bluuebunny/crossref_metadata_2025 huggingface dataset:
On the evaluation of the Gottfried sum and d̄−ū asymmetry with the use of the TMM method
Authors: Kotlorz A. , Kotlorz D. , Teryaev O. V.
Published: December 2021

In this paper, we obtain the integrated flavor asymmetry of the sea quarks in the proton, [Formula: see text], with the help of the truncated moments approach elaborated in our previous papers. We use the difference between the light sea-quark distributions [Formula: see text] extracted from Drell–Yan (DY) NuSea/E866 measurements of the cross-section ratio [Formula: see text] and from the recent global analysis of deep inelastic scattering (DIS) [Formula: see text] data incorporating the reanalyzed neutron structure function. In our analysis, we also include the most recent DY data from the Fermilab SeaQuest/E906 experiment.
Exact solutions and aysmptotic state analysis of a modified Toda lattice system with a perturbation parameter
Authors: Qin Meng-Li , Wen Xiao-Yong , Yuan Cui-Lian
Published: December 2021

Under consideration is a modified Toda lattice system with a perturbation parameter, which may describe the particle motion in a lattice. With the aid of symbolic computation Maple, the discrete generalized [Formula: see text]-fold Darboux transformation (DT) of this system is constructed for the first time. Different types of exact solutions are derived by applying the resulting DT through choosing different [Formula: see text]. Specifically, standard soliton solutions, rational solutions and their mixed solutions are given via the [Formula: see text]-fold DT, [Formula: see text]-fold DT and [Formula: see text]-fold DT, respectively. Limit states of various exact solutions are analyzed via the asymptotic analysis technique. Compared with the known results, we find that the asymptotic states of mixed solutions of standard soliton and rational solutions are consistent with the asymptotic analysis results of solitons and rational solutions alone. Soliton interaction and propagation phenomena are shown graphically. Numerical simulations are used to explore relevant soliton dynamical behaviors. These results and properties might be helpful for understanding lattice dynamics.
Local-moves on knots and products of knots II
Authors: Kauffman Louis H. , Ogasa Eiji
Published: December 2021

We use the terms, knot product and local-move, as defined in the text of this paper. Let [Formula: see text] be an integer [Formula: see text]. Let [Formula: see text] be the set of simple spherical [Formula: see text]-knots in [Formula: see text]. Let [Formula: see text] be an integer [Formula: see text]. We prove that the map [Formula: see text] is bijective, where [Formula: see text]Hopf, and Hopf denotes the Hopf link.

Let [Formula: see text] and [Formula: see text] be 1-links in [Formula: see text]. Suppose that [Formula: see text] is obtained from [Formula: see text] by a single pass-move, which is a local-move on 1-links. Let [Formula: see text] be a positive integer. Let [Formula: see text] denote the knot product [Formula: see text]. We prove the following: The [Formula: see text]-dimensional submanifold [Formula: see text] [Formula: see text] is obtained from [Formula: see text] by a single [Formula: see text]-pass-move, which is a local-move on [Formula: see text]-submanifolds contained in [Formula: see text]. See the body of this paper for the definitions of all local-moves in this abstract.

We prove the following: Let [Formula: see text], and [Formula: see text] be positive integers. If the [Formula: see text] torus link is pass-move-equivalent to the [Formula: see text] torus link, then the Brieskorn manifolds, [Formula: see text] and [Formula: see text], are diffeomorphic as abstract manifolds.

Let [Formula: see text] and [Formula: see text] be (not necessarily connected or spherical) 2-dimensional closed oriented submanifolds in [Formula: see text]. Suppose that [Formula: see text] is obtained from [Formula: see text] by a single ribbon-move, which is a local-move on 2-dimensional submanifolds contained in [Formula: see text]. Let [Formula: see text] be an integer [Formula: see text]. We prove the following: The [Formula: see text]-submanifold [Formula: see text] [Formula: see text] is obtained from [Formula: see text] by a single [Formula: see text]-pass-move, which is a local-move on [Formula: see text]-dimensional submanifolds contained in [Formula: see text].
Characters of infinite-dimensional quantum classical groups: BCD cases
Authors: Sato Ryosuke
Published: December 2021

We study the character theory of inductive limits of [Formula: see text]-deformed classical compact groups. In particular, we clarify the relationship between the representation theory of Drinfeld–Jimbo quantized universal enveloping algebras and our previous work on the quantized characters. We also apply the character theory to construct Markov semigroups on unitary duals of [Formula: see text], [Formula: see text], and their inductive limits.
On the divisibility among power GCD and power LCM matrices on gcd-closed sets
Authors: Zhu Guangyan
Published: December 2021

