The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various ...mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other components which may affect its performance, such as data aggregation component that aggregates workers' data and data perturbation component that protects workers' privacy. Therefore, different from the past literature, we capture such interactive effect and propose INCEPTION, a novel MCS system framework that integrates an incentive, a data aggregation, and a data perturbation mechanism. Specifically, its incentive mechanism selects workers who are more likely to provide reliable data and compensates their costs for both sensing and privacy leakage. Its data aggregation mechanism also incorporates workers' reliability to generate highly accurate aggregated results, and its data perturbation mechanism ensures satisfactory protection for workers' privacy and desirable accuracy for the final perturbed results. We validate the desirable properties of INCEPTION through theoretical analysis as well as extensive simulations.
Carbohydrates are the most abundant and one of the most important biomacromolecules in Nature. Except for energy-related compounds, carbohydrates can be roughly divided into two categories: ...Carbohydrates as matter and carbohydrates as information. As matter, carbohydrates are abundantly present in the extracellular matrix of animals and cell walls of various plants, bacteria, fungi, etc., serving as scaffolds. Some commonly found polysaccharides are featured as biocompatible materials with controllable rigidity and functionality, forming polymeric biomaterials which are widely used in drug delivery, tissue engineering, etc. As information, carbohydrates are usually referred to the glycans from glycoproteins, glycolipids, and proteoglycans, which bind to proteins or other carbohydrates, thereby meditating the cell–cell and cell–matrix interactions. These glycans could be simplified as synthetic glycopolymers, glycolipids, and glycoproteins, which could be afforded through polymerization, multistep synthesis, or a semisynthetic strategy. The information role of carbohydrates can be demonstrated not only as targeting reagents but also as immune antigens and adjuvants. The latter are also included in this review as they are always in a macromolecular formulation. In this review, we intend to provide a relatively comprehensive summary of carbohydrate-based macromolecular biomaterials since 2010 while emphasizing the fundamental understanding to guide the rational design of biomaterials. Carbohydrate-based macromolecules on the basis of their resources and chemical structures will be discussed, including naturally occurring polysaccharides, naturally derived synthetic polysaccharides, glycopolymers/glycodendrimers, supramolecular glycopolymers, and synthetic glycolipids/glycoproteins. Multiscale structure–function relationships in several major application areas, including delivery systems, tissue engineering, and immunology, will be detailed. We hope this review will provide valuable information for the development of carbohydrate-based macromolecular biomaterials and build a bridge between the carbohydrates as matter and the carbohydrates as information to promote new biomaterial design in the near future.
We developed an updated nonstationary bias‐correction method for a monthly global climate model of temperature and precipitation. The proposed method combines two widely used quantile mapping ...bias‐correction methods to eliminate potential illogical values of the variable. Instead of empirical parameter estimation in the more‐common quantile mapping method, our study compared bias‐correction performance when parametric or nonparametric procedures were used to estimate the probability distribution. The results showed our proposed bias‐correction method to be very effective in reducing the model bias: it removed over 80% and 83% of model bias for surface air temperature and precipitation, respectively, during the validation period. Compared with a widely used method of bias correction (delta change), our proposed technique demonstrates improved correction of the distribution of variables. In addition, nonparametric estimation procedures further reduced the mean absolute errors in temperature and precipitation during the validation period by approximately 2% and 0.4%, respectively, compared with parametric procedures. The proposed method can remove over 40% and 60% of the uncertainty from model temperature and precipitation projections, respectively, at the global land scale.
Key Points
The proposed bias‐correction method removed over 80% and 83% of model bias for temperature and precipitation, respectively
The nonparametric estimation procedures further reduced the MAE in GCM simulation compared with parametric procedures
The proposed method can remove over 40% and 60% of the uncertainty from model temperature and precipitation projections, respectively
Deep Learning for the Internet of Things Yao, Shuochao; Zhao, Yiran; Zhang, Aston ...
Computer (Long Beach, Calif.),
05/2018, Letnik:
51, Številka:
5
Journal Article
Recenzirano
Odprti dostop
How can the advantages of deep learning be brought to the emerging world of embedded IoT devices? The authors discuss several core challenges in embedded and mobile deep learning, as well as recent ...solutions demonstrating the feasibility of building IoT applications that are powered by effective, efficient, and reliable deep learning models.
Recent years have witnessed the proliferation of mobile crowd sensing (MCS) systems that leverage the public crowd equipped with various mobile devices (e.g., smartphones, smartglasses, smartwatches) ...for large scale sensing tasks. Because of the importance of incentivizing worker participation in such MCS systems, several auction-based incentive mechanisms have been proposed in past literature. However, these mechanisms fail to consider the preservation of workers' bid privacy. Therefore, different from prior work, we propose a differentially private incentive mechanism that preserves the privacy of each worker's bid against the other honest-but-curious workers. The motivation of this design comes from the concern that a worker's bid usually contains her private information that should not be disclosed. We design our incentive mechanism based on the single-minded reverse combinatorial auction. Specifically, we design a differentially private, approximately truthful, individual rational, and computationally efficient mechanism that approximately minimizes the platform's total payment with a guaranteed approximation ratio. The advantageous properties of the proposed mechanism are justified through not only rigorous theoretical analysis but also extensive simulations.
Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved ...significant efforts to prevent sleep-related disorders. To alleviate the time consumption caused by manual visual inspection of PSG, automatic multivariate sleep stage classification has become an important research topic in medical and bioinformatics.
We present a unified hybrid self-attention deep learning framework, namely HybridAtt, to automatically classify sleep stages by capturing channel and temporal correlations from multivariate PSG records. We construct a new multi-view convolutional representation module to learn channel-specific and global view features from the heterogeneous PSG inputs. The hybrid attention mechanism is designed to further fuse the multi-view features by inferring their dependencies without any additional supervision. The learned attentional representation is subsequently fed through a softmax layer to train an end-to-end deep learning model.
We empirically evaluate our proposed HybridAtt model on a benchmark PSG dataset in two feature domains, referred to as the time and frequency domains. Experimental results show that HybridAtt consistently outperforms ten baseline methods in both feature spaces, demonstrating the effectiveness of HybridAtt in the task of sleep stage classification.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
With the wide bandwidths in millimeter wave (mmWave) frequency band that results in unprecedented accuracy, mmWave sensing has become vital for many applications, especially in autonomous vehicles ...(AVs). In addition, mmWave sensing has superior reliability compared to other sensing counterparts such as camera and LiDAR, which is essential for safety-critical driving. Therefore, it is critical to understand the security vulnerabilities and improve the security and reliability of mmWave sensing in AVs. To this end, we perform the end-to-end security analysis of a mmWave-based sensing system in AVs, by designing and implementing practical physical layer attack and defense strategies in a state-of-the-art mmWave testbed and an AV testbed in real-world settings. Various strategies are developed to take control of the victim AV by spoofing its mmWave sensing module, including adding fake obstacles at arbitrary locations and faking the locations of existing obstacles. Five real-world attack scenarios are constructed to spoof the victim AV and force it to make dangerous driving decisions leading to a fatal crash. Field experiments are conducted to study the impact of the various attack scenarios using a Lincoln MKZ-based AV testbed, which validate that the attacker can indeed assume control of the victim AV to compromise its security and safety. To defend the attacks, we design and implement a challenge-response authentication scheme and a RF fingerprinting scheme to reliably detect aforementioned spoofing attacks.
Fascinating properties are displayed by synthetic multicomponent supramolecular systems that comprise a manifold of competitive interactions, thereby mimicking natural processes. We present the ...integration of two reentrant phase transitions based on an unexpected dilution-induced assembly process using supramolecular polymers and surfactants. The co-assembly of the water-soluble benzene-1,3,5-tricarboxamide (BTA-EG
4
) and a surfactant at a specific ratio yielded small-sized aggregates. These interactions were modeled using the competition between self-sorting and co-assembly of both components. The small-sized aggregates were transformed into supramolecular polymer networks by a twofold dilution in water without changing their ratio. Kinetic experiments show the in situ growth of micrometer-long fibers in the dilution process. We were able to create systems that undergo fully reversible hydrogel-solution-hydrogel-solution transitions upon dilution by introducing another orthogonal interaction.
Dilution-induced ordering
Many molecules, such as surfactants, can form ordered structures when placed in solution. Typically, the systems become more ordered and the structures change from spheres to elongated shapes as the concentration is increased. Su
et al
. studied a system of benzene-1,3,5-tricarboxamide) (BTA-EG4) with the cationic surfactant octyltrimethylammonium bromide (OTAB), in water (see the Perspective by Webber). BTA-EG4 undergoes supramolecular polymerization in water and will form hydrogels at higher concentrations, whereas OTAB will form small aggregates. However, when combined, the OTAB initially disrupts the BTA-EG4 hydrogels, but these can be reestablished upon dilution because this lessens the effect of the surfactant. With careful engineering, this can be expanded to a gel-sol-gel-sol system as a function of concentration. —MSL
Dilution-induced gelation in multicomponent supramolecular systems leads to highly adaptive hydrogel materials.
The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers ...that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations.
Professional identity (PI) is culturally shaped. It is associated with a sufficient and stable workforce of professionals. China has a relatively low ratio of nursing professionals to its population.
...This scoping review aims to obtain comprehensive knowledge of the influencing factors and PI development process among nursing students and nurses in China.
A scoping review was conducted. The most common Chinese databases, China National Knowledge Infrastructure and Wanfang Data were searched for publications in Chinese. The databases of EBSCOhost and ProQuest Dissertation & Thesis Global (Full Text) were searched for publications in English. After screening the title and abstract of the articles and further assessing the full text of the articles identified after the initial screening, 53 articles were included for analysis.
The influencing factors to PI development in nursing were grouped into four dimensions: personal, family, institutional, and social factors. The social factors tended to negatively affect professional identity whereas the factors of the three other dimensions exerted influence in different directions. A framework was established based on PI levels in different career stages of nurses to depict the continuum and dynamic nature of the development process.
The PI development in nursing is a dynamic process shaped by multidimensional factors. Changes in policy should be made to reverse the nursing profession stereotype of being an assistant role to medicine.
•Construction of professional identity in nursing is influenced by multidimensional factors.•Social attitudes toward nursing exert negative impacts on professional identity development.•Advancement in nursing professional proficiency does not necessarily lead to heightened professional identity.•Nursing students and clinical nurses in China display an overall low passion for nursing.
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