bluuebunny/crossref_metadata_2025 huggingface dataset:
Weight gain in Chinese youth during a 4-month COVID-19 lockdown: a retrospective observational study
Authors: Dun Yaoshan , Ripley-Gonzalez Jeffrey W. , Zhou Nanjiang , You Baiyang , Li Qiuxia , Li Hui , Zhang Wenliang , Thomas Randal J. , Olson Thomas P. , Liu Jie , Dong Yuchen , Liu Suixin
Published: July 2021

Objectives

To observe the weight change in Chinese youth during a 4-month COVID-19 lockdown, and the association between weight change and mental health, physical activity and sedentary time changes, and dietary habits.

Design

A retrospective observational study.

Settings

Two universities located in Zhejiang and Hunan provinces, China.

Participants

This study enrolled 12 889 college students whose body weight was measured before the lockdown (1 December 2019–20 January 2020) at the two universities, and reported their weight measured at home or community after the end of the lockdown (1–23 May 2020) via an online follow-up questionnaire.

Primary and secondary outcome measures

The primary outcome was the weight change in Chinese youth during a 4-month lockdown resulting from the COVID-19 pandemic. The secondary outcomes were the relationships of weight change to COVID-19-related stress, depression, anxiety, physical activity and sedentary time changes, and dietary habits.

Results

Participants’ ages ranged from 17 to 27 years (M=19, SD=1) with 80.2% identified as female. The average absolute and relative changes in body weight were 2.6 (95% CI 2.0 to 3.2)) kg and 4.2% (95% CI 4.0% to 4.3%) for men, and 2.1 (1.9 to 2.4) kg and 4.2% (95% CI 3.9% to 4.4%) for women. An increase in overweight and obese individuals according to Asian cut-off points as a demographic percentage by 4.5% and 2.7% and 4.8% and 3.4% in men and women, respectively (P<0.001), was observed. Weight gain was significantly associated with increased sedentary time and an increase in COVID-19-related stress and depression score.

Conclusion

The present study’s results suggest that the risk of weight gain in Chinese youth during the lockdown increased and that strategies to decrease sedentary time and improve mental health may be warranted to mitigate weight gain during and after the COVID-19 pandemic.
Breaking up prolonged sitting with a 6 min walk improves executive function in women and men esports players: a randomised trial
Authors: DiFrancisco-Donoghue Joanne , Jenny Seth E , Douris Peter C , Ahmad Sophia , Yuen Kyle , Hassan Tamzid , Gan Hillary , Abraham Kenney , Sousa Amber
Published: July 2021

Objective

The effect of prolonged sitting on executive function and performance in competitive esports players are unknown. This study aimed to evaluate executive function following a 6 min bout of walking or rest during prolonged gaming in competitive esports players.

Methods

12 men and 9 women completed three separate 2-hour gaming session days assigned in randomised order consisting of a 6 min walk break, 6 min rest break and continuous before and after each session. Postintervention participant exit survey data were also collected.

Results

The walk condition produced a significantly faster mean solution time (7613.6±3060.5 min, p=0.02) and planning time (5369.0±2802.09, p=0.04) compared with the resting condition (9477±3547.4; 6924±3247.7) and continuous play (8200.0±3031.6; 5862.7±2860.7). The rest condition resulted in the slowest mean solution time (9477.0±3547.4) and planning time (6924.0±3247.7), with the continuous play resulting in a faster mean solution time (8200.1±3031.6) and planning time (5862.7±2860.7) than the rest condition. There was no impact on game performance in any of the conditions. However, over 70% of participants felt that the walk break improved esports performance.

Conclusions

Reducing sit time and breaking up prolonged sitting have acute and chronic health benefits. This study provides evidence that a 6 min walking break in the middle of 2 hours of gameplay allows gamers to have these health benefits while improving processing speed and executive function.

Trial registration number

NCT04674436.
The utility of strain imaging in the cardiac surveillance of bone marrow transplant patients
Authors: Deshmukh Tejas , Emerson Peter , Geenty Paul , Mahendran Shehane , Stefani Luke , Hogg Megan , Brown Paula , Panicker Shyam , Chong James , Altman Mikhail , Gottlieb David , Thomas Liza
Published: July 2021

Objective

To evaluate the utility of two-dimensional multiplanar speckle tracking strain to assess for cardiotoxicity post allogenic bone marrow transplantation (BMT) for haematological conditions.

Methods

Cross-sectional study of 120 consecutive patients post-BMT (80 pretreated with anthracyclines (BMT+AC), 40 BMT alone) recruited from a late effects haematology clinic, compared with 80 healthy controls, as part of a long-term cardiotoxicity surveillance study (mean duration from BMT to transthoracic echocardiogram 6±6 years). Left ventricular global longitudinal strain (LV GLS), global circumferential strain (LV GCS) and right ventricular free wall strain (RV FWS) were compared with traditionl parameters of function including LV ejection fraction (LVEF) and RV fractional area change.

Results

LV GLS (−17.7±3.0% vs −20.2±1.9%), LV GCS (−14.7±3.5% vs −20.4±2.1%) and RV FWS (−22.6±4.7% vs −28.0±3.8%) were all significantly (p=0.001) reduced in BMT+AC versus controls, while only LV GCS (−15.9±3.5% vs −20.4±2.1%) and RV FWS (−23.9±3.5% vs −28.0±3.8%) were significantly (p=0.001) reduced in BMT group versus controls. Even in patients with LVEF >53%, ~75% of patients in both BMT groups demonstrated a reduction in GCS.

Conclusion

Multiplanar strain identifies a greater number of BMT patients with subclinical LV dysfunction rather than by GLS alone, and should be evaluated as part of post-BMT patient surveillence. Reduction in GCS is possibly due to effects of preconditioning, and is not fully explained by AC exposure.
First-in-human DR5 PET reveals insufficient DR5 expression in patients with gastrointestinal cancer
Authors: Wang Shujing , Zhu Hua , Li Yingjie , Ding Jin , Wang Feng , Ding Lixin , Wang Xinyu , Zhao Jun , Zhang Yan , Yao Yunfeng , Zhou Tong , Li Nan , Wu Aiwen , Yang Zhi
Published: July 2021

Background

Death receptor 5 (DR5) is a promising therapeutic target for cancer therapy. However, many clinical trials of DR5 agonists failed to show significant therapeutic efficacy in patients with cancer. The study aimed to investigate the feasibility of using89Zr-CTB006 positron emission tomography (PET) for noninvasive imaging of DR5 expression in preclinical models and patients with gastrointestinal (GI) cancers.

Methods

Balb/c, Sp2/0 xenograft and patient-derived tumor xenograft were employed for micro-PET/CT imaging in vivo. In the clinical study, patients with GI cancers planning to undergo surgical operation were enrolled and underwent18F-FDG and89Zr-CTB006 PET/CT. The tumor tissues were obtained through surgical operation and DR5 expression levels were confirmed by RNAscope.

Results

Preclinical studies showed that89Zr-CTB006 PET could specifically detect DR5 expression levels in vivo. Twenty-one patients, including nine gastric cancers and 12 colorectal cancers, were enrolled. The biodistribution showed high uptake in the liver and spleen and low uptake in the brain, lung and muscle with an acceptable whole-body dosimetry of 0.349 mSv/MBq. Strikingly, the adrenal glands maintained stable high uptake over the entire examination in all patients. The tumor lesions showed different levels of uptake of89Zr-CTB006 with a mean maximum standardized uptake value (SUVmax) of 6.63±3.29 (range 1.8–13.8). Tumor tissue was obtained from 18 patients, and89Zr-CTB006 uptake in patients with RNAscope scores of 3–4 was significantly higher than that in patients with scores of 0–2. An SUVmaxof 9.3 at 48 hours and 6.3 at 72 hours could be used to discriminate the DR5 expression status of tumors both with a sensitivity and specificity of 100% and 92.9%, respectively.

Conclusions

89Zr-CTB006 PET/CT is capable of detecting DR5 expression in cancer patients and is a promising approach to screen patients with DR5 overexpression.
Internal Medicine–Paediatrics residents’ application of life design principles to career decisions
Authors: Conyers Lynae , Tackett Sean , Wright Scott
Published: July 2021

Background

Residency is an important time for career planning. Little is known about how trainees make career decisions during this formative period.

Objective

The objective of this study was to understand whether ‘design thinking’ activities help to inform Internal Medicine–Paediatrics (Med-Peds) residents’ career decisions.

Methods

We performed a cross-sectional survey of a national sample of fourth-year Med-Peds residents in May–July 2019 covering intended career, helpfulness of ‘life design’ strategies used to inform career decision and confidence with intended career plans. Bivariate analyses were performed to evaluate associations between used strategies and confidence with career plans.

Results

A total of 86/145 (59%) of residents responded to the survey. The most helpful activities for clarifying career decisions were immersive exposure to the career during residency, and reflecting on compatibility of careers with their views of life and work. Finding the right mentor was associated with higher confidence in one's intended career path (p<0.05). There were no significant differences with confidence in intended career plan by gender or career path. Career decisions made during the first 2 years of residency were associated with higher confidence than those made prior to or in the second half of residency (p=0.01 and p=0.004).

Conclusion

This national survey of graduating Med-Peds residents reveals that proactive life design strategies were helpful in bringing clarity to their decision and were associated with higher confidence in intended career plans. These data should be of interest to residency training programmes in their efforts to support trainees with their career decisions.
Tobacco retail availability and cigarette and e-cigarette use among youth and adults: a scoping review
Authors: Travis Nargiz , Levy David T , McDaniel Patricia A , Henriksen Lisa
Published: July 2021

Objective

States and localities are formulating strategies to reduce the widespread retail availability of tobacco products. Evidence of associations between retailer density/proximity and tobacco use outcomes can help inform those strategies. We conducted a scoping review on tobacco retail availability and cigarette/e-cigarette use in adults and youth, and considered variations in spatial units, measures of retailer exposure and outcomes across studies.

Methods

A systematic search for studies examining the association between retailer density/proximity and youth and adult cigarette/e-cigarette use was conducted across MEDLINE (PubMed), Web of Science and Google Scholar through 27 August 2020 with no restrictions.

Results

Thirty-five studies were included in our qualitative synthesis. While there were differences in neighbourhood definitions (eg, egocentric vs administrative), there is evidence for a positive association between higher retailer density in egocentric neighbourhoods around homes and current smoking in adults and adolescents. Administrative unit measures in some studies showed associations with adult current smoking, and adolescent lifetime and current smoking. Studies on tobacco outlet proximity to homes obtained mixed results. Density/proximity of tobacco outlets around schools showed no or inverse association with adolescent smoking, but suggests higher susceptibility to smoking. Evidence of an association between e-cigarette retail availability and e-cigarette use is limited due to a small number of studies.

Conclusion

The current literature provides limited empirical evidence of the association between tobacco retailer availability and smoking or e-cigarette use. More research with uniform measures of environmental exposure to tobacco retailers is needed to allow for greater comparability between studies.
Impact of flavours, device, nicotine levels and price on adult e-cigarette users’ tobacco and nicotine product choices
Authors: Yang Yong , Lindblom Eric N. , Salloum Ramzi G. , Ward Kenneth D.
Published: July 2021

Introduction

To understand the impact of e-cigarette devices, flavours, nicotine levels and prices on adult e-cigarette users’ choices among closed-system and open-system e-cigarettes, cigarettes and heated tobacco products (HTPs).

Methods

Online discrete choice experiments were conducted among adult (≥18 years) e-cigarette users (n=2642) in August 2020. Conditional logit regressions were used to assess the relative impact of product attributes and the interactions between product attributes and user characteristics, with stratified analyses to examine differences by smoking status and primarily used e-cigarette device and flavour.

Results

On average, participants preferred non-tobacco and non-menthol flavours most, preferred open-system over closed-system e-cigarettes and preferred regular nicotine level over low nicotine level. However, the preference varied by demographics, smoking status and the primarily used e-cigarette device and flavour. The differences in preference among products/devices were larger than the difference among flavours or nicotine levels. Participants who primarily used closed-system e-cigarettes exhibited similar preferences for closed-system and open-system e-cigarettes, but those who primarily used open-system e-cigarettes preferred much more open-system over closed-system e-cigarettes. HTP was the least preferred product, much lower than cigarettes in general, but participants living in states where IQOS is being sold had similar preferences to cigarettes and HTPs.

Conclusions

People are unlikely to switch to another product/device because of the restriction of flavour or nicotine level. If non-tobacco and non-menthol flavours were banned from open-system e-cigarettes, users may switch to menthol flavour e-cigarettes. Intervention strategies should be tailored to specific groups.
Concurrent training remodels the subcutaneous adipose tissue extracellular matrix of people living with HIV: a non-randomized clinical trial
Authors: Silva B.F. , Barletta F. , Pedro R.E. , Batista M.L. , Hernandes L. , de Moraes S.M.F. , Peres S.B.
Published: July 2021

Evaluate the effect of 12 wks of concurrent training (CT) in the extracellular matrix (ECM) of subcutaneous adipose tissue (SAT) in people living with HIV/AIDS (PLWHA). In the non-randomized clinical trial, 19 participants, 11 healthy (HIV–) and 18 PLWHA under the use of highly active antiretroviral therapy (HAART) for at least 1 year (HIV+). All participants engaged in a moderate-intensity CT program for 12 weeks, 3 times a week. Before and after CT, aerobic and strength performance were assessed, as well as anthropometric and biochemical blood profiles. In addition, SAT biopsies were performed for histologic and morphometric analyses. Statistical analysis was carried out with R Studio, using descriptive and inferential analysis, ANOVA test, and mixed-effect model (P < 0.05). HIV+ showed higher levels of very-low-density lipoproteins and triglycerides and lower levels of high-density lipoproteins at baseline than HIV– (P < 0.05). All groups showed improved aerobic and strength performances (P < 0.05). Both groups showed reduced adipocyte sizes after CT (P < 0.05). Lastly, HIV+ presented smaller adipocytes and higher elastic fiber deposition at baseline and decreased after training only in HIV+, similar to the HIV group. Thus, CT in PLWHA promoted a decrease in the size heterogeneity of adipocytes and elastic fiber deposition, remodeling the ECM, and improving the SAT fibrosis profile. Brazilian Clinical Trials Registry (ensaiosclinicos.gov.br – UTN: U1111-1214-3022).

Novelty: Adipose tissue fibrosis is improved by training in people living with HIV. Concurrent training remodels adipose tissue extracellular matrix.
Compact objects by gravitational decoupling in f(R) gravity
Authors: Sharif M. , Aslam Mariyah
Published: July 2021

The objective of this paper is to discuss anisotropic solutions representing static spherical self-gravitating systems in f(R) theory. We employ the extended gravitational decoupling approach and transform temporal as well as radial metric potentials which decomposes the system of non-linear field equations into two arrays: one set corresponding to seed source and the other one involves additional source terms. The domain of the isotropic solution is extended in the background of f(R) Starobinsky model by employing the metric potentials of Krori–Barua spacetime. We determine two anisotropic solutions by employing some physical constraints on the extra source. The values of unknown constants are computed by matching the interior and exterior spacetimes. We inspect the physical viability, equilibrium and stability of the obtained solutions corresponding to the star Her X-I. It is observed that one of the two extensions satisfies all the necessary physical requirements for particular values of the decoupling parameter.
Three-dimensional exotic Newtonian supergravity theory with cosmological constant
Authors: Concha Patrick , Ravera Lucrezia , Rodríguez Evelyn
Published: July 2021

We present a supersymmetric extension of the exotic Newtonian Chern–Simons gravity theory in three spacetime dimensions. The underlying new non-relativistic superalgebra is obtained by expanding the $${\mathcal {N}}=2$$

N
=
2

AdS superalgebra and can be written as two copies of the enhanced Nappi–Witten algebra, one of which is augmented by supersymmetry. We show that the exotic Newtonian superalgebra allows to introduce a cosmological constant to the extended Newtonian supergravity. Interestingly, the obtained supergravity action contains the extended Newton–Hooke supergravity as a sub-case.
DBP-GAPred: An intelligent method for prediction of DNA-binding proteins types by enhanced evolutionary profile features with ensemble learning
Authors: Barukab Omar , Ali Farman , Khan Sher Afzal
Published: July 2021

DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung, breast, and liver cancer) and chronic diseases (i.e. AIDS/HIV, asthma), while other kinds are involved in antibiotics, steroids, and anti-inflammatory drugs designing. These crucial processes are closely related to DBPs types. DBPs are categorized into single-stranded DNA-binding proteins (ssDBPs) and double-stranded DNA-binding proteins (dsDBPs). Few computational predictors have been reported for discriminating ssDBPs and dsDBPs. However, due to the limitations of the existing methods, an intelligent computational system is still highly desirable. In this work, features from protein sequences are discovered by extending the notion of dipeptide composition (DPC), evolutionary difference formula (EDF), and K-separated bigram (KSB) into the position-specific scoring matrix (PSSM). The highly intrinsic information was encoded by a compression approach named discrete cosine transform (DCT) and the model was trained with support vector machine (SVM). The prediction performance was further boosted by the genetic algorithm (GA) ensemble strategy. The novel predictor (DBP-GAPred) acquired 1.89%, 0.28%, and 6.63% higher accuracies on jackknife, 10-fold, and independent dataset tests, respectively than the best predictor. These outcomes confirm the superiority of our method over the existing predictors.
Language in Economics and Accounting Research: The Role of Linguistic History
Authors: Gotti Giorgio , Roberts Seán G. , Fasan Marco , Robertson Cole B. J.
Published: July 2021

This paper investigates whether a consideration of linguistic history is important when studying the relationship between economic and linguistic behaviors. Several recent economic studies have suggested that differences between languages can affect the way people think and behave (linguistic relativity or Sapir–Whorf hypothesis). For example, the way a language obliges one to talk about the future might influence intertemporal decisions, such as a company’s earnings management. However, languages have historical relations that lead to shared features—they do not constitute independent observations. This can inflate correlations between variables if not dealt with appropriately (Galton’s problem). We discuss this problem and provide an overview of the latest methods to control linguistic history. We then provide an empirical demonstration of how Galton’s problem can bias results in an investigation of whether a company’s earnings management behavior is predicted by structural features of its employees’ language. We find a strong relationship when not controlling linguistic history, but the relationship disappears when controls are applied. In contrast, economic predictors of earnings management remain robust. Overall, our results suggest that careful consideration of linguistic history is important for distinguishing true causes from spurious correlations in economic behaviors.
A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures
Authors: Otoum Safa , Kantarci Burak , Mouftah Hussein
Published: July 2021

Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with

detection rate while SARSA-IDS and TD-IDS perform at the order of

.
Global-Local Enhancement Network for NMF-Aware Sign Language Recognition
Authors: Hu Hezhen , Zhou Wengang , Pu Junfu , Li Houqiang
Published: July 2021

Sign language recognition (SLR) is a challenging problem, involving complex manual features (i.e., hand gestures) and fine-grained non-manual features (NMFs) (i.e., facial expression, mouth shapes,
etc
.). Although manual features are dominant, non-manual features also play an important role in the expression of a sign word. Specifically, many sign words convey different meanings due to non-manual features, even though they share the same hand gestures. This ambiguity introduces great challenges in the recognition of sign words. To tackle the above issue, we propose a simple yet effective architecture called Global-Local Enhancement Network (GLE-Net), including two mutually promoted streams toward different crucial aspects of SLR. Of the two streams, one captures the global contextual relationship, while the other stream captures the discriminative fine-grained cues. Moreover, due to the lack of datasets explicitly focusing on this kind of feature, we introduce the first non-manual-feature-aware isolated Chinese sign language dataset (NMFs-CSL) with a total vocabulary size of 1,067 sign words in daily life. Extensive experiments on NMFs-CSL and SLR500 datasets demonstrate the effectiveness of our method.
IRTS: An Intelligent and Reliable Transmission Scheme for Screen Updates Delivery in DaaS
Authors: Zheng Hongdi , Wang Junfeng , Zhang Jianping , Li Ruirui
Published: July 2021

Desktop-as-a-service (DaaS) has been recognized as an elastic and economical solution that enables users to access personal desktops from anywhere at any time. During the interaction process of DaaS, users rely on screen updates to perceive execution results remotely, and thus the reliability and timeliness of screen updates transmission have a great influence on users’ quality of experience (QoE). However, the efficient transmission of screen updates in DaaS is facing severe challenges: most transmission schemes applied in DaaS determine sending strategies in terms of pre-set rules, lacking the intelligence to utilize bandwidth rationally and fit new network scenarios. Meanwhile, they tend to focus on reliability or timeliness and perform unsatisfactorily in ensuring reliability and timeliness simultaneously, leading to lower transmission efficiency of screen updates and users’ QoE when network conditions turn unfavorable. In this article, an intelligent and reliable end-to-end transmission scheme (IRTS) is proposed to cope with the preceding issues. IRTS draws support from reinforcement learning by adopting SARSA, an online learning method based on the temporal difference update rule, to grasp the optimal mapping between network states and sending actions, which extricates IRTS from the reliance on pre-set rules and augments its adaptability to different network conditions. Moreover, IRTS guarantees reliability and timeliness via an adaptive loss recovery method, which intends to recover lost screen updates data automatically with fountain code while controlling the number of redundant packets generated. Extensive performance evaluations are conducted, and numerical results show that IRTS outperforms the reference schemes in display quality, end-to-end delay/delay jitter, and fairness when transferring screen updates under various network conditions, proving that IRTS can enhance the transmission efficiency of screen updates and users’ QoE in DaaS.
MFECN: Multi-level Feature Enhanced Cumulative Network for Scene Text Detection
Authors: Liu Zhandong , Zhou Wengang , Li Houqiang
Published: July 2021

Recently, many scene text detection algorithms have achieved impressive performance by using convolutional neural networks. However, most of them do not make full use of the context among the hierarchical multi-level features to improve the performance of scene text detection. In this article, we present an efficient multi-level features enhanced cumulative framework based on instance segmentation for scene text detection. At first, we adopt a Multi-Level Features Enhanced Cumulative (
MFEC
) module to capture features of cumulative enhancement of representational ability. Then, a Multi-Level Features Fusion (
MFF
) module is designed to fully integrate both high-level and low-level MFEC features, which can adaptively encode scene text information. To verify the effectiveness of the proposed method, we perform experiments on six public datasets (namely, CTW1500, Total-text, MSRA-TD500, ICDAR2013, ICDAR2015, and MLT2017), and make comparisons with other state-of-the-art methods. Experimental results demonstrate that the proposed Multi-Level Features Enhanced Cumulative Network (MFECN) detector can well handle scene text instances with irregular shapes (i.e., curved, oriented, and horizontal) and achieves better or comparable results.
Residual Refinement Network with Attribute Guidance for Precise Saliency Detection
Authors: Lin Feng , Zhou Wengang , Deng Jiajun , Li Bin , Lu Yan , Li Houqiang
Published: July 2021