Let [Formula: see text] and [Formula: see text] be positive integers and let [Formula: see text] be a set of [Formula: see text] distinct positive integers. For [Formula: see text], one defines [Formula: see text]. We denote by [Formula: see text] (respectively, [Formula: see text]) the [Formula: see text] matrix having the [Formula: see text]th power of the greatest common divisor (respectively, the least common multiple) of [Formula: see text] and [Formula: see text] as its [Formula: see text]-entry. In this paper, we show that for arbitrary positive integers [Formula: see text] and [Formula: see text] with [Formula: see text], the [Formula: see text]th power matrices [Formula: see text] and [Formula: see text] are both divisible by the [Formula: see text]th power matrix [Formula: see text] if [Formula: see text] is a gcd-closed set (i.e. [Formula: see text] for all integers [Formula: see text] and [Formula: see text] with [Formula: see text]) such that [Formula: see text]. This confirms two conjectures of Shaofang Hong proposed in 2008.
THE STRATEGIC ROLE OF PRIVATE EQUITY IN THE INTERNATIONALIZATION OF ITALIAN SMEs
Authors: BOSIO ANDREA ODILLE , GERVASONI ANNA , BOLLAZZI FRANCESCO
Published: December 2021

The internationalization of the portfolio company is a key strategy used by private equity (PE) investors to create value and produce returns. In recent years, the focus on the strategies for value-creation through operational improvement has become essential to achieve the exponential growth required to the portfolio company, given the low multiples and the market risk of leverage. In this paper, we define the key types of contribution that a PE investor can provide in order to support the internationalization process and their effects on the portfolio company’s performance. The research is based on a survey administered to 47 PE fund managers, which covers 156 deals involving Italian companies. The results offer insight into the contribution to the corporate governance, strategy and management that PE provides in addition to the monetary support. The findings show that the non-financial support given to the portfolio companies has a positive impact on the performance and that the most impactful contribution the PE can give is the support to the relational network when the company strategy involves a foreign direct investment.
A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices
Authors: Shen Chaonan , Zhang Kai , Tang Jinshan
Published: December 2021

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the
Internet of Things (IoT)
can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from
Chest X-ray (CXR)
images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a
discrete social learning particle swarm optimization
algorithm
(DSLPSO)
is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a
Support Vector Machine (SVM)
for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.
Adversarial Separation Network for Text Style Transfer
Authors: Yang Haitong , Zhou Guangyou , He Tingting
Published: December 2021

This article considers the task of text style transfer: transforming a specific style of sentence into another while preserving its style-independent content. A dominate approach to text style transfer is to learn a good content factor of text, define a fixed vector for every style and recombine them to generate text in the required style. In fact, there are a large number of different words to convey the same style from different aspects. Thus, using a fixed vector to represent one style is very inefficient, which causes the weak representation power of the style vector and limits text diversity of the same style. To address this problem, we propose a novel neural generative model called Adversarial Separation Network (ASN), which can learn the content and style vector jointly and the learnt vectors have strong representation power and good interpretabilities. In our method, adversarial learning is implemented to enhance our model’s capability of disentangling the two factors. To evaluate our method, we conduct experiments on two benchmark datasets. Experimental results show our method can perform style transfer better than strong comparison systems. We also demonstrate the strong interpretability of the learnt latent vectors.
Using Pre-trained Language Model to Enhance Active Learning for Sentence Matching
Authors: Bai Guirong , He Shizhu , Liu Kang , Zhao Jun
Published: December 2021

Active learning is an effective method to substantially alleviate the problem of expensive annotation cost for data-driven models. Recently, pre-trained language models have been demonstrated to be powerful for learning language representations. In this article, we demonstrate that the pre-trained language model can also utilize its learned textual characteristics to enrich criteria of active learning. Specifically, we provide extra textual criteria with the pre-trained language model to measure instances, including noise, coverage, and diversity. With these extra textual criteria, we can select more efficient instances for annotation and obtain better results. We conduct experiments on both English and Chinese sentence matching datasets. The experimental results show that the proposed active learning approach can be enhanced by the pre-trained language model and obtain better performance.
Another Look at Privacy-Preserving Automated Contact Tracing
Authors: Tang Qiang
Published: December 2021

In the current COVID-19 pandemic, manual contact tracing has been proven to be very helpful to reach close contacts of infected users and slow down spread of the virus. To improve its scalability, a number of automated contact tracing (ACT) solutions have been proposed, and some of them have been deployed. Despite the dedicated efforts, security and privacy issues of these solutions are still open and under intensive debate. In this article, we examine the ACT concept from a broader perspective, by focusing on not only security and privacy issues but also functional issues such as interface, usability, and coverage. We first elaborate on these issues and particularly point out the inevitable privacy leakages in existing Bluetooth Low Energy based ACT solutions, including centralized and decentralized ones. In addition, we examine the existing venue-based ACT solutions and identify their privacy and security concerns. Then, we propose a generic venue-based ACT solution and a concrete instantiation based on Bluetooth Low Energy technology. Our solution monitors users’ contacting history only in virus-spreading-prone venues and offers higher-level protection for both security and privacy than its predecessors. Finally, we evaluate our solution from security, privacy, and efficiency perspectives, and also highlight how to reduce false positives in some specific indoor environments.
PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment
Authors: Kato Fumiyuki , Cao Yang , Yoshikawa Mastoshi
Published: December 2021

Existing Bluetooth-based private contact tracing (PCT) systems can privately detect whether people have come into
direct contact
with patients with COVID-19. However, we find that the existing systems lack
functionality
and
flexibility
, which may hurt the success of contact tracing. Specifically, they cannot detect
indirect contact
(e.g., people may be exposed to COVID-19 by using a contaminated sheet at a restaurant without making direct contact with the infected individual); they also cannot flexibly change the rules of “risky contact,” such as the duration of exposure or the distance (both spatially and temporally) from a patient with COVID-19 that is considered to result in a risk of exposure, which may vary with the environmental situation.

In this article, we propose an efficient and secure contact tracing system that enables us to trace both direct contact and indirect contact. To address the above problems, we need to utilize users’ trajectory data for PCT, which we call
trajectory-based PCT
. We formalize this problem as a
spatiotemporal private set intersection
that satisfies both the security and efficiency requirements. By analyzing different approaches such as homomorphic encryption, which could be extended to solve this problem, we identify the trusted execution environment (TEE) as a candidate method to achieve our requirements. The major challenge is how to design algorithms for a spatiotemporal private set intersection under the limited secure memory of the TEE. To this end, we design a TEE-based system with flexible trajectory data encoding algorithms. Our experiments on real-world data show that the proposed system can process hundreds of queries on tens of millions of records of trajectory data within a few seconds.
Be Consistent, Work the Program, Be Present Every Day
Authors: Jones Jasmine , Yuan Ye , Yarosh Svetlana
Published: December 2021

Recovery from substance abuse disorders (SUDs) is a lifelong process of change. Self-tracking technologies have been proposed by the recovery community as a beneficial design space to support people adopting positive lifestyles and behaviors in their recovery. To explore the potential of this design space, we designed and deployed a technology probe consisting of a mobile app, wearable visualization, and ambient display to enable people to track and reflect on the activities they adopted in their recovery process. With this probe we conducted a four-week exploratory field study with 17 adults in early recovery to investigate 1) what activities people in recovery desire to track, 2) how people perceive self-tracking tools in relation to their recovery process, and 3) what digital resources self-tracking tools can provide to aid the recovery process. Our findings illustrate the array of activities that people track in their recovery, along with usage scenarios, preferences and design tensions that arose. We discuss implications for holistic self-tracking technologies and opportunities for future work in behavior change support for this context.
Unobtrusive Pedestrian Identification by Leveraging Footstep Sounds with Replay Resistance
Authors: Huang Long , Wang Chen
Published: December 2021

The ability to identify pedestrians unobtrusively is essential for smart buildings to provide customized environments, energy saving, health monitoring and security-enhanced services. In this paper, we present an unobtrusive pedestrian identification system by passively listening to people's walking sounds. The proposed acoustic system can be easily integrated with the widely deployed voice assistant devices while providing the context awareness ability. This work focuses on two major tasks. Firstly, we address the challenge of recognizing footstep sounds in complex indoor scenarios by exploiting deep learning and the advanced stereo recording technology that is available on most voice assistant devices. We develop a Convolutional Neural Network-based algorithm and the footstep sound-oriented signal processing schemes to identify users by their footstep sounds accurately. Secondly, we design a "live" footstep detection approach to defend against replay attacks. By deriving the novel inter-footstep and intra-footstep characteristics, we distinguish live footstep sounds from the machine speaker's replay sounds based on their spatial variances. The system is evaluated under normal scenarios, traditional replay attacks and the advanced replays, which are designed to forge footstep sounds both acoustically and spatially. Extensive experiments show that our system identifies people with up to 94.9% accuracy in one footstep and shields 100% traditional replay attacks and up to 99% advanced replay attacks.
Understanding User Perceptions of Proactive Smart Speakers
Authors: Wei Jing , Dingler Tilman , Kostakos Vassilis
Published: December 2021

