Discriminative Feature Learning with Foreground Attention for Person Re-Identification

The performance of person re-identification (Re-ID) has been seriously effected by the large cross-view appearance variations caused by mutual occlusions and background clutters. Hence learning a feature representation that can adaptively emphasize the foreground persons becomes very critical to solve the person Re-ID problem. In this paper, we propose a simple yet effective foreground attentive neural network (FANN) to learn a discriminative feature representation for person Re-ID, which can adaptively enhance the positive side of foreground and weaken the negative side of background. Specifically, a novel foreground attentive subnetwork is designed to drive the network's attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons. The resulting feature maps of encoder network are further fed into the body part subnetwork and feature fusion subnetwork to learn discriminative features. Besides, a novel symmetric triplet loss function is introduced to supervise feature learning, in which the intra-class distance is minimized and the inter-class distance is maximized in each triplet unit, simultaneously. Training our FANN in a multi-task learning framework, a discriminative feature representation can be learned to find out the matched reference to each probe among various candidates in the gallery. Extensive experimental results on several public benchmark datasets are evaluated, which have shown clear improvements of our method over the state-of-the-art approaches

Chinese university students' academic vocabulary learning using mobile technology in English medium instruction settings

The advent of mobile technology has ushered in transformative changes across various domains, including the educational landscape. Within this context, the present study embarks on an in-depth exploration of the utilization and effectiveness of mobile technology for vocabulary learning in English Medium Instruction (EMI) settings in Chinese higher education institutions. Employing a robust mixed-methods research design, the study meticulously examines multiple facets of mobile vocabulary learning, including the efficacy of specialized mobile applications, the pedagogical strategies they employ, and the socio-cultural factors that influence their adoption and implementation. One of the pivotal findings of this research is that mobile applications serve as potent tools for enhancing academic vocabulary acquisition. They do so by fostering learner engagement, facilitating personalized learning experiences, and implementing pedagogical strategies grounded in established learning theories. However, the study also uncovers a set of challenges that learners encounter, such as issues related to time management and the lack of structured pedagogical guidance, which can impede the effective utilization of these applications. To provide a holistic understanding of these complex dynamics, the study introduces an innovative conceptual framework. This framework not only synthesizes the empirical data but also situates it within the broader academic discourse, thereby offering a comprehensive lens through which the challenges and advantages of mobile-assisted vocabulary learning can be viewed. The contributions of this research are manifold. It offers groundbreaking insights that have the potential to shape pedagogical practices in the fields of Teaching English to Speakers of Other Languages (TESOL) and mobile learning. Furthermore, it provides actionable recommendations for educational stakeholders, ranging from instructors to policymakers, who are involved in the integration of mobile technology in EMI settings. Lastly, by identifying areas that warrant further investigation, the study lays a robust foundation for subsequent research endeavors, thereby enriching the academic dialogue surrounding mobile-assisted vocabulary learning in EMI contexts

Annual report on research activities 2014/15

https://commons.ln.edu.hk/research_annual_report/1013/thumbnail.jp

Channel Estimation and ICI Cancelation in Vehicular Channels of OFDM Wireless Communication Systems

Orthogonal frequency division multiplexing (OFDM) scheme increases bandwidth efficiency (BE) of data transmission and eliminates inter symbol interference (ISI). As a result, it has been widely used for wideband communication systems that have been developed during the past two decades and it can be a good candidate for the emerging communication systems such as fifth generation (5G) cellular networks with high carrier frequency and communication systems of high speed vehicles such as high speed trains (HSTs) and supersonic unmanned aircraft vehicles (UAVs). However, the employment of OFDM for those upcoming systems is challenging because of high Doppler shifts. High Doppler shift makes the wideband communication channel to be both frequency selective and time selective, doubly selective (DS), causes inter carrier interference (ICI) and destroys the orthogonality between the subcarriers of OFDM signal. In order to demodulate the signal in OFDM systems and mitigate ICIs, channel state information (CSI) is required. In this work, we deal with channel estimation (CE) and ICI cancellation in DS vehicular channels. The digitized model of the DS channels can be short and dense, or long and sparse. CE methods that perform well for short and dense channels are highly inefficient for long and sparse channels. As a result, for the latter type of channels, we proposed the employment of compressed sensing (CS) based schemes for estimating the channel. In addition, we extended our CE methods for multiple input multiple output (MIMO) scenarios. We evaluated the CE accuracy and data demodulation fidelity, along with the BE and computational complexity of our methods and compared the results with the previous CE procedures in different environments. The simulation results indicate that our proposed CE methods perform considerably better than the conventional CE schemes

The role of airports in national civil aviation policies

The concept of a hub airport has evolved widening its scope as a national civil aviation policy-making tool, due to the ability to deliver wider socio-economic benefits to a country. However, not all airports can be converted into hubs. This research proposes a methodological approach to structural analysis of the airport industry, that could be applied to determine the competitive position of an airport in a given aviation network and devise airport strategies and national policy measures to improve the current position of the airport. This study presents a twelve-group taxonomy of airports, which analyses the changing geography of the airport industry in the East (Asia and The Middle East). Multivariate data have been used in a two-step Agglomerative Hierarchical Clustering exercise which represents three airport strategies: namely, degree-of-airport-activity (size and intensity of operations), network strategies (international and domestic hub), and the market segmentation strategies (service and destination orientation). Principal Component Analysis has been utilised as a data reduction tool. The study confirms the general hypothesis that a sound macro environment and liberalised approach to economic regulation in the air transport industry are important for successful hub operations. In addition, it sheds light on the fact that while the factors of geographical advantage, economic development, urbanisation, tourism and business attractiveness, physical and intellectual infrastructure, and political and administrative frameworks, are all basic prerequisites (qualifiers) for successful hubbing in the region, those factors would not necessarily guarantee a hub status unless the governments are also committed to develop the sector and take timely decisions (differentiators) to allow airports to benefit from the first mover advantage. Application of the proposed taxonomy was tested on a case study of the major international airport of Sri Lanka, to provide policy inputs to develop the airport that is currently identified as being overshadowed by the mega hubs in the region

METROPOLITAN ENCHANTMENT AND DISENCHANTMENT. METROPOLITAN ANTHROPOLOGY FOR THE CONTEMPORARY LIVING MAP CONSTRUCTION

We can no longer interpret the contemporary metropolis as we did in the last century. The thought of civil economy regarding the contemporary Metropolis conflicts more or less radically with the merely acquisitive dimension of the behaviour of its citizens. What is needed is therefore a new capacity for imagining the economic-productive future of the city: hybrid social enterprises, economically sustainable, structured and capable of using technologies, could be a solution for producing value and distributing it fairly and inclusively. Metropolitan Urbanity is another issue to establish. Metropolis needs new spaces where inclusion can occur, and where a repository of the imagery can be recreated. What is the ontology behind the technique of metropolitan planning and management, its vision and its symbols? Competitiveness, speed, and meritocracy are political words, not technical ones. Metropolitan Urbanity is the characteristic of a polis that expresses itself in its public places. Today, however, public places are private ones that are destined for public use. The Common Good has always had a space of representation in the city, which was the public space. Today, the Green-Grey Infrastructure is the metropolitan city's monument that communicates a value for future generations and must therefore be recognised and imagined; it is the production of the metropolitan symbolic imagery, the new magic of the city

Recent advances in low-cost particulate matter sensor: calibration and application

Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment