bluuebunny/crossref_metadata_2025 huggingface dataset:
Conventional-based contact lens fitting in pure microphthalmia associated with irregular cornea
Authors: Prabhu Manikanda , Bhalerao Sushank , Gogri Pratik Yeshwant , Dalal Samruddhi
Published: July 2022

In this case, a female teenager presented to the clinic with blurring of vision at a distance in both eyes since 2 years. All preliminary examinations were recorded. Retinoscopy revealed split reflex in both eyes. Both anterior and posterior segment findings seem to be within normal limits except for the microcornea. Topography depicts an asymmetric bow tie with superior steeping in both eyes. This case was diagnosed as pure microphthalmos associated with irregular corneal curvature, suggestive of keratoconus without corneal thinning with the help of topography and biometric measurements. Contact lens trial was advised to improve the visual potential. Fitting this patient in RGP lenses has been a challenging task because of narrow palpebral fissure height and small cornea.
Strengthening the primary care workforce to deliver high-quality care for non-communicable diseases in refugee settings: lessons learnt from a UNHCR partnership
Authors: Harris Philippa , Kirkland Ros , Masanja Saimon , Le Feuvre Peter , Montgomery Sarah , Ansbro Éimhín , Woodman Michael , Harris Matthew
Published: July 2022

Non-communicable disease (NCD) prevention and care in humanitarian contexts has been a long-neglected issue. Healthcare systems in humanitarian settings have focused heavily on communicable diseases and immediate life-saving health needs. NCDs are a significant cause of morbidity and mortality in refugee settings, however, in many situations NCD care is not well integrated into primary healthcare services. Increased risk of poorer outcomes from COVID-19 for people living with NCDs has heightened the urgency of responding to NCDs and shone a spotlight on their relative neglect in these settings. Partnering with the United Nations Refugee Agency (UNHCR) since 2014, Primary Care International has provided clinical guidance and Training of Trainer (ToT) courses on NCDs to 649 health professionals working in primary care in refugee settings in 13 countries. Approximately 2300 healthcare workers (HCW) have been reached through cascade trainings over the last 6 years. Our experience has shown that, despite fragile health services, high staff turnover and competing clinical priorities, it is possible to improve NCD knowledge, skills and practice. ToT programmes are a feasible and practical format to deliver NCD training to mixed groups of HCW (doctors, nurses, technical officers, pharmacy technicians and community health workers). Clinical guidance must be adapted to local settings while co-creating an enabling environment for health workers is essential to deliver accessible, high-quality continuity of care for NCDs. On-going support for non-clinical systems change is equally critical for sustained impact. A shared responsibility for cascade training—and commitment from local health partners—is necessary to raise NCD awareness, influence local and national policy and to meet the UNHCR’s objective of facilitating access to integrated prevention and control of NCDs.
Cognitive prehabilitation for geriatric patients undergoing elective surgery: a PRISMA-P-compliant protocol for a systematic review
Authors: He Yu , Zou Wenchen , Han Xiaochai , Chen Caitao , Zhang Anren , Sun Nianyi
Published: July 2022

Introduction

The ageing of the population, as well as advances and improvements in surgery, and anaesthesia have greatly increased the demand for surgical services in geriatric patients. Neurocognitive disorders are the most common complications experienced in the postoperative period by older individuals. Improving perioperative brain health in older adults has become key actions for the multidisciplinary perioperative care teams. This comprehensive systematic review will assess the effectiveness and safety of cognitive prehabilitation programmes prior to surgery on cognitive functional capacity and postoperative cognitive outcomes in geriatric patients undergoing elective surgery.

Methods and analysis

This protocol was prepared using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. The following key electronic bibliographic databases will be searched from inception to July 2022: MEDLINE, EMBASE, CINAHL, CENTRAL, PEDro, PsycINFO, CBM, CNKI, WANFANG database and VIP. We will include randomised controlled trials published in English or Chinese that examine the effects of cognitive prehabilitation programmes on geriatric patients undergoing elective surgery. To construct the search strategy, the Patient, Intervention, Comparison, Outcome, Study scheme will be used. Two reviewers will independently complete the study screening, selection, data extraction and quality rating. The Physiotherapy Evidence Database scale will be used to assess the methodological quality of the included studies. A narrative or quantitative synthesis will be conducted based on the final data. The planned start and end dates for the study were 1 September 2021 and 1 August 2023.

Ethics and dissemination

Ethical approval will not be required for this protocol. The results of the final review will be disseminated via peer-reviewed journals and conference presentations.

PROSPERO registration number

CRD42021277191.
‘Communication, that is the key’: a qualitative investigation of how essential workers with COVID-19 responded to public health information
Authors: Roe Mark , Buggy Conor , Ingram Carolyn , Codd Mary , Buckley Claire , Archibald Mary , Rachwal Natalia , Downey Vicky , Chen Yanbing , Sripaiboonkij Penpatra , Drummond Anne , Alvarez Elizabeth , Perrotta Carla
Published: July 2022

Objectives

To understand how essential workers with confirmed infections responded to information on COVID-19.

Design

Qualitative analysis of semistructured interviews conducted in collaboration with the national contact tracing management programme in Ireland.

Setting

Semistructured interviews conducted via telephone and Zoom Meetings.

Participants

18 people in Ireland with laboratory confirmed SARS-CoV-2 infections using real-time PCR testing of oropharyngeal and nasopharyngeal swabs. All individuals were identified as part of workplace outbreaks defined as ≥2 individuals with epidemiologically linked infections.

Results

A total of four high-order themes were identified: (1) accessing essential information early, (2) responses to emerging ‘infodemic’, (3) barriers to ongoing engagement and (4) communication strategies. Thirteen lower order or subthemes were identified and agreed on by the researchers.

Conclusions

Our findings provide insights into how people infected with COVID-19 sought and processed related health information throughout the pandemic. We describe strategies used to navigate excessive and incomplete information and how perceptions of information providers evolve overtime. These results can inform future communication strategies on COVID-19.
Measuring anti-transgender stigma in low-income and middle-income countries: a scoping review protocol
Authors: Tanner Amanda E , Goldenberg Tamar , Erausquin Jennifer Toller , Mertus Sulianie , Jibriel Mohammed Sheikh Eldin , Barrington Clare
Published: July 2022

Introduction

Globally, transgender and other gender diverse (trans) people face pervasive stigma, which contributes to health inequities across multiple health outcomes. Stigma is a fundamental cause of health inequities because it simultaneously limits access to resources, contributes to systemic vulnerability and generates chronic stress. Anti-trans stigma occurs across multiple socioecological levels, resulting in multiple possible definitions and measurements of trans stigma. Understanding how trans stigma has been measured in low-income and middle-income countries (LMICs) is critical to health promotion efforts for trans communities. Accordingly, this scoping review will identify and examine how anti-trans stigma has been measured in existing LMIC-specific research to inform best practices for measurement of anti-trans stigma that includes consideration of local context.

Methods and analysis

This is the protocol for a scoping review of anti-trans stigma in LMICs. We will search (from January 2001 to December 2021) PubMed, WHO Global Medicus and EBSCO. Study selection will conform to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews Checklist. Original studies in English, Spanish, Arabic or Russian will be included. Reviewers will independently screen all citations, full-text articles and abstract data. Data analysis will involve quantitative and qualitative methods. A narrative summary of findings will be conducted.

Ethics and dissemination

As a scoping review (no direct interaction with participants), this study is exempt from human subjects oversight. Understanding context-specific ways to measure anti-trans stigma is urgently needed to support trans health globally. The planned scoping review will help to address this gap. Results of the review will be disseminated in a peer-reviewed journal and likely in other media such as conferences, seminars and symposia.

Protocol registration number

osf.io/qcs2v
Predictive value of retinal nerve fibre layer thickness for postoperative visual improvement in patients with pituitary macroadenoma
Authors: Kurian Deepthi Elizabeth , V Rajshekhar , Horo Saban , Chacko Ari G , Prabhu Krishna , Mahasampath Gowri , Korah Sanita
Published: July 2022

Objective

To determine the usefulness of retinal nerve fibre layer (RNFL) thickness and a reliable cut-off value that can predict postoperative visual function improvement in patients with pituitary macroadenoma.

Methods and Analysis

This was a prospective observational study. Preoperative optical coherence tomography of the RNFL was performed in patients with pituitary macroadenoma. Postoperative visual function changes (acuity and visual fields) were identified using predefined criteria. Receiver operating characteristic curves were constructed for RNFL values to define the ideal cut-off value that predicted improvement. Other variables including preoperative visual acuity, mean deviation, visual field index and tumour volume were also analysed.

Results

Twenty-nine eligible subjects (58 eyes) were recruited. The mean (±SD) age was 43.9 (±12.85) years and 65.5% were male. The mean (±SE) follow-up duration was 20.8 (±6.42) months. RNFL thickness was significantly thinner in eyes with visual dysfunction and optic disc pallor. Better preoperative logarithmic minimum angle of resolution (logMAR) visual acuity, higher RNFL thickness and smaller tumour volume were associated with postoperative visual field improvement on univariate analysis; however, only mean RNFL thickness had significant association on multivariate analysis. None of the preoperative variables showed significant association with improvement in visual acuity. The best cut-off of mean RNFL thickness for visual field improvement was estimated at 81 μm with 73.1% sensitivity and 62.5% specificity.

Conclusion

Preoperative RNFL thickness can be an objective predictor of visual field outcomes in patients undergoing surgery for pituitary macroadenomas, with moderate sensitivity and specificity. It is, however, not a good predictor of visual acuity outcome.
Efficacy and safety of anti-PD1 monotherapy or in combination with ipilimumab after BRAF/MEK inhibitors in patients with BRAF mutant metastatic melanoma
Authors: Pires da Silva Ines , Zakria Danny , Ahmed Tasnia , Trojanello Claudia , Dimitriou Florentia , Allayous Clara , Gerard Camille , Zimmer Lisa , Lo Serigne , Michielin Olivier , Lebbe Celeste , Mangana Johanna , Ascierto Paolo Antonio , Johnson Douglas B , Carlino Matteo , Menzies Alexander , Long Georgina
Published: July 2022

Background

Patients with V600BRAF mutant metastatic melanoma have higher rates of progression-free survival (PFS) and overall survival (OS) with first-line anti-PD1 (PD1]+anti-CTLA-4 (IPI) versus PD1. Whether this is also true after BRAF/MEKi therapy is unknown. We aimed to determine the efficacy and safety of PD1 versus IPI +PD1 after BRAF/MEK inhibitors (BRAF/MEKi).

Methods

Patients with V600BRAF mutant metastatic melanoma treated with BRAF/MEKi who had subsequent PD1 versus IPI+PD1 at eight centers were included. The endpoints were objective response rate (ORR), PFS, OS and safety in each group.

Results

Of 200 patients with V600E (75%) or non-V600E (25%) mutant metastatic melanoma treated with BRAF/MEKi (median time of treatment 7.6 months; treatment cessation due to progressive disease in 77%), 115 (57.5%) had subsequent PD1 and 85 (42.5%) had IPI+PD1. Differences in patient characteristics between PD1 and IPI+PD1 groups included, age (med. 63 vs 54 years), time between BRAF/MEKi and PD1±IPI (16 vs 4 days), Eastern Cooperative Oncology Group Performance Status (ECOG PS) of ≥1 (62% vs 44%), AJCC M1C/M1D stage (72% vs 94%) and progressing brain metastases at the start of PD1±IPI (34% vs 57%). Median follow-up from PD1±IPI start was 37.8 months (95% CI, 33.9 to 52.9). ORR was 36%; 34% with PD1 vs 39% with IPI+PD1 (p=0.5713). Median PFS was 3.4 months; 3.4 with PD1 vs 3.6 months with IPI+PD1 (p=0.6951). Median OS was 15.4 months; 14.4 for PD1 vs 20.5 months with IPI+PD1 (p=0.2603). The rate of grade 3 or 4 toxicities was higher with IPI+PD1 (31%) vs PD1 (7%). ORR, PFS and OS were numerically higher with IPI+PD1 vs PD1 across most subgroups except for females, those with <10 days between BRAF/MEKi and PD1±IPI, and those with stage III/M1A/M1B melanoma. The combination of ECOG PS=0 and absence of liver metastases identified patients with >3 years OS (area under the curve, AUC=0.74), while ECOG PS ≥1, progressing brain metastases and presence of bone metastases predicted primary progression (AUC=0.67).

Conclusions

IPI+PD1 and PD1 after BRAF/MEKi have similar outcomes despite worse baseline prognostic features in the IPI+PD1 group, however, IPI+PD1 is more toxic. A combination of clinical factors can identify long-term survivors, but less accurately those with primary resistance to immunotherapy after targeted therapy.
A new methodology to measure faultlines at scale leveraging digital traces
Authors: Mehrjoo Amir , Cuevas Rubén , Cuevas Ángel
Published: July 2022

The definition of society is tight with human group-level behavior. Groupfaultlinesdefined ashypothetical lines splitting groups into homogeneous subgroups based on members’ attributeshave been proposed as a theoretical method to identify conflicts within groups. For instance, crusades and women’s rights protests are the consequences of strong faultlines in societies with diverse cultures.

Measuring the presence and strength of faultlines represents an important challenge. Existing literature resorts in questionnaires as traditional tool to find group-level behavioral attributes and thus identify faultlines. However, questionnaire data usually come with limitations and biases, especially for large-scale human group-level research. On top of that, questionnaires limit faultline research due to the possibility of dishonest answers, unconscientious responses, and differences in understanding and interpretation.

In this paper, we propose a new methodology for measuring faultlines in large-scale groups, which leverages data readily available from online social networks’ marketing platforms. Our methodology overcomes the limitations of traditional methods to measure group-level attributes and group faultlines at scale.

To prove the applicability of our methodology, we analyzed the faultlines between people living in Spain, grouped by geographical regions. We collected data on 67,270 interest topics from Facebook users living in Spain, France, Germany, Greece, Italy, Portugal, and the United Kingdom. We computed existing metrics to measure faultlines’ distance and strenght using our data to identify potential faultlines existing among Spanish regions. The results reveal that the strongest faultlines in Spain belong to Spanish Islands (the Canary Islands and the Balearic Islands), Catalonia, and Basque regions. These findings are aligned with the historical secessionist movements and cultural diversity reports supporting the validity of our methodology.
WePos
Authors: Guo Baoshen , Zuo Weijian , Wang Shuai , Lyu Wenjun , Hong Zhiqing , Ding Yi , He Tian , Zhang Desheng
Published: July 2022

On-demand delivery is an emerging business in recent years where accurate indoor locations of Gig couriers play an important role in the order dispatch and delivery process. To cater to this need, WiFi-based indoor positioning methods have become an alternative method for on-demand delivery thanks to extensive WiFi deployment in the indoor environment. Existing WiFi-based indoor localization and positioning methods are not suitable for large-scale on-demand delivery scenarios due to high costs (e.g., high labor cost to collect fingerprints) and limited coverage due to limited labeled data. In this work, we explore (i) massive crowdsourced WiFi data collecting from wearable or mobile devices of couriers with little extra effort and (ii) natural manual reports data in the delivery process as two opportunities to perform merchant-level indoor positioning in a weak-supervised manner. Specifically, we proposed WePos, an end-to-end weak-supervised-based merchant-level positioning framework, which consists of the following three parts: (i) a Bidirectional Encoder Representations from Transformers (BERT) based pre-training module to learn latent embeddings of WiFi access points, (ii) a contrastive label self-generate module to produce pseudos for WiFi scanning lists by matching similarity embedding clustering results and couriers' reporting behaviors. (iii) a deep neural network-based classifier to fine-tune the whole training process and conduct online merchant-level position inference. To evaluate the performance of our system, we conduct extensive experiments in both a large-scale public crowdsourcing dataset with over 50 GB of WiFi signal records and a real-world WiFi crowdsourced dataset collected from Eleme, (i.e., one of the largest on-demand delivery platforms in China) in four multi-floor malls in Shanghai. Experimental results show that WePos outperforms state-of-the-art baselines in the merchant-level positioning performance, offer up to 91.4% in positioning accuracy.
VoLearn
Authors: Xia Chengshuo , Fang Xinrui , Arakawa Riku , Sugiura Yuta
Published: July 2022

Conventional motion tutorials rely mainly on a predefined motion and vision-based feedback that normally limits the application scenario and requires professional devices. In this paper, we propose VoLearn, a cross-modal system that provides operability for user-defined motion learning. The system supports the ability to import a desired motion from RGB video and animates the motion in a 3D virtual environment. We built an interface to operate on the input motion, such as controlling the speed, and the amplitude of limbs for the respective directions. With exporting of virtual rotation data, a user can employ a daily device (i.e., smartphone) as a wearable device to train and practice the desired motion according to comprehensive auditory feedback, which is able to provide both temporal and amplitude assessment. The user study demonstrated that the system helps reduce the amplitude and time errors of motion learning. The developed motion-learning system maintains the characteristics of high user accessibility, flexibility, and ubiquity in its application.
Combating False Data Injection Attacks on Human-Centric Sensing Applications
Authors: Xin Jingyu , Phoha Vir V. , Salekin Asif
Published: July 2022

The recent prevalence of machine learning-based techniques and smart device embedded sensors has enabled widespread human-centric sensing applications. However, these applications are vulnerable to false data injection attacks (FDIA) that alter a portion of the victim's sensory signal with forged data comprising a targeted trait. Such a mixture of forged and valid signals successfully deceives the continuous authentication system (CAS) to accept it as an authentic signal. Simultaneously, introducing a targeted trait in the signal misleads human-centric applications to generate specific targeted inference; that may cause adverse outcomes. This paper evaluates the FDIA's deception efficacy on sensor-based authentication and human-centric sensing applications simultaneously using two modalities - accelerometer, blood volume pulse signals. We identify variations of the FDIA such as different forged signal ratios, smoothed and non-smoothed attack samples. Notably, we present a novel attack detection framework named Siamese-MIL that leverages the Siamese neural networks' generalizable discriminative capability and multiple instance learning paradigms through a unique sensor data representation. Our exhaustive evaluation demonstrates Siamese-MIL's real-time execution capability and high efficacy in different attack variations, sensors, and applications.
Predicting Post-Operative Complications with Wearables
Authors: Zhang Jingwen , Li Dingwen , Dai Ruixuan , Cos Heidy , Williams Gregory A. , Raper Lacey , Hammill Chet W. , Lu Chenyang
Published: July 2022

Post-operative complications and hospital readmission are of great concern to surgical patients and health care providers. Wearable devices such as Fitbit wristbands enable long-term and non-intrusive monitoring of patients outside clinical environments. To build accurate predictive models based on wearable data, however, requires effective feature engineering to extract high-level features from time series data collected by the wearable sensors. This paper presents a pipeline for developing clinical predictive models based on wearable sensors. The core of the pipeline is a multi-level feature engineering framework for extracting high-level features from fine-grained time series data. The framework integrates a set of techniques tailored for noisy and incomplete wearable data collected in real-world clinical studies: (1) singular spectrum analysis for extracting high-level features from daily features over the course of the study; (2) a set of daily features that are resilient to missing data in wearable time series data; (3) a K-Nearest Neighbors (KNN) method for imputing short missing heart rate segments; (4) the integration of patients' clinical characteristics and wearable features. We evaluated the feature engineering approach and machine learning models in a clinical study involving 61 patients undergoing pancreatic surgery. Linear support vector machine (SVM) with integrated feature engineering achieved an AUROC of 0.8802 for predicting post-operative readmission or severe complications, which significantly outperformed the existing rule-based model used in clinical practice and other state-of-the-art feature engineering approaches.
Gesture Recognition Method Using Acoustic Sensing on Usual Garment
Authors: Amesaka Takashi , Watanabe Hiroki , Sugimoto Masanori , Shizuki Buntarou
Published: July 2022

