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Obtaining fine-grained crowd flow distribution with recognized human intention is extremely valuable for a series of applications for a metropolitan city. We address this problem by leveraging a key insight people's intention behind their movement is highly correlated with the point-of-interest (POI) distribution of the corresponding regions. We propose DeepFlowGen to model the complicated relationship between crowd flow, POI, and time to generate intention-aware fine-grained crowd flow. Specifically, we solve the conflict between dynamic crowd flow and static POI distribution by fusing the information in both time and POI domains. In addition, we employ a sequence of residual blocks in DeepFlowGen to address the challenges of modeling the diverse temporal rhythms and heterogeneous influence of POI. Extensive experiments demonstrate that our model outperforms the state-of-the-art solutions by more than 15% in terms of RMSE of total crowd flow. Moreover, the correlation between the generated intention-aware crowd flow and the check-in distribution across different categories of POIs is as high as 0.90 and 0.75 in Beijing and Shanghai. Combined with extensive case studies, we demonstrate the strong ability of our model in generating intention-aware fine-grained crowd flow.

With the improvement of the economic level and the prosperity of the commodity market, consumers’ demand for commodities is not only at the material level. The packaging design of the story illustration has strong readability. On the basis of satisfying the visual needs of consumers, it can also show the cultural background behind the tea products through illustration. Through the research on the feasibility and implementation method of story graphics in packaging design, it is confirmed that story graphics is a direct and efficient packaging graphics language, which has certain significance for the improvement of product quality and the shaping of brand image and can provide new ideas and methods for the development of packaging design.

In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication resources, device scheduling is crucial to the convergence rate of FL. In this paper, we propose a joint device scheduling and resource allocation policy to maximize the model accuracy within a given total training time budget for latency constrained wireless FL. A lower bound on the reciprocal of the training performance loss, in terms of the number of training rounds and the number of scheduled devices per round, is derived. Based on the bound, the accuracy maximization problem is solved by decoupling it into two sub-problems. First, given the scheduled devices, the optimal bandwidth allocation suggests allocating more bandwidth to the devices with worse channel conditions or weaker computation capabilities. Then, a greedy device scheduling algorithm is introduced, which in each step selects the device consuming the least updating time obtained by the optimal bandwidth allocation, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy. Experiments show that the proposed policy outperforms state-of-the-art scheduling policies under extensive settings of data distributions and cell radius., Comment: submitted to IEEE Trans. Wireless Communications

In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance.

In this paper, the graduation design course in the digital media art major of College of Sino-Korea Multimedia Design subordinate to Shanghai University of Engineering Science is taken as an example to expound four key factors of construction of the graduation design course, together with exploration in and reflection on the teaching and instruction modes of such course.

Origin-Destination (OD) flow contains the information of direction and volume of population mobility between different regions in a city, having significant value in public transportation resource allocation. In this paper, we explore population distribution to infer OD flows, which is called pop2flow (population distribution to OD flows) problem. Compared to the conventional OD forecasting problem by using the historical OD matrix, pop2flow is more challenging because the population distribution carries much less information. In order to solve the pop2flow problem, we proposed a model, Graph-based Spatial-temporal Embedding with Dynamic Fusion (GSTE-DF). Specifically, GSTE-DF is composed of two parts: node embedding learning and flow prediction. The node embedding learning part captures the dynamic spatial-temporal features of population distribution into each nodes embedding. The flow prediction part adopts the learned embeddings and POI (points of interesting) distribution of every two regions to infer the population interaction between them. By conducting extensive experiments on real-world datasets collected in Beijing and New York City, we demonstrate the superiority of GSTE-DF compared to state-of-the-art baselines

Starting from the application purpose of real distribution network planning, for the high-voltage distribution network connected by medium-voltage tie lines, the power supply capacity is predicted according to current feeder loads and their changing trends, so that the results are more realistic. Based on the above calculation process, the weakness of a distribution network can be identified intuitively and conveniently, which can be used as the basis for generating the measures or schemes to improve power supply capacities. Numerical examples show that the presented model and method are intuitive, simple, and stable and effective compared with the existing methods, and are convenient for application in practice.

With the popularity of smart phones, the advent of 4G network era, and the establishment of the “Internet + education” ecosystem, the mobile terminal construction witnesses the high-speed development, especially the mobile education campus APP on teaching services centered on college students. This kind of mobile interactive application provides the teachers and students with a more convenient resource sharing and campus life of higher education. In this paper, the teaching service requirements of the School are analyzed, and the needs of the target users are explored. Based on the interactive process in conformity with the user's behaviors, the user interface information architecture of the APP is constructed. Moreover, the visual design is created in accordance with the APP design requirements of iOS system, in order to design the interface prototype of APP “i Sino-Korea” launched by Sino-Korean School of Multimedia Design of Shanghai University of Engineering Science.

As a novel cross-subject, digital media art is the combination of technology and art. The program design course is an important part of computer teaching course system. Considering the characteristics of the major and knowledge of the students, The Basic Course of Animation Programming is chosen as an introductory programming course. This paper proposes the reform of practicing on the content of course and teaching method and it also explores the measures combining teaching and ability training so as to raise the students' interest in learning and improve their programming knowledge and methods of quick understanding and application.

We consider multiple base station (BS) cooperative transmission in downlink cellular networks to improve the spectral efficiency and the system capacity. Grouping BSs into clusters is a practical solution to realize BSs cooperation and reduce system complexity. However, it still suffers from inter-cluster interference, especially for the cluster-edge users. In this paper, clustering and scheduling are jointly considered to deal with the problem. The clusters are formed dynamically from users' point of view to minimize the inter-cluster interference, and are allowed to be overlapped. Accordingly, coordinated precoding scheme is designed to manage the intra-cluster interference. A greedy scheduling algorithm is proposed jointly with dynamic clustering. Simulations show that the proposed joint algorithm provides impressive average throughput gain over the non-joint ones, and the user fairness is improved significantly.