Voice assistants, such as Amazon's Alexa and Google Home, increasingly find their way into consumer homes. Their functionality, however, is currently limited to being passive answer machines rather than proactively engaging users in conversations. Speakers' proactivity would open up a range of important application scenarios, including health services, such as checking in on patient states and triggering medication reminders. It remains unclear how passive speakers should implement proactivity. To better understand user perceptions, we ran a 3-week field study with 13 participants where we modified the off-the-shelf Google Home to become proactive. During the study, our speaker proactively triggered conversations that were essentially Experience Sampling probes allowing us to identify when to engage users. Applying machine-learning, we are able to predict user responsiveness with a 71.6% accuracy and find predictive features. We also identify self-reported factors, such as boredom and mood, that are significantly correlated with users' perceived availability. Our prototype and findings inform the design of proactive speakers that verbally engage users at opportune moments and contribute to the design of proactive application scenarios and voice-based experience sampling studies.
RFTemp
Authors: Banerjee Avishek , Srinivasan Kannan
Published: December 2021

Microwave ovens have been widely used in recent years to heat food quickly and efficiently. Users estimate the time to heat the food by prior knowledge or by trial and error process. However, this often results in the food being over-heated or under-heated, destroying the nutrients. In this paper, we present RFTemp, a system that can monitor microwave oven leakage to estimate the temperature of the food that is being heated and thus estimate the accurate time when the food has reached the targeted temperature. To design such a system, we propose an innovative microwave leakage sensing procedure and a novel water-equivalent food model to estimate food temperature. To evaluate the real-world performance of RFTemp we build a prototype using software defined radios and conducted experiments on various food items using household microwave ovens. We show that RFTemp can estimate the temperature of the food with a mean error of 5°C, 2x improvement over contactless infrared thermometer and sensors.
Wi-Fruit
Authors: Liu Yutong , Jiang Landu , Kong Linghe , Xiang Qiao , Liu Xue , Chen Guihai
Published: December 2021

People usually assess fruit qualities from external features such as color, shape, size, and texture. However, it is quite common that we select fruits with perfect appearances but rotten inside, especially for fruits with thick pericarps. Thus the accurate measurement is desirable to evaluate the internal conditions of fruits. As two key features of fruit internal qualities, existing methods on measuring fruit moisture and soluble solid contents (SSC) are either destructive or costly, limiting their adoption in daily life. In this paper, we propose Wi-Fruit, a non-destructive and low-cost fruit moisture and SSC measurement system leveraging Wi-Fi channel state information (CSI). First, to cope with the fruit structure dependency challenge, we propose a double-quotient model to pre-process CSI on adjacent antennas. Second, to address the fruit size and type dependency challenges, a lightweight artificial neural network (ANN) model with visual information fusion is proposed for fruit moisture and SSC estimations. Extensive evaluations are conducted on 6 types of fruits with both thick (i.e., watermelon and grapefruit) and thin pericarps (i.e., dragon fruit, apple, pear, and orange) over a month in either an empty laboratory room or a library with massive books. Results demonstrate that Wi-Fruit achieves an acceptable estimation accuracy (RMSE=0.319). It is independent of various fruit structures, sizes, and types, while also robust to time and environmental changes. The fruit internal sensing capabilities of Wi-Fruit can help fruit saving and safety in both pre-harvest and post-harvest applications.
Winect
Authors: Ren Yili , Wang Zi , Tan Sheng , Chen Yingying , Yang Jie
Published: December 2021

WiFi human sensing has become increasingly attractive in enabling emerging human-computer interaction applications. The corresponding technique has gradually evolved from the classification of multiple activity types to more fine-grained tracking of 3D human poses. However, existing WiFi-based 3D human pose tracking is limited to a set of predefined activities. In this work, we present Winect, a 3D human pose tracking system for free-form activity using commodity WiFi devices. Our system tracks free-form activity by estimating a 3D skeleton pose that consists of a set of joints of the human body. In particular, we combine signal separation and joint movement modeling to achieve free-form activity tracking. Our system first identifies the moving limbs by leveraging the two-dimensional angle of arrival of the signals reflected off the human body and separates the entangled signals for each limb. Then, it tracks each limb and constructs a 3D skeleton of the body by modeling the inherent relationship between the movements of the limb and the corresponding joints. Our evaluation results show that Winect is environment-independent and achieves centimeter-level accuracy for free-form activity tracking under various challenging environments including the none-line-of-sight (NLoS) scenarios.
Constructing Floor Plan through Smoke Using Ultra Wideband Radar
Authors: Chen Weiyan , Zhang Fusang , Gu Tao , Zhou Kexing , Huo Zixuan , Zhang Daqing
Published: December 2021