In this study, we show a new gesture recognition method for clothing-based gesture input methods using active and passive acoustic sensing. Our system consists of a piezoelectric speaker and a microphone. The speaker transmits ultrasonic swept sine signals, and the microphone simultaneously records the ultrasonic signals that propagate through the garment and the rubbing sounds generated by the gestures on the garment. Our method recognizes a variety of gestures, such as pinch, twist, touch, and swipe, by incorporating active and passive acoustic sensing. An important feature of our method is that it does not require a dedicated garment or embroidery embedded since our system only requires a pair of piezoelectric elements to be attached to the usual garment with a magnet. We performed recognition experiments of 11 gestures on the forearm with four types of garments made from different materials and recognition experiments of five one-handed gestures on the button of a shirt and the pocket of pants. The results of a per-user classifier confirmed that the f-scores were 83.9% and 95.9% for 11 gestures with four different types of garments and 5 gestures that were selected assuming actual use, respectively. In addition, we confirmed that the system recognizes five gestures, which can be performed with one hand, with 89.2% and 92.6% accuracy in the button and pocket sites, respectively.
TransRisk
Authors: Xie Xiaoyang , Hong Zhiqing , Qin Zhou , Fang Zhihan , Tian Yuan , Zhang Desheng
Published: July 2022

Human mobility data may lead to privacy concerns because a resident can be re-identified from these data by malicious attacks even with anonymized user IDs. For an urban service collecting mobility data, an efficient privacy risk assessment is essential for the privacy protection of its users. The existing methods enable efficient privacy risk assessments for service operators to fast adjust the quality of sensing data to lower privacy risk by using prediction models. However, for these prediction models, most of them require massive training data, which has to be collected and stored first. Such a large-scale long-term training data collection contradicts the purpose of privacy risk prediction for new urban services, which is to ensure that the quality of high-risk human mobility data is adjusted to low privacy risk within a short time. To solve this problem, we present a privacy risk prediction model based on transfer learning, i.e., TransRisk, to predict the privacy risk for a new target urban service through (1) small-scale short-term data of its own, and (2) the knowledge learned from data from other existing urban services. We envision the application of TransRisk on the traffic camera surveillance system and evaluate it with real-world mobility datasets already collected in a Chinese city, Shenzhen, including four source datasets, i.e., (i) one call detail record dataset (CDR) with 1.2 million users; (ii) one cellphone connection data dataset (CONN) with 1.2 million users; (iii) a vehicular GPS dataset (Vehicles) with 10 thousand vehicles; (iv) an electronic toll collection transaction dataset (ETC) with 156 thousand users, and a target dataset, i.e., a camera dataset (Camera) with 248 cameras. The results show that our model outperforms the state-of-the-art methods in terms of RMSE and MAE. Our work also provides valuable insights and implications on mobility data privacy risk assessment for both current and future large-scale services.
Are You Left Out?
Authors: Zhou Pengyuan , Xu Hengwei , Lee Lik Hang , Fang Pei , Hui Pan
Published: July 2022

Wearable computers engage in percutaneous interactions with human users and revolutionize the way of learning human activities. Due to rising privacy concerns, federated learning has been recently proposed to train wearable data with privacy preservation collaboratively. However, under the state-of-the-art (SOTA) schemes, user profiles on wearable devices of inferior networking conditions are regarded as 'left out'. Such schemes suffer from three fundamental limitations: (1) the widely adopted network-capacity-based client selection leads to biased training; (2) the aggregation has low communication efficiency; (3) users lack convenient channels for providing feedback on wearable devices.

Therefore, this paper proposes a Fair and Communication-efficient Federated Learning scheme, namely FCFL. FCFL is a full-stack learning system specifically designed for wearable computers, improving the SOTA performance in terms of communication efficiency, fairness, personalization, and user experience. To this end, we design a technique named ThrowRightAway (TRA) to loose the network capacity constraints. Clients with poor networks are allowed to be selected as participators to improve the representation and guarantee the model's fairness. Remarkably, we propose Movement Aware Federated Learning (MAFL) to aggregate only the model updates with top contributions to the global model for the sake of communication efficiency. Accordingly, we implemented an FCFL-supported prototype as a sports application on smartwatches. Our comprehensive evaluation demonstrated that FCFL is a communication efficient scheme significantly reducing uploaded data by up to 29.77%, with a prominent feature of guaranteeing enhanced fairness up to 65.07%. Also, FCFL achieves robust personalization performance (i.e., 20% improvements of global model accuracy) in the face of packet loss below a certain fraction (10%-30%). A follow-up user survey shows that our FCFL-supported prototypical system on wearable devices significantly reduces users' workload.
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
Authors: Lu Wang , Wang Jindong , Chen Yiqiang , Pan Sinno Jialin , Hu Chunyu , Qin Xin
Published: July 2022

It is expensive and time-consuming to collect sufficient labeled data to build human activity recognition (HAR) models. Training on existing data often makes the model biased towards the distribution of the training data, thus the model might perform terribly on test data with different distributions. Although existing efforts on transfer learning and domain adaptation try to solve the above problem, they still need access to unlabeled data on the target domain, which may not be possible in real scenarios. Few works pay attention to training a model that can generalize well to unseen target domains for HAR. In this paper, we propose a novel method called Semantic-Discriminative Mixup (SDMix) for generalizable cross-domain HAR. Firstly, we introduce semantic-aware Mixup that considers the activity semantic ranges to overcome the semantic inconsistency brought by domain differences. Secondly, we introduce the large margin loss to enhance the discrimination of Mixup to prevent misclassification brought by noisy virtual labels. Comprehensive generalization experiments on five public datasets demonstrate that our SDMix substantially outperforms the state-of-the-art approaches with 6% average accuracy improvement on cross-person, cross-dataset, and cross-position HAR.
Modeling the Noticeability of User-Avatar Movement Inconsistency for Sense of Body Ownership Intervention
Authors: Li Zhipeng , Jiang Yu , Zhu Yihao , Chen Ruijia , Wang Ruolin , Wang Yuntao , Yan Yukang , Shi Yuanchun
Published: July 2022

An avatar mirroring the user's movement is commonly adopted in Virtual Reality(VR). Maintaining the user-avatar movement consistency provides the user a sense of body ownership and thus an immersive experience. However, breaking this consistency can enable new interaction functionalities, such as pseudo haptic feedback [45] or input augmentation [37, 59], at the expense of immersion. We propose to quantify the probability of users noticing the movement inconsistency while the inconsistency amplitude is being enlarged, which aims to guide the intervention of the users' sense of body ownership in VR. We applied angular offsets to the avatar's shoulder and elbow joints and recorded whether the user identified the inconsistency through a series of three user studies and built a statistical model based on the results. Results show that the noticeability of movement inconsistency increases roughly quadratically with the enlargement of offsets and the offsets at two joints negatively affect the probability distributions of each other. Leveraging the model, we implemented a technique that amplifies the user's arm movements with unnoticeable offsets and then evaluated implementations with different parameters(offset strength, offset distribution). Results show that the technique with medium-level and balanced-distributed offsets achieves the best overall performance. Finally, we demonstrated our model's extendability in interventions in the sense of body ownership with three VR applications including stroke rehabilitation, action game and widget arrangement.
Wavesdropper
Authors: Wang Chao , Lin Feng , Ba Zhongjie , Zhang Fan , Xu Wenyao , Ren Kui
Published: July 2022

Most existing eavesdropping attacks leverage propagating sound waves for speech retrieval. However, soundproof materials are widely deployed in speech-sensitive scenes (e.g., a meeting room). In this paper, we reveal that human speech protected by an isolated room can be compromised by portable and commercial off-the-shelf mmWave devices. To achieve this goal, we develop Wavesdropper, a word detection system that utilizes a mmWave probe to sense the targeted speaker's throat vibration and recover speech contents in the obstructed condition. We proposed a CEEMD-based method to suppress dynamic clutters (e.g., human movements) in the room and a wavelet-based processing method to extract the delicate vocal vibration information from the hybrid signals. To recover speech contents from mmWave signals related to the vocal vibration, we designed a neural network to infer the speech contents. Moreover, we explored word detection on a conversation with multiple (two) probes and reveal that the adversary can detect words on multiple people simultaneously with only one mmWave device. We performed extensive experiments to evaluate the system performance with over 60,000 pronunciations. The experimental results indicate that Wavesdropper can achieve 91.3% accuracy for 57-word recognition on 23 volunteers.
CSI:DeSpy
Authors: Salman Muhammad , Dao Nguyen , Lee Uichin , Noh Youngtae
Published: July 2022

