Reviewerly/exHarmony huggingface dataset:
BodyTouch

This paper presents a study on the touch precision of an eye-free, body-based interface using on-body and near-body touch methods with and without skin contact. We evaluate user touch accuracy on four different button layouts. These layouts progressively increase the number of buttons between adjacent body joints, resulting in 12, 20, 28, and 36 touch buttons distributed across the body. Our study indicates that the on-body method achieved an accuracy beyond 95% for the 12- and 20-button layouts, whereas the near-body method only for the 12-button layout. Investigating user touch patterns, we applied SVM classifiers, which boost both the on-body and near-body methods to support up to the 28-button layouts by learning individual touch patterns. However, using generalized touch patterns did not significantly improve accuracy for more complex layouts, highlighting considerable differences in individual touch habits. When evaluating user experience metrics such as workload perception, confidence, convenience, and willingness-to-use, users consistently favored the 20-button layout regardless of the touch technique used. Remarkably, the 20-button layout, when applied to on-body touch methods, does not necessitate personal touch patterns, showcasing an optimal balance of practicality, effectiveness, and user experience without the need for trained models. In contrast, the near-body touch targeting the 20-button layout needs a personalized model; otherwise, the 12-button layout offers the best immediate practicality.
Blockchain Adoption in Product Lifecycle Management: Challenges and Potential Directions

Blockchain is a leading-edge technology that has received great public attention in the past few years. Due to its eccentric features i.e. transparency, immutability and decentralization, blockchain intends to targets any domain that deals with transactional data. In production sectors, significant efforts are in progress to make use of this disruptive technology for efficient collaboration among stakeholders and management of their products lifecycles. However, despite positive feedback and great aspiration, certain challenges still exist over which the executive level management has great concern. In this regard, this study tries to highlight and focus on what are the potential challenges in blockchain technology adoption? and provide recommendations for how organization can address those challenges? Initially, we elaborate and classify the potential challenges into two categories i.e. key challenges specific to blockchain adoption in PLM and some further challenges that are generic in nature and applicable to most of the applications of blockchain. The study then scientifically argues for potential directions and solutions to address the key challenges. Afterward, the general-purpose challenges are explored and broken down into four different aspects i.e. technical, organizational, environmental & social, and financial challenges. It is emphasized that both internal and external level decisions are need of the day to address these challenges. Finally, the evolution of a new era i.e. blockchain 5.0, its aims and capabilities to overcome the existing technical concerns has been discussed. Multiple Industrial sectors can follow the recommendation presented in this paper to setup their own secure and full-fledged blockchain-based PLM systems.
Do I Just Tap My Headset?

A variety of consumer Augmented Reality (AR) applications have been released on mobile devices and novel immersive headsets over the last five years, creating a breadth of new AR-enabled experiences. However, these applications, particularly those designed for immersive headsets, require users to employ unfamiliar gestural input and adopt novel interaction paradigms. To better understand how everyday users discover gestures and classify the types of interaction challenges they face, we observed how 25 novices from diverse backgrounds and technical knowledge used four different AR applications requiring a range of interaction techniques. A detailed analysis of gesture interaction traces showed that users struggled to discover the correct gestures, with the majority of errors occurring when participants could not determine the correct sequence of actions to perform or could not evaluate their actions. To further reflect on the prevalence of our findings, we carried out an expert validation study with eight professional AR designers, engineers, and researchers. We discuss implications for designing discoverable gestural input techniques that align with users' mental models, inventing AR-specific onboarding and help systems, and enhancing system-level machine recognition.
Quality Analysis Framework based on Complexity for Change Management Using Intelligent Digital Twin

Software attributes analysis in industrial automation systems due to the higher proportion of using software-defined systems is highly significant. The requirement of higher flexibility, adaptivity, quality, less cost, and time to market as main classes of manufacturing attributes in dynamic and heterogeneous environments induce a large amount of complexity, which directly deteriorates the external software quality attributes. Complexity is a comprehension of quality management factor that influences the quality attributes of an asset such as testability, reusability, reliability, and maintainability. In order to manage the continuous requirement of software-defined systems’ updating efficiently, an automatic method for estimating these quality attributes is required. This paper proposes a novel approach for quality attributes analysis with the factor of complexity estimation. The proposed approach is realized in a framework that exploits the operational simulation capability of the intelligent Digital Twin concept to decrease the cost of untoward consequences of complexity increase on software external attributes by using operational data. It uses the simulated asset to predict how complexity due to applying new changes would impact the software quality attributes. The quality analysis process contains the intelligence process of monitoring the complexity metrics vector and quality attributes vector, the analysis of training models, the prediction of attributes, and the evaluation of measurements to enhance the quality attributes in advance. A comprehensive evaluation using the example of a Robotino 3 Premium from Festo, an autonomous mobile robot, is used to show the consequences of changes in software quality attributes for validation of the proposed framework.
Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition

Human Activity Recognition (HAR) models often suffer from performance degradation in real-world applications due to distribution shifts in activity patterns across individuals. Test-Time Adaptation (TTA) is an emerging learning paradigm that aims to utilize the test stream to adjust predictions in real-time inference, which has not been explored in HAR before. However, the high computational cost of optimization-based TTA algorithms makes it intractable to run on resource-constrained edge devices. In this paper, we propose an Optimization-Free Test-Time Adaptation (OFTTA) framework for sensor-based HAR. OFTTA adjusts the feature extractor and linear classifier simultaneously in an optimization-free manner. For the feature extractor, we propose Exponential Decay Test-time Normalization (EDTN) to replace the conventional batch normalization (CBN) layers. EDTN combines CBN and Test-time batch Normalization (TBN) to extract reliable features against domain shifts with TBN's influence decreasing exponentially in deeper layers. For the classifier, we adjust the prediction by computing the distance between the feature and the prototype, which is calculated by a maintained support set. In addition, the update of the support set is based on the pseudo label, which can benefit from reliable features extracted by EDTN. Extensive experiments on three public cross-person HAR datasets and two different TTA settings demonstrate that OFTTA outperforms the state-of-the-art TTA approaches in both classification performance and computational efficiency. Finally, we verify the superiority of our proposed OFTTA on edge devices, indicating possible deployment in real applications. Our code is available at https://github.com/Claydon-Wang/OFTTA.
Industrial Cross-Robot Transfer Learning

With increased complexity and individualization of processes and products, the manufacturing industry experiences growing pressure to automate. To meet these constantly growing requirements regarding the flexibility of production and logistic systems, intelligent robotic systems that can adapt dynamically to changing process conditions are essential. Deep reinforcement learning provides a solution and offers a paradigm of learning adaptive control strategies through autonomous agents. However, the corresponding training procedure is based on trial-and-error interactions with an environment and is thus very data inefficient and costly in real-world scenarios. Transfer learning approaches can mitigate these costs and improve learning by leveraging control capabilities acquired in other settings, such as simulations, or from previously learning a different task. In this paper, we investigate the transfer of trajectories between different robot models. We propose a methodology that maps trajectory demonstrations from a source robot into trajectories of a target robot by using forward and inverse kinematics models. We introduce a similarity measure, that one the one hand compares different robot models using key poses along the kinematic chains, and on the other hand captures the efficiency of transitions. This similarity measure is used to select from the mapped trajectories those which are feasible, efficient and retain the general shape of the robotic arm. Finally, we perform behavioral cloning to pre-train a deep reinforcement learning policy for the target domain and demonstrate significant benefits compared to learning the target policy from scratch.
TrackPose

Several studies have explored the estimation of finger pose/angle to enhance the expressiveness of touchscreens. However, the accuracy of previous algorithms is limited by large estimation errors, and the sequential output angles are unstable, making it difficult to meet the demands of practical applications. We believe the defect arises from improper rotation representation, the lack of time-series modeling, and the difficulty in accommodating individual differences among users. To address these issues, we conduct in-depth study of rotation representation for the 2D pose problem by minimizing the errors between representation space and original space. A deep learning model, TrackPose, using a self-attention mechanism is proposed for time-series modeling to improve accuracy and stability of finger pose. A registration application on a mobile phone is developed to collect touchscreen images of each new user without the use of optical tracking device. The combination of the three measures mentioned above has resulted in a 33% reduction in the angle estimation error, 47% for the yaw angle especially. Additionally, the instability of sequential estimations, measured by the proposed metric MAEΔ, is reduced by 62%. User study further confirms the effectiveness of our proposed algorithm.
Towards Multimodal Information Systems for Assisting Humans in Production and Logistics Processes

Information support systems for coping with and mastering increasing process complexity are becoming a success factor for many companies. The information systems available today are predominantly implemented as mobile applications and, as the range of functions grows, place increasingly high demands on the user interface. To reduce the cognitive load, these are tailored to the user and are limited in functionality or require special training, which is costly and impractical for some user groups. Current voice interaction interfaces offer advantages in this context, but the communication of data to the user can be done more meaningfully and clearly in graphical or multimodal form. Furthermore, both interaction modes neglect essential sources of information in industrial and logistics use cases, such as available information about documents and physical properties of objects. Methodologically, this contribution proceeds as follows: First, a literature review was conducted on Cognitive Load Theory, Human-Computer Interaction, Social Response Theory, and the cooperative principle of conversation with a focus on human-machine interaction. Subsequently, expert interviews on relevant application areas in the field of production and logistics were conducted in order to identify use cases and derive system requirements. Based on the findings, a concept for an stationary adaptive multimodal information system was developed, which, to our knowledge, does not exist yet due to the combination of a 2D/3D scanner and conversational intelligence and can be used to improve human-machine interaction in the context of Industry 5.0.
Application Fields of Flexible Production Systems and Their Impact on Agile Product Creation

Agility is a central instrument for reacting appropriately to dynamic influences and enabling innovation capability. However, rigid production systems can no longer meet the requirements of agile product development and innovation processes. Flexible production systems like the 'Value Stream Kinematics' provide benefits for producing companies. In this research effort, the ‘Persona-method’ has been used to derive typical individuals who are influenced by a socio-technical system, specifically by a flexible production system. Based on these results holistic use cases of flexible production systems for the product development process and production system development were identified and described as well as discussed in detail. The comparison of agility barriers within industrial companies and the identified specific use cases of a flexible production system shows that agility barriers in product and production system development can be overcome by utilizing a flexible production system.
Ways of expressing the category of instrumentality in retranslated texts

The category of instrumentality remains a research niche due to the interrelation of many cognitive and linguistic factors during its implementation in language. This study can contribute to the improvement of methodology and tools for automated propositional analysis of natural language texts. The article aims to identify the types and frequency of instrumentality manifestations in oral immediate recalls. The dataset comprises two reading texts on Social Studies and 34 immediate text-based oral recalls (2779 words in total). The study involved 34 native Russian 5th graders aged 11-12. For the types and range of modifications in expressing the semantic roles of instrumentality, the recalls were analyzed individually and later contrasted with the original texts. The findings showed that the most typical modifications of Instruments in Russian recalls are diathesis shift, excorporation or dismantling of verbs integrated into constructions, and redistribution of semantic roles including replacement of Instrument with Causator, Theme and the Circumstants of Place. Based on the variety of modifications, all the means of manifesting the semantic role of instrumentality registered in our dataset fall into two groups. The first group comprises all the above types of modification, as well as Human Psychological State and Perception. The second group consists of Incorporated verbs and Instrumental actions that were reproduced by respondents using the “extraction” strategy with virtually no lexical loss. The findings expand our understanding of the varieties of expressing the category of instrumentality in oral speech, as well as the specifics of generating secondary texts. They can find application in the studies of receptive and productive speech activity as well as in automating propositional analysis of Russian texts.
MagDot

Tracking the angular movement of body joints has been a critical enabler for various applications, such as virtual and augmented reality, sports monitoring, and medical rehabilitation. Despite the strong demand for accurate joint tracking, existing techniques, such as cameras, IMUs, and flex sensors, suffer from major limitations that include occlusion, cumulative error, and high cost. These issues collectively undermine the practicality of joint tracking. We introduce MagDot, a new magnetic-based joint tracking method that enables high-accuracy, drift-free, and wearable joint angle tracking. To overcome the limitations of existing techniques, MagDot employs a novel tracking scheme that compensates for various real-world impacts, achieving high tracking accuracy. We tested MagDot on eight participants with a professional motion capture system, i.e., Qualisys motion capture system with nine Arqus A12 cameras. The results indicate MagDot can accurately track major body joints. For example, MagDot can achieve tracking accuracy of 2.72°, 4.14°, and 4.61° for elbow, knee, and shoulder, respectively. With a power consumption of only 98 mW, MagDot can support one-day usage with a small battery pack.
PASTEL

