CSS3与网页视觉设计及技术呈现研究

目的研究网页视觉设计中,使得网页负载更小、交互更友好和更便捷、自适应性更好的方法和技术。基于当前CSS3、HTML5和JavaScript技术的甄别与筛选,基本涵盖了网页视觉设计呈现所必需的基本语言代码规范和格式要求。方法利用CSS3、HTML5和JavaScript等进行联合测试,重点在于CSS3的源代码测试,比较使用CSS3与使用最基础版本的CSS代码实现同一效果的差异,这些差异包括HTML文件的大小和加载时间、线程比较等。结论有无受体(如加载外部图片),均可通过CSS3实现动画演绎和图片常见效果的变化,提高页面性能,降低网页载荷;使用CSS3实现网页自适应,使不同浏览终端设备之间更易实现无障碍切换,同时DIV不会错乱;CSS3的新特性让微交互更轻松、更便捷且兼具景深效果;CSS3更能有效优化页面性能,节省开发成本。     

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user’s behavior. However, in general, the sequential rule-based approaches have poor generalization capability. Moreover, existing approaches mostly consider an aspect as a noun or noun phrase, so these approaches are unable to extract verb aspects. In this article, we have proposed a multi-layered rule-based (ML-RB) technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects. Additionally, after rigorous analysis, we have also constructed rules for the extraction of verb aspects. These verb rules primarily based on the association between verb and opinion words. The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets. The F1 score for both the datasets are 0.90 and 0.88, respectively, which are better than existing approaches. These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.     

栅格系统方法在网页界面设计中的应用研究

目的 研究栅格系统方法在网页界面设计中的发展现状、应用范式及创新途径。方法 从网页界面设计的特点与规律出发,分析其作为一种视觉传达媒介在交互方式与设计流程上的特性,研究栅格系统方法的基本原理及其在网页界面设计中的重要作用,结合对栅格系统发展现状与应用范式的归纳分析,探讨其在网页界面设计中的创新途径与发展方向。结论 栅格系统本质上是一种可以数学描述的界面设计方法,能够指导网页元素的布局与信息内容的组织传达,有效提升网页产品的用户体验。信息技术软硬件的发展对网页界面设计提出了新的要求,可以从典型栅格系统的扩展应用、响应式网页设计方法的推广完善等方向探索创新。同时,栅格系统方法能够从非结构化数据的处理、学习模型建立等方面,推动人工智能技术在设计领域内的应用。     

Prediction of users webpage access behaviour using association rule mining

Web Usage mining is a technique used to identify the user needs from the web log. Discovering hidden patterns from the logs is an upcoming research area. Association rules play an important role in many web mining applications to detect interesting patterns. However, it generates enormous rules that cause researchers to spend ample time and expertise to discover the really interesting ones. This paper works on the server logs from the MSNBC dataset for the month of September 1999. This research aims at predicting the probable subsequent page in the usage of web pages listed in this data based on their navigating behaviour by using Apriori prefix tree (PT) algorithm. The generated rules were ranked based on the support, confidence and lift evaluation measures. The final predictions revealed that the interestingness of pages mainly depended on the support and lift measure whereas confidence assumed a uniform value among all the pages. It proved that the system guaranteed 100% confidence with the support of 1.3E ?05. It revealed that the pages such as Front page, On-air, News, Sports and BBS attracted more interested subsequent users compared to Travel, MSN-News and MSN-Sports which were of less interest.     

Comparison of visual similarity judgement on computer screens and on paper

This article reports a study of how visual similarity judgement differs between the two media. Participants of the study judged dissimilarities of web pages both on screens in computer-based tasks and on printouts in paper-based tasks. Significant difference in similarity judgement was found between computer and paper tasks. This difference could neither be explained by the ecological difference in using computers and printouts, nor be explained by visual difference. The availability of interactive features of web pages on screens, compared to the lack of them on printouts, could account for the observed difference in similarity judgement, even though the features are not relevant to the tasks. The result highlights the significance of interactive features as parts of context change when tasks are moved between the two media.     

Suitability of Searching and Representing Multimedia Learning Resources in a 3-D Virtual Gaming Environment

Search engines usually publish search results on web pages that are filled with a certain number of matched records that can be further navigated through successive back and forth operations. While this approach proves to be useful, it fails to show the context and relationship among displayed results. We argue that new and intuitive user interfaces, such as 3-D virtual environment interfaces, may provide an alternative to the traditional presentation techniques, particularly in a learning environment where learners are looking for multimedia learning resources that match their interests. We propose a 3-D car gaming environment such as a search metaphor and analyze the suitability of such an approach in terms of user perception, interaction, visual quality, graphics-rendering performance, and other parameters. Our experiment suggests that the proposed approach not only shows an intuitive way of information visualization but also integrates education and entertainment to serve the vision of Edutainment.      

An unsupervised approach for fault diagnosis of power transformers

Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.     

Performance Anomaly Detection in Web Services: An RNN- Based Approach Using Dynamic Quality of Service Features

Performance anomaly detection is the process of identifying occurrences that do not conform to expected behavior or correlate with other incidents or events in time series data. Anomaly detection has been applied to areas such as fraud detection, intrusion detection systems, and network systems. In this paper, we propose an anomaly detection framework that uses dynamic features of quality of service that are collected in a simulated setup. Three variants of recurrent neural networks-SimpleRNN, long short term memory, and gated recurrent unit are evaluated. The results reveal that the proposed method effectively detects anomalies in web services with high accuracy. The performance of the proposed anomaly detection framework is superior to that of existing approaches using maximum accuracy and detection rate metrics.     

Information Classification and Extraction on Official Web Pages of Organizations

As a real-time and authoritative source, the official Web pages of organizations contain a large amount of information. The diversity of Web content and format makes it essential for pre-processing to get the unified attributed data, which has the value of organizational analysis and mining. The existing research on dealing with multiple Web scenarios and accuracy performance is insufficient. This paper aims to propose a method to transform organizational official Web pages into the data with attributes. After locating the active blocks in the Web pages, the structural and content features are proposed to classify information with the specific model. The extraction methods based on trigger lexicon and LSTM (Long Short-Term Memory) are proposed, which efficiently process the classified information and extract data that matches the attributes. Finally, an accurate and efficient method to classify and extract information from organizational official Web pages is formed. Experimental results show that our approach improves the performing indicators and exceeds the level of state of the art on real data set from organizational official Web pages.     

Learning dispatching rules using random forest in flexible job shop scheduling problems

In this paper, we address the flexible job-shop scheduling problem (FJSP) with release times for minimising the total weighted tardiness by learning dispatching rules from schedules. We propose a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) in order to extract dispatching rules from the best schedules. RANFORS consists of three phases: schedule generation, rule learning with data transformation, and rule improvement with discretisation. In the schedule generation phase, we present three solution approaches that are widely used to solve FJSPs. Based on the best schedules among them, the rule learning with data transformation phase converts them into training data with constructed attributes and generates a dispatching rule with inductive learning. Finally, the rule improvement with discretisation improves dispatching rules with a genetic algorithm by discretising continuous attributes and changing parameters for random forest with the aim of minimising the average total weighted tardiness. We conducted experiments to verify the performance of the proposed approach and the results showed that it outperforms the existing dispatching rules. Moreover, compared with the other decision-tree-based algorithms, the proposed algorithm is effective in terms of extracting scheduling insights from a set of rules.     

Learning keyword phrases from query logs of USPTO patent examiners for automatic query scope limitation in patent searching

Professional search in patent repositories poses several unique challenges. One key requirement is to search the entire affected space of concepts, following well-defined procedures to ensure traceability of results obtained. Several techniques have been introduced to enhance query generation, preferably via automated query term expansion, to improve retrieval effectiveness. Currently, these approaches are mostly limited to computing additional query terms from patent documents based on statistical measures. For conceptual search to solve the limitation of traditional keyword search standard dictionaries are used to provide synonyms and keyword phrases for query refinement. Studies show that these are insufficient in such highly specialized domains. In this paper, we present an approach to extract keyword phrases from query logs created during the validation procedure of the patent applications. This creates valuable domain-specific lexical databases for several specific patent classes that can be used to both expand as well as limit the scope of a patent search. This provides a more powerful means to guide a professional searcher through the search process. We evaluate the lexical databases based on real query sessions of patent examiners.     

Ontology-based Tamil–English cross-lingual information retrieval system

Cross-lingual information retrieval (CLIR) systems facilitate users to query for information in one language and retrieve relevant documents in another language. In general, CLIR systems translate query in source language to target language and retrieve documents in target language based on the keywords present in the translated query. However, the presence of ambiguity in source and translated queries reduces the performance of the system. Ontology can be used to address this problem. The current approaches to ontology-based CLIR systems use manually constructed multilingual ontology, which is expensive. However, many methods exist to automatically construct ontology for any domain in English but not in other languages like Tamil. We propose a methodology for Tamil–English CLIR system by translating the Tamil query to English and retrieve pages in English to address these issues. Our approach uses a word sense disambiguation module to resolve the ambiguity in Tamil query. An automatically constructed ontology in English is used to address the ambiguity of English query. We have developed a morphological analyser for Tamil language, Tamil–English bilingual dictionary and named entity database to translate a Tamil query to English. The translated query is reformulated using ontology and the reformulated queries are given to a search engine to retrieve English documents from the Internet. We have evaluated our methodology for agriculture domain and the evaluation results show that our approach outperforms other approaches in terms of precision.     

