bluuebunny/crossref_metadata_2025 huggingface dataset:
Arthroscopic capsular shift surgery in patients with atraumatic shoulder joint instability: a randomised, placebo-controlled trial
Authors: Jaggi Anju , Herbert Robert D , Alexander Susan , Majed Addie , Butt David , Higgs Deborah , Rudge Will , Ginn Karen A
Published: June 2023

Objectives

To determine the effect of arthroscopic capsular shift surgery on pain and functional impairment for people with atraumatic shoulder (glenohumeral) joint instability.

Methods

We conducted a randomised, placebo-controlled clinical trial in a specialist secondary care facility. Patients aged 18 years and over who reported insecurity (apprehension) in their shoulder joint and had evidence of capsulolabral damage on arthroscopic examination were included. Patients were excluded if their shoulder apprehension symptoms were precipitated by a high velocity shoulder injury, they had bony or neural damage, a rotator cuff or labral tear, or previous surgery on the symptomatic shoulder. Sixty-eight participants were randomised and received diagnostic arthroscopy, followed by arthroscopic capsular shift or diagnostic arthroscopy alone. All participants received the same postoperative clinical care. The primary outcome was pain and functional impairment measured with the Western Ontario Shoulder Instability Index. The prespecified minimum clinically important effect was a reduction in pain and disability of 10.4 points.

Results

Mean reductions in pain and functional impairment for both groups were similar. Compared with diagnostic arthroscopy, arthroscopic capsular shift increased pain and functional impairment by means of 5 points (95% CI −6 to 16 points) at 6 months, 1 point (95% CI −11 to 13 points) at 12 months and 2 points (95% CI −12 to 17 points) at 24 months.

Conclusions

Compared with diagnostic arthroscopy alone, arthroscopic capsular shift confers, at best, only minimal clinically important benefit in the medium term.

Trial registration number

NCT01751490.
Lessons learnt in scaling up evidence-based comprehensive health sector responses addressing female genital mutilation in highly prevalent settings
Authors: Ahmed Wisal , Gebretsadik Eyob , Gbenou Dina , Hien Yelmali , Dramou Bernadette , Ambelu Haimanot , Hussein Hiba , Lavussa Joyce , Mamo Anabay , Cherono Matilda , Kessi Mary , El Hennawy Hala , Gholbzouri Karima , Ouedraogo Leopold , Chilanga Asmani , Elamin Hayfa , Pallitto Christina
Published: June 2023

Female genital mutilation (FGM) affects over 200 million girls and women. Its health complications include acute and potentially lifelong urogenital, reproductive, physical, mental health complications with estimated health treatment costs of US$1.4 billion per year. Moreover, there is a concerning rise in the trend of FGM medicalisation with almost one in five FGM cases being performed by a health worker.

The WHO developed several evidence-based resources to apply a comprehensive health approach to strengthen FGM prevention and care services. However, there has been limited uptake of this comprehensive approach in FGM prevalent settings. To address this, a three-step multicountry participatory process was used to engage health sector players from FGM prevalent settings to develop comprehensive action plans, implement foundational activities and harness the learnings to inform subsequent planning and implementation. Support to adapt evidence-based resources and seed funding were also provided to initiate foundational activities that had potential for scale up.

A total of 15 countries participated in this three-step this approach between 2018 and 2022. Ten countries developed comprehensive national action plans and eight WHO resources were adapted for foundational activities.

This scale up approach can be strengthened by increasing the frequency of multicountry experience sharing meetings, identifying in-country champions to continuously advocate for FGM integration within (public and private) health services and securing stable funding to implement foundational activities. Documentation of each country’s experience as case studies including monitoring and evaluation are essential to expand the learning and quality of the health interventions addressing FGM.
Secular trends in premature and early menopause in low-income and middle-income countries
Authors: Leone Tiziana , Brown Laura , Gemmill Alison
Published: June 2023

Background

While secular trends in high-income countries show an increase in the mean age at menopause, it is unclear if there is a similar pattern in low-income and middle-income countries (LMICs), where women’s exposure to biological, environmental and lifestyle determinants of menopause may differ. Premature (before age 40 years) and early (ages 40–44 years) menopause could have negative repercussions on later life health outcomes which in ageing societies could mean further stress on low-resource health systems. An evaluation of such trends in LMICs has been hampered by the suitability, quality and comparability of data from these countries.

Methods

Using 302 standardised household surveys from 1986 to 2019, we estimate trends and CIs using bootstrapping in the prevalence of premature and early menopause in 76 LMICs. We also developed a summary measure of age at menopause for women who experience menopause before the age of 50 years based on demographic estimation methods that can be used to measure menopausal status in surveys with truncated data.

Results

Trends indicate an increasing prevalence of early and premature menopause in LMICs, in particular in sub-Saharan Africa and South/Southeast Asia. These regions also see a suggested decline of the mean age at menopause with greater variation across continents.

Conclusions

This study enables the analysis of menopause timing by exploiting data generally used for the study of fertility by methodologically allowing the use of truncated data. Findings show a clear increase in prevalence of premature and early menopause in the regions with the highest fertility with possible consequences for later life health. They also show a different trend compared with high-income regions, confirming a lack of generalisability and the importance of accounting for nutritional and health transitions at the local level. This study calls for further data and research on menopause on a global scale.
Incidence and prevalence of interstitial lung diseases worldwide: a systematic literature review
Authors: Shah Gupta Rikisha , Koteci Ardita , Morgan Ann , George Peter M , Quint Jennifer K
Published: June 2023

Interstitial lung disease (ILD) is a collective term representing a diverse group of pulmonary fibrotic and inflammatory conditions. Due to the diversity of ILD conditions, paucity of guidance and updates to diagnostic criteria over time, it has been challenging to precisely determine ILD incidence and prevalence. This systematic review provides a synthesis of published data at a global level and highlights gaps in the current knowledge base. Medline and Embase databases were searched systematically for studies reporting incidence and prevalence of various ILDs. Randomised controlled trials, case reports and conference abstracts were excluded. 80 studies were included, the most described subgroup was autoimmune-related ILD, and the most studied conditions were rheumatoid arthritis (RA)-associated ILD, systemic sclerosis associated (SSc) ILD and idiopathic pulmonary fibrosis (IPF). The prevalence of IPF was mostly established using healthcare datasets, whereas the prevalence of autoimmune ILD tended to be reported in smaller autoimmune cohorts. The prevalence of IPF ranged from 7 to 1650 per 100 000 persons. Prevalence of SSc ILD and RA ILD ranged from 26.1% to 88.1% and 0.6% to 63.7%, respectively. Significant heterogeneity was observed in the reported incidence of various ILD subtypes. This review demonstrates the challenges in establishing trends over time across regions and highlights a need to standardise ILD diagnostic criteria.PROSPERO registration number: CRD42020203035.
Development of carbon materials for sulfur cathodes in inorganic-based solid-state lithium–sulfur batteries
Authors: Yang Zhao , Yu Qihang , Thinphang-nga Anna , Li Xia
Published: June 2023

Inorganic-based solid-state lithium–sulfur batteries (SSLSBs) with high energy density and high safety have attracted wide attention as they are one of the most promising energy storage devices to meet future market requirements. However, the development of SSLSBs faces various challenges due to the unreasonable structural design in sulfur cathodes. Carbon is one of the indispensable components in sulfur cathodes. The rational design of carbon materials becomes an important strategy to address the challenges in sulfur cathodes. This review summarizes recent literature about the design and application of carbon materials for sulfur cathodes in inorganic-based SSLSBs. It starts with the introduction of different carbon materials from zero-dimensional to three-dimensional carbon materials. Particularly, this review paper highlights the structural design of carbon materials and the cathode fabrication methods, toward improving the conductivity of cathodes, buffering volume changes in cathodes, reducing interfacial resistance among cathode components, and increasing the mass loading of active materials. Finally, the existing challenges and promising solutions for carbon materials in the cathodes are discussed and proposed.
Chemically reactive MHD peristaltic flow of Jeffrey nanofluid via a vertical porous conduit with complaint walls under the effects of bioconvection and double diffusion
Authors: Ajithkumar M. , Lakshminarayana P.
Published: June 2023

Inspired by significant physiological applications of peristaltic transport, a mathematical model for the peristaltic pumping of a Jeffrey nanofluid containing gyrotactic microorganisms through a flexible and vertical symmetric channel is investigated, along with the effects of thermal radiation, heat source/sink, chemical reaction, porous medium and Hall current. Swimming microorganisms in a model of non-Newtonian fluid has several biological and ecological benefits, including in the pharmaceutical industry, biofertilizers, biofuel technology, biosensors, etc. With these presumptions in mind, a conducting Jeffery fluid flow containing microorganisms via a porous vertical conduit is proposed. The expressions for the flow quantities are established by solving the governing equations of this study analytically using the homotopy perturbation method (HPM). A detailed examination is performed using the tables and graphical representations to comprehend the impact of significant components on this analysis. In addition, the results for Newtonian nanofluid are obtained as a special case and discovered that its velocity is lower than the Jeffrey nanofluid. It is observed that the best velocity distribution can be seen at larger thermal Grashof number and Darcy number. The size of the trapped bolus enhances with the increase of the Hall parameter. Furthermore, the density of the motile microorganism distribution declines for higher bioconvection Peclet number and bioconvection constant.
Signatures of two- and three-dimensional semimetals from circular dichroism
Authors: Mandal Ipsita
Published: June 2023

Topological invariants are crucial quantities for classifying materials with topological phases. Hence, their connections with experimentally measurable quantities are extremely important. In this context, circular dichroism (CD) provides a protocol to detect the Chern number [Formula: see text] of the lowest energy Bloch band (LBB) of a semimetal. This hinges on the unequal depletion rates of the Bloch electrons from a filled LBB, under the action of a time-periodic circular drive, depending on the chirality of the polarization. According to the dimensionality of the system (i.e., whether it is two- or three-dimensional), the integrated differential rate for depletion has to be formulated a bit differently in order to relate it to [Formula: see text]. Our aim is to capture the nature of the CD response for semimetals with anisotropic band dispersions. We show that while the quantization of the CD response for the three-dimensional cases is strongly sensitive to anisotropy, the two-dimensional counterparts show a perfectly quantized response.
Many Worlds and the Vacuum Energy Problem
Authors: Williams Hollis
Published: June 2023

We suggest that it might be possible to resolve the vacuum energy problem by assuming the reality of a many worlds interpretation of quantum mechanics. The suggested resolution is that the enormous theoretical prediction for the vacuum energy density is actually the value distributed across all the parallel universes in a superposition. It is assumed that branching of all the universes into a larger superposition is a physical process which is extremely rare, but which has occurred sufficiently often since the Big Bang that the discrepancy for the experimentally measured value of the energy density can be explained.
Career perceptions and professional plans of undergraduate students majoring in preventive medicine since the COVID-19 pandemic in China: A survey-based cross-sectional study
Authors: Wang Yan , Zhang Mo , Zhang Tao , Deng Yang , Wang Fang , Wang Ling , Du Yan
Published: June 2023

College students majoring in preventive medicine are the future main task force in combating the COVID-19 pandemic. This cross-sectional study aimed to evaluate career perceptions and professional plans of these students after the COVID-19 pandemic in China. A total of 372 (response rate: 93%) participants completed the survey. We observed that after the admission, students reported better comprehension of the preventive medicine major and employment prospect (dependent t-test: [Formula: see text] vs. [Formula: see text], [Formula: see text]). The overall career perceptions of undergraduate students majoring in preventive medicine were positive [Formula: see text]. Especially for junior (Grades 1 and 2) students, which were more willing to recommend preventive medicine major to prospective students compared with senior (Grades 3–5) students. Junior students were also more enthusiastic about learning professional knowledge, had a stronger belief that their employment prospective would become better, and were more willing to pursue a relevant career. More than three-quarters (287, 78.85%) of the students tended to pursue graduate education, and the majority (228, 62.47%) of them preferred public institutions as future employers. With the increasing society-wide recognition of the importance of public health in epidemic prevention and control, the confidence of undergraduate students majoring in preventive medicine has been improved.
ConvBoost
Authors: Shao Shuai , Guan Yu , Zhai Bing , Missier Paolo , Plötz Thomas
Published: June 2023

Human activity recognition (HAR) is one of the core research themes in ubiquitous and wearable computing. With the shift to deep learning (DL) based analysis approaches, it has become possible to extract high-level features and perform classification in an end-to-end manner. Despite their promising overall capabilities, DL-based HAR may suffer from overfitting due to the notoriously small, often inadequate, amounts of labeled sample data that are available for typical HAR applications. In response to such challenges, we propose ConvBoost -- a novel, three-layer, structured model architecture and boosting framework for convolutional network based HAR. Our framework generates additional training data from three different perspectives for improved HAR, aiming to alleviate the shortness of labeled training data in the field. Specifically, with the introduction of three conceptual layers--Sampling Layer, Data Augmentation Layer, and Resilient Layer--we develop three "boosters"--R-Frame, Mix-up, and C-Drop--to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively. These new conceptual layers and boosters, that are universally applicable for any kind of convolutional network, have been designed based on the characteristics of the sensor data and the concept of frame-wise HAR. In our experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, GOTOV) we demonstrate the effectiveness of our ConvBoost framework for HAR applications based on variants of convolutional networks: vanilla CNN, ConvLSTM, and Attention Models. We achieved substantial performance gains for all of them, which suggests that the proposed approach is generic and can serve as a practical solution for boosting the performance of existing ConvNet-based HAR models. This is an open-source project, and the code can be found at https://github.com/sshao2013/ConvBoost
HyWay
Authors: Vijay Harsh , Pushp Saumay , Mittal Amish , Gupta Praveen , Gupta Meghna , Gambhira Sirish , Chopra Shivang , Baranwal Mayank , Arya Arshia , Manchepalli Ajay , Padmanabhan Venkata N.
Published: June 2023

We present HyWay, short for "Hybrid Hallway", to enable mingling and informal interactions among physical and virtual users, in casual spaces and settings, such as office water cooler areas, conference hallways, trade show floors, and more. We call out how the hybrid and unstructured (or semi-structured) nature of such settings set these apart from the all-virtual and/or structured settings considered in prior work. Key to the design of HyWay is bridging the awareness gap between physical and virtual users, and providing the virtual users the same agency as physical users.

