Renewable Energy-Aware Routing in the Internet

The increasing power consumption of the Internet infrastructure has attracted a lot of world-wide attention because of the severe impact on the environment. Many research works have started to search for solutions of how to reduce the energy consumption in data networks. Other works have considered that generation of electricity from fossil-based fuel emits greenhouse gases into the atmosphere, which leads to global warming. Consequently, another approach for sustainable networks is the utilization of renewable energy to power the infrastructure. This thesis introduces a new backbone Internet routing protocol that performs routing considering the different renewable energy availability at various geographical locations. A Border Gateway Protocol (BGP)-based routing algorithm using a new metric is proposed to increase the utilization of renewable energy. The aim of the presented protocol is to maximize the total renewable energy usage of the backbone network and reduce the non-renewable energy consumption for different traffic load. The new metric is based on a linear energy power consumption model for the selected routers. This linear model describes the power efficiency of routers using a scaling factor (SF), which the proposed algorithm incorporates into the routing metric and combines with a per-packet load balancing scheme to increase the renewable energy power consumption. Simulations with various configurations were implemented to evaluate the performance of the presented routing algorithm

Identifying meaningful return information for XML keyword search

Keyword search enables web users to easily access XML data with-out the need to learn a structured query language and to study pos-sibly complex data schemas. Existing work has addressed the prob-lem of selecting qualied data nodes that match keywords and con-necting them in a meaningful way, in the spirit of inferring a where clause in XQuery. However, how to infer the return clause for key-word search is an open problem. To address this challenge, we present an XML keyword search en-gine, XSeek, to infer the semantics of the search and identify return nodes effectively. XSeek recognizes possible entities and attributes inherently represented in the data. It also distinguishes betwee

An Empirical Study on the Influencing Factors of Customers\u27 Acceptance Intention towards Online Behavioral Advertising

Big data mining and analysis technology greatly influence the development of the advertising industry. In order to capture large information on consumers\u27 online behaviour, cookie files and Hadoop are widely adopted by advertisers to reach targeted consumers, which leads to online behavioural advertising. Based on an empirical study, this research mainly analyzes the factors influencing customers\u27 acceptance intention towards OBA from developing a conceptual framework. By collecting data through questionnaires and using SPSS and AMOS for data analysis, the result indicates that the factors of performance expectancy, effort expectancy, social influence and facilitating conditions have a positive relationship with customer acceptance intention. Moreover, performance expectancy, effort expectancy, and facilitating conditions have a positive relationship with attitudes towards OBA. However, attitudes do not positively impact customer acceptance intention and social influence has no significant relationship with attitudes, which could attribute to privacy concern and the rising of personality consciousness respectively. The result of this study is of great significance to the way of improving advertising effectiveness

Sparse Attention-Based Neural Networks for Code Classification

Categorizing source codes accurately and efficiently is a challenging problem in real-world programming education platform management. In recent years, model-based approaches utilizing abstract syntax trees (ASTs) have been widely applied to code classification tasks. We introduce an approach named the Sparse Attention-based neural network for Code Classification (SACC) in this paper. The approach involves two main steps: In the first step, source code undergoes syntax parsing and preprocessing. The generated abstract syntax tree is split into sequences of subtrees and then encoded using a recursive neural network to obtain a high-dimensional representation. This step simultaneously considers both the logical structure and lexical level information contained within the code. In the second step, the encoded sequences of subtrees are fed into a Transformer model that incorporates sparse attention mechanisms for the purpose of classification. This method efficiently reduces the computational cost of the self-attention mechanisms, thus improving the training speed while preserving effectiveness. Our work introduces a carefully designed sparse attention pattern that is specifically designed to meet the unique needs of code classification tasks. This design helps reduce the influence of redundant information and enhances the overall performance of the model. Finally, we also deal with problems in previous related research, which include issues like incomplete classification labels and a small dataset size. We annotated the CodeNet dataset with algorithm-related labeling categories, which contains a significantly large amount of data. Extensive comparative experimental results demonstrate the effectiveness and efficiency of SACC for the code classification tasks.Comment: 2023 3rd International Conference on Digital Society and Intelligent Systems (DSInS 2023

Prediction of Soil Layer R-Value Dependence on Moisture Content

This study focuses on how green roof thermal performance is affected by the soil moisture in summer condition. It aims to determine whether moist soil is a better insulator during the summer months than dry soil. A soil model is developed to predict simultaneous conduction, convection, and surface evaporation for a layer of moist soil representing a green roof. It used to analyze evaporation process and its affect on the soil resistance. The model considers only bare soil without vegetation on the roof. The model predicts the soil surface temperature as it is affected by soil moisture content, which can then be used to calculate heat transfer through the soil layer. An experimental dry out test was conducted to measure the soil moisture and soil temperature histories. Comparison of the predicted and measured sol surface temperature shows that the model reasonably captures the actual behavior. The evaporative cooling effectively reduces the soil surface temperature and heat flux in moist soil and can be used as an effective way to insulate the roof