Applying an extended prototype willingness model to predict back seat safety belt use in China

The risk of injury and death in traffic accidents for passengers in the back and front seats can be reduced by utilizing safety belts. However, passengers use back seatbelts far less frequently than those in the front. More investigation is therefore required into the psychological constructs that affect individuals\u27 attitudes toward using back seat belts. In this study, four models were used to analyze individual intentions and actual back seat belt use: the standard theory of planned behavior (TPB); the standard prototype willingness model (PWM); a model that integrates the TPB and PWM constructs; and a model that integrates the TPB construct, PWM constructs, descriptive norms and perceived law enforcement. The results showed that the standard PWM has much more explanatory power than the standard TPB in explaining the variance in behavioral intention and behavior. Incorporating perceived behavioral control (PBC) into the standard PWM did not improve the model fit considerably, while incorporating descriptive norms and perceived law enforcement moderately improved the model fit. Attitude greatly impacted behavioral intention and the use of back seat belts, followed by perceived law enforcement and descriptive norms, while subjective norms, prototype favorability, prototype similarity and PBC had no significant effect

Collaborative streaming of on demand videos for mobile devices

The 3G and LTE technologies made video on-demand a popular entertainment for users on the go. However, bandwidth insufficiency is an obstacle in providing high quality and smooth video playout in cellular networks. The objective of the proposed PhD research is to provide a user with high quality video streaming with minimal stalling time by aggregating bandwidth from ubiquitous nearby devices that may be using different radio networks

Advancing Android Activity Recognition Service with Markov Smoother: Practical Solutions

Common use of smartphones is a compelling reason for performing activity recognition with on-board sensors as it is more practical than other approaches, such as wearable sensors and augmented environments. Many solutions have been proposed by academia, but practical use is limited to experimental settings. Ad hoc solutions exist with different degrees in recognition accuracy and efficiency. To ease the development of activity recognition for the mobile application eco-system, Google released an activity recognition service on their Android platform. In this paper, we present a systematic evaluation of this activity recognition service and share the lesson learnt. Through our experiments, we identified scenarios in which the recognition accuracy was barely acceptable. We analyze the cause of the inaccuracy and propose four practical and light-weight solutions to significantly improve the recognition accuracy and efficiency. Our evaluation confirmed the improvement. As a contribution, we released the proposed solutions as open-source projects for developers who want to incorporate activity recognition into their applications

Sensor-based activity recognition with dynamically added context

An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods

Randomized algorithms for fully online multiprocessor scheduling with testing

We contribute the first randomized algorithm that is an integration of arbitrarily many deterministic algorithms for the fully online multiprocessor scheduling with testing problem. When there are two machines, we show that with two component algorithms its expected competitive ratio is already strictly smaller than the best proven deterministic competitive ratio lower bound. Such algorithmic results are rarely seen in the literature. Multiprocessor scheduling is one of the first combinatorial optimization problems that have received numerous studies. Recently, several research groups examined its testing variant, in which each job $J_j$ arrives with an upper bound $u_j$ on the processing time and a testing operation of length $t_j$; one can choose to execute $J_j$ for $u_j$ time, or to test $J_j$ for $t_j$ time to obtain the exact processing time $p_j$ followed by immediately executing the job for $p_j$ time. Our target problem is the fully online version, in which the jobs arrive in sequence so that the testing decision needs to be made at the job arrival as well as the designated machine. We propose an expected $(\sqrt{\varphi + 3} + 1) (\approx 3.1490)$-competitive randomized algorithm as a non-uniform probability distribution over arbitrarily many deterministic algorithms, where $\varphi = \frac {\sqrt{5} + 1}2$ is the Golden ratio. When there are two machines, we show that our randomized algorithm based on two deterministic algorithms is already expected $\frac {3 \varphi + 3 \sqrt{13 - 7\varphi}}4 (\approx 2.1839)$-competitive. Besides, we use Yao's principle to prove lower bounds of $1.6682$ and $1.6522$ on the expected competitive ratio for any randomized algorithm at the presence of at least three machines and only two machines, respectively, and prove a lower bound of $2.2117$ on the competitive ratio for any deterministic algorithm when there are only two machines.Comment: 21 pages with 1 plot; an extended abstract to be submitte

Activity recognition with weighted frequent patterns mining in smart environments

In the past decades, activity recognition has aroused a great interest for the research groups majoring in context-awareness computing and human behaviours monitoring. However, the correlations between the activities and their frequent patterns have never been directly addressed by traditional activity recognition techniques. As a result, activities that trigger the same set of sensors are difficult to differentiate, even though they present different patterns such as different frequencies of the sensor events. In this paper, we propose an efficient association rule mining technique to find the association rules between the activities and their frequent patterns, and build an activity classifier based on these association rules. We also address the classification of overlapped activities by incorporating the global and local weight of the patterns. The experiment results using publicly available dataset demonstrate that our method is able to achieve better performance than traditional recognition methods such as Decision Tree, Naive Bayesian and HMM. Comparison studies show that the proposed association rule mining method is efficient, and we can further improve the activity recognition accuracy by considering global and local weight of frequent patterns of activities

Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort

Most existing public transit systems have a fixed dispatching and service mode, which cannot effectively allocate resources from the perspective of the interests of all participants, resulting in resource waste and dissatisfaction. Low passenger satisfaction leads to a considerable loss of bus passengers and further reduces the income of bus operators. This study develops an optimization model for bus schedules that considers vehicle types and offers two service levels based on heterogeneous passenger demands. In this process, passenger satisfaction, bus company income, and government subsidies are considered. A bilevel model is proposed with a lower-level passenger ride simulation model and an upper-level multiobjective optimization model to maximize the interests of bus companies, passengers, and the government. To verify the effectiveness of the proposed methodology, a real-world case from Guangzhou is presented and analyzed using the nondominated sorting genetic algorithm-II (NSGA-II), and the related Pareto front is obtained. The results show that the proposed bus operation system can effectively increase the benefits for bus companies, passengers, and the governmen

Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations

Buses and metros are two main public transit modes, and these modes are crucial components of sustainable transportation systems. Promoting reciprocal integration between bus and metro systems requires a deep understanding of the effects of multiple factors on transfers among integrated public transportation transfer modes, i.e., metro-to-bus and bus-to-metro. This study aims to reveal the determinants of the transfer ratio between bus and metro systems and quantify the associated impacts. The transfer ratio between buses and metros is identified based on large-scale transaction data from automated fare collection systems. Meanwhile, various influencing factors, including weather, socioeconomic, the intensity of business activities, and built environment factors, are obtained from multivariate sources. A multivariate regression model is used to investigate the associations between the transfer ratio and multiple factors. The results show that the transfer ratio of the two modes significantly increases under high temperature, strong wind, rainfall, and low visibility. The morning peak hours attract a transfer ratio of up to 57.95%, and the average hourly transfer volume is 0.94 to 1.38 times higher at this time than in other periods. The intensity of business activities has the most significant impact on the transfer ratio, which is approximately 1.5 to 15 times that of the other independent variables. Moreover, an adaptative geographically weighted regression is utilized to investigate the spatial divergences of the influences of critical factors on the transfer ratio. The results indicate that the impact of a factor presents spatial heterogeneity and even shows opposite effects (in terms of positive and negative) on the transfer ratio in different urban contexts. For example, among the related socioeconomic variables, the impact of the housing price on the downtown transfer ratio is larger than that in the suburbs. Crowd density positively influences the transfer ratio at most stations in the northern region, whereas it shows negative results in the southern region. These findings provide valuable insights for public transportation management and promote the effective integration of bus and metro systems to provide enhanced transfer services

Primordial magnetic field as a common solution of nanohertz gravitational waves and Hubble tension

The origin of interstellar and intergalactic magnetic fields is largely unknown, and the primordial magnetic fields (PMFs) produced by, e.g., phase transitions of the early Universe are expected to provide seeds for those magnetic fields. The PMFs affect the evolution of the Universe at an early time, resulting in a series of phenomena. In this work, we show that the PMF-induced turbulence can give rise to nanohertz (nHz) gravitational waves reported by several pulsar timing arrays, including NANOGrav, PPTA, EPTA, and CPTA. Using the nHz gravitational wave data, we obtain the constraints on the characteristic magnetic field strength ($B_{\rm ch}^* \sim \mathcal{O}(1)~\rm{\mu G}$) and coherent length scale ($\ell_{\rm ch}^* \sim \mathcal{O}(1)~\rm{pc}$) of PMFs, assuming a generation temperature of approximately the QCD temperature ($\sim 100$ MeV). In addition, the PMFs which evolve to the recombination era can induce baryon density inhomogeneities, and then alter the ionization process. This naturally results in an alleviation of the tension of the Hubble parameter $H_0$ and the matter clumpiness parameter $S_8$ between early and late-time measurements. Assuming an evolution form of $B_{\rm ch}\sim \ell_{\rm ch}^{-\alpha}$ from the epoch of the production of PMFs to the epoch of recombination, we find $0.91<\alpha<1.08$ (95\% credible region).Comment: 7 pages, 4 figure

Meeting-Merging-Mission: A Multi-robot Coordinate Framework for Large-Scale Communication-Limited Exploration

This letter presents a complete framework Meeting-Merging-Mission for multi-robot exploration under communication restriction. Considering communication is limited in both bandwidth and range in the real world, we propose a lightweight environment presentation method and an efficient cooperative exploration strategy. For lower bandwidth, each robot utilizes specific polytopes to maintains free space and super frontier information (SFI) as the source for exploration decision-making. To reduce repeated exploration, we develop a mission-based protocol that drives robots to share collected information in stable rendezvous. We also design a complete path planning scheme for both centralized and decentralized cases. To validate that our framework is practical and generic, we present an extensive benchmark and deploy our system into multi-UGV and multi-UAV platforms