Mobile Intellectual Property Marketplace Model for Commercialization of Intellectual Property Rights

This study discusses the development of the intellectual property (IP) marketplace model based on mobile location-aware computing. Referring to statistics released by the Directorate General of Intellectual Property, there has been a growth in the number of intellectual property rights (IPR) applications in recent years, even during the Covid-19 pandemic. On the other hand, after IPR protection, the commercialization of IPR is one of the pillars of the IP system. Nevertheless, research institutions such as LIPI/BRIN indicate that the potential for commercializing IPR is still low. Furthermore, the opportunity is that cellular networks have covered almost all parts of Indonesia, and there has been significant growth in smartphone users. The method utilized in this research is prototyping. This research results from an IP marketplace model based on mobile location-aware computing in Indonesia. Using the smartphone user's location, contextual IPR information from the user's location related to IPR will enter their smartphone. The experimental results indicate that the application can display a list of IPR information according to the smartphone user's location. Furthermore, the search feature can forage IPR listing information based on user queries. © 2022 IEEE.

LLDSNet: Limited labeled data segmentation network for COVID-19 CT lesion segmentation

With the COVID-19 outbreak in 2019, the world is facing a major crisis and people's health is at serious risk. Accurate segmentation of lesions in CT images can help doctors understand disease infections, prescribe the right medicine and control patients' conditions. Fast and accurate diagnosis not only can make the limited medical resources get reasonable allocation, but also can control the spread of disease, and computer-aided diagnosis can achieve this purpose, so this paper proposes a deep learning segmentation network LLDSNet based on a small amount of data, which is divided into two modules: contextual feature-aware module (CFAM) and shape edge detection module (SEDM). Due to the different morphology of lesions in different CT, lesions with dispersion, small lesion area and background area imbalance, lesion area and normal area boundary blurred, etc. The problem of lesion segmentation in COVID-19 poses a major challenge. The CFAM can effectively extract the overall and local features, and the SEDM can accurately find the edges of the lesion area to segment the lesions in this area. The hybrid loss function is used to avoid the class imbalance problem and improve the overall network performance. It is demonstrated that LLDSNet dice achieves 0.696 for a small number of data sets, and the best performance compared to five currently popular segmentation networks. © 2023 SPIE.

Competitive gamification in crowdsourcing-based contextual-aware recommender systems.

During the COVID-19 outbreak, crowdsourcing-based context-aware recommender systems (CARS) which capture the real-time context in a contactless manner played an important role in the "new normal". This study investigates whether this approach effectively supports users' decisions during epidemics and how different game designs affect users performing crowdsourcing tasks. This study developed a crowdsourcing-based CARS focusing on restaurant recommendations. We used four conditions (control, self-competitive, social-competitive, and mixed gamification) and conducted a two-week field study involving 68 users. The system provided recommendations based on real-time contexts including restaurants' epidemic status, allowing users to identify suitable restaurants to visit during COVID-19. The result demonstrates the feasibility of crowdsourcing to collect real-time information for recommendations during COVID-19 and reveals that a mixed competitive game design encourages both high- and low-performance users to engage more and that a game design with self-competitive elements motivates users to take on a wider variety of tasks. These findings inform the design of restaurant recommender systems in an epidemic context and serve as a comparison of incentive mechanisms for gamification of self-competition and competition with others.

11-Year Trend in Antibiotic Consumption in a South-Eastern European Country; the Situation in Albania and the Implications for the Future.

There are growing concerns with rising antimicrobial resistance (AMR) across countries. These concerns are enhanced by the increasing and inappropriate utilization of 'Watch' antibiotics with their greater resistance potential, AMR is further exacerbated by the increasing use of antibiotics to treat patients with COVID-19 despite little evidence of bacterial infections. Currently, little is known about antibiotic utilization patterns in Albania in recent years, including the pandemic years, the influence of an ageing population, as well as increasing GDP and greater healthcare governance. Consequently, total utilization patterns in the country were tracked from 2011 to 2021 alongside key indicators. Key indicators included total utilization as well as changes in the use of 'Watch' antibiotics. Antibiotic consumption fell from 27.4 DIDs (defined daily doses per 1000 inhabitants per day) in 2011 to 18.8 DIDs in 2019, which was assisted by an ageing population and improved infrastructures. However, there was an appreciable increase in the use of 'Watch' antibiotics during the study period. Their utilization rose from 10% of the total utilization among the top 10 most utilized antibiotics (DID basis) in 2011 to 70% by 2019. Antibiotic utilization subsequently rose after the pandemic to 25.1 DIDs in 2021, reversing previous downward trends. Alongside this, there was increasing use of 'Watch' antibiotics, which accounted for 82% (DID basis) of the top 10 antibiotics in 2021. In conclusion, educational activities and antimicrobial stewardship programs are urgently needed in Albania to reduce inappropriate utilization, including 'Watch' antibiotics, and hence AMR.

