Reviewerly/exHarmony huggingface dataset:
Compacting MocapNET-based 3D Human Pose Estimation via Dimensionality Reduction

Abstract. MocapNETs are state of the art Neural Network (NN) ensembles that estimate 3D human pose based on visual input in the form of an RGB image. They do so by deriving a 3D Bio Vision Hierarchy (BVH) skeleton from estimated 2D human body joint projections. BVH output makes MocapNETs directly compatible with a large variety of 3D graphics engines, where virtual avatars can be directly animated from RGB sources and off-the-shelf webcam input. MocapNETs have satisfactory accuracy and state of the art computational performance that, however, prior to this work was not sufficient for their deployment on embedded devices. In this paper we explore dimensionality reduction via the use of Principal Components Analysis (PCA) as a means to optimize their size and make them applicable to mobile and edge devices. PCA allows (a) reduction of input dimensionality, (b) fine-grained control over the variance covered by the maintained dimensions and, (c) drastic reduction of the total number of model/network parameters without compromising regression accuracy. Extensive experiments on the CMU BVH dataset provide insight on the effective receptive fields for densely connected networks. Moreover, PCA-based dimensionality reduction results in a 35% smaller NN compared to the baseline (original NN without any dimension reduction) and derives BVH skeletons without accuracy degradation. As such, the proposed compact NN solution becomes deployable on the Raspberry Pi 4 ARM CPU @ 23Hz.
Effects of Presence on Human Performance and Workload in Simulated VR-based Telerobotics

The Sense of Presence (SoP), which is typically described as the sensation of "being there", is an influential phenomenon that can be elicited in virtual environments. It becomes particularly important in Virtual Reality (VR)-based platforms for remote work or assistance (e.g., digital twins), whereby the quality of the interactions between the human and any virtual object determine the industrial performance and well-being of the operators. While SoP is typically assumed to have a positive impact on task performance, and also to affect mental workload, systematic investigations addressing this matter in the telerobotics field, also leveraging a mixed-method approach, are missing. We here covered these aspects by analyzing the effects of SoP on users’ performance and workload during simulated teleoperation of an industrial robotic arm in VR. Our participants guided the robot through a pick-and-place task via different control modalities and under different task loads. We operationalized the users’ performance in terms of operation times at the pick-and-place task; the explicit workload was inferred via self-report, while the implicit workload was deduced from the users’ pupil size variations. Our results demonstrate a positive effect of the SoP on task performance, suggesting higher efficiency when feeling a higher SoP. Little to no impact of the SoP on the users’ workload was observed, which however is worthy of further investigation.
An Integrated Development Environment for the Design of Fuzzy Human-centric System in Accordance with IEEE Standard 1855-2016

A fuzzy Logic System (FLS) has the unique potential of intelligent decision-making capability with the input data, which are imprecise and unpredictable but can be expressed using linguistic rules. However, such FLS strongly depends on the hardware architecture of the systems under consideration. A language called Fuzzy Markup Language (FML), which is based on XML, has the ability to express fuzzy systems without taking into account either hardware or software restrictions, thus enabling sharing effectively among distributed environments. FML creates a unified and clearly defined FLS that is independent of its previous representation. It also increases the capabilities of system designers by giving them access to a range of services that accelerate the process of creating a distributed or centralised fuzzy system. This can be done either through a centralised cloud-based platform or through a distributed cloud-based platform. FML-related software libraries, such as Simpful (a Python-based library) and Java FML (JFML, a Java-based library), have added to the novelty in it. However, JFML and Simpful enable fuzzy system modeling for programmers only in the abovementioned programming languages. Hence a need for the development of an open-source FLS which will be capable of enabling fuzzy system-based modelling is felt. The present communication introduces an enhancement of JFML through a visual environment based on GUI following IEEE Std 1855-2016. The functionalities of user-friendly visual FML are developed considering the data related to Human Activity Recognition (HAR) and other physiological data like SpO2 and pulse rate obtained from wearable sensors to compare the performances.
Real-Time Activity Recognition for Surveillance Applications on Edge Devices

Human Activity Recognition is a crucial task for surveillance systems that has seen great advancements with the emergence of Artificial Intelligence. At the same time, hardware advances have allowed for development of systems that operate in real-time. However real-time performance is still a far from solved problem for wearable devices when it comes to computer vision tasks such as activity recognition. In this paper a hybrid solution for Human Activity Recognition is proposed that exploits a lightweight method for on-device posture recognition and a more heavyweight activity recognition method executed on the cloud. The experimental evaluation for the activity recognition module indicates superior performance compared to existing methods and the lightweight posture method can predict satisfactorily the desired classes. The developed system offers a user-friendly Augmented Reality application that provides scene annotations to the user including the activity of the detected persons.
Evaluation of Machine Learning based Pose Estimation of Surfers on River Waves

In this work we are describing our preliminary experimental evaluation of open-source machine learning based pose estimation solutions for tracking a surfer's skeleton on a stationary wave. We are briefly describing the sport's environment and development and the current state-of-the-art of machine learning based single camera 2D and 3D pose estimation solutions. The main part of the paper deals with the experiment's setup and the interpretation of the resulting data of the movements of one surfer. We are closing with our lessons learned and our next steps.
Decentralized Resilient Smart Lock System with Offline Capabilities - ChainLock

The management of authorizations for access to rooms, buildings and similar is of great importance for many facilities and institutions. However, similar with increasing size and variability, the complexity and the effort required for updating also increases significantly. The presented decentralized solution with locks featuring no permanent connection represents a significant improvement and simplification in this regard. In addition, its distributed blockchain architecture significantly reduces the possibilities for manipulation.
A Framework Towards Ambient Assisted Living Enhanced by Service Robots

Automated Human Activity Recognition (HAR) is a crucial technology for Ambient Assisted Living applications (AAL), which uses sensor data to detect and recognize human activities and behaviors. However, this technology faces several challenges, such as coping with the inherent variability of human motions, the presence of multiple people, nuisance factors such as partial occlusion, changes in lighting or viewpoint, etc. The proposed research work of ACTIVE aims to address these challenges by developing a digital assistant capable of accurately recognizing a wide range of human activities by introducing new methods and technologies for dynamic, adaptive multimodal data fusion and analysis. Additionally, the research work focuses on the incorporation of a mobile robotic platform to assist in the monitoring of human activities in dynamic environments. The ultimate goal is to provide a better understanding of human behavior, improve quality of life, and enable personalized services for individuals.
Voice-Based Conversational Agents and Knowledge Graphs for Improving News Search in Assisted Living

As the healthcare sector faces major challenges, such as aging populations, staff shortages, and common chronic diseases, delivering high-quality care to individuals has become very difficult. Conversational agents have shown to be a promising technology to alleviate some of these issues. In the form of digital health assistants, they have the potential to improve the everyday life of the elderly and chronically ill people. This includes, for example, medication reminders, routine checks, or social chit-chat. In addition, conversational agents can satisfy the fundamental need of having access to information about daily news or local events, which enables individuals to stay informed and connected with the world around them. However, finding relevant news sources and navigating the plethora of online news articles can be overwhelming, particularly for those with limited technological literacy or health-related impairments. To address this challenge, we propose an innovative solution that combines knowledge graphs and conversational agents for news search in assisted living. By leveraging graph databases to semantically structure news data and implementing an intuitive voice-based interface, our system can help care-dependent people easily discover relevant news articles and give personalized recommendations. We explain our design choices, provide a system architecture, share insights of an initial user test, and give an outlook on planned future work.
Employee Behavior Analysis Towards Multi-Label Classification of Customer Reviews

With the surge in competitive e-commerce demands occurring in recent years, most logistics companies have been compelled to create more efficient and successful delivery organizations. Also, new logistics companies which provide different opportunities to customers and employees have entered the market and led to a boost in competition. In this work, a behavior analysis approach using customer reviews is developed for couriers of a private logistics company using real textual data about the attitudes of the couriers, facilities of the company, delivery system, product, seller, etc. In the experiments, the review analysis approach based on BERT achieved the best performance for the multi-label classification of reviews in terms of precision as 0.927, recall as 0.916, and F1-score as 0.921, and for the sentiment analysis with AUC score of 0.977. Based on the sentiment analysis, the review polarization is conceptualized for employee behavior analysis and segmentation of cross-docks.
Remote Operated Human Robot Interactive System using Hand Gestures for Persons with Disabilities

This paper proposes a novel Human-Robot Interactive System where users can interact with the robotic system using hand gestures, even from a distance, through a smartphone-based IoT-Controller Framework. The system is primarily designed to help people with vocal and hearing impairments. A mobile application records user gestures shown in front of a smartphone and sends the data to a server. The server performs gesture recognition and forms a command which is sent to the robotic system. The robotic system performs the required task based on the given command. The robotic system is set to carry out an object pick and place operation based on the gesture commands in the system’s initial experiments. The accuracy of both the gesture recognition model and the object grasping task were measured, along with the time taken for the system to complete the entire task. Experimental results showed that the system performed well, achieving 100% accuracy in object grabbing and gesture recognition, and the average time taken by the system to complete the robotic pick and place task was around 78 seconds.
Case study analysis of medical and pharmaceutical chatbots in digital marketing and proposal to create a reliable chatbot with summary extraction based on users' keywords.

With the recent advances of AI and its current adoption in healthcare, the associated social and ethical issues of its impact are of utmost importance. We study the current state of the art of chatbots and virtual assistants in the domain of medicine and pharmacology and propose the framework for a more-advanced solution which is based on a chatbot that supports dialogue flow about disease factors, connected to heterogeneous data assets and tools like publications lists, summarization components, entity extractors, sentiment identifiers, etc. The novelty of the proposed work lies to the fact that it copes with disinformation, which is a crucial success factor of virtual assistants, particularly in the health universe. Our chatbot was designed and deployed using the pandorabots platform an open source framework.
Towards Deriving Organizational Key Performance Indicators in a Metaverse Workplace: A Systematic Literature Review

Human resources of several businesses worldwide are already active in metaverses. This has led Human Resource Management (HRM) professionals exploring and applying new methods and practices to manage this technological transformation in the workplace. However, despite the growing volume of research on metaverse work environments, there is still limited research on avatar performance management. The emergence of metaverse applications in the workplace brings new key performance indicators (KPIs) requirements that are different from those in conventional work environments. This article aims to fill this gap by identifying the key metrics for measuring and managing avatar performance. For this purpose, a systematic literature review is carried out based on the PRISMA methodology. In total, 357 articles were screened, from which 31 met the predefined inclusion criteria and were reviewed based on their risk of bias. As a result, the required KPIs suitable for a metaverse workplace are proposed. By contributing to knowledge on performance in virtual work environments, this article extends and advances theory and research on the Strategic Human Resource Management of metaverse workplaces.
Artificial cognitive functions towards AI-enabled collaborative robots

In this paper we discuss work in progress and our approach to next generation robotics. The use of cognitive architectures can overcome some of the challenges encountered when Artificial Intelligence (AI) algorithms are introduced to robotic systems. New cognitive functionality is required to that end. We discuss system requirements that are currently not sufficiently addressed, and present our current work.
Towards Accelerating the Adoption of Federated Learning for Heterogeneous Data

Federated Machine Learning (FML) is a distributed machine learning approach that solves basic AI and data problems such as data heterogeneity, privacy preservation, and data ownership. This technology enables organizations to collaborate on the model building while retaining control over their data, making it particularly useful when data is sensitive or too large to be collected in a central location. Numerous open-source frameworks for FML have been developed, each with different capabilities. In this paper, we use a popular framework to implement a proposed algorithm and tackle the significant problem of data heterogeneity in AI. Specifically, we integrated the FEDMA algorithm to simulate the data heterogeneity problem with the FEMNIST dataset, a widely used benchmark dataset in the research community.
An RGB-D Fusion System for Indoor Wheelchair Navigation

We present a method for extracting high-level semantic information through successful landmark detection using feature fusion between RGB and depth information. We focus on the classification of specific labels (open path, humans, staircases, doorways, obstacles) in the encountered scene, which can be a fundamental source of information enhancing scene understanding, and acting towards the safe navigation of the mobile unit. Experiments are conducted using a manual wheelchair equipped with a stereo RGB-D camera that captures image instances consisting of multiple labels before fine-tuning on a pre-trained Vision Transformer (ViT).
Toward a holistic elderly-centred behaviour monitoring solution: Achievements and Opportunities

Despite extensive research on human-centric domestic behaviour monitoring for various essential applications, there are still significant challenges to deploying such systems on a larger scale. One of the main obstacles is adjusting the gap between privacy, performance, and the cost of assistive technologies to support older adults living independently in their homes. For instance, while traditional vision-based sensing approaches offer high performance, they compromise human privacy in domestic environments. On the other hand, ambient sensing approaches, such as the use of Passive Infra-Red (PIR) sensors, maintain human privacy but are hindered significantly in real-world scenarios, such as multi-occupancy environments. Inspired by our previous research work, this paper proposes a holistic system approach of several functional phases that can be used together to monitor and facilitate the independence of older adults using the Thermal Sensor Array.
An Intelligent and Accessible Wardrobe

Vesti-AmI is a work-in-progress prototype of an intelligent wardrobe cooperating with the technological infrastructure of the Intelligent Home, aiming to assist a wide variety of users (i.e. adults, children, elderly, and people with disabilities) in getting ready for their daily activities in a context-sensitive, user-friendly and intelligent manner.
The DARLENE XR platform for intelligent surveillance applications

Closed-circuit television (CCTV) systems play a significant role in the prevention and handling of criminal events. Although multiple cameras can reduce blind spots in surveillance areas, they tend to lack tools for automatic scene comprehension that could eliminate distractions and missed events. A novel intelligent surveillance system based on eXtended Reality (XR) and Computer Vision (CV) is developed to help analysts identify security incidents while improving their situational awareness by presenting them with a global representation of the area in VR. In addition, AR supervision and the ability to communicate with officers in the field, help headquarters to improve operational planning and simplify the coordination processes. The proposed platform offers real-time 3D human tracking and pose estimation in a virtual photo-realistic 3D environment equipped with a transparency layer that allows visibility in all premises of a building. In addition, informative visualizations overlay camera streams regarding 2D pose estimation, instance segmentation and activity recognition. The developed application has been tested in simulated security events, evaluating the provided functionalities.
Towards Shareable and Reproducible Cloud Computing Experiments

Containerization has emerged as a systematic way of sharing experiments comprising of code, data, and environment. Containerization isolates dependencies of an experiment and allows the computational results to be regenerated. Several new advancements within containerization make it further easy to encapsulate applications and share lighter-weight containers. However, using containerization for cloud computing experiments requires further improvements both at the container runtime level and the infrastructure-level. In this paper, we lay a vision for using containers as a dominant method for efficient sharing and improved reproducibility of cloud computing experiments. We advocate use of container-compliant cloud infrastructures, inclusion of performance profiles of the application or system architecture on which experiments were performed, and methods for statistical comparison across different container executions. We also outline challenges in the achieving this vision and propose existing solutions that can be adapted and propose new methods that can help with automation.
CDFMR: A Distributed Statistical Analysis of Stock Market Data using MapReduce with Cumulative Distribution Function

The stock market generates massive data daily on top of a deluge of historical data. Investors and traders look to stock market data analysis for assurance in their investments, a prime indicator of our global economy. This has led to immense popularity in the topic, and consequently, much research has been done on stock market predictions and future trends. However, due to the relatively slow electronic trading systems and order processing times, the velocity of data, the variety of data, and social factors, there is a need for gaining speed, control, and continuity in data processing (real-time stream processing) considering the amount of data that is being produced daily. Unfortunately, processing this massive amount of data on a single node is inefficient, time-consuming, and unsuitable for real-time processing. Recently, there have been many advancements in Big Data processing technologies such as Hadoop, Cloud MapReduce, and HBase. This paper proposes a MapReduce algorithm for statistical stock market analysis with a Cumulative Distribution Function (CDF). We also highlight the challenges we faced during this work and their solutions. We further showcase how our algorithm is spanned across multiple functions, which are run using multiple MapReduce jobs in a cascaded fashion.
A Probabilistic Approach for Secure and Verifiable Computation of kNN Queries in Cloud

The tremendous benefits of outsourcing have influenced many organizations to migrate their data and DBMS functionalities to the cloud infrastructure. Since cloud service providers offer advanced security services, data migration typically boosts the security of business data and operations. Nonetheless, since a cloud is an untrusted third party, it is important for organizations to ensure confidentiality of the outsourced data and integrity of results generated by the cloud. In this paper, we focus on the Secure and Verifiable Computation for k-Nearest Neighbor (SVC-kNN) problem. Existing SVC-kNN solutions delegate the verification task solely to a single semi-trusted party and are unreliable in terms of security as the verification server could be either dishonest or compromised. To address these issues, we propose a novel and probabilistic SVC-kNN protocol that utilizes a randomization technique and additive homomorphic properties under the two-cloud model. Our proposed solution facilitates the clouds to jointly compute verification proofs and allows the end-user to verify the computation results efficiently. Additionally, our solution is highly efficient from the data owner and query issuers’ perspective as most of the critical computations are performed by the clouds. Furthermore, our experimental results using a real dataset clearly demonstrate the effectiveness of our protocol.