As an important topic in the multimedia and computer vision fields, salient object detection has been researched for years. Recently, state-of-the-art performance has been witnessed with the aid of the fully convolutional networks (FCNs) and the various pyramid-like encoder-decoder frameworks. Starting from a common encoder-decoder architecture, we enhance a residual refinement network with feature purification for better saliency estimation. To this end, we improve the global knowledge streams with intermediate supervisions for global saliency estimation and design a specific feature subtraction module for residual learning, respectively. On the basis of the strengthened network, we also introduce an attribute encoding sub-network (AENet) with a grid aggregation block (GAB) to guide the final saliency predictor to obtain more accurate saliency maps. Furthermore, the network is trained with a novel constraint loss besides the traditional cross-entropy loss to yield the finer results. Extensive experiments on five public benchmarks show our method achieves better or comparable performance compared with previous state-of-the-art methods.
Part-wise Spatio-temporal Attention Driven CNN-based 3D Human Action Recognition
Authors: Dhiman Chhavi , Vishwakarma Dinesh Kumar , Agarwal Paras
Published: July 2021

Recently, human activity recognition using skeleton data is increasing due to its ease of acquisition and finer shape details. Still, it suffers from a wide range of intra-class variation, inter-class similarity among the actions and view variation due to which extraction of discriminative spatial and temporal features is still a challenging problem. In this regard, we present a novel Residual Inception Attention Driven CNN (RIAC-Net) Network, which visualizes the dynamics of the action in a part-wise manner. The complete skeletonis partitioned into five key parts: Head to Spine, Left Leg, Right Leg, Left Hand, Right Hand. For each part, a Compact Action Skeleton Sequence (CASS) is defined. Part-wise skeleton-based motion dynamics highlights discriminative local features of the skeleton that helps to overcome the challenges of inter-class similarity and intra-class variation with improved recognition performance. The RIAC-Net architecture is inspired by the concept of inception-residual representation that unifies the Attention Driven Residues (ADR) with inception-based Spatio-Temporal Convolution Features (STCF) to learn efficient salient action features. An ablation study is also carried out to analyze the effect of ADR over simple residue-based action representation. The robustness of the proposed framework is evaluated by performing an extensive experiment on four challenging datasets: UT Kinect Action 3D, Florence 3D action, MSR Daily Action3D, and NTU RGB-D datasets, which consistently demonstrate the superiority of the proposed method over other state-of-the-art methods.
Is the Reign of Interactive Search Eternal? Findings from the Video Browser Showdown 2020
Authors: Lokoč Jakub , Veselý Patrik , Mejzlík František , Kovalčík Gregor , Souček Tomáš , Rossetto Luca , Schoeffmann Klaus , Bailer Werner , Gurrin Cathal , Sauter Loris , Song Jaeyub , Vrochidis Stefanos , Wu Jiaxin , Jónsson Björn þóR
Published: July 2021

Comprehensive and fair performance evaluation of information retrieval systems represents an essential task for the current information age. Whereas Cranfield-based evaluations with benchmark datasets support development of retrieval models, significant evaluation efforts are required also for user-oriented systems that try to boost performance with an interactive search approach. This article presents findings from the 9th Video Browser Showdown, a competition that focuses on a legitimate comparison of interactive search systems designed for challenging known-item search tasks over a large video collection. During previous installments of the competition, the interactive nature of participating systems was a key feature to satisfy known-item search needs, and this article continues to support this hypothesis. Despite the fact that top-performing systems integrate the most recent deep learning models into their retrieval process, interactive searching remains a necessary component of successful strategies for known-item search tasks. Alongside the description of competition settings, evaluated tasks, participating teams, and overall results, this article presents a detailed analysis of query logs collected by the top three performing systems, SOMHunter, VIRET, and vitrivr. The analysis provides a quantitative insight to the observed performance of the systems and constitutes a new baseline methodology for future events. The results reveal that the top two systems mostly relied on temporal queries before a correct frame was identified. An interaction log analysis complements the result log findings and points to the importance of result set and video browsing approaches. Finally, various outlooks are discussed in order to improve the Video Browser Showdown challenge in the future.
Lifelog Image Retrieval Based on Semantic Relevance Mapping
Authors: Xu Qianli , Molino Ana Garcia Del , Lin Jie , Fang Fen , Subbaraju Vigneshwaran , Li Liyuan , Lim Joo-Hwee
Published: July 2021

