Reviewerly/exHarmony huggingface dataset:
Activity Recognition over Temporal Distance using Supervised Learning in the Context of Dementia Diagnostics

Englisch: In the case of neurological diseases, the progression of the disease can be detected by monitoring movements and activities. Documenting such monitoring requires time-consuming work, which can hardly be covered in the context of a constantly decreasing availability of nursing staff. In cooperation with two dementia residential communities, we incrementally develop a process that supports the nursing staff by providing an approach for a semi-automated documentation. This paper presents an approach to aggregate individual activities over a care period using smartwatches in combination with supervised learning algorithms. A smartwatch offers the opportunity to integrate sensor technology into a patient’s daily routine without disturbing them, as many patients already wear watches.
Physics-Based Hand-Object-Interaction for Immersive Environments

In this work, we developed a physics-based hand-object interaction that can be used in immersive virtual environments. The resulting interaction technique enables the user to interact with objects in a pseudo-realistic way that works on the calculation of forces, friction and other factors. Even though this approach can lead to a grasping method that is suitable for arbitrary interactions and is not limited by predefined gestures and data, our prototype is limited to sliding objects on a plane. We evaluated our implementation in a user study in which users had to move an object with their virtual hands, and made a comparison with a rule-based interaction method.
Analyzing Eye Tracking Data in Mirror Exposure

Mirror exposure is an important method in the treatment of body image disturbances. Eye tracking can support the unaffected assessment of attention biases during mirror exposure. However, the analysis of eye tracking data in mirror exposure comes with various difficulties and is associated with a high manual workload during data processing. We present an automated data processing framework that enables us to determine any body part as an area of interest without placing markers on the bodies of participants. A short, formative user study proved the quality compared to the gold standard. The automatic processing and openness for different systems allow a broad range of applications.
“Hopohopo”: A Virtual Reality Awareness Application about Social Anxiety Disorder

Virtual Reality (VR) applications play an increasingly important role in the context of mental health. While VR-based therapeutic interventions are increasingly well-established, the application of VR in the field of mental health awareness represents a novel and promising development. We present our mental health awareness VR application “hopohopo”, in which the user takes the perspective of a person suffering from social anxiety. Through the VR journey, the user experiences what it could feel like to suffer from constant fear of being negatively judged by others in everyday social situations. The aim of the application is to allow nonaffected users to expand their knowledge and to raise mental health awareness. In this paper, we report our findings on the potential of this immersive VR application for psychoeducation and destigmatization of social anxiety. In this respect, we provide the scope of our VR application and first results of its ongoing usability and usefulness evaluation process.
Usability and User Experience of Virtual Objects Supporting Learning and Communicating in Virtual Reality

This study aims to evaluate the usability and user experience of the InteractionSuitcase. This is a collection of virtual objects that have cultural connotations (associated with German or Anglo-American culture). They are intended to promote communication about cultural differences and similarities in English lessons at German schools. They are thus part of a didactic concept for using social VR for trans- and intercultural learning. Future teachers used the virtual objects during a practical seminar and rated them helpful to support trans- and intercultural encounters. Further, they associate a positive user experience. Since the virtual objects should encourage communication, the sense of connectedness instead of isolation is a meaningful result. The results proved the readiness of the InteractionSuitcase for further pedagogical applications.
Life-Death Interfaces: Tangible Ways of Legacy-Making, Grief, and Remembrance

In this paper, we present the results of a design project that investigated new ways of preparing for one’s own death, of remembering loved ones and of joining others in grief. These ways include a pillow for remembering the voices of loved ones, an hourglass containing human ashes as a ritual object for grieving, an additive monument for collective mourning, and a set of nested gift boxes for staying present in the life of a loved one after one’s death. We present our designs and initial user reactions. We discuss our findings, concluding that actively engaging with the difficult topic of death and dying can be rewarding.
Footprints Without Borders: An Interactive Installation for Tangible Communication About Consumption-Based CO₂ Emissions in an Exhibition Context

English Looking at consumption-based emissions is directly relevant to policy discussions. In order to develop effective climate strategies, climate policy needs to address the root causes, which requires an understanding of outsourced CO₂ emissions. Using a design science research methodology process, an interactive installation was developed to communicate scientifically collected data in an exhibition environment. A three-dimensional and tangible world map allows to explore territorial emissions in contrast to cosumption-based emissions visually and haptically. Evaluation findings indicate that the exhibit supports different levels of understanding according to Bloom's taxonomy. The developed design and interaction concept was evaluated in early user tests showing high pragmatic and very high hedonic qualities. Based on first results, the designed learning object offers an alternative approach to inform about outsourced emissions and to promote the discourse about effective climate strategies.
Investigating External Airflow and Reduced Room Temperature to Reduce Virtual Reality Sickness