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.
Exploring Multiple Antennas for Long-range WiFi Sensing
Authors: Zeng Youwei , Liu Jinyi , Xiong Jie , Liu Zhaopeng , Wu Dan , Zhang Daqing
Published: December 2021

Despite extensive research effort on contactless WiFi sensing over the past few years, there are still significant barriers hindering its wide application. One key issue is the limited sensing range due to the intrinsic nature of employing the weak target-reflected signal for sensing and therefore the sensing range is much smaller than the communication range. In this work, we address this challenging issue, moving WiFi sensing one step closer to real-world adoption. The key idea is to effectively utilize the multiple antennas widely available on commodity WiFi access points to simultaneously strengthen the target-reflected signal and reduce the noise. Although traditional beamforming schemes can help increase the signal strength, they are designed for communication and can not be directly applied to benefit sensing. To effectively increase the WiFi sensing range using multiple antennas, we first propose a new metric that quantifies the signal sensing capability. We then propose novel signal processing methods, which lay the theoretical foundation to support beamforming-based long-range WiFi sensing. To validate the proposed idea, we develop two sensing applications: fine-grained human respiration monitoring and coarse-grained human walking tracking. Extensive experiments show that: (i) the human respiration sensing range is significantly increased from the state-of-the-art 6-8 m to 11 m;1 and (ii) human walking can be accurately tracked even when the target is 18 m away from the WiFi transceivers, outperforming the sensing range of the state-of-the-art by 50%.
Quantifying the Causal Effect of Individual Mobility on Health Status in Urban Space
Authors: Zhang Yunke , Xu Fengli , Xia Tong , Li Yong
Published: December 2021

How does individual mobility in the urban environment impact their health status? Previous works have explored the correlation between human mobility behaviour and individual health, yet the study on the underlying causal effect is woefully inadequate. However, the correlation analysis can sometimes be bewildering because of the confounding effects. For example, older people visit park more often but have worse health status than younger people. The common associations with age will lead to a counter-intuitive negative correlation between park visits and health status. Obtaining causal effects from confounded observations remains a challenge. In this paper, we construct a causal framework based on propensity score matching on multi-level treatment to eliminate the bias brought by confounding effects and estimate the total treatment effects of mobility behaviours on health status. We demonstrate that the matching procedure approximates a de-confounded randomized experiment where confounding variables are balanced substantially. The analysis on the directions of estimated causal effects reveals that fewer neighbouring tobacco shops and frequent visits to sports facilities are related with higher risk in health status, which differs from their correlation directions. Physical mobility behaviours and environment features have more significant estimated effects on health status than contextual mobility behaviours. Moreover, we embed our causal analysis framework in health prediction models to filter out features with superficial correlation but insignificant effects that might lead to over-fitting. This strategy achieves better model robustness with more features filtered out than L1-regularization. Our findings shed light on individual healthy lifestyle and mobility-related health policymaking.
FaceBit
Authors: Curtiss Alexander , Rothrock Blaine , Bakar Abu , Arora Nivedita , Huang Jason , Englhardt Zachary , Empedrado Aaron-Patrick , Wang Chixiang , Ahmed Saad , Zhang Yang , Alshurafa Nabil , Hester Josiah
Published: December 2021

The COVID-19 pandemic has dramatically increased the use of face masks across the world. Aside from physical distancing, they are among the most effective protection for healthcare workers and the general population. Face masks are passive devices, however, and cannot alert the user in case of improper fit or mask degradation. Additionally, face masks are optimally positioned to give unique insight into some personal health metrics. Recognizing this limitation and opportunity, we present FaceBit: an open-source research platform for smart face mask applications. FaceBit's design was informed by needfinding studies with a cohort of health professionals. Small and easily secured into any face mask, FaceBit is accompanied by a mobile application that provides a user interface and facilitates research. It monitors heart rate without skin contact via ballistocardiography, respiration rate via temperature changes, and mask-fit and wear time from pressure signals, all on-device with an energy-efficient runtime system. FaceBit can harvest energy from breathing, motion, or sunlight to supplement its tiny primary cell battery that alone delivers a battery lifetime of 11 days or more. FaceBit empowers the mobile computing community to jumpstart research in smart face mask sensing and inference, and provides a sustainable, convenient form factor for health management, applicable to COVID-19 frontline workers and beyond.
Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction
Authors: Wang Huandong , Yu Qiaohong , Liu Yu , Jin Depeng , Li Yong
Published: December 2021