Federated Learning (FL) aims to improve machine learning privacy by allowing several data owners in edge and ubiquitous computing systems to collaboratively train a model, while preserving their local training data private, and sharing only model training parameters. However, FL systems remain vulnerable to privacy attacks, and in particular, to membership inference attacks that allow adversaries to determine whether a given data sample belongs to participants' training data, thus, raising a significant threat in sensitive ubiquitous computing systems. Indeed, membership inference attacks are based on a binary classifier that is able to differentiate between member data samples used to train a model and non-member data samples not used for training. In this context, several defense mechanisms, including differential privacy, have been proposed to counter such privacy attacks. However, the main drawback of these methods is that they may reduce model accuracy while incurring non-negligible computational costs. In this paper, we precisely address this problem with PASTEL, a FL privacy-preserving mechanism that is based on a novel multi-objective learning function. On the one hand, PASTEL decreases the generalization gap to reduce the difference between member data and non-member data, and on the other hand, PASTEL reduces model loss and leverages adaptive gradient descent optimization for preserving high model accuracy. Our experimental evaluations conducted on eight widely used datasets and five model architectures show that PASTEL significantly reduces membership inference attack success rates by up to -28%, reaching optimal privacy protection in most cases, with low to no perceptible impact on model accuracy.
Utilizing ISA-95 in an Industrial Knowledge Graph for Material Flow Simulation - Semantic Model Extensions and Efficient Data Integration

Despite access to emerging technologies for effective data representation and interoperability between different types of data, many enterprises struggle to use the information and knowledge available in manufacturing data to increase the efficiency and quality of their production. This is especially true for knowledge graphs and semantic web technologies. Nevertheless, breaking down the rigid and historical manufacturing data systems and transferring them to knowledge graphs often involves a lot of manual work for cleaning, mapping, and transforming the data. To address this issue, we have developed a methodology to efficiently utilize ISA-95 in knowledge graphs for manufacturing. The methodology is based on two main building blocks. First, we built an ontology based on the ISA-95 standard and extended it with additional semantic concepts. The second building block is an extract, transform, and load pipeline, to allow an efficient mapping of different data formats into the concepts specified by the ontology, and consequently a fast data integration into the knowledge graph. The created knowledge graph serves as basis for efficient management of material flow simulations.
Augmented reality support to employ tacit knowledge in non-conforming operations

Organisations often rely, implicitly, on the tacit knowledge of their employees, especially in non-conforming operations. These activities include (dis)assembly tasks, manual machining, troubleshooting, and tailored adjustments. While a traditional master-apprentice approach is a proven way to transfer tacit knowledge between employees, this approach is not feasible in current industrial situations. This research proposes a contemporary master-apprentice approach to support and improve non-conforming operations in situations that cannot rely on all parties being present at the same time, in the same location, while co-operating on the same task. The approach demonstrates the use of augmented reality as an implicit and discreet tool that purposefully addresses both capturing tacit knowledge, as well as employing it during operations - without disruptive interference in the primary processes.
SF-Adapter

Wearable sensor-based human activity recognition (HAR) has gained significant attention due to the widespread use of smart wearable devices. However, variations in different subjects can cause a domain shift that impedes the scaling of the recognition model. Unsupervised domain adaptation has been proposed as a solution to recognize activities in new, unlabeled target domains by training the source and target data together. However, the need for accessing source data raises privacy concerns. Source-free domain adaptation has emerged as a practical setting, where only a pre-trained source model is provided for the unlabeled target domain. This setup aligns with the need for personalized activity model adaptation on target local devices. As the edge devices are resource-constrained with limited memory, it is crucial to take the computational efficiency, i.e., memory cost into consideration. In this paper, we develop a source-free domain adaptation framework for wearable sensor-based HAR, with a focus on computational efficiency for target edge devices. Firstly, we design a lightweight add-on module called adapter to adapt the frozen pre-trained model to the unlabeled target domain. Secondly, to optimize the adapter, we adopt a simple yet effective model adaptation method that leverages local representation similarity and prediction consistency. Additionally, we design a set of sample selection optimization strategies to select samples effective for adaptation and further enhance computational efficiency while maintaining adaptation performance. Our extensive experiments on three datasets demonstrate that our method achieves comparable recognition accuracy to the state-of-the-art source free domain adaptation methods with fewer than 1% of the parameters updated and saves up to 4.99X memory cost.