The Integration of Risk Aspects into Business Process Management: The e-BPRIM Modeling Method

Risk consideration in enterprise engineering is gaining attention since the business environment is becoming more and more competitive, complex, and unpredictable. Risk-aware Business Process Management (R-BPM) is a recently emerged management paradigm, which assists organizations in addressing this concern. R-BPM strives to integrate two traditionally isolated areas: risk management and business process management. This chapter will present recent achievements of our long-term research devoted to this field. It consists in developing an integrated process-risk management methodological framework, named BPRIM, and its related multi-view modeling method, called e-BPRIM, which promotes and supports risk-aware process management with ADOBPRIM, a computer-assisted modeling environment based on ADOXX. A case study related to the management of the COVID-19 pandemic in France shall illustrate the usage of the e-BPRIM method with the ADOBPRIM modeling environment. © Springer International Publishing AG 2018.

Nonlinear analysis and dynamics of COVID-19 mathematical model with optimal control strategies

Efficacy of the healthcare system and illumination (awareness) activities control COVID-19. To defend public health, the spreading pandemic of COVID-19 disease necessitates social distancing, wearing masks, personal cleanliness, and precautions. Due to inadequate awareness programs, COVID-19 rapidly increases in India. The primary goal of this research is to investigate the spreading behavior of the COVID-19 virus in India when people are aware of the disease. We find the optimum value of disease transmission rate and detection of the unidentified asymptomatic and symptomatic populations. An optimal control problem is designed with limited resource allocation to improve the recovered individuals. A stability analysis presents for emphasizes the relevance of disease awareness in preventing the spread of the disease. The control parameters are used to explore the increase and decrease of the infected individual with and without control in optimal control analysis. The model is simulated using the Hattaf-fractional derivative to study the memory effect in the epidemic. To adapt the model to the total number of reported COVID-19 cases in India, we collected data from March 20, 2021 to September 30, 2021. According to the simulation results, the pandemic would spread faster if awareness campaigns were improperly carried out.

Personalized, context-aware, and adaptable persuasive approach for encouraging physical activity among older adults

Seclusion and sedentary lifestyle are the main causes of many psychological and physical health problems. They may be among the top 10 causes of death and disability in the world. The pandemic crisis context of COVID has deepened these problems, especially for older adults who have been isolated, deprived of their relatives and of doing physical activities. In this paper, we introduce an adaptive, personalized, and context-aware persuasive platform to stimulate physical activities of older adults without deception or coercion. Our persuasion approach is customizable, in the sense that every older adult has its personal profile. It is also adaptive because it can use a persuasion loop to change the persuasion strategy when the older adult does not adhere to the proposed persuasion strategy. Furthermore, our persuasion approach is context-aware as it takes account of contextual location and weather information in the provision of the persuasion strategy. To validate our approach, we implemented "ActiveSenior”. Then, we carried out a large-scale challenge for one month to approve the results of our persuasive approach. The evaluation of the acceptance of our ActiveSenior system was encouraging as most of the interviewed participants were satisfied. In addition, the obtained results showed a marked improvement in the physical activity of older adults, quantified by the number of steps taken per day. © 2023 Elsevier B.V.

Antibiotic Overprescribing among Neonates and Children Hospitalized with COVID-19 in Pakistan and the Implications.