Recently, the spy cameras spotted in private rental places have raised immense privacy concerns. The existing solutions for detecting them require additional support from synchronous external sensing or stimulus hardware such as on/off LED circuits, which require extra obligations from the user. For example, a user needs to carry a smartphone and laboriously perform preset motions (e.g., jumping, waving, and preplanned walking pattern) for synchronous sensing of acceleration signals. These requirements cause considerable discomfort to the user and limit the practicability of prevalent solutions. To cope with this, we propose CSI:DeSpy, an efficient and painless method by leveraging video bitrate fluctuations of the WiFi camera and the passively obtained Channel States Information (CSI) from user motion. CSI:DeSpy includes a self-adaptive feature that makes it robust to detect motion efficiently in multipath-rich environments. We implemented CSI:DeSpy on the Android platform and assessed its performance in diverse real-life scenarios, namely; (1) its reliability with the intensities of physical activities in diverse multipath-rich environments, (2) its practicability with activities of daily living, (3) its unobtrusiveness with passive sensing, and (4) its robustness to different network loads. CSI:DeSpy attained average detection rates of 96.6%, 96.2%, 98.5%, and 93.6% respectively.
DepreST-CAT
Authors: Tlachac ML , Flores Ricardo , Reisch Miranda , Houskeeper Katie , Rundensteiner Elke A.
Published: July 2022

The rates of mental illness, especially anxiety and depression, have increased greatly since the start of the COVID-19 pandemic. Traditional mental illness screening instruments are too cumbersome and biased to screen an entire population. In contrast, smartphone call and text logs passively capture communication patterns and thus represent a promising screening alternative. To facilitate the advancement of such research, we collect and curate the DepreST Call and Text log (DepreST-CAT) dataset from over 365 crowdsourced participants during the COVID-19 pandemic. The logs are labeled with traditional anxiety and depression screening scores essential for training machine learning models. We construct time series ranging from 2 to 16 weeks in length from the retrospective smartphone logs. To demonstrate the screening capabilities of these time series, we then train a variety of unimodal and multimodal machine and deep learning models. These models provide insights into the relative screening value of the different types of logs, lengths of log time series, and classification methods. The DepreST-CAT dataset is a valuable resource for the research community to model communication patterns during the COVID-19 pandemic and further the development of machine learning algorithms for passive mental illness screening.
A Review of Personal Informatics Research for People with Motor Disabilities
Authors: Motahar Tamanna , Wiese Jason
Published: July 2022

Personal informatics (PI) has become an area of significant research over the past decade, maturing into a sub-field that seeks to support people from many backgrounds and life contexts in collecting and finding value in their personal data. PI research includes a focus on people with chronic conditions as a monolithic group, but currently fails to distinguish the needs of people with motor disabilities (MD). To understand how current PI literature addresses those needs, we conducted a mapping review on PI publications engaged with people with MD. We report results from 50 publications identified in the ACM DL, Pubmed, JMIR, SCOPUS, and IEEE Xplore. Our analysis shows significant incompatibilities between the needs of individuals with MD and the ways that PI literature supports them. We also found inconsistencies in the ways that disability levels are reported, that PI literature for MD excludes non-health-related data domains, and an insufficient focus on PI tools' accessibility and usability for some MD users. In contrast with Epstein et al.'s [36] recent PI review, behavior change and habit awareness were the most common motivation in these publications. Finally, many of the reviewed articles reported involvement by caregivers, trainers, healthcare providers, and researchers across the PI stages. In addition to these insights, we provide recommendations for designing PI technology through a user-centric lens that will broaden the scope of PI and include people regardless of their motor abilities.
Shoes++
Authors: Yan Zihan , Zhou Jiayi , Wu Yufei , Liu Guanhong , Luo Danli , Zhou Zihong , Mi Haipeng , Sun Lingyun , Chen Xiang 'Anthony' , Tao Ye , Zhang Yang , Wang Guanyun
Published: July 2022

Feet are the foundation of our bodies that not only perform locomotion but also participate in intent and emotion expression. Thus, foot gestures are an intuitive and natural form of expression for interpersonal interaction. Recent studies have mostly introduced smart shoes as personal gadgets, while foot gestures used in multi-person foot interactions in social scenarios remain largely unexplored. We present Shoes++, which includes an inertial measurement unit (IMU)-mounted sole and an input vocabulary of social foot-to-foot gestures to support foot-based interaction. The gesture vocabulary is derived and condensed by a set of gestures elicited from a participatory design session with 12 users. We implement a machine learning model in Shoes++ which can recognize two-person and three-person social foot-to-foot gestures with 94.3% and 96.6% accuracies (N=18). In addition, the sole is designed to easily attach to and detach from various daily shoes to support comfortable social foot interaction without taking off the shoes. Based on users' qualitative feedback, we also found that Shoes++ can support team collaboration and enhance emotion expression, thus making social interactions or interpersonal dynamics more engaging in an expanded design space.
Social Emotional Learning with Conversational Agents
Authors: Fu Yue , Michelson Rebecca , Lin Yifan , Nguyen Lynn K. , Tayebi Tala June , Hiniker Alexis
Published: July 2022

Social emotional skills are foundational competencies upon which children draw throughout their lives. This work investigates current, commercially available experiences for social emotional learning (SEL) through conversational agents (CAs). Specifically, we reviewed 3,767 Skills available in the "Kids" category of the Alexa Skills Marketplace and found 42 working Skills with connections to SEL. We found that the most common scenarios these Skills sought to support were: active listening, emotional wellbeing, conversation with other people, and politeness. The interaction patterns used by these Skills distilled into a taxonomy of styles we labeled: The Delegator, The Lecturer, The Bulldozer, and The One-Track Mind. We found that, collectively, these Skills provide shallow experiences and lack contingent feedback. To examine the gap between current offerings and families' needs, we also conducted 26 interviews with parents to probe parents' ideas about CAs supporting children's SEL. Parents see potential for CAs to support children in four concrete ways, including attuning to others, cultivating curiosity, reinforcing politeness, and developing emotional awareness. Despite their optimism about these opportunities, parents expressed skepticism about CAs' impoverished conversational abilities and worry about CAs advancing values and behavioral norms that are at odds with their own.
DiverSense
Authors: Li Yang , Wu Dan , Zhang Jie , Xu Xuhai , Xie Yaxiong , Gu Tao , Zhang Daqing
Published: July 2022

The ubiquity of Wi-Fi infrastructure has facilitated the development of a range of Wi-Fi based sensing applications. Wi-Fi sensing relies on weak signal reflections from the human target and thus only supports a limited sensing range, which significantly hinders the real-world deployment of the proposed sensing systems. To extend the sensing range, traditional algorithms focus on suppressing the noise introduced by the imperfect Wi-Fi hardware. This paper picks a different direction and proposes to enhance the quality of the sensing signal by fully exploiting the signal diversity provided by the Wi-Fi hardware. We propose DiverSense, a system that combines sensing signal received from all subcarriers and all antennas in the array, to fully utilize the spatial and frequency diversity. To guarantee the diversity gain after signal combining, we also propose a time-diversity based signal alignment algorithm to align the phase of the multiple received sensing signals. We implement the proposed methods in a respiration monitoring system using commodity Wi-Fi devices and evaluate the performance in diverse environments. Extensive experimental results demonstrate that DiverSense is able to accurately monitor the human respiration even when the sensing signal is under noise floor, and therefore boosts sensing range to 40 meters, which is a 3x improvement over the current state-of-the-art. DiverSense also works robustly under NLoS scenarios, e.g., DiverSense is able to accurately monitor respiration even when the human and the Wi-Fi transceivers are separated by two concrete walls with wooden doors.
Decoloniality in computing
Authors: Taylor Jordan
Published: July 2022

This interview explores the relationship between social computing technology and decolonization and the relationship between coloniality and computing research.
Forging a Path: Faculty Interviews on the Present and Future of Computer Science Education in the United States
Authors: Lunn Stephanie , Samary Maíra Marques , Hambrusch Susanne , Yadav Aman
Published: July 2022

Computer science education (CSEd) is a growing interdisciplinary area that continues to gain momentum from students, researchers, and educators. Yet, there are few formal programs or degree options for students interested in pursuing graduate work in CSEd. This article explores the existing state of CSEd in the United States (U.S.) through semi-structured interviews with (
n
= 15) faculty engaged in CSEd research. Thematic coding of the transcripts revealed the complexities involved in the development of formal programs, the distinct considerations for faculty, and the value of having strong ties to both computer science and education. The themes described positive aspects of support and cohesion within the larger community and opportunities to expand knowledge across fields. Applying Cornell and Parker’s principles of interdisciplinary science to the field of CSEd, we provide recommendations for ways forward and discuss the potential impact on institutional structures, research capacity, individual and group identities, and teaching and learning. The findings from this investigation not only inform on the present state of CSEd in the U.S., but also offer guidance for CSEd-focused graduate programs.
An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence
Authors: Arivazhagan N. , Venkatesh J. , Somasundaram K. , Vijayalakshmi K. , Priya S. Sathiya , Suresh Thangakrishnan M. , Senthamilselvan K. , Lakshmi Dhevi B. , Vijendra Babu D. , Chandragandhi S. , Ashine Chamato Fekadu
Published: July 2022

In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors’ time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods.
LSTM-Based Deep Model for Investment Portfolio Assessment and Analysis
Authors: Yang Haohua
Published: July 2022

In recent years, within the scope of financial quantification, quantitative investment models that support human-oriented algorithms have been proposed. These models attempt to characterize fiat-delayed series through intelligent acquaintance methods to predict data and arrange investment strategies. The standard long short-term memory (LSTM) neural network has the shortcoming of low effectiveness of the fiscal cycle sequence. This work utters throughout the amended LSTM design. The augury result of the neural reticulation was upgraded by coalesce attentional propose to the LSTM class, and a genetic algorithmic program product was formulated. Genetic algorithm (GA) updates the inalienable parameters to a higher generalization aptitude. Using man stock insignitor future data from January 2019 to May 2020, we accomplish a station-of-the-contrivance algorithmic rule. Inferences have shown that the improved LSTM example proposed in this paper outperforms other designs in multiple respect, and it performs effectively in investment portfolio design, which is suitable for future investment.