With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, the rising paradigm of knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based on knowledge graph techniques, and predicting users' future movement based on the "knowledge" extracted from multiple sources in a cohesive manner. Specifically, we propose a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph (STKG), where mobility trajectories, category information of venues, and temporal information are jointly modeled by the facts with different relation types in STKG. The mobility prediction problem is converted to the knowledge graph completion problem in STKG. Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge. Extensive evaluations confirm the high accuracy of our model in predicting users' mobility, i.e., improving the accuracy by 5.04% compared with the state-of-the-art algorithms. In addition, PoI categories as the background knowledge and auxiliary information are confirmed to be helpful by improving the performance by 3.85% in terms of accuracy. Additionally, experiments show that our proposed method is time-efficient by reducing the computational time by over 43.12% compared with existing methods.
Zero-Shot Learning for IMU-Based Activity Recognition Using Video Embeddings
Authors: Tong Catherine , Ge Jinchen , Lane Nicholas D.
Published: December 2021

The Activity Recognition Chain generally precludes the challenging scenario of recognizing new activities that were unseen during training, despite this scenario being a practical and common one as users perform diverse activities at test time. A few prior works have adopted zero-shot learning methods for IMU-based activity recognition, which work by relating seen and unseen classes through an auxiliary semantic space. However, these methods usually rely heavily on a hand-crafted attribute space which is costly to define, or a learnt semantic space based on word embedding, which lacks motion-related information crucial for distinguishing IMU features. Instead, we propose a strategy to exploit videos of human activities to construct an informative semantic space. With our approach, knowledge from state-of-the-art video action recognition models is encoded into video embeddings to relate seen and unseen activity classes. Experiments on three public datasets find that our approach outperforms other learnt semantic spaces, with an additional desirable feature of scalability, as recognition performance is seen to scale with the amount of data used. More generally, our results indicate that exploiting information from the video domain for IMU-based tasks is a promising direction, with tangible returns in a zero-shot learning scenario.
Twin Meander Coil
Authors: Takahashi Ryo , Yukita Wakako , Sasatani Takuya , Yokota Tomoyuki , Someya Takao , Kawahara Yoshihiro
Published: December 2021

Energy-efficient and unconstrained wearable sensing platforms are essential for ubiquitous healthcare and activity monitoring applications. This paper presents Twin Meander Coil for wirelessly connecting battery-free on-body sensors to a textile-based reader knitted into clothing. This connection is based on passive inductive telemetry (PIT), wherein an external reader coil collects data from passive sensor coils via the magnetic field. In contrast to standard active sensing techniques, PIT does not require the reader to power up the sensors. Thus, the reader can be fabricated using a lossy conductive thread and industrial knitting machines. Furthermore, the sensors can superimpose information such as ID, touch, rotation, and pressure on its frequency response. However, conventional PIT technology needs a strong coupling between the reader and the sensor, requiring the reader to be small to the same extent as the sensors' size. Thus, applying this technology to body-scale sensing systems is challenging. To enable body-scale readout, Twin Meander Coil enhances the sensitivity of PIT technology by dividing the body-scale meander-shaped reader coils into two parts and integrating them so that they support the readout of each other. To demonstrate its feasibility, we built a prototype with a knitting machine, evaluated its sensing ability, and demonstrated several applications.
iMon
Authors: Huynh Sinh , Balan Rajesh Krishna , Ko JeongGil
Published: December 2021

Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to overcome errors caused by microsaccade eye motions that cause the exact gaze point to be uncertain. Second, we design an image enhancement model to refine visual details and remove motion blur effects of input eye images. Finally, we apply a calibration scheme to correct for differences between the perceived and actual gaze points caused by individual Kappa angle differences. With all these improvements, iMon achieves a person-independent per-frame tracking error of 1.49 cm (on smartphones) and 1.94 cm (on tablets) when tested with the GazeCapture dataset and 2.01 cm with the TabletGaze dataset. This outperforms the previous state-of-the-art solutions by ~22% to 28%. By averaging multiple per-frame estimations that belong to the same fixation point and applying personal calibration, the tracking error is further reduced to 1.11 cm (smartphones) and 1.59 cm (tablets). Finally, we built implementations that run on an iPhone 12 Pro and show that our mobile implementation of iMon can run at up to 60 frames per second - thus making gaze-based control of applications possible.
Auditable Augmented/Mixed/Virtual Reality
Authors: Cloete Richard , Norval Chris , Singh Jatinder
Published: December 2021

Virtual, Augmented and Mixed Reality (XR) technologies are becoming increasingly pervasive. However, the contextual nature of XR, and its tight coupling of the digital and physical environments, brings real propensity for loss and harm.

This means that auditability---the ability to inspect how a system operates---will be crucial for dealing with incidents as they occur, by providing the information enabling rectification, repair and recourse. However, supporting audit in XR brings considerations, as the process of capturing audit data itself has implications and challenges, both for the application (e.g., overheads) and more broadly.