There are concerns with excessive antibiotic prescribing among patients admitted to hospital with COVID-19, increasing antimicrobial resistance (AMR). Most studies have been conducted in adults with limited data on neonates and children, including in Pakistan. A retrospective study was conducted among four referral/tertiary care hospitals, including the clinical manifestations, laboratory findings, the prevalence of bacterial co-infections or secondary bacterial infections and antibiotics prescribed among neonates and children hospitalized due to COVID-19. Among 1237 neonates and children, 511 were admitted to the COVID-19 wards and 433 were finally included in the study. The majority of admitted children were COVID-19-positive (85.9%) with severe COVID-19 (38.2%), and 37.4% were admitted to the ICU. The prevalence of bacterial co-infections or secondary bacterial infections was 3.7%; however, 85.5% were prescribed antibiotics during their hospital stay (average 1.70 ± 0.98 antibiotics per patient). Further, 54.3% were prescribed two antibiotics via the parenteral route (75.5%) for ≤5 days (57.5), with most being 'Watch' antibiotics (80.4%). Increased antibiotic prescribing was reported among patients requiring mechanical ventilation and high WBCs, CRP, D-dimer and ferritin levels (p < 0.001). Increased COVID-19 severity, length of stay and hospital setting were significantly associated with antibiotic prescribing (p < 0.001). Excessive antibiotic prescribing among hospitalized neonates and children, despite very low bacterial co-infections or secondary bacterial infections, requires urgent attention to reduce AMR.

MSCAF-Net: A General Framework for Camouflaged Object Detection via Learning Multi-Scale Context-Aware Features

The aim of camouflaged object detection (COD) is to find objects that are hidden in their surrounding environment. Due to the factors like low illumination, occlusion, small size and high similarity to the background, COD is recognized to be a very challenging task. In this paper, we propose a general COD framework, termed as MSCAF-Net, focusing on learning multi-scale context-aware features. To achieve this target, we first adopt the improved Pyramid Vision Transformer (PVTv2) model as the backbone to extract global contextual information at multiple scales. An enhanced receptive field (ERF) module is then designed to refine the features at each scale. Further, a cross-scale feature fusion (CSFF) module is introduced to achieve sufficient interaction of multi-scale information, aiming to enrich the scale diversity of extracted features. In addition, inspired the mechanism of the human visual system, a dense interactive decoder (DID) module is devised to output a rough localization map, which is used to modulate the fused features obtained in the CSFF module for more accurate detection. The effectiveness of our MSCAF-Net is validated on four benchmark datasets. The results show that the proposed method significantly outperforms state-of-the-art (SOTA) COD models by a large margin. Besides, we also investigate the potential of our MSCAF-Net on some other vision tasks that are highly related to COD, such as polyp segmentation, COVID-19 lung infection segmentation, transparent object detection and defect detection. Experimental results demonstrate the high versatility of the proposed MSCAF-Net. The source code and results of our method are available at https://github.com/yuliu316316/MSCAF-COD. IEEE

Forecasting Emerging Pandemics with Transfer Learning and Location-aware News Analysis

Monitoring and forecasting epidemic diseases are of prime importance to public health organizations and policymakers in taking proper measures and adjusting prevention tactics. Early prediction is especially important to restrict the spread of emerging pandemics such as COVID-19. However, despite increasing research and development for various epidemics, several challenges remain unresolved. On the one hand, early-stage epidemic prediction for emerging new diseases is difficult because of data paucity and lack of experience. On the other hand, many existing studies ignore or fail to leverage the contribution of social factors such as news, geolocations, and climate. Even though some researchers have recognized the profound impact of social features, capturing the dynamic correlation between these features and pandemics requires an extensive understanding of heterogeneous formats of data and mechanisms. In this paper, we design TLSS, a neural transfer learning architecture for learning and transferring general characteristics of existing epidemic diseases to predict a new pandemic. We propose a new feature module to learn the impact of news sentiment and semantic information on epidemic transmission. We then combine this information with historical time-series features to forecast future infection cases in a dynamic propagation process. We compare the proposed model with several state-of-the-art statistics approaches and deep learning methods in epidemic prediction with different lead times of ground truth. We conducted extensive experiments on three stages of COVID-19 development in the United States. Our experiment demonstrates that our approach has strong predictive performance for COVID infection cases, especially with longer lead times. © 2022 IEEE.

A Temporal-Context-Aware Approach for Individual Human Mobility Inference Based on Sparse Trajectory Data

Inferring individual human mobility at a given time is not only beneficial for personalized location-based services, but also crucial for trajectory tracking of the confirmed cases in the context of the COVID-19 pandemic. However, individual generated trajectory data using mobile Apps is characterized by implicit feedback, which means only a few individual-location interactions can be observed. Existing studies based on such sparse trajectory data are not sufficient to infer individual's missing mobility in his/her historical trajectory and further predict individual's future mobility given a specific time. To address this concern, in this paper, we propose a temporal-context-aware approach that incorporates multiple factors to model the time sensitive individual-location interactions in a bottom-up way. Based on the idea of feature fusion, the driving effect of heterogeneous information such as time, space, category and sentiment on individual's mobile behavior is gradually strengthened, so that the temporal context when a check-in occurs can be accurately depicted. We leverage Bayesian Personalized Ranking (BPR) to optimize the model, where a novel negative sampling method is employed to alleviate data sparseness. Based on three real-world datasets, we evaluate the proposed approach with regard to two different tasks, namely, missing mobility inference and future mobility prediction at a given time. The empirical results encouragingly demonstrate that our approach outperforms multiple baselines in terms of two evaluation metrics, i.e., accuracy and average percentile rank. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification.

The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health. It is of great importance to rapidly and accurately screen patients with COVID-19 from community acquired pneumonia (CAP). In this study, a total of 1658 patients with COVID-19 and 1027 CAP patients underwent thin-section CT. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to conventional CT severity score (CT-SS) and Radiomics features. An infection Size Aware Random Forest method (iSARF) was used for classification. Experimental results show that the proposed method yielded best performance when using the handcrafted features with sensitivity of 91.6%, specificity of 86.8%, and accuracy of 89.8% over state-of-the-art classifiers. Additional test on 734 subjects with thick slice images demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making. Furthermore, the data of extracted features will be made available after the review process.

Boosting COVID-19 Severity Detection with Infection-Aware Contrastive Mixup Classification

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifically, we train two segmentation networks to first extract the lung region and then the inner lesion region. The lesion segmentation mask serves as complementary information for the original CT slices. To relieve the issue of imbalanced data distribution, we further improve the advanced Contrastive Mixup Classification network by weighted cross-entropy loss. On the COVID-19 severity detection leaderboard, our approach won the first place with a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by over 11.46%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Antimicrobial Consumption from 2017 to 2021 in East Trinidad and Tobago: A Study in the English-Speaking Caribbean.

An antimicrobial consumption (AMC) study was performed in Trinidad and Tobago at the Eastern Regional Health Authority (ERHA). A retrospective, cross-sectional survey was conducted from 1 November 2021 to 30 March 2022. Dosage and package types of amoxicillin, azithromycin, co-amoxiclav, cefuroxime, ciprofloxacin, levofloxacin, moxifloxacin, nitrofurantoin and co-trimoxazole were investigated. Consumption was measured using the World Health Organization's Antimicrobial Resistance and Consumption Surveillance System methodology version 1.0, as defined daily doses (DDD) per 1000 population per day (DID). They were also analyzed using the 'Access', 'Watch' and 'Reserve' classifications. In the ERHA, AMC ranged from 6.9 DID to 4.6 DID. With regards to intravenous formulations, the 'Watch' group displayed increased consumption, from 0.160 DID in 2017 to 0.238 DID in 2019, followed by a subsequent drop in consumption with the onset of the COVID-19 pandemic. Oral co-amoxiclav, oral cefuroxime, oral azithromycin and oral co-trimoxazole were the most highly consumed antibiotics. The hospital started off as the higher consumer of antibiotics, but this changed to the community. The consumption of 'Watch' group antibiotics increased from 2017 to 2021, with a drop in consumption of 'Access' antibiotics and at the onset of COVID-19. Consumption of oral azithromycin was higher in 2021 than 2020.

Knowledge, Attitude and Practices of Self-Medication Including Antibiotics among Health Care Professionals during the COVID-19 Pandemic in Pakistan: Findings and Implications.