To this end, we have designed HyWay to incorporate reciprocity (users can see and hear others only if they can be seen and heard), porosity (conversations in physical space are porous and not within airtight compartments), and agency (the ability for users to seamlessly move between conversations). We present our implementation of HyWay and the user survey findings from multiple deployments in unstructured settings (e.g., social gatherings), and semi-structured ones (e.g., a poster event). Results from these deployments show that HyWay enables effective mingling between physical and virtual users.
On the Long-Term Effects of Continuous Keystroke Authentication
Authors: Huh Jun Ho , Kwag Sungsu , Kim Iljoo , Popov Alexandr , Park Younghan , Cho Geumhwan , Lee Juwon , Kim Hyoungshick , Lee Choong-Hoon
Published: June 2023

One of the main challenges in deploying a keystroke dynamics-based continuous authentication scheme on smartphones is ensuring low error rates over time. Unstable false rejection rates (FRRs) would lead to frequent phone locks during long-term use, and deteriorating attack detection rates would jeopardize its security benefits. The fact that it is undesirable to train complex deep learning models directly on smartphones or send private sensor data to servers for training present unique deployment constraints, requiring on-device solutions that can be trained fully on smartphones.

To improve authentication accuracy while satisfying such real-world deployment constraints, we propose two novel feature engineering techniques: (1) computation of pair-wise correlations between accelerometer and gyroscope sensor values, and (2) on-device feature extraction technique to compute dynamic time warping (DTW) distance measurements between autoencoder inputs and outputs via transfer-learning. Using those two feature sets in an ensemble blender, we achieved 6.4 percent equal error rate (EER) in a public dataset. In comparison, blending two state-of-the-art solutions achieved 14.1 percent EER in the same test settings. Our real-world dataset evaluation showed increasing FRRs (user frustration) over two months; however, through periodic model retraining, we were able to maintain average FRRs around 2.5 percent while keeping attack detection rates around 89 percent. The proposed solution has been deployed in the latest Samsung Galaxy smartphone series to protect secure workspace through continuous authentication.
Automatic Update for Wi-Fi Fingerprinting Indoor Localization via Multi-Target Domain Adaptation
Authors: Wang Jiankun , Zhao Zenghua , Ou Mengling , Cui Jiayang , Wu Bin
Published: June 2023

Wi-Fi fingerprinting system in the long term suffers from gradually deteriorative localization accuracy, leading to poor user experiences. To keep high accuracy yet at a low cost, we first study long-term variation of access points (APs) and characteristics of their Wi-Fi signals through over-one-year experiments. Motivated by the experimental findings, we then design MTLoc, a Multi-Target domain adaptation network-based Wi-Fi fingerprinting Localization system. As the core, MTDAN (Multi-Target Domain Adaptation Network) model adopts the framework of generative adversarial network to learn time-invariant, time-specific, and location-aware features from the source and target domains. To enhance the alignment among the source and targets, two-level cycle consistency constraints are proposed. Hence, MTDAN is able to transfer location knowledge from the source domain to multiple targets. In addition, domain selection and outlier detection are designed to avoid explosive growth of storage for targets and to limit the impact of random variations of Wi-Fi signals. Extensive experiments are carried out on five datasets collected over two years in various real-world indoor environments with a total area of 8, 350 m2. Experimental results demonstrate that MTLoc retains high localization accuracy with limited storage and training cost in the long term, which significantly outperforms its counterparts. We share our dataset to the community for other researchers to validate our results and conduct further research.
Supporting Solar Energy Coordination among Communities
Authors: Panagiotidou Georgia , Costanza Enrico , Fell Michael J. , Samanani Farhan , Knox Hannah
Published: June 2023

The transition to renewable energy is likely to require the creation of growing numbers of energy communities: collectives organized around shared, local renewable resources. Unlike individual households however, the requirements for such communities to share a resource and demand-shift their consumption are still unexplored. By deploying a custom sensor energy monitoring kit and data physicalization workshops with 17 households, we examine the factors that impact their coordination around the shared resource. We found that collective demand-shifting has an extended set of considerations including trade-offs related to privacy, flexibility and social cohesion which are core for navigating already delicate neighborly relations. We use these factors to propose design considerations for a digital system that can act as a mediator among households. Such a system should enable multiple levels of immediacy to account for people's routines, should have adjustable levels of privacy to balance policing and fairness and should be able to offload some of the mundane decision-making. This study moves beyond individual energy consumption behavior to help identify energy as a collective issue that demands collective action. Accordingly, our findings contribute to the development of a next generation of Ubicomp technologies that can support collective action for environmental sustainability.
LemurDx
Authors: Arakawa Riku , Ahuja Karan , Mak Kristie , Thompson Gwendolyn , Shaaban Sam , Lindhiem Oliver , Goel Mayank
Published: June 2023

