DEVELOPMENT OF MOBILE TRAVEL GUIDE APPLICATION FOR MUSEUMS

In recent years, the development of worldwide travel has been outstanding. At the same time, due to the fast development of the travel business, it is important to offer tourists an enhanced travel platform. In China, most of museums still use narrators to do presentation about exhibition. In order to support museums to provide service in various ways, online product is essential. During the research process for completing this Thesis work, a mobile travel guide application was developed according to the existing condition of museums, which is based on an existing application. The objective of this project was to develop a mobile travel guide application with added functions to an existing application. The main goal of the application is to provide a wide variety of functions for museums to benefit visitors to museums. Moreover, this application aims to reach the right target group, and do real-time data analysis. The research draws from the actual demand analysis, developing a mobile application on the Android operating system. The application is an interaction application, and is added with some functions that are different from the existing one. The constructive research method was used to specify and analyze possible existing problem, in order to achieve a satisfactory result. Database concept was studied to construct database for the application. Usability studies were used to create graphical interfaces. There were several tools used in the development. Android studio was utilized to create the Android development environment. Hierarchy viewer is a tool to examine and design the User Interface. The test tool Android Virtual Device Nexus 5 in Android studio was mainly used for program automatic testing. The outcome of this research is a practical and easy application that everyone is able to use. It includes basic function which is textual and audio explanation, and added functions which are online ticketing sale and online shop service

Coverage Performance Analysis of FeICIC Low Power Subframes

Although the Almost Blank Subframes (ABSF) proposed in heterogeneous cellular networks can enhance the performance of the Cell Range Expansion (CRE) User Equipments (UEs), it significantly degrades macro-cell total throughput. To address this problem, the Low Power Subframes (LPSF) are encouraged to be applied in macro-cell center region by the Further-enhanced Inter-cell Interference Coordination (FeICIC). However, the residual power of the LPSF which interferes the CRE UEs, and the proportion of the LPSF affect the downlink throughput together. To achieve a better rate coverage probability, appropriate LPSF power and proportion are required. In this paper, the analytical results of the overall Signal to Interference and Noise Ratio (SINR) coverage probability and the rate coverage probability are derived under the stochastic geometric framework. The optimal region bias ranges for maximizing the rate coverage probability are also analysed. The results show that the ABSF still outperform the LPSF in terms of rate with the optimal range expansion bias, but lead to a heavier burden on the back-haul of the pico-cell. However, with a static range expansion bias, the LPSF provide better rate coverage than the ABSF. Also, in a low range expansion scenario, the reduced power of the LPSF has negligible effect on the rate coverage with the optimal resource partitioning

On a question of Drinfeld on the Weil representation I: the finite field case

Let F be a finite field of odd cardinality, and let G= GL2(F). The group G \times G \times G acts on F^2 \otimes F^2 \otimes F^2 via symplectic similitudes, and has a natural Weil representation. Answering a question rasised by V. Drinfeld, we decompose that representation into irreducibles. We also decompose the analogous representation of GL2(A), where A is a cubic algebra over F.Comment: 29 pages. Comments welcom

ConsPrompt: Easily Exploiting Contrastive Samples for Few-shot Prompt Learning

Prompt learning recently become an effective linguistic tool to motivate the PLMs' knowledge on few-shot-setting tasks. However, studies have shown the lack of robustness still exists in prompt learning, since suitable initialization of continuous prompt and expert-first manual prompt are essential in fine-tuning process. What is more, human also utilize their comparative ability to motivate their existing knowledge for distinguishing different examples. Motivated by this, we explore how to use contrastive samples to strengthen prompt learning. In detail, we first propose our model ConsPrompt combining with prompt encoding network, contrastive sampling module, and contrastive scoring module. Subsequently, two sampling strategies, similarity-based and label-based strategies, are introduced to realize differential contrastive learning. The effectiveness of proposed ConsPrompt is demonstrated in five different few-shot learning tasks and shown the similarity-based sampling strategy is more effective than label-based in combining contrastive learning. Our results also exhibits the state-of-the-art performance and robustness in different few-shot settings, which proves that the ConsPrompt could be assumed as a better knowledge probe to motivate PLMs

SeqVISTA: a graphical tool for sequence feature visualization and comparison

BACKGROUND: Many readers will sympathize with the following story. You are viewing a gene sequence in Entrez, and you want to find whether it contains a particular sequence motif. You reach for the browser's "find in page" button, but those darn spaces every 10 bp get in the way. And what if the motif is on the opposite strand? Subsequently, your favorite sequence analysis software informs you that there is an interesting feature at position 13982–14013. By painstakingly counting the 10 bp blocks, you are able to examine the sequence at this location. But now you want to see what other features have been annotated close by, and this information is buried several screenfuls higher up the web page. RESULTS: SeqVISTA presents a holistic, graphical view of features annotated on nucleotide or protein sequences. This interactive tool highlights the residues in the sequence that correspond to features chosen by the user, and allows easy searching for sequence motifs or extraction of particular subsequences. SeqVISTA is able to display results from diverse sequence analysis tools in an integrated fashion, and aims to provide much-needed unity to the bioinformatics resources scattered around the Internet. Our viewer may be launched on a GenBank record by a single click of a button installed in the web browser. CONCLUSION: SeqVISTA allows insights to be gained by viewing the totality of sequence annotations and predictions, which may be more revealing than the sum of their parts. SeqVISTA runs on any operating system with a Java 1.4 virtual machine. It is freely available to academic users at