Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Testing web applications

Traditional testing techniques are not adequate for web-based applications, since they miss their additional features such as their multi-tier nature, hyperlink-based structure, and event-driven feature. Limited work has been done on testing web applications. In this paper, we propose new techniques for white box testing of web applications developed in the .NET environment with emphasis on their event-driven feature. We extend recent work on modeling of web applications by enhancing previous dependence graphs and proposing an event-based dependence graph model. We apply data flow testing techniques to these dependence graphs and propose an event flow testing technique. Also, we present a few coverage testing approaches for web applications. Further, we propose mutation testing operators for evaluating the adequacy of web application tests.     

Extended Graetz problem including streamwise conduction and viscous dissipation in microchannel

Extended Graetz problem in microchannel is analyzed by using eigenfunction expansion to solve the energy equation. The hydrodynamically developed flow is assumed to enter the microchannel with uniform temperature or uniform heat flux boundary condition. The effects of velocity and temperature jump boundary condition on the microchannel wall, streamwise conduction and viscous dissipation are all included. From the temperature field obtained, the local Nusselt number distributions are shown as the dimensionless parameters (Peclet number, Knudsen number, Brinkman number) vary. The fully developed Nusselt number for each boundary condition is obtained also in terms of these parameters.     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity

Distributed and Parallel Databases - The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form...     

An efficient and security dynamic identity based authentication protocol for multi-server architecture using smart cards

Generally, if a user wants to use numerous different network services, he/she must register himself/herself to every service providing server. It is extremely hard for users to remember these different identities and passwords. In order to resolve this problem, various multi-server authentication protocols have been proposed. Recently, Sood et al. analyzed Hsiang and Shih's multi-server authentication protocol and proposed an improved dynamic identity based authentication protocol for multi-server architecture. They claimed that their protocol provides user's anonymity, mutual authentication, the session key agreement and can resist several kinds of attacks. However, through careful analysis, we find that Sood et al.'s protocol is still vulnerable to leak-of-verifier attack, stolen smart card attack and impersonation attack. Besides, since there is no way for the control server CS to know the real identity of the user, the authentication and session key agreement phase of Sood et al.'s protocol is incorrect. We propose an efficient and security dynamic identity based authentication protocol for multi-server architecture that removes the aforementioned weaknesses. The proposed protocol is extremely suitable for use in distributed multi-server architecture since it provides user's anonymity, mutual authentication, efficient, and security.     

A power-saving data storage scheme for wireless sensor networks

In wireless sensor network (WSN), sensors are small, inexpensive, and computable. However, they are limited in power, memory, and computational capacities. A large number of tiny sensors are usually deployed randomly to monitor one or more phenomena to collect and process the sensed data, and to send the data back to the sink. Many literatures focus on developing power-saving protocols. In addition, many papers present data storage schemes but they do not take power saving into consideration. Hence, these data storage schemes cannot perform well based on power-saving protocols. Therefore, it is very critical to propose a data storage scheme to support power-saving mechanism. In this paper, we propose a power-saving data storage scheme for WSN. Our scheme adopts grid-based architecture, in which each grid guarantees that two sensors will stay in active mode while the others stay in sleep mode to save energy. Simulation results show that our power-saving data storage scheme can reduce energy consumption.     

深度学习的人体图像半自动标注系统

针对目前数据标注过于依赖硬件、手动数据标注效率低下的问题,提出了基于深度学习的人体图像半自动标注系统.系统通过对算法进行改进,增加人体关键点个数进行特征提取和加入运动信息的约束,提高了视频分阶段标注的准确率.使用真实数据集仿真实验证明了通过深度学习算法进行数据标注的可行性,并且使用半自动标注的速度快、准确率高.     

Testing scientific software: A systematic literature review

ContextScientific software plays an important role in critical decision making, for example making weather predictions based on climate models, and computation of evidence for research publications. Recently, scientists have had to retract publications due to errors caused by software faults. Systematic testing can identify such faults in code.ObjectiveThis study aims to identify specific challenges, proposed solutions, and unsolved problems faced when testing scientific software.MethodWe conducted a systematic literature survey to identify and analyze relevant literature. We identified 62 studies that provided relevant information about testing scientific software.ResultsWe found that challenges faced when testing scientific software fall into two main categories: (1) testing challenges that occur due to characteristics of scientific software such as oracle problems and (2) testing challenges that occur due to cultural differences between scientists and the software engineering community such as viewing the code and the model that it implements as inseparable entities. In addition, we identified methods to potentially overcome these challenges and their limitations. Finally we describe unsolved challenges and how software engineering researchers and practitioners can help to overcome them.ConclusionsScientific software presents special challenges for testing. Specifically, cultural differences between scientist developers and software engineers, along with the characteristics of the scientific software make testing more difficult. Existing techniques such as code clone detection can help to improve the testing process. Software engineers should consider special challenges posed by scientific software such as oracle problems when developing testing techniques.