On the use of information fusion techniques to improve information quality: Taxonomy,opportunities and challenges

The information fusion field has recently been attracting a lot of interest within the scientific community, as it provides, through the combination of different sources of heterogeneous information, a fuller and/or more precise understanding of the real world than can be gained considering the above sources separately. One of the fundamental aims of computer systems, and especially decision support systems, is to assure that the quality of the information they process is high. There are many different approaches for this purpose, including information fusion. Information fusion is currently one of the most promising methods. It is particularly useful under circumstances where quality might be compromised, for example, either intrinsically due to imperfect information (vagueness, uncertainty, …) or because of limited resources (energy, time, …). In response to this goal, a wide range of research has been undertaken over recent years. To date, the literature reviews in this field have focused on problem-specific issues and have been circumscribed to certain system types. Therefore, there is no holistic and systematic knowledge of the state of the art to help establish the steps to be taken in the future. In particular, aspects like what impact different information fusion methods have on information quality, how information quality is characterised, measured and evaluated in different application domains depending on the problem data type or whether fusion is designed as a flexible process capable of adapting to changing system circumstances and their intrinsically limited resources have not been addressed. This paper aims precisely to review the literature on research into the use of information fusion techniques specifically to improve information quality, analysing the above issues in order to identify a series of challenges and research directions, which are presented in this paper.     

Using localizing learning to improve supervised learning algorithms

Slow learning of neural-network function approximators can frequently be attributed to interference, which occurs when learning in one area of the input space causes unlearning in another area. To mitigate the effect of unlearning, this paper develops an algorithm that adjusts the weights of an arbