preparations of a Pharmacy Warehouse

Reducing the time, it takes to search for the right medicine in a pharmacy warehouse is an urgent task for activating the highly qualified work of a pharmacist. In the practice of modern pharmacies, automated cassette cabinets (modified snack machines) are widely used with a set of the most popular medicines and goods for free sale. To automate the sale of prescription drugs, the dispatching of the drug dispensing process is required. As is known, coordinate robots in computer technology are widely used. In the case of the pharmacy business, these robots are searching in a vertical plane in a two-dimensional matrix of cellsshelves with medicines. In this case, it is necessary to carry out complex interaction between the pharmacist-seller, the client and the coordinating warehouse robot. The robot is entrusted with the mechanical work associated with the search and identification of the ordered medicine. The article shows that the most rational way to accurately identify goods is to use a QR code. For this, an actuator-capture is proposed, equipped with computer vision based on the Raspberry Pi 4 mini-computer. At the same time, the coordinate search method is supplemented by product recognition by a QR code, which significantly intellectualizes the software of previously existing coordinate pharmacy robots. As a result, the proposed method for creating an intelligent robot allows not only to intensify the work of pharmacists by reducing the physical load on pharmacists, but also creates the prerequisites for the intelligent search for drugs by various pharmacological characteristics.
Extractive Conversation Summarization Driven by Textual Entailment Prediction

Summarizing conversations like meetings, email threads or discussion forums poses relevant challenges on how to model the dialogue structure. Existing approaches mainly focus on premise-claim entailment relationships while neglecting contrasting or uncertain assertions. Furthermore, existing techniques are abstractive, thus requiring a training set consisting of humanly generated summaries. With the twofold aim of enriching the dialogue representation and addressing conversation summarization in the absence of training data, we present an extractive conversation summarization pipeline. We explore the use of contradictions and neutral premise-claim relations, both in the same document or in different documents. The results achieved on four datasets covering different domains show that applying unsupervised methods on top of a refined premise-claim selection achieves competitive performance in most domains.
The GapApp: Matching Military Occupations and Civil Jobs in Cyber Security for Women

This paper describes the activities and results of a George Washington University – USA Department of Defense Cyber Scholarship Program capacity building project during the 2022 academic year. In particular, the report provides information about the design and development of the GapApp, an application based on the outcome recommendations of the May 2021 conference. The GapApp is intended to streamline and to make accessible the multitude of competing information streams for careers, especially in the high demand field of cyber security to active women military nearing separation from the service. The existing workforce role and skill taxonomies in cyber security and military were studied, a technique for mapping one to another and evaluating the match was proposed. A working pilot application on a platform that can be ported to a mobile phone was successfully demonstrated.
Online Doctor Recommendation Method for Doctor-Patient Interaction Data and Doctor Attributes

At present, LDA topic model, Word2Vec model and knowledge graph have been used to recommend doctors for online consultation, and have achieved certain results. However, the single word vector model will lead to some deficiencies in the recommendation results. In online consultation, patients have short questions and doctors have long answers, which is difficult to deal with uniformly. Therefore, this paper combines Sentence BERT sentence vector model, ELMO word vector model and doctor attribute model to build a set of combined recommendation model for intelligent doctor recommendation. In the empirical study, this paper chooses a large number of test patients as the research object. The results show that compared with the single word vector model, the combined recommendation model proposed in this paper is better in the accuracy rate, mean reciprocal rank and coverage rate of doctor recommendation.
Teaching Method for Electrical Machines Focusing on Differences Between Simulation and Experimental Verification

In modern times, electrical machines are used in a wide range of fields such as home appliances and automobile industries, so it is important for the development of these industries to train engineers with knowledge of electrical machines at universities. On the other hand, companies generally use finite element analysis (FEA) to minimize the number of motor prototypes and experiments to reduce development spans and costs. However, FEA has many differences from the actual electrical machine. Therefore, it is important for designers to understand these differences at the design stage using FEA. However, as mentioned above, it is usually difficult in the industry for engineers who design electrical machines using FEA to experience multiple prototypes and experiments in companies. Therefore, in our laboratory, we are implementing education that actively incorporates prototyping and experiments into the research cycle. In this paper, we introduce a teaching method focusing on the difference between experiments and FEA in electrical machines, as well as laboratory equipment. Finally, using a student questionnaire, we consider its educational effect.
A Viewpoint on Software Supply Chain Security: Are We Getting Lost in Translation?

Many of the adoption challenges in securing the software supply chain are largely caused by the language we use to describe risk and defenses and other sociocultural gaps. We shed light on the impacts of these gaps and opportunities to overcome them.
CoBaIR: A Python Library for Context-Based Intention Recognition in Human-Robot-Interaction

Human-Robot Interaction (HRI) becomes more and more important in a world where robots integrate fast in all aspects of our lives but HRI applications depend massively on the utilized robotic system as well as the deployment environment and cultural differences. Because of these variable dependencies it is often not feasible to use a data-driven approach to train a model for human intent recognition. Expert systems have been proven to close this gap very efficiently. Furthermore, it is important to support understandability in HRI systems to establish trust in the system. To address the above-mentioned challenges in HRI we present an adaptable python library in which current state-of-the-art Models for context recognition can be integrated. For Context-Based Intention Recognition a two-layer Bayesian Network (BN) is used. The bayesian approach offers explainability and clarity in the creation of scenarios and is easily extendable with more modalities. Additionally, it can be used as an expert system if no data is available but can as well be fine-tuned when data becomes available
Older Adults’ Emotional Challenges and Co-design Preferences for a Social Robot after the COVID-19 Pandemic

Mental health challenges became more prevalent during the COVID-19 pandemic, especially among older adults. Consequently, we witnessed an uptake of new technologies, including social robots to address these challenges. However, we observed limited inclusion of older adults in the design process to design these technologies to cater user needs during the pandemic. To address this gap, we conducted a co-design workshop with 17 older adults and explored their emotional challenges after the COVID-19 pandemic. They evaluated the current social robot designs available in the literature and elicited the design preferences for a social robot to address their current emotional challenges. Our results based on thematic analysis show that the impact of the pandemic on older adults’ emotional challenges is persisting, and the companionship of a social robot is preferred to enhance their mental well-being. We also show that older adults preferred an animal-like robot design embodied with soft skin possessing a medium size. These findings highlighted older adults’ design choices of a social robot and affirmed their potential to support older adults’ mental well-being.
Action-conditioned Deep Visual Prediction with RoAM, a new Indoor Human Motion Dataset for Autonomous Robots

With the increasing adoption of robots across industries, it is crucial to focus on developing advanced algorithms that enable robots to anticipate, comprehend, and plan their actions effectively in collaboration with humans. We introduce the Robot Autonomous Motion (RoAM) video dataset, which is collected with a custom-made turtlebot3 Burger robot in a variety of indoor environments recording various human motions from the robot’s ego-vision. The dataset also includes synchronized records of the LiDAR scan and all control actions taken by the robot as it navigates around static and moving human agents. The unique dataset provides an opportunity to develop and benchmark new visual prediction frameworks that can predict future image frames based on the action taken by the recording agent in partially observable scenarios or cases where the imaging sensor is mounted on a moving platform. We have benchmarked the dataset on our novel deep visual prediction framework called ACPNet where the approximated future image frames are also conditioned on action taken by the robot and demonstrated its potential for incorporating robot dynamics into the video prediction paradigm for mobile robotics and autonomous navigation research.
A Study on Customer’s Perception of Robot Nonverbal Communication Skills in a Service Environment

Nonverbal communication has the potential to enable robots to interact with customers in service environments efficiently. While previous efforts in this domain have been paid to the understanding of customers’ interaction experience from different aspects, there is a lack of studies on the configuration of multimodal interaction (i.e., the combination of nonverbal gestures, voice, and touch) in service environments and the effect of nonverbal communication styles when performed in this setting. This paper aims to address the gap in the literature by introducing a multimodal HRI framework operated in a cafe shop setting. A systematic study is conducted with 171 customers. It is followed by an in-depth analysis based on objective and subjective measurements to build an understanding of customers’ attitudes towards the robot’s nonverbal behaviours.
Let me Be your Service Robot: Exploring Early User Experiences of Human-Robot Collaboration for Service Domains

There has been increasing interest in the application of service robots in retail service domains in recent years. In most cases, deployed robot systems focus on serving customer needs autonomously. Specific and individual customer needs often cannot be addressed by these systems, promoting frustration and dissatisfaction in customers. In this study, we investigate the potential of human-robot-collaboration in service domains and how humans may be kept in the loop while customers interact with autonomous service robots. Therefore, we developed a graphical user interface allowing users to control a service robot remotely. We tested the interface with 19 participants to understand their perceptions on the usability of the interface and their user experiences while serving customers in two different use cases. Results illustrate that participants easily learned interacting with the robot and successfully completed the service use cases. They reported diverse user experiences, ranging from feeling odd to having great experiences while remotely operating the robot. We discuss implications of our results for the design of human-robot-interaction in service domains and emphasize a shift of focus from full robot automatization to human-robot collaboration.
Shaping Imbalance into Balance: Active Robot Guidance of Human Teachers for Better Learning from Demonstrations

Learning from Demonstrations (LfD) transfers skills from human teachers to robots. However, data imbalance in demonstrations can bias policies towards majority situations. Previous work attempted to solve this problem after data collection, but few efforts were made to maintain a balanced distribution from the phase of data acquisition. Our method accounts for the influence of robots on human teachers and enables robots to actively guide interaction to approximate demonstration distributions to target distributions. Simulated and real-world experiments validated the method’s efficacy in shaping demonstration distribution into various target distributions and robustness to various levels of uncertainties. Also, our method significantly improved the generalization ability of robot learning when LfD policies were trained with data collected by our method compared to natural data collection.
To Err Is Robotic; to Earn Trust, Divine: Comparing ChatGPT and Knowledge Graphs for HRI

The paper discusses two current approaches to conversational AI, using large language models and knowledge graphs, and compares types of errors that occur in human-robot interactions based on these approaches. It provides example dialogues and describes solutions to several error types including false implications, ontological errors, theory of mind errors, and handling of speech recognition errors. The paper addresses issues of particular concern for earning user trust.
High-Speed, High-Quality Robotic Portrait Drawing System

Although robotic portrait drawing has been a recurring topic in robotics, most robotic portrait drawing systems have focused on either speed or quality of the drawing due to various technical difficulties in pursuing both goals. In this work, we propose a novel robotic portrait drawing system that uses advanced machine-learning techniques and a variable line width Chinese calligraphy pen to draw a high-quality portrait in a short time. Our approach first detects the human keypoints from the incoming video stream and extracts the dominant human face from the video, and then uses a CycleGAN based algorithm to convert the image style into a black-and-white line drawing. After a number of optimization steps, we use a 6-DOF robotic arm and a calligraphy pen to quickly draw the portrait. The system has been openly demonstrated to the general public at the RoboWorld 2022 exhibition, where the system has drawn portraits of more than 40 visitors with a satisfaction rate of 95%.
A Case of Identity: Enacting Robot Identity with Belief Propagation for Decentralized Multi-Agent Task Allocation

Advancements in autonomous agents have led to an increasingly ubiquitous presence of robots in human environments where social and physical interaction is expected. Such environments are often composed of heterogeneous agents with disparate action capabilities, intentions, and motivations. Intra- and inter-agent dissimilarity often prevents enacting effective behavioral skills (e.g., collaboration, communication, coordination) towards dynamic task allocation objectives. We propose a Bayesian probabilistic inference approach, MultiRobot Belief Propagation with Identity constraints (MRPBI), for 1) decentralized task allocation in multi-agent systems and 2) modeling task affinity using personal identities. MRPBI leverages competing costs of individual and group capabilities that result in less error-prone convergence to steady state, scalability without loss of accuracy, and sensitivity to environmental dynamics. An implementation of MRBP-I as a distributed algorithm that weighs factors of both individual and cooperative perception in an energy-minimizing task allocation scheme is presented.
Rush-Out Risk Mapping from Human Operational Commands Considering Field Context<sup>*</sup>

Collaborative delivery robots in hospitals are required to move safely and efficiently in a short time, without colliding with people. Hence, they must consider the risk of people rushing out from blind spots or rooms, including field context such as the role and usage of the location. However, these factors are difficult to extract solely from geometric information. Therefore, we propose a method for generating a rush-out risk map considering the field context from the hospital staf’s operation data of an electric wheelchair. We convert the wheelchair’s speed operated by staff into rush-out risk, and then place rush-out risk potentials at positions where rush-outs may occur. Subsequently, we optimize the mapping position of rush-out risk and parameters of each potential to minimize the error to obtain a rush-out risk map. We collected actual staff operation data in the hospital and confirmed that we could generate a rush-out risk map with small errors.
Humanoid Robots in Healthcare: Lessons Learned from an Innovation Project

This paper presents lessons learned from an ongoing innovation project exploring the integration of a humanoid robot at a rehabilitation hospital in Norway. By combining human factors and human-robot interaction approaches, we suggest a framework to identify key concepts and methods to evaluate the healthcare staff and patients’ perceptions of the robot. Findings from initial studies on the topic are presented, based upon semi-structured interviews with stakeholders; a survey identifying specific challenges in today’s activities at the hospital; and a meeting and a workshop with key stakeholders to identify concrete scenarios and tasks where the humanoid robot could support staff. We wrap up by assessing the usefulness of the framework and discussing key challenges and opportunities for humanoid robots within healthcare, as identified in the current experiences in the project.