This paper explores the practicalities of auditing XR systems, characterises the tensions between audit and other considerations, and argues the need for flexible tools enabling the management of such. In doing so, we introduce Droiditor, a configurable open-source Android toolkit that enables the runtime capture of audit-relevant data from mobile applications. We use Droiditor as a means to indicate some potential implications of audit data capture, demonstrate how greater configurability can assist in managing audit-related concerns, and discuss the potential considerations that result. Given the societal demands for more transparent and accountable systems, our broader aim is to draw attention to auditability, highlighting tangible ways forward and areas for future work.
Spatio-Temporal Graph Attention Embedding for Joint Crowd Flow and Transition Predictions
Authors: Yang Xi , He Suining , Wang Bing , Tabatabaie Mahan
Published: December 2021

Crowd mobility prediction, in particular, forecasting flows at and transitions across different locations, is essential for crowd analytics and management in spacious environments featured with large gathering. We propose GAEFT, a novel crowd mobility analytics system based on the multi-task graph attention neural network to forecast crowd flows (inflows/outflows) and transitions. Specifically, we leverage the collective and sanitized campus Wi-Fi association data provided by our university information technology service and conduct a relatable case study. Our comprehensive data analysis reveals the important challenges of sparsity and skewness, as well as the complex spatio-temporal variations within the crowd mobility data. Therefore, we design a novel spatio-temporal clustering method to group Wi-Fi access points (APs) with similar transition features, and obtain more regular mobility features for model inputs. We then propose an attention-based graph embedding design to capture the correlations among the crowd flows and transitions, and jointly predict the AP-level flows as well as transitions across buildings and clusters through a multi-task formulation. Extensive experimental studies using more than 28 million association records collected during 2020-2021 academic year validate the excellent accuracy of GAEFT in forecasting dynamic and complex crowd mobility.
Escape Path Obstacle-Based Mobility Model (EPOM) for Campus Delay-Tolerant Network
Authors: Bakura Sirajo Abdullahi , Lambert Alain , Nowak Thomas
Published: December 2021

In Delay-Tolerant Networks (DTNs), humans are the main carriers of mobile devices, signifying that human mobility can be exploited by extracting nodes’ interests, social behavior, and spatiotemporal features for the performance evaluation of DTNs protocols. This paper presents a new mobility model that describes students’ daily activities in a campus environment. Unlike the conventional random walk models, which use a free space environment, our model includes a collision-avoidance technique that generates an escape path upon encountering obstacles of different shapes and sizes that obstruct pedestrian movement. We evaluate the model’s usefulness by comparing the distributions of its synthetic traces with realistic traces in terms of spatial, temporal, and connectivity features of human mobility. Similarly, we analyze the concept of dynamic movement clusters observed on the location-based trajectories of the studied real traces. The model synthetically generates traces with the distribution of the intercluster travel distance, intracluster travel distance, direction of movement, contact duration, intercontact time, and pause time similar to the distribution of real traces.
Comparison of Patient Outcomes following Implantation of Trifocal and Extended Depth of Focus Intraocular Lenses: A Systematic Review and Meta-Analysis
Authors: Guo Yining , Wang Yinhao , Hao Ran , Jiang Xiaodan , Liu Ziyuan , Li Xuemin
Published: December 2021

Purpose. The purpose is to compare the outcomes of implantation of trifocal intraocular lenses (TIOLs) and extended depth of focus (EDOF) intraocular lenses (IOLs). Methods. A comprehensive search of PubMed, Cochrane Library, EMBASE, and ClinicalTrial.gov was conducted in March 2020 to identify relevant studies. A meta-analysis of the results was performed. Patients implanted with EDOF IOLs or TIOLs in previous studies were included. The primary outcomes of the study were uncorrected distance visual acuity (UDVA), uncorrected intermediate visual acuity (UIVA), uncorrected near visual acuity (UNVA), and defocus curve. Results. TIOLs and EDOF IOLs provided comparable binocular UDVA (MD = -0.01, 95% CI: -0.04, 0.03, logMAR). However, EDOF IOLs provided better UIVA (MD: -0.08, 95% CI: -0.14, -0.01, logMAR) and worse UNVA (MD: 0.10, 95% CI: 0.06, 0.14, logMAR) than TIOLs. Fewer patients achieved spectacle independence after implantation of EDOF IOLs (RR: 0.74, 95% CI: 0.63, 0.87) than after implantation of TIOLs, especially for near vision (RR = 0.82, 95% CI: 0.68, 0.99). There was no statistically significant difference in contrast sensitivity (CS) under photopic or mesopic conditions with both IOLs. Patient satisfaction after implantation of both IOLs was high. Conclusion. EDOF IOLs and TIOLs provide comparable distance vision. However, EDOF IOLs provide better intermediate vision and worse near vision than TIOLs. The advantages of EDOF IOLs over TIOLs in terms of CS, aberrations, and visual disturbance are not significant. Patients are satisfied with both types of IOLs.
Surgical Challenges in Multi-Vessel Minimally Invasive Coronary Artery Bypass Grafting
Authors: Liu Jia-Ji , Kong Qing-Yu , You Bin , Liang Lin , Xiao Wei , Ma Xiao-long , Pan Feng , Chi Li-Qun
Published: December 2021

