Annotated bibliography of community music research review, AHRC connected communities programme

This research review, consisting of a 90-entry annotated bibliography, was produced as part of an AHRC Connected Communities programme project entitled Community Music, its History and Current Practice, its Constructions of ‘Community’, Digital Turns and Future Soundings. It supports a 2,500 word report written with this same title for the AHRC

People don't use the shortest path

Most recent route choice models, following either Random Utility Maximization or rule-based paradigm, require explicit enumeration of feasible routes. The quality of model estimation and prediction is sensitive to the appropriateness of consideration set. However, few empirical studies of revealed route characteristics have been reported in the literature. Such study could also help practitioners and researchers evaluate widely applied shortest path assumptions. This study aims at bridging the gap by evaluating morning commute routes followed by residents at the Twin Cities, Minnesota. Accurate GPS and GIS data were employed to reveal routes people utilized. Findings from this study could also provide guidance for future efforts in building better travel demand models.Rationality, travel behavior, transport geography, commuting, transportation networks

In the Battle for Reality: Social Documentaries in the U.S.

Provides an overview of documentaries that address social justice and democracy issues, and includes case studies of successful strategic uses of social documentaries

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Impact of a Non-Traditional Research Approach

abstract: Construction Management research has not been successful in changing the practices of the construction industry. The method of receiving grants and the peer review paper system that academics rely on to achieve promotion, does not align to academic researchers becoming experts who can bring change to industry practices. Poor construction industry performance has been documented for the past 25 years in the international construction management field. However, after 25 years of billions of dollars of research investment, the solution remains elusive. Research has shown that very few researchers have a hypothesis, run cycles of research tests in the industry, and result in changing industry practices. The most impactful research identified in this thesis, has led to conclusions that pre-planning is critical, hiring contractors who have expertise will result in better performance, and risk is mitigated when the supply chain partners work together and expertise is utilized at the beginning of projects. The problems with construction non-performance have persisted. Legal contract issues have become more important. Traditional research approaches have not identified the severity and the source of construction non-performance. The problem seems to be as complex as ever. The construction industry practices and the academic research community remain in silos. This research proposes that the problem may be in the traditional construction management research structure and methodology. The research has identified a unique non-traditional research program that has documented over 1700 industry tests, which has resulted in a decrease in client management by up to 79%, contractors adding value by up to 38%, increased customer satisfaction by up to 140%, reduced change order rates as low as -0.6%, and decreased cost of services by up to 31%. The purpose of this thesis is to document the performance of the non-traditional research program around the above identified results. The documentation of such an effort will shed more light on what is required for a sustainable, industry impacting, and academic expert based research program.Dissertation/ThesisMasters Thesis Construction 201

Transformer-based NMT : modeling, training and implementation

International trade and industrial collaborations enable countries and regions to concentrate their developments on specific industries while making the most of other countries' specializations, which significantly accelerates global development. However, globalization also increases the demand for cross-region communication. Language barriers between many languages worldwide create a challenge for achieving deep collaboration between groups speaking different languages, increasing the need for translation. Language technology, specifically, Machine Translation (MT) holds the promise to enable communication between languages efficiently in real-time with minimal costs. Even though nowadays computers can perform computation in parallel very fast, which provides machine translation users with translations with very low latency, and although the evolution from Statistical Machine Translation (SMT) to Neural Machine Translation (NMT) with the utilization of advanced deep learning algorithms has significantly boosted translation quality, current machine translation algorithms are still far from accurately translating all input. Thus, how to further improve the performance of state-of-the-art NMT algorithm remains a valuable open research question which has received a wide range of attention. In the research presented in this thesis, we first investigate the long-distance relation modeling ability of the state-of-the-art NMT model, the Transformer. We propose to learn source phrase representations and incorporate them into the Transformer translation model, aiming to enhance its ability to capture long-distance dependencies well. Second, though previous work (Bapna et al., 2018) suggests that deep Transformers have difficulty in converging, we empirically find that the convergence of deep Transformers depends on the interaction between the layer normalization and residual connections employed to stabilize its training. We conduct a theoretical study about how to ensure the convergence of Transformers, especially for deep Transformers, and propose to ensure the convergence of deep Transformers by putting the Lipschitz constraint on its parameter initialization. Finally, we investigate how to dynamically determine proper and efficient batch sizes during the training of the Transformer model. We find that the gradient direction gets stabilized with increasing batch size during gradient accumulation. Thus we propose to dynamically adjust batch sizes during training by monitoring the gradient direction change within gradient accumulation, and to achieve a proper and efficient batch size by stopping the gradient accumulation when the gradient direction starts to fluctuate. For our research in this thesis, we also implement our own NMT toolkit, the Neutron implementation of the Transformer and its variants. In addition to providing fundamental features as the basis of our implementations for the approaches presented in this thesis, we support many advanced features from recent cutting-edge research work. Implementations of all our approaches in this thesis are also included and open-sourced in the toolkit. To compare with previous approaches, we mainly conducted our experiments on the data from the WMT 14 English to German (En-De) and English to French (En-Fr) news translation tasks, except when studying the convergence of deep Transformers, where we alternated the WMT 14 En-Fr task with the WMT 15 Czech to English (Cs-En) news translation task to compare with Bapna et al. (2018). The sizes of these datasets vary from medium (the WMT 14 En-De, ~ 4.5M sentence pairs) to very large (the WMT 14 En-Fr, ~ 36M sentence pairs), thus we suggest our approaches help improve the translation quality between popular language pairs which are widely used and have sufficient data.China Scholarship Counci