Lifelog analytics is an emerging research area with technologies embracing the latest advances in machine learning, wearable computing, and data analytics. However, state-of-the-art technologies are still inadequate to distill voluminous multimodal lifelog data into high quality insights. In this article, we propose a novel
semantic relevance mapping
(
SRM
) method to tackle the problem of lifelog information access. We formulate lifelog image retrieval as a series of mapping processes where a semantic gap exists for relating basic semantic attributes with high-level query topics. The SRM serves both as a formalism to construct a trainable model to bridge the semantic gap and an algorithm to implement the training process on real-world lifelog data. Based on the SRM, we propose a computational framework of lifelog analytics to support various applications of lifelog information access, such as image retrieval, summarization, and insight visualization. Systematic evaluations are performed on three challenging benchmarking tasks to show the effectiveness of our method.
A Densely Connected Network Based on U-Net for Medical Image Segmentation
Authors: Yang Zhenzhen , Xu Pengfei , Yang Yongpeng , Bao Bing-Kun
Published: July 2021

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.
Video Decolorization Based on the CNN and LSTM Neural Network
Authors: Liu Shiguang , Wang Huixin , Zhang Xiaoli
Published: July 2021

Video decolorization is the process of transferring three-channel color videos into single-channel grayscale videos, which is essentially the decolorization operation of video frames. Most existing video decolorization algorithms directly apply image decolorization methods to decolorize video frames. However, if we only take the single-frame decolorization result into account, it will inevitably cause temporal inconsistency and flicker phenomenon meaning that the same local content between continuous video frames may display different gray values. In addition, there are often similar local content features between video frames, which indicates redundant information. To solve the preceding problems, this article proposes a novel video decolorization algorithm based on the convolutional neural network and the long short-term memory neural network. First, we design a local semantic content encoder to learn and extract the same local content of continuous video frames, which can better preserve the contrast of video frames. Second, a temporal feature controller based on the bi-directional recurrent neural networks with Long short-term memory units is employed to refine the local semantic features, which can greatly maintain temporal consistency of the video sequence to eliminate the flicker phenomenon. Finally, we take advantages of deconvolution to decode the features to produce the grayscale video sequence. Experiments have indicated that our method can better preserve the local contrast of video frames and the temporal consistency over the state of the-art.
Cognitive Wearable Robotics for Autism Perception Enhancement
Authors: Chen Min , Xiao Wenjing , Hu Long , Ma Yujun , Zhang Yin , Tao Guangming
Published: July 2021

Autism spectrum disorder (ASD) is a serious hazard to the physical and mental health of children, which limits the social activities of patients throughout their lives and places a heavy burden on families and society. The developments of communication techniques and artificial intelligence (AI) have provided new potential methods for the treatment of autism. The existing treatment systems based on AI for children with ASD focus on detecting health status and developing social skills. However, the contradiction between the terminal interaction capability and availability cannot meet the needs for real application scenarios. At the same time, the lack of diverse data cannot provide individualized care for autistic children. To explore this robot-based approach, a novel AI-based first-view-robot architecture is proposed in this article. By providing care from the first-person perspective, the proposed wearable robot overcomes the difficulty of the absence of cognitive ability in the third-view of traditional robotics and improves the social interaction ability of children with ASD. The first-view-robot architecture meets the requirements of dynamic, individualized, and highly immersed interaction services for autistic children. First, the multi-modal and multi-scene data collection processes of standard, static, and dynamic datasets are introduced in detail. Then, to comprehensively evaluate the learning ability of children with ASD through mental states and external performances, a learning assessment model with emotion correction is proposed. Besides, a wearable robot-assisted environment perception and expression enhancement mechanism for children with ASD is realized by reinforcement learning, which can be adapted to interactive environments with optimal action policies. An interactive testbed for children with ASD treatments is demonstrated and experimental cases for test subjects are presented. Last, three open issues are discussed from data processing, robot designing, and service responding perspectives.
Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection
Authors: Qian Shengsheng , Hu Jun , Fang Quan , Xu Changsheng
Published: July 2021

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.
Characterizing Student Proficiency in Software Modeling in Terms of Functions, Structures, and Behaviors
Authors: Thomas Paul J. , Patel Devang , Magana Alejandra J.
Published: July 2021

Software modeling is an integral practice for software engineers, especially as the complexity of software solutions increases. Unified Modeling Language (UML) is the industry standard for software modeling. however, it is often used incorrectly and misunderstood by novice software designers. This study is centered around understanding patterns of student proficiency of abstraction and systems thinking within a software modeling context. The participants of this study (n = 97) belonged to a systems analysis and design course that is primarily taken by second-year university students. The exam solutions to a case study from the course were evaluated for modeling proficiency. As evidence of proficiency in abstract thinking and systems thinking, we evaluated UML activity diagrams, class diagrams, and sequence diagrams and the alignment between these representations in terms of functions, structures, and behaviors. The results suggest students being proficient in modeling the functional aspects of an information system while facing some difficulty in capturing the structural and behavioral aspects of an information system. Clustering analysis revealed two groups within the sample, with one group displaying a significantly higher abstraction and systems thinking ability. Statistically significant correlations were also found between student proficiency of abstraction and their modeling proficiency in terms of functions, structures, and behaviors.
Recorp: Receiver-oriented Policies for Industrial Wireless Networks
Authors: Brummet Ryan , Hossain Md Kowsar , Chipara Octav , Herman Ted , Stewart David E.
Published: July 2021

Future Industrial Internet-of-Things (IIoT) systems will require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge in such networks is to provide predictable performance and adaptability in response to link quality variations. We address this challenge by developing RECeiver ORiented Policies (Recorp), which leverages the stability of IIoT workloads by combining offline policy synthesis and run-time adaptation. Compared to schedules that service a single flow in a slot, Recorp policies share slots among multiple flows by assigning a coordinator and a list of flows that may be serviced in the same slot. At run-time, the coordinator will execute one of the flows depending on which flows the coordinator has already received. A salient feature of Recorp is that it provides predictable performance: a policy meets the end-to-end reliability and deadline of flows when the link quality exceeds a user-specified threshold. Experiments show that across IIoT workloads, policies provided a median increase of 50% to 142% in real-time capacity and a median decrease of 27% to 70% in worst-case latency when schedules and policies are configured to meet an end-to-end reliability of 99%.
Lock-free Contention Adapting Search Trees
Authors: Winblad Kjell , Sagonas Konstantinos , Jonsson Bengt
Published: July 2021

Concurrent key-value stores with range query support are crucial for the scalability and performance of many applications. Existing lock-free data structures of this kind use a fixed synchronization granularity. Using a fixed synchronization granularity in a concurrent key-value store with range query support is problematic as the best performing synchronization granularity depends on a number of factors that are difficult to predict, such as the level of contention and the number of items that are accessed by range queries. We present the first linearizable lock-free key-value store with range query support that dynamically adapts its synchronization granularity. This data structure is called the lock-free contention adapting search tree (LFCA tree). An LFCA tree automatically performs local adaptations of its synchronization granularity based on heuristics that take contention and the performance of range queries into account. We show that the operations of LFCA trees are linearizable, that the lookup operation is wait-free, and that the remaining operations (insert, remove and range query) are lock-free. Our experimental evaluation shows that LFCA trees achieve more than twice the throughput of related lock-free data structures in many scenarios. Furthermore, LFCA trees are able to perform substantially better than data structures with a fixed synchronization granularity over a wide range of scenarios due to their ability to adapt to the scenario at hand.
Scalable Privacy-preserving Geo-distance Evaluation for Precision Agriculture IoT Systems
Authors: Yan Qiben , Lou Jianzhi , Vuran Mehmet C. , Irmak Suat
Published: July 2021

Precision agriculture has become a promising paradigm to transform modern agriculture. The recent revolution in big data and Internet-of-Things (IoT) provides unprecedented benefits including optimizing yield, minimizing environmental impact, and reducing cost. However, the mass collection of farm data in IoT applications raises serious concerns about potential privacy leakage that may harm the farmers’ welfare. In this work, we propose a novel scalable and private geo-distance evaluation system, called SPRIDE, to allow application servers to provide geographic-based services by computing the distances among sensors and farms privately. The servers determine the distances without learning any additional information about their locations. The key idea of SPRIDE is to perform efficient distance measurement and distance comparison on encrypted locations over a sphere by leveraging a homomorphic cryptosystem. To serve a large user base, we further propose SPRIDE+ with novel and practical performance enhancements based on pre-computation of cryptographic elements. Through extensive experiments using real-world datasets, we show SPRIDE+ achieves private distance evaluation on a large network of farms, attaining 3+ times runtime performance improvement over existing techniques. We further show SPRIDE+ can run on resource-constrained mobile devices, which offers a practical solution for privacy-preserving precision agriculture IoT applications.