Objectives. Minimally invasive coronary artery bypass grafting (MICS CABG) has emerged as an alternative treatment for patients with multi-vessel coronary artery disease, but there are certain surgical challenges inherent in the adoption of this approach. The present study was conducted to provide insight regarding the outcomes associated with our first 118 cases, to discuss the surgical difficulties encountered in these patients, and to outline the potential countermeasures. Methods. Between January 2017 and January 2020, 118 patients underwent multi-vessel MICS CABG. These patients were stratified into two groups based upon whether they did or did not experience surgical challenges, and early clinical outcomes were compared between these groups to assess the incidence of technical difficulties and associated factors. Results. Surgical challenges arose in 38 of the 118 cases in this study, including 13 cases of exposure-related difficulties, 11 cases of proximal anastomosis-related difficulties, 15 cases of distal anastomosis-related difficulties, 4 cases of LITA-related difficulties, and 3 cases of lung-related difficulties. Relative to the other 80 patients, those patients for whom intraoperative technical challenges arose experience significant increases in operative duration (4.94 ± 0.89 vs. 5.59 ± 1.11 h,

P
=
0.001

), intraoperative blood loss (667 ± 313 vs. 892 ± 532 mL,

P
=
0.005

), length of the ICU admission (17.59 ± 3.51 vs. 22.59 ± 17.31 h,

P
=
0.015

), and the duration of postoperative hospitalization (5.96 ± 1.23 vs. 6.71 ± 1.92 days,

P
=
0.012

). There were no significant differences between these groups with respect to the mean graft number, major complications such as stroke or organ dysfunction, or one-year graft patency. Conclusions. There is a substantial learning curve associated with performing off-pump MICS CABG to treat multi-vessel disease. Surgical challenges encountered during this procedure may increase the operative duration, intraoperative blood loss, ICU admission, and the duration of postoperative hospitalization. However, these issues do not appear to compromise the efficacy of complete revascularization, and early clinical outcomes associated with this procedure remain acceptable.
ZC3H13 Inhibits the Progression of Hepatocellular Carcinoma through m6A-PKM2-Mediated Glycolysis and Enhances Chemosensitivity
Authors: Wang Qibo , Xie Haichuan , Peng Hao , Yan Jianjian , Han Limin , Ye Gang
Published: December 2021

Objective. N6-Methyladenosine (m6A) is the most prevalent RNA epigenetic modulation in eukaryotic cells, which serves a critical role in diverse physiological processes. Emerging evidences indicate the prognostic significance of m6A regulator ZC3H13 in hepatocellular carcinoma (HCC). Herein, this study was conducted for revealing biological functions and mechanisms of ZC3H13 in HCC. Methods. Expression of ZC3H13 was examined in collected HCC and normal tissues, and its prognostic significance was investigated in a public database. Gain/loss of functional assays were presented for defining the roles of ZC3H13 in HCC progression. The specific interactions of ZC3H13 with PKM2 were validated in HCC cells via mRNA stability, RNA immunoprecipitation, and luciferase reporter and MeRIP‐qPCR assays. Moreover, rescue experiments were carried out for uncovering the mechanisms. Results. ZC3H13 expression was downregulated in HCC, and its loss was in relation to dismal survival outcomes. Functionally, overexpressed ZC3H13 suppressed proliferation, migration, and invasion and elevated apoptotic levels of HCC cells. Moreover, ZC3H13 overexpression sensitized to cisplatin and weakened metabolism reprogramming of HCC cells. Mechanically, ZC3H13-induced m6A modified patterns substantially abolished PKM2 mRNA stability. ZC3H13 facilitated malignant behaviors of HCC cells through PKM2-dependent glycolytic signaling. Conclusion. Collectively, ZC3H13 suppressed the progression of HCC through m6A-PKM2-mediated glycolysis and sensitized HCC cells to cisplatin, which offered a fresh insight into HCC therapy.