网页界面设计中的平面视觉元素

分析了网页界面设计中平面视觉元素的6个特性。结合传统平面构成方式在网页界面设计中的运用情况,阐述了文字与字体、图形图像、色彩、版面等平面视觉元素适应网页界面设计发展的新特点。针对网页界面设计中的交互元素和动态元素,提出了与平面视觉元素相结合的设计方法。     

基于生理电的移动网页时延对体验质量的影响

目的从用户角度出发,研究移动设备网页浏览过程中网页时延与用户体验质量之间的关系。方法通过记录多类网页浏览过程中不同时延下用户皮电、心电两类生理指标的变化,利用方差分析以及t检验计算其特征值发生显著性差异时对应的时延长度并对该时延长度进行标记,结合MOS评分量表,对用户的主观感受作出评价。结果实验得到了较为可靠的结论,研究发现不同移动网页时延下用户的体验质量是不同的。用户在打开网页时的主观体验与网页的时延长度整体存在非线性负相关关系。结论当移动网页缓冲时延小于1 s时,用户体验良好;当时延超过1 s时,用户体验质量在一定时延范围内随时延增加而下降且该变化呈现较大的个人差异性;当时延超过4 s时,用户负面体验较为严重。     

面向语义的精简化多关系频繁模式发现方法

多关系频繁模式发现能够直接从复杂结构化数据中发现涉及多个关系的复杂频繁模式,避免了传统方法的局限。有别于主流基于归纳逻辑程序设计技术的方法,提出了基于合取查询包含关系的面向语义的精简化多关系频繁模式发现方法,具有理论与技术基础的新颖性,解决了两种语义冗余问题。实验表明,该方法在可理解性、功能、效率以及可扩展性方面具有优势。     

基于TRIZ的卷筒纸包装机折边机构设计

针对现有卷筒纸包装机包装后存在的问题和实现产品模块重用的问题,利用基于问题的创新方法,在设计中采用TRIZ理论的理想解方法,在对该机构进行工艺分析、运动分析和力分析的基础上,得到了该机构的理想解,然后对其进行了设计,采用差速系统使卷筒纸在移动中旋转,设计了燕尾形折边结构,使包装膜均匀自然地向中心收缩,使包装膜成型更加美观,同时通过提高零部件的通用性,实现了其在产品中的重用。经实际使用,该机构能够满足应用需求。     

网页界面优化设计的分析与研究

网络是现代化的、最能影响人们生活方式的信息传媒系统.以感性工学理论为指导,针对网页界面优化设计的相关文献进行整合探讨,并在此基础之上进一步分析论述了网页界面优化设计的内容、方法,为设计学理论与计算机技术相结合的网页界面优化设计构建了科学的设计方法体系.     

Multi-dimensional Security Range Query for Industrial IoT

The Internet of Things (IoT) has allowed for significant advancements in applications not only in the home, business, and environment, but also in factory automation. Industrial Internet of Things (IIoT) brings all of the benefits of the IoT to industrial contexts, allowing for a wide range of applications ranging from remote sensing and actuation to decentralization and autonomy. The expansion of the IoT has been set by serious security threats and obstacles, and one of the most pressing security concerns is the secure exchange of IoT data and fine-grained access control. A privacy-preserving multi-dimensional secure query technique for fog-enhanced IIoT was proposed in light of the fact that most existing range query schemes for fog-enhanced IoT cannot provide both multi-dimensional query and privacy protection. The query matrix was then decomposed using auxiliary vectors, and the auxiliary vector was then processed using BGN homomorphic encryption to create a query trapdoor. Finally, the query trapdoor may be matched to its sensor data using the homomorphic computation used by an IoT device terminal. With the application of particular auxiliary vectors, the spatial complexity might be efficiently decreased. The homomorphic encryption property might ensure the security of sensor data and safeguard the privacy of the user's inquiry mode. The results of the experiments reveal that the computing and communication expenses are modest.     

A new query expansion method for document retrieval based on the inference of fuzzy rules

Abstract

Automatic query expansion based on user relevance feedback techniques can improve the performance of document retrieval systems. In this paper, we present a new query expansion method based on the inference of fuzzy rules and user relevance feedback techniques to deal with document retrieval. The proposed method uses membership functions and fuzzy rules to infer relevant degrees of expansion terms and puts the expansion terms with larger relevant degrees into the original user's query. Then, the system calculates the degree of similarity of each document with respect to the expanded user's query. The proposed method gets a higher average precision rate and a higher average recall rate than the existing methods for document retrieval.