Hyperactivity is the most dominant presentation of Attention-Deficit/Hyperactivity Disorder in young children. Currently, measuring hyperactivity involves parents' or teachers' reports. These reports are vulnerable to subjectivity and can lead to misdiagnosis. LemurDx provides an objective measure of hyperactivity using passive mobile sensing. We collected data from 61 children (25 with hyperactivity) who wore a smartwatch for up to 7 days without changing their daily routine. The participants' parents maintained a log of the child's activities at a half-hour granularity (e.g., sitting, exercising) as contextual information. Our ML models achieved 85.2% accuracy in detecting hyperactivity in children (using parent-provided activity labels). We also built models that estimated children's context from the sensor data and did not rely on activity labels to reduce parent burden. These models achieved 82.0% accuracy in detecting hyperactivity. In addition, we interviewed five clinicians who suggested a need for a tractable risk score that enables analysis of a child's behavior across contexts. Our results show the feasibility of supporting the diagnosis of hyperactivity by providing clinicians with an interpretable and objective score of hyperactivity using off-the-shelf watches and adding no constraints to children or their guardians.
Fingerprinting IoT Devices Using Latent Physical Side-Channels
Authors: Feng Justin , Zhao Tianyi , Sarkar Shamik , Konrad Dominic , Jacques Timothy , Cabric Danijela , Sehatbakhsh Nader
Published: June 2023

The proliferation of low-end low-power internet-of-things (IoT) devices in "smart" environments necessitates secure identification and authentication of these devices via low-overhead fingerprinting methods. Previous work typically utilizes characteristics of the device's wireless modulation (WiFi, BLE, etc.) in the spectrum, or more recently, electromagnetic emanations from the device's DRAM to perform fingerprinting. The problem is that many devices, especially low-end IoT/embedded systems, may not have transmitter modules, DRAM, or other complex components, therefore making fingerprinting infeasible or challenging. To address this concern, we utilize electromagnetic emanations derived from the processor's clock to fingerprint. We present Digitus, an emanations-based fingerprinting system that can authenticate IoT devices at range. The advantage of Digitus is that we can authenticate low-power IoT devices using features intrinsic to their normal operation without the need for additional transmitters and/or other complex components such as DRAM. Our experiments demonstrate that we achieve ≥ 95% accuracy on average, applicability in a wide range of IoT scenarios (range ≥ 5m, non-line-of-sight, etc.), as well as support for IoT applications such as finding hidden devices. Digitus represents a low-overhead solution for the authentication of low-end IoT devices.
Scalability in External Communication of Automated Vehicles
Authors: Colley Mark , Britten Julian , Rukzio Enrico
Published: June 2023

Automated vehicles will alter traffic fundamentally. While users can engage in non-driving-related tasks such as reading or even sleeping, the possibility to interact with other road users such as pedestrians via, for example, eye contact vanishes. Therefore, external communication of automated vehicles is currently researched with various concepts spanning dimensions such as anthropomorphism, technology, viewpoint, locus, message type, and others. However, the proposed concepts are mostly evaluated in simple scenarios, such as one person trying to cross in front of one automated vehicle. Therefore, we implemented a WebGL application of a four-lane road and conducted a within-subject study (N=46) to study the effects of nine concepts with and without the presence of other pedestrians and altering the yielding target of the automated vehicle. We found that all concepts were rated better than having no external communication. However, the effects were not uniform across the concepts.
Understanding In-Situ Programming for Smart Home Automation
Authors: Liu Xiaoyi , Shi Yingtian , Yu Chun , Gao Cheng , Yang Tianao , Liang Chen , Shi Yuanchun
Published: June 2023

Programming a smart home is an iterative process in which users configure and test the automation during the in-situ experience with IoT space. However, current end-user programming mechanisms are primarily preset configurations on GUI and fail to leverage in-situ behaviors and context. This paper proposed in-situ programming (ISP) as a novel programming paradigm for AIoT automation that extensively leverages users' natural in-situ interaction with the smart environment. We built a Wizard-of-Oz system and conducted a user-enactment study to explore users' behavior models in this paradigm. We identified a dynamic programming flow in which participants iteratively configure and confirm through query, control, edit, and test. We especially identified a novel method "snapshot" for automation configuration and a novel method "simulation" for automation testing, in which participants leverage ambient responses and in-situ interaction. Based on our findings, we proposed design spaces on dynamic programming flow, coherency and clarity of interface, and state and scene management to build an ideal in-situ programming experience.
DAPPER
Authors: Gong Taesik , Kim Yewon , Orzikulova Adiba , Liu Yunxin , Hwang Sung Ju , Shin Jinwoo , Lee Sung-Ju
Published: June 2023

Many applications utilize sensors in mobile devices and machine learning to provide novel services. However, various factors such as different users, devices, and environments impact the performance of such applications, thus making the domain shift (i.e., distributional shift between the training domain and the target domain) a critical issue in mobile sensing. Despite attempts in domain adaptation to solve this challenging problem, their performance is unreliable due to the complex interplay among diverse factors. In principle, the performance uncertainty can be identified and redeemed by performance validation with ground-truth labels. However, it is infeasible for every user to collect high-quality, sufficient labeled data. To address the issue, we present DAPPER (Domain AdaPtation Performance EstimatoR) that estimates the adaptation performance in a target domain with only unlabeled target data. Our key idea is to approximate the model performance based on the mutual information between the model inputs and corresponding outputs. Our evaluation with four real-world sensing datasets compared against six baselines shows that on average, DAPPER outperforms the state-of-the-art baseline by 39.8% in estimation accuracy. Moreover, our on-device experiment shows that DAPPER achieves up to 396x less computation overhead compared with the baselines.
Touch-and-Heal
Authors: Guo Shihui , Zhan Lishuang , Cao Yancheng , Zheng Chen , Zhou Guyue , Gong Jiangtao
Published: June 2023

