Belief Decay or Persistence? A Mixed-method Study on Belief Movement Over Time

When individuals encounter new information (data), that information is incorporated with their existing beliefs (prior) to form a new belief (posterior) in a process referred to as belief updating. While most studies on rational belief updating in visual data analysis elicit beliefs immediately after data is shown, we posit that there may be critical movement in an individual's beliefs when elicited immediately after data is shown v. after a temporal delay (e.g., due to forgetfulness or weak incorporation of the data). Our paper investigates the hypothesis that posterior beliefs elicited after a time interval will “decay” back towards the prior beliefs compared to the posterior beliefs elicited immediately after new data is presented. In this study, we recruit 101 participants to complete three tasks where beliefs are elicited immediately after seeing new data and again after a brief distractor task. We conduct (1) a quantitative analysis of the results to understand if there are any systematic differences in beliefs elicited immediately after seeing new data or after a distractor task and (2) a qualitative analysis of participants' reflections on the reasons for their belief update. While we find no statistically significant global trends across the participants beliefs elicited immediately v. after the delay, the qualitative analysis provides rich insight into the reasons for an individual's belief movement across 9 prototypical scenarios, which includes (i) decay of beliefs as a result of either forgetting the information shown or strongly held prior beliefs, (ii) strengthening of confidence in updated beliefs by positively integrating the new data and (iii) maintaining a consistently updated belief over time, among others. These results can guide subsequent experiments to disambiguate when and by what mechanism new data is truly incorporated into one's belief system.     

Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

Cloud storage is essential for managing user data to store and retrieve from the distributed data centre. The storage service is distributed as pay a service for accessing the size to collect the data. Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy, duplication leads to increase storage space. The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis. It creates a complex nature to increase the storage consumption under cost. To resolve this problem, this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication (HIBD) based on Segmented Bind Linkage (SBL) Methods for reducing storage in a cloud environment. Initially, preprocessing is done using the sparse augmentation technique. Further, the preprocessed files are segmented into blocks to make Hash-Index. The block of the contents is compared with other files through Semantic Content Source Deduplication (SCSD), which identifies the similar content presence between the file. Based on the content presence count, the Distance Vector Weightage Correlation (DVWC) estimates the document similarity weight, and related files are grouped into a cluster. Finally, the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case. This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.     

面向关系型数据与知识图谱的数据集成技术综述

目前,各个国家和地区均已将大数据视为重要的战略资源.然而,大数据时代普遍存在数据流通困难、数据监管不足等问题,致使数据孤岛现象严重,数据质量低下,数据要素潜能难以释放.这驱使研究人员探索数据集成技术,以打破数据壁垒、实现信息共享、提升数据质量,进而激活数据要素潜能.关系型数据和知识图谱作为两种至关重要的数据组织与存储形式,在现实生活中应用广泛.为此,聚焦关系型数据和知识图谱,归纳总结并分析实体解析、数据融合、数据清洗3方面的数据集成关键技术,最后展望未来研究方向与趋势.     

An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However, there is no standard guideline for data transformation from relational database to NoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processing time when compared to the relational database query processing time. However, these approaches produced redundant tables in the resulted schema that in turn consume large unnecessary storage size and produce high query processing time due to the generated schema with redundant column families in the transformed column-oriented database. In this paper, an efficient data transformation technique from relational database to column-oriented database is proposed. The proposed schema transformation technique is based on the combination of denormalization approach, data access pattern and multiple-nested schema. In order to validate the proposed work, the proposed technique is implemented by transforming data from MySQL database to HBase database. A benchmark transformation technique is also performed in which the query processing time and the storage size are compared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storage space usage due to the reduced number of column families in the column-oriented database.     

Multi-response optimization of the electrical discharge machining of insulating zirconia

In this study, electrical discharge machining has been used to machine insulating zirconia via the assisting electrode method. The process parameter optimization was investigated by combining the Taguchi method with grey relational analysis. The application of Taguchi–grey relational analysis is proven to effectively improve the performance of electrical discharge machining in drilling insulating zirconia. The results of this analysis indicate that the final optimal process parameters are a peak current of 8 A, a pulse duration of 16?µs, a duty cycle of 0.5, and a flushing pressure of 6?MPa. Additionally, the material removal rate, electrode wear rate, and hole taper ratio increase by 39%, 1.5%, and 1.3%, respectively, which improves the grey relational grade by 6.8%. The electrical resistance test confirms that the conductivity of the conductive layer obtained using the final optimal process parameters is better than that of the conductive layer obtained using the initial optimal process parameters. Energy spectrum analysis reveals that the conductive layer is composed of C, Cu, Zn, Zr, and O. Analysis of variance shows that the most significant component of the multi-responses is the peak current, with a 51.4% contribution.     

电网调度管理演绎数据库模型研究与实现

本文针对传统专家系统在具有海量数据特征的电力系统的应用中存在的问题,提出了一个电网调度管理演绎数据库模型,介绍了该模型中基于关系数据模式的知识表示方法和基于查询的推理机制,并基于中文Ｗｉｎｄｏｗｓ环境和关系型ＤＢＭＳＦｏｘｐｒｏ２．５Ｂ实现了该演绎数据库模型,实践表明,该实际系统在演绎推理的有效性和可靠性方面都大为提高。     

Recognizing interleaved and concurrent activities using qualitative and quantitative temporal relationships

The majority of approaches to activity recognition in sensor environments are either based on manually constructed rules for recognizing activities or lack the ability to incorporate complex temporal dependencies. Furthermore, in many cases, the rather unrealistic assumption is made that the subject carries out only one activity at a time. In this paper, we describe the use of Markov logic as a declarative framework for recognizing interleaved and concurrent activities incorporating both input from pervasive lightweight sensor technology and common-sense background knowledge. In particular, we assess its ability to learn statistical-temporal models from training data and to combine these models with background knowledge to improve the overall recognition accuracy. We also show the viability and the benefit of exploiting both qualitative and quantitative temporal relationships like the duration of the activities and their temporal order. To this end, we propose two Markov logic formulations for inferring the foreground activity as well as each activities’ start and end times. We evaluate the approach on an established dataset where it outperforms state-of-the-art algorithms for activity recognition.     

面向动态模糊数据的关系数据库系统的研究

为使关系数据库系统具备处理动态模糊数据的能力,将动态模糊理论和关系数据库理论相结合,通过分析面向动态模糊数据的关系数据库系统的功能,构造了该数据库的系统结构,探讨了实现该数据库的方法,对面向动态模糊数据的关系数据库系统做了初步的研究。     

iFIX组态软件在污水处理控制系统中的应用

介绍组态软件iFIX在污水控制系统中的应用,提出了系统进行组态过程中的两个主要技术问题,iFIX和关系数据库的通信以及日报表的生成,并给出解决方法。     

浅谈产品设计中的细节意识的重要性

现在,产品设计对大家已不是一个陌生的概念,细节设计体现在产品设计的方方面面,从产品设计之初到设计结束,细节设计一直贯穿在产品设计之中。细节往往决定一件设计的成败,注重细节往往能让我们把想做的事完成的更好。