Virtual Reality (VR) provides a new way for people to interact with technology, but it comes with its own challenges that can restrict its accessibility for the general public. Not everyone is able to use VR due to a phenomenon known as VR sickness, where users experience motion sickness like symptoms when using VR. This paper presents two studies that investigates the impact of introducing an external airflow and reducing room temperature on people during a VR experience. 33 participants were used across these studies who played the same VR game in each condition, a control, an airflow and a reduced room temperature. Our results show participants had a 28% reduction in average VR sickness with the external airflow, as compared against the control and reduced room temperature. Most participants also responded that they preferred the airflow condition the most, citing that it made them less sick and more comfortable.
Exploration of Electrochromics for Calm Reminder: A Customizable Bracelet

At the crossroad of calm computing and personalization technology, we present a calm reminder, taking the form of a customizable bracelet. The concept is based on a perpetual and automatic slow switching between two graphical states, only reminding personal values or actions to the user when fortuitously noticed. Switching and customization of the graphical states increases the reminding personal signification, aesthetics, and noticeability by providing a visual change to the user’s periphery. We report on building an actual prototype as well as a survey study (n = 62) and a workshop inspired by co-design practices (n = 17) to understand why and how this concept of calm reminder could be used.
Gaze-Based Authentication Method Using Graphical Passwords Featuring Keypoints

We propose a gaze-based authentication method using a new type of graphical password. After registering keypoints in an image as a secret, the user can unlock the device by gazing at them in the registered order on a randomly displayed image. We present the design and implementation of our authentication method and report the results of a feasibility study utilizing mobile devices. Our prototype implementation achieved an acceptance rate of 86.0%.
Exploring the Usage of Digital Technology for Active Play During the COVID-19 Pandemic Among Families with Young Children

Lockdowns in response to the COVID-19 pandemic have disrupted how family live, play, and stay active. Based on interviews and activity diaries with 8 families with young children (three to five years of age), this study investigated how COVID-19 affected active play in families as well as how digital technologies have been used to keep children active and well. Key challenges reported by families were limited access to outdoor spaces, upheaval of routines, increased screen-time, and struggles with the lack of boundaries between work and family lives. These challenges in turn led parents to invent new ways to keep their children to active. Parents used digital technologies to promote music and dance, imaginative play, and parent-facilitated play. Based on these findings, we discuss design opportunities to reframe the home context into an environment for active play.
Undergraduates’ Perception Towards Incorporating Video Repository with Institutional Repository at the Library, Trincomalee Campus, Eastern University, Sri Lanka: A need Analysis

Undergraduates of the Department of Languages and Communication Studies of the Faculty of Communication and Business Studies, Trincomalee Campus have to produce a digital story for the course ‘basic video production’. As the students’ productions are preserved only in the form of digital video discs, a lack of efficiency in preservation and retrieval was identified. Hence, the aim is to explore the preferences and perceptions of the undergraduates regarding initiating a video repository on the Institutional Repository. Therefore, the preferences and perceptions of the undergraduates on video repositories, the present status of retrieval and referencing of the videos produced by the undergraduates, and their willingness toward archiving the digital story produced by them on the Institutional Repository were analysed. A descriptive survey was carried out for data collection by self-administrated semi-structured questionnaires using Google forms. Data were analyzed using MS office Excel. According to the preferences and perceptions on video repositories, more than 72% prefer to refer to videos produced by previous batches of the undergraduates. According to the present status of the video referencing and retrieval only about 22% of them are aware of video repositories and only 1.6% have access to repositories related to their field. Further, their dissatisfaction regarding the current retrieval system, lack of access to other repositories and lack of availability of their preservation systems were also identified. Further, their positive attitude towards preserving their creation in the repositories was identified. However, utilizing cloud storage and requesting approval from creators to download the creation were recommended along with expanding this study with all other students and staff of the Trincomalee Campus.
Super-resolved synthetic aperture radar image reconstruction based on multiresolution fusion discrimination

Generative adversarial networks (GANs) are utilized for synthetic aperture radar (SAR) image super-resolution reconstruction, affording realistic texture details. However, existing GANs only discriminate the final generated high-resolution (HR) image after two consecutive upsampling processes, which ignore some high-frequency information of the reconstructed images. To resolve this issue, a multiresolution fusion discrimination (MRFD) algorithm is proposed to discriminate the reconstructed feature maps after each upsampling. First, a multiresolution discrimination process discriminates the authenticity of each upsampled feature map separately, which reduces the image distortion imposed during two consecutive upsampling processes. Besides, multiresolution feature fusion further preserves the consistent high-frequency texture structures. Finally, a multiscale dense network extracts image features in different scales, with multiscale dense block’s dense connections improving parameter utilization. The experimental results on a SAR dataset demonstrate that the proposed MRFD algorithm performs better in reconstructing the texture details of HR images.
Gradual System F

Bringing the benefits of gradual typing to a language with parametric polymorphism like System F, while preserving relational parametricity, has proven extremely challenging: first attempts were formulated a decade ago, and several designs have been recently proposed, with varying syntax, behavior, and properties. Starting from a detailed review of the challenges and tensions that affect the design of gradual parametric languages, this work presents an extensive account of the semantics and metatheory of GSF, a gradual counterpart of System F. In doing so, we also report on the extent to which the Abstracting Gradual Typing methodology can help us derive such a language. Among gradual parametric languages that follow the syntax of System F, GSF achieves a unique combination of properties. We clearly establish the benefits and limitations of the language, and discuss several extensions of GSF towards a practical programming language.
AANet: an attention-based alignment semantic segmentation network for high spatial resolution remote sensing images

In this paper, we present an efficient network to tackle three critical problems in high spatial resolution (HSR) remote sensing image segmentation: (i) feature misalignment, (ii) insufficient contextual information extraction and (iii) various class imbalance issues. In detail, we propose a novel Feature Alignment Block (FAB) to suppress misalignment issues with the guide of an anchor map. Further, to extract sufficient information, we design a Contextual Augmentation Block (CAB) to augment features of different semantic levels. Finally, we present an Annealing Online Hard Example Mining (AOHEM) strategy to handle the various class imbalance issues with a view to dynamically adjust the focus of the network. We apply the above proposed designs to FPN to form our Attention-based Alignment Network (AANet). Experimental results demonstrate that the proposed method achieves promising results on the challenging iSAID and Vaihingen datasets with a better trade-off between accuracy and complexity.
Explore the Conceptual Design of a Journal Sharing Application Platform Based on the "Internet Plus"

With the continuous improvement of material living standards, people's spiritual and cultural needs are also constantly improving. This article will be based on the current popular journal culture to explore, focus on the project background, market analysis, product development and other aspects of the shared concept application platform research, in order to create a comprehensive function, convenient operation, high usage, strong security, and leading strong application software.
The architecture of network virtual experiment environment based on cloud computing

In order to improve the user access success rate, cloud service success rate and experiment resource sharing and retrieval efficiency of network virtual experiment environment, a network virtual experiment environment architecture based on cloud computing was proposed. Through the CVM cloud virtual experiment platform and LIMP Laboratory integrated management platform, the system architecture of network virtual experiment environment is designed. Based on cloud computing, the access control algorithm of network virtual experiment environment is designed, and the resource sharing of network virtual experiment environment is realized by data mining technology. The experimental results show that the architecture proposed in this paper has a high success rate of user access and cloud service, and a good efficiency of experimental resource sharing and retrieval, indicating that the architecture proposed in this paper has a good application effect.
Perspective Chapter: Cryptocurrencies Effectiveness for Nature

The rise of cryptocurrencies based on Blockchain platforms have provided multiple solutions for social and nature projects supported by concerned investors with sustainable development initiatives. Speculation and unclear uses of a cryptocurrency plays a negative role for the projects they claim to support. A positive relationship between coin investors and supported projects must position the coin value on the scale of the community involvement among the coin and project issues, thus placing the project results above speculative moves. Coin nature and social based projects may include a decentralized autonomous organization (DAO), combined with a digital currency to contribute to social and nature improvements. This organization provides a framework for the engagement of investors, beneficiaries, and implementation partners, with results measured by reliable third parties. The potential funding from non fiduciary sources for sustainable development targets may be framed under the fundraising and financial solutions models, addressing the cryptocurrency volatility risks with responsible tokenomics in attention to transaction and regulatory issues. Overall, the more clear are the object and transaction issues of a nature conservation project supported by a currency, the more successful it will be in terms of nature and social improvements and the currency valuation for all parties involved.
Nonlinear system identification based on generalized adaptive exponential function link networks model

Extension functions based on the model of the trigonometric functional link networks (TFLN) and the adaptive exponential functional link networks (AEFLN) have been widely applied in nonlinear identification systems. However, these models are lack of cross-terms (multiple input sample and its past samples). This degrades their performance, especially in nonlinear systems containing strong nonlinear distortion. In this paper, we propose a generalized AEFLN model (GAEFLN) for nonlinear system identification. Since the GAEFLN contains sine, exponential, and cross-terms expansion function, its convergence characteristics will be significantly improved. Simulation results based on nonlinear system identification show that the performance of the proposed GAEFLN is superior to those of the TFLN and AEFLN.
Evaluation of Machine Learning Algorithms for the Classification of Lithology Using Geophysical Logs

Summary Classification of subsurface formation lithology from well log data is a significant task in geoscience, petroleum exploration and engineering. Presently, several machine learning algorithms have been implemented for lithology classification to improve the prediction accuracy. However, due to the complex geological conditions, such algorithms are hardly adopted for mineral deposits. In his paper, we evaluated three popular machine learning algorithms, such as the Support Vector Machine, Random Forest and Gradient Boosting Decision Tree. This study used the process of grid search and 10-fold cross-validation to optimize the hyperparameters of each model and avoid overfitting. The performance of each model is evaluated using metrics of accuracy, precision, recall and F1-score of predicted labels of lithology against the true labels. The results show that the Gradient Boosting Decision Tree model has better lithology classification performance, with a precision of 97.74%, recall of 98.67% and F1-score of 98.20% among other models. The interpretation of GBDT model shows that the order of features contributing to the lithology classification is VP &gt;Density &gt;Vs &gt; natural gamma. The study reveals that GBDT model can provide significant information for further exploration targeting of deep mineral deposits.
Structural and Geophysical Surveys Supported by Remote Sensing Data for Geo-Hazards Assessment In Highly Deformed Districts

Summary The present research introduces precautionary incorporated surveys for geo-hazards assessment and risks mitigation, especially for new urban areas situated in highly deformed districts. The present work focuses on delineating surface geological structures and their near-surface extension in selected new urban city from Cairo-Suez Province, Egypt. The study is started by outcrop investigation for determining the distributed rock units and delineating surface geologic structures considering their attitudes, types and geometrical parameters. This is followed by geoelectrical surveys measuring several 2D profiles along selected highly deformed areas applying advanced inversion approach. The geophysical results show a clear image for the near-surface faults extension as well as the near-surface shale layers distribution. Obviously, the applied inversion scheme enhances the subsurface structural boundaries mapping. Moreover, geotechnical characterization of soil is achieved via free swelling test for selected shale samples. The weighted overlay analysis GIS tool is used for constructing a geo-hazard map considering several risk factors including faults, drainage, seismicity, land use-land cover (LU-LC) and shale layers distribution. Additionally, a risk assessment map is produced based on degree of vulnerability which can be useful for future planning and urbanization. In particular, the results provide a preliminary scheme for sustainable development planning.
Factors Driving IoT Testing/V&amp;V Selection and Planning

The previous chapter introduced lessons learned in test planning and V&V, so this chapter defines the detailed factors that a project working on IoT should consider in their planning efforts. As shown in Figure 4-1, four factors combine to create a decision matrix for planning purposes. This chapter explains each factor quantitatively and defines IoT test planning levels. One way to start planning is to determine a factor score based on integrity levels, product maturity, organizational (Org) development (Dev) ability, project size, and the other factors defined in this chapter. This scoring can be used numerically as outlined in this chapter or as guidance using each factor taken together.
Self-Organizing Data Analytics (SODA): IoT Data Analytics, AI, and Statistics

After IV&V, testing, test environment, and STA concerns of the previous chapters, a final development and test support environment will be the tools and analysis used to understand the IoT data in support of engineering and particularly of testing (Part 1). In this chapter, I address the idea of self-organizing data analytics (SODA) [1]. Many testers and engineers have limited data analytics skills or experience, yet SODA can offer a positive path for them. This chapter outlines an environment to address this limitation but leads to better testing and better products and sales.