Affective touch plays an important role in human-robot interaction. However, it is challenging for robots to perceive various natural human tactile gestures accurately, and feedback human intentions properly. In this paper, we propose a data-driven affective computing system based on a biomimetic quadruped robot with large-format, high-density flexible pressure sensors, which can mimic the natural tactile interaction between humans and pet dogs. We collect 208-minute videos from 26 participates and construct a dataset of 1212 human gestures-dog actions interaction sequences. The dataset is manually annotated with an 81-tactile-gesture vocabulary and a 44-corresponding-dog-reaction vocabulary, which are constructed through literature, questionnaire, and video observation. Then, we propose a deep learning algorithm pipeline with a gesture classification algorithm based on ResNet and an action prediction algorithm based on Transformer, which achieve the classification accuracy of 99.1% and the 1-gram BLEU score of 0.87 respectively. Finally, we conduct a field study to evaluate the emotion regulation effects through tactile affective interaction, and compare it with voice interaction. The results show that our system with tactile interaction plays a significant role in alleviating user anxiety, stimulating user excitement and improving the acceptability of robotic dogs.
Exploring Smart Standing Desks to Foster a Healthier Workplace
Authors: Haliburton Luke , Kheirinejad Saba , Schmidt Albrecht , Mayer Sven
Published: June 2023

Sedentary behavior is endemic in modern workplaces, contributing to negative physical and mental health outcomes. Although adjustable standing desks are increasing in popularity, people still avoid standing. We developed an open-source plug-and-play system to remotely control standing desks and investigated three system modes with a three-week in-the-wild user study (N=15). Interval mode forces users to stand once per hour, causing frustration. Adaptive mode nudges users to stand every hour unless the user has stood already. Smart mode, which raises the desk during breaks, was the best rated, contributing to increased standing time with the most positive qualitative feedback. However, non-computer activities need to be accounted for in the future. Therefore, our results indicate that a smart standing desk that shifts modes at opportune times has the most potential to reduce sedentary behavior in the workplace. We contribute our open-source system and insights for future intelligent workplace well-being systems.
TelecomTM
Authors: Liu Jingxiao , Yuan Siyuan , Dong Yiwen , Biondi Biondo , Noh Hae Young
Published: June 2023

We introduce the TelecomTM system that uses pre-existing telecommunication fiber-optic cables as virtual strain sensors to sense vehicle-induced ground vibrations for fine-grained and ubiquitous traffic monitoring and characterization. Here we call it a virtual sensor because it is a software-based representation of a physical sensor. Due to the extensively installed telecommunication fiber-optic cables at the roadside, our system using redundant dark fibers enables to monitor traffic at low cost with low maintenance. Many existing traffic monitoring approaches use cameras, piezoelectric sensors, and smartphones, but they are limited due to privacy concerns and/or deployment requirements. Previous studies attempted to use telecommunication cables for traffic monitoring, but they were only exploratory and limited to simple tasks at a coarse granularity, e.g., vehicle detection, due to their hardware constraints and real-world challenges. In particular, those challenges are 1) unknown and heterogeneous properties of virtual sensors and 2) large and complex noise conditions. To this end, our TelecomTM system first characterizes the geographic location and analyzes the signal pattern of each virtual sensor through driving tests. We then develop a spatial-domain Bayesian filtering and smoothing algorithm to detect, track, and characterize each vehicle. Our approach uses the spatial dependency of multiple virtual sensors and Newton's laws of motion to combine the distributed sensor data to reduce uncertainties in vehicle detection and tracking. In our real-world evaluation on a two-way traffic road with 1120 virtual sensors, TelecomTM achieved 90.18% vehicle detection accuracy, 27x and 5x error reduction for vehicle position and speed tracking compared to a baseline method, and ±3.92% and ±11.98% percent error for vehicle wheelbase and weight estimation, respectively.
TouchKey
Authors: Miao Yuchen , Gu Chaojie , Yan Zhenyu , Chau Sze Yiu , Tan Rui , Lin Qi , Hu Wen , He Shibo , Chen Jiming
Published: June 2023

