When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social network, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.Comment: To appear in Oct. IEEE Communications Magazine, feature topic on "Social Networks Meet Next Generation Mobile Multimedia Internet

Noninvasive prediction of Blood Lactate through a machine learning-based approach.

We hypothesized that blood lactate concentration([Lac]blood) is a function of cardiopulmonary variables, exercise intensity and some anthropometric elements during aerobic exercise. This investigation aimed to establish a mathematical model to estimate [Lac]blood noninvasively during constant work rate (CWR) exercise of various intensities. 31 healthy participants were recruited and each underwent 4 cardiopulmonary exercise tests: one incremental and three CWR tests (low: 35% of peak work rate for 15 min, moderate: 60% 10 min and high: 90% 4 min). At the end of each CWR test, venous blood was sampled to determine [Lac]blood. 31 trios of CWR tests were employed to construct the mathematical model, which utilized exponential regression combined with Taylor expansion. Good fitting was achieved when the conditions of low and moderate intensity were put in one model; high-intensity in another. Standard deviation of fitting error in the former condition is 0.52; in the latter is 1.82 mmol/liter. Weighting analysis demonstrated that, besides heart rate, respiratory variables are required in the estimation of [Lac]blood in the model of low/moderate intensity. In conclusion, by measuring noninvasive cardio-respiratory parameters, [Lac]blood during CWR exercise can be determined with good accuracy. This should have application in endurance training and future exercise industry

Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin

Factors Influence Information and Knowledge Sharing in Organization

In today\u27s business environment, competitive advantage increasingly requires the open sharing of knowledge by organizational members [22]. Although the practitioners place emphasis on the importance of knowledge sharing, empirical researches on knowledge sharing are still limited, and little research has been done to understand the factors that influence knowledge sharing in organizations. This study investigates cultural and interpersonal factors that influence an individual’s propensity to share information and knowledge that he or she has created. Three different situations of sharing (information product, self-developed knowledge and organization-developed knowledge) were considered. The study found that organization culture influenced individual’s beliefs of organization trust and psychological safety, and those who perceived higher trust and psychological safety seemed more likely to share information and knowledge with others

Improving One-class Recommendation with Multi-tasking on Various Preference Intensities

In the one-class recommendation problem, it's required to make recommendations basing on users' implicit feedback, which is inferred from their action and inaction. Existing works obtain representations of users and items by encoding positive and negative interactions observed from training data. However, these efforts assume that all positive signals from implicit feedback reflect a fixed preference intensity, which is not realistic. Consequently, representations learned with these methods usually fail to capture informative entity features that reflect various preference intensities. In this paper, we propose a multi-tasking framework taking various preference intensities of each signal from implicit feedback into consideration. Representations of entities are required to satisfy the objective of each subtask simultaneously, making them more robust and generalizable. Furthermore, we incorporate attentive graph convolutional layers to explore high-order relationships in the user-item bipartite graph and dynamically capture the latent tendencies of users toward the items they interact with. Experimental results show that our method performs better than state-of-the-art methods by a large margin on three large-scale real-world benchmark datasets.Comment: RecSys 2020 (ACM Conference on Recommender Systems 2020

Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis

Document representation is the core of many NLP tasks on machine understanding. A general representation learned in an unsupervised manner reserves generality and can be used for various applications. In practice, sentiment analysis (SA) has been a challenging task that is regarded to be deeply semantic-related and is often used to assess general representations. Existing methods on unsupervised document representation learning can be separated into two families: sequential ones, which explicitly take the ordering of words into consideration, and non-sequential ones, which do not explicitly do so. However, both of them suffer from their own weaknesses. In this paper, we propose a model that overcomes difficulties encountered by both families of methods. Experiments show that our model outperforms state-of-the-art methods on popular SA datasets and a fine-grained aspect-based SA by a large margin.Comment: International ACM SIGIR Conference 201

Decrease of Klotho in the Kidney of Streptozotocin-Induced Diabetic Rats

The klotho gene is expressed in a limited number of tissues, most notably in distal convoluted tubules in the kidney and choroid plexus in the brain. A previous study suggested that Klotho increases resistance to oxidative stress. However, changes of Klotho expression in high glucose-induced oxidative stress remain unclear. In the present study, we used streptozotocin-induced diabetic rats (STZ rats) to examine the effects of insulin, phloridzin or antioxidant, tiron on diabetic nephropathy. Both insulin and phloridzin reversed the lower Klotho expression levels in kidneys of STZ rats by the correction of hyperglycemia. Also, renal functions were improved by these treatments. In addition to the improvement of renal functions, the decrease of Klotho expression in kidney of STZ rats was also reversed by tiron without changing blood glucose levels. The reduction of oxidative stress induced by high glucose can be considered for this action of tiron. This view was further confirmed in vitro using high glucose-exposed Madin-Darby canine kidney (MDCK) epithelial cells. Thus, we suggest that decrease of oxidative stress is not only responsible for the improvement of renal function but also for the recovery of Klotho expression in kidney of STZ rats

Plasmonic Circular Nanostructure for Enhanced Light Absorption in Organic Solar Cells

This study attempts to enhance broadband absorption in advanced plasmonic circular nanostructures (PCN). Experimental results indicate that the concentric circular metallic gratings can enhance broadband optical absorption, due to the structure geometry and the excitation of surface plasmon mode. The interaction between plasmonic enhancement and the absorption characteristics of the organic materials (P3HT:PCBM and PEDOT:PSS) are also examined. According to those results, the organic material's overall optical absorption can be significantly enhanced by up to ~51% over that of a planar device. Additionally, organic materials are enhanced to a maximum of 65% for PCN grating pitch = 800 nm. As a result of the PCN's enhancement in optical absorption, incorporation of the PCN into P3HT:PCBM-based organic solar cells (OSCs) significantly improved the performance of the solar cells: short-circuit current increased from 10.125 to 12.249 and power conversion efficiency from 3.2% to 4.99%. Furthermore, optimizing the OSCs architectures further improves the performance of the absorption and PCE enhancement

On qq-Counting of Noncrossing Chains and Parking Functions

For a finite Coxeter group $W$, Josuat-Verg\`es derived a $q$-polynomial counting the maximal chains in the lattice of noncrossing partitions of $W$ by weighting some of the covering relations, which we call bad edges, in these chains with a parameter $q$. We study the connection of these weighted chains with parking functions of type $A$ ($B$, respectively) from the perspective of the $q$-polynomial. The $q$-polynomial turns out to be the generating function for parking functions (of either type) with respect to the number of cars that do not park at their preferred spaces. In either case, we present a bijective result that carries bad edges to unlucky cars while preserving their relative order. Using this, we give an interpretation of the $\gamma$-positivity of the $q$-polynomial in the case that $W$ is the hyperoctahedral group.Comment: 32 pages, to be published in SIDM