Since the emergence of COVID-19, several different medicines including antimicrobials have been administered to patients to treat COVID-19. This is despite limited evidence of the effectiveness of many of these, fueled by misinformation. These utilization patterns have resulted in concerns for patients' safety and a rise in antimicrobial resistance (AMR). Healthcare workers (HCWs) were required to serve in high-risk areas throughout the pandemic. Consequently, they may be inclined towards self-medication. However, they have a responsibility to ensure any medicines recommended or prescribed for the management of patients with COVID-19 are evidence-based. However, this is not always the case. A descriptive cross-sectional study was conducted among HCWs in six districts of the Punjab to assess their knowledge, attitude and practices of self-medication during the ongoing pandemic. This included HCWs working a range of public sector hospitals in the Punjab Province. A total of 1173 HCWs were included in the final analysis. The majority of HCWs possessed good knowledge regarding self-medication and good attitudes. However, 60% were practicing self-medication amid the COVID-19 pandemic. The most frequent medicines consumed by the HCWs under self-medication were antipyretics (100%), antibiotics (80.4%) and vitamins (59.9%). Azithromycin was the most commonly purchase antibiotic (35.1%). In conclusion, HCWs possess good knowledge of, and attitude regarding, medicines they purchased. However, there are concerns that high rates of purchasing antibiotics, especially "Watch" antibiotics, for self-medication may enhance AMR. This needs addressing.

Assessment of antibiotic prescribing patterns at dental and primary health care clinics according to WHO Access, Watch, Reserve (AWaRe) classification.

BACKGROUND: The 2019 WHO Access, Watch, Reserve (AWaRe) antibiotic classification framework aims to prevent irrational prescribing of antibiotics used to treat widespread infections. This study explored antibiotic prescribing pattern for appropriate indications by family physicians and general dentists in primary health care practices. METHODS: A retrospective review of patients' electronic medical records was conducted over 6 months, from May 1, 2020, to November 30, 2020. The data were collected from 24 general family medicine and dental practices within the North West Armed Forces in Tabuk city. Antibiotic prescribing for systemic use (J01) was assessed by the number of prescriptions and the number defined daily doses (DDDs) and then analyzed according to the AWaRe classification. The prescribing of antibiotics for appropriate indications was assessed through comparing the prescription pattern with the recently published and relevant clinical guidelines. Multivariate logistic regression analysis was used to predict the association between the prescribing of AWaRe category and some demographic and disease-related factors. RESULTS: In total, 752 prescriptions of antibiotics were collected. Watch-group antibiotics such as second-generation cephalosporin and macrolides were more likely prescribed (51.1%) based on the number of prescriptions and (52.2%) based on DDDs compared with Access-group antibiotics (48.9%) and (47.8%), respectively. The percentages of Watch group antibiotics for children and adults were 66.7% and 42.9%, respectively. Adherence to prescribing guidelines was poor for children (27.2%) and adults (64%). Being a child (adjusted odds ratio [OR]: 2.89; 95% confidence interval [CI] = 1.46-5.78), diagnosis with acute respiratory tract infection (adjusted OR, 2.62; 95% CI = 1.03-6.69), and urinary tract infection (adjusted OR, 4.69; 95% CI = 2.09-10.56) were associated with higher prescriptions of Watch-group antibiotics. CONCLUSIONS: a higher prescribing of Watch-group antibiotics and poor adherence to antibiotic guidelines were observed, especially for children. The findings of this study identified targets for further improvement and interventions needed to develop better antibiotic-prescribing practices.

Therapeutic work with clients living in poverty.

BACKGROUND: Financial inequalities appear to be increasing and poverty is becoming ubiquitous. Poverty affects mental health but its impact on mental health and wellbeing is rarely highlighted within health research. AIMS: The Covid-19 pandemic, the Ukrainian invasion and other international and national events have led to a cost-of-living crisis for many people. This is likely to lead to an increase in related referrals and therefore active consideration of the relevant issues relating to poverty appears vital. This paper reports a study which sought to understand how therapists experienced their work with clients who self-refer due to living in poverty. METHOD: Eight therapists participated in semi-structured interviews analysed using Interpretative Phenomenological Analysis (IPA). RESULTS: Three superordinate themes were elicited: firstly 'Resilience in the struggle to engage with therapeutic work', secondly 'Struggling to promote social activism' and thirdly, 'Navigating multiple challenges and barriers'. Each superordinate theme contains two or three sub themes. CONCLUSIONS: Issues of structural inequality (including but not limited to poverty) impact significantly on people's lives but are often ignored or minimised in therapeutic work. It is important that therapists are aware of poverty and take this into account when working with clients.

Analysis Of COVID-19 Effects On Wellbeing - Study Of Reddit Posts Using Natural Language Processing Techniques

The COVID-19 pandemic continues to negatively impact people's mental health worldwide. Due to the rise in unemployment, loss of income, and lack of social interaction, people are now more likely to feel lonely, go on fewer outings, and dread the unexpected nature of viral transmission. Meanwhile, Public Health authorities are interested in monitoring people's mental and emotional well-being. In this paper, natural language processing is used to analyze human sentiments concerning the COVID-19 pandemic that has been dangerously affecting individuals' mental and physical well-being for more than two years now. Even though several waves of COVID-19 have passed, of which the first and third waves i.e., the initial pandemic period from 20th March 2020 and the rise of the Delta variant from January 2020 had the most impact on the mental health of individuals, this is further evident by the results of this paper. This research focuses on how severely this virus has affected people's mental health and emotions. After processing the data i.e., cleaning, formatting, and removing irregularities from the data, feature engineering models are applied to acquire the results. The results through VADER (valence-aware dictionary and sentiment reasoning) indicate an increase in overall negative sentiments between two mentioned periods. Additionally, the NRC-EIL (National Research Council of Canada - Emotion Intensity Lexicon) analysis showed that 'fear' and 'sadness' occurred during those times. © 2022 IEEE.

Antibiotic Consumption in Vanuatu before and during the COVID-19 Pandemic, 2018 to 2021: An Interrupted Time Series Analysis.

The study objectives were to examine antibiotic consumption at Vila Central Hospital (VCH), Vanuatu between January 2018 and December 2021 and the influence of the COVID-19 pandemic on antibiotic consumption during this period. Data on antibiotic usage were obtained from the Pharmacy database. We used the WHO's Anatomical Therapeutic Classification/Defined Daily Dose (ATC/DDD) index, VCH's inpatient bed numbers and the hospital's catchment population to calculate monthly antibiotic consumption. The results were expressed as DDDs per 100 bed days for inpatients (DBDs) and DDDs per 1000 inhabitants per day for outpatients (DIDs). Interrupted time series (ITS) was used to assess the influence of COVID-19 by comparing data before (January 2018 to January 2020) and during (February 2020 to December 2021) the pandemic. Ten antibiotics were examined. In total, 226 DBDs and 266 DBDs were consumed before and during COVID-19 by inpatients, respectively with mean monthly consumption being significantly greater during COVID-19 than before the pandemic (2.66 (p = 0.009, 95% CI 0.71; 4.61)). Whilst outpatients consumed 102 DIDs and 92 DIDs before and during the pandemic, respectively, the difference was not statistically significant. Findings also indicated that outpatients consumed a significantly lower quantity of Watch antibiotics during COVID-19 than before the pandemic (0.066 (p = 0.002, 95% CI 0.03; 0.11)). The immediate impact of COVID-19 caused a reduction in both inpatient and outpatient mean monthly consumption by approximately 5% and 16%, respectively, and this was followed by an approximate 1% monthly increase until the end of the study. By mid-2021, consumption had returned to pre-pandemic levels.

Semantic Communication for Capacity-aware Remote Collaboration

The global spread of coronavirus has sparked a considerable interest in technologies that facilitate seamless communication between users which are physically or spatially distant. Using current remote collaboration systems that utilize 3D sensing with LiDAR and depth cameras, point cloud streaming, and MR/VR devices, distant users can communicate with each other as if they did in person. However, these systems may violate users' privacy since they can share information of their entire personal space with other users. In addition, although various point cloud compression methods have been proposed, remote transmission of 3D scenes still requires significant bandwidth. This paper proposes a 3D spatial data sharing system based on the paradigm of 'semantic communication', i.e., controlling communication in the units of semantic objects. Our system understands the semantics of the scene and leverages point cloud streaming, thereby enabling users to assert fine-grained control over their privacy. Further, the system adaptively controls the size of the data frame based on network capacity and scene context. The experimental results show that the network delay can be reduced by 96%. We have also tested our system in a commercial 4G network, showing that 3-D spatial sharing with point clouds over severe networks is possible. © 2022 IEEE.