Secure device pairing is important to wearables. Existing solutions either degrade usability due to the need of specific actions like shaking, or they lack universality due to the need of dedicated hardware like electrocardiogram sensors. This paper proposes TouchKey, a symmetric key generation scheme that exploits the skin electric potential (SEP) induced by powerline electromagnetic radiation. The SEP is ubiquitously accessible indoors with analog-to-digital converters widely available on Internet of Things devices. Our measurements show that the SEP has high randomness and the SEPs measured at two close locations on the same human body are similar. Extensive experiments show that TouchKey achieves a high key generation rate of 345 bit/s and an average success rate of 99.29%. Under a range of adversary models including active and passive attacks, TouchKey shows a low false acceptance rate of 0.86%, which outperforms existing solutions. Besides, the overall execution time and energy usage are 0.44 s and 2.716 mJ, which make it suitable for resource-constrained devices.
CircuitGlue
Authors: Lambrichts Mannu , Ramakers Raf , Hodges Steve , Devine James , Underwood Lorraine , Finney Joe
Published: June 2023

We present CircuitGlue, an electronic converter board that allows heterogeneous electronic components to be readily interconnected. Electronic components are plugged into an eight-pin programmable header on the board, and the assignment of each pin in the header is configured in software. CircuitGlue supports a variety of connections, including power, ground, analog signals, and various digital protocols at different voltages. As such, off-the-shelf electronic components and modules are instantly compatible no matter what voltage levels, interface types, communication protocols, and pinouts they use. In this paper, we demonstrate the use of CircuitGlue to ease and expedite prototyping with electronics and we explore new opportunities enabled by CircuitGlue. Finally, we reflect on the results of a preliminary user study evaluating the usability of CircuitGlue for people new to electronics.
VoiceCloak
Authors: Chen Meng , Lu Li , Wang Junhao , Yu Jiadi , Chen Yingying , Wang Zhibo , Ba Zhongjie , Lin Feng , Ren Kui
Published: June 2023

Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma when enjoying the utility of voice services. Existing machine-centric studies employ direct modification or text-based re-synthesis to de-identify users' voices but cause inconsistent audibility for human participants in emerging online communication scenarios, such as virtual meetings. In this paper, we propose a human-centric voice de-identification system, VoiceCloak, which uses adversarial examples to balance the privacy and utility of voice services. Instead of typical additive examples inducing perceivable distortions, we design a novel convolutional adversarial example that modulates perturbations into real-world room impulse responses. Benefiting from this, VoiceCloak could preserve user identity from exposure by Automatic Speaker Identification (ASI), while remaining the voice perceptual quality for non-intrusive de-identification. Moreover, VoiceCloak learns a compact speaker distribution through a conditional variational auto-encoder to synthesize diverse targets on demand. Guided by these pseudo targets, VoiceCloak constructs adversarial examples in an input-specific manner, enabling any-to-any identity transformation for robust de-identification. Experimental results show that VoiceCloak could achieve over 92% and 84% successful de-identification on mainstream ASIs and commercial systems with excellent voiceprint consistency, speech integrity, and audio quality.
Navigating the Data Avalanche
Authors: Ekambaranathan Anirudh , Zhao Jun , Chalhoub George
Published: June 2023

This paper critically examines the intersection of privacy concerns in children's apps and the support required by developers to effectively address these concerns. Third-party libraries and software development kits (SDKs) are widely used in mobile app development, however, these libraries are commonly known for posing significant data privacy risks to users. Recent research has shown that app developers for children are particularly struggling with the lack of support in navigating the complex market of third-party SDKs. The support needed for developers to build privacy-friendly apps is largely understudied. Motivated by the needs of developers and an empirical analysis of 137 'expert-approved' children's apps, we designed DataAvalanche.io, a web-based tool to support app developers in navigating the privacy and legal implications associated with common third-party SDKs on the market. Through semi-structured interviews with 12 app developers for children, we demonstrate that app developers largely perceive the transparency supported by our tool positively. However, they raised several barriers, including the challenges of adopting privacy-friendly alternatives and the struggle to safeguard their own legal interests when facing the imbalance of power in the app market. We contribute to our understanding of the open challenges and barriers faced by app developers in creating privacy-friendly apps for children and provide critical future design and policy directions.
BMAR
Authors: Meier Manuel , Holz Christian
Published: June 2023

A requirement of cross-modal signal processing is accurate signal alignment. Though simple on a single device, accurate signal synchronization becomes challenging as soon as multiple devices are involved, such as during activity monitoring, health tracking, or motion capture---particularly outside controlled scenarios where data collection must be standalone, low-power, and support long runtimes. In this paper, we present BMAR, a novel synchronization method that operates purely based on recorded signals and is thus suitable for offline processing. BMAR needs no wireless communication between devices during runtime and does not require any specific user input, action, or behavior. BMAR operates on the data from devices worn by the same person that record barometric pressure and acceleration---inexpensive, low-power, and thus commonly included sensors in today's wearable devices. In its first stage, BMAR verifies that two recordings were acquired simultaneously and pre-aligns all data traces. In a second stage, BMAR refines the alignment using acceleration measurements while accounting for clock skew between devices. In our evaluation, three to five body-worn devices recorded signals from the wearer for up to ten hours during a series of activities. BMAR synchronized all signal recordings with a median error of 33.4 ms and reliably rejected non-overlapping signal traces. The worst-case activity was sleeping, where BMAR's second stage could not exploit motion for refinement and, thus, aligned traces with a median error of 3.06 s.
Towards a Dynamic Fresnel Zone Model to WiFi-based Human Activity Recognition
Authors: Liu Jinyi , Li Wenwei , Gu Tao , Gao Ruiyang , Chen Bin , Zhang Fusang , Wu Dan , Zhang Daqing
Published: June 2023

The passive WiFi sensing research has largely centered on activity sensing using fixed-location WiFi transceivers, leading to the development of several theoretical models that aim to map received WiFi signals to human activity. Of these models, the Fresnel zone model has shown to be particularly noteworthy. However, the growing popularity of mobile WiFi receivers has not been matched by corresponding research on mobile receiver-based theoretical models. This paper fills this gap by presenting the first theoretical model to quantify the impact of moving a moving receiver for WiFi sensing. We propose a novel dynamic Fresnel zone model in the free space of an indoor environment, which takes the form of a cluster of concentric hyperbolas centered on the transmitter and reflection subject. We examine three properties of this model, i.e., relating the variation in RF signals received by the receiver to the position and orientation of the human, the movement of the receiver, and the presence of other objects in the environment. To validate this model, we develop a prototype system and conduct extensive experiments. The results are consistent with our theoretical analysis, and the system is able to detect the direction of the transmitter with an accuracy of 10° or better, measure the receiver's relative motion displacement within 1 cm a millimeter-level accuracy, and classify five receiver-side activities with an accuracy of 98%. Our work moves a significant step forward in WiFi sensing and may potentially open up new avenues for future research.
Techno-Economic Optimization of Flow Batteries Using the Optimization Potential to Prioritize Different Optimization Possibilities
Authors: Gerlach Daniel , Noack Jens , Bischof Katharina , Le Boulch Chloé , Trupp Sabine
Published: June 2023

In order to achieve a successful broad market entry of redox flow batteries, not only the technical performance but also the economic efficiency of the system is important. Known flow batteries must therefore be optimized techno-economically for specific applications. Not every technically possible improvement has the same positive influence on the economically relevant performance or energy-related specific costs. With the help of an optimization potential as a value, prioritizations of the optimization approaches possible in concrete cases can be obtained. Extensive comparative measurements on redox flow batteries based on vanadium and on methyl viologen and TEMPO served to establish a data basis for all input values for the model presented here. The data came from measurements of laboratory batteries, since only from these real numerical values for the costs could be obtained. The theoretical model developed herein can be used as a profound basis in other investigations with e.g. industrial batteries in order to be able to carry out goal-oriented optimization and more realistic comparisons.
Graphene Oxide Addition Induces the Improvement of ORR Catalysis Properties of Low-Cost Mn-Based Catalyst Used for Al-Air Battery
Authors: Liu Yi , Chen Mi , Shah Mussadiq , Liu Zhiwei
Published: June 2023

To further improve the oxygen reduction reaction (ORR) activity of low-cost Mn based catalyst, graphene oxide (GO) was added in the preparation of one dimensional (1D) α-MnO2 nanorod using KMnO4-MnSO4 system via hydrothermal method. Experimental results showed that the GO addition (20 wt%) could induce the formation of MnO(OH) nanorod. The Mn based@GO catalyst had more surface defects and oxygen vacancies compared with pure α-MnO2. The onset potential, half-wave potential (E1/2) and limiting current density were significantly enhanced from 0.86 V/0.66 V/3.56 mA cm-2 to 0.91 V/0.77 V/5.41 mA cm-2, indicating that GO addition could greatly improve the catalytic activity of Mn based catalyst. Furthermore, the discharge voltage, power density, mass energy density of Al-air battery using Mn based@GO catalyst were greatly improved comparing with the usage of pure MnO2 catalyst, and it was also found that the application effect of Mn based @GO catalyst in the Al-air battery was almost comparable to the commercial 20% Pt/C catalyst. Our research revealed for the first time the commercial potential of the novel and low-cost MnO2/MnO(OH)@GO nanocomposite in the Al-air battery.
β-Cell function during a high-fat meal in young versus old adults: role of exercise
Authors: Malin Steven K. , Frick Hannah , Wisseman William S. , Edwards Elizabeth S. , Edwards David A. , Emerson Sam R. , Kurti Stephanie P.
Published: June 2023

High-fat diets may induce β-cell dysfunction. This study compared how young and older adults responded to a high-fat meal with regard to β-cell function and whether exercise comparably impacted glucose regulation. Older adults secreted more insulin during the high-fat meal than younger adults. Although exercise increased β-cell function adjusted for skeletal muscle insulin sensitivity in relation to glucose tolerance, it raised adipose insulin resistance and reduced pancreatic β-cell function relative to adipose tissue in older adults. Additional work is needed to discern nutrient-exercise interactions across age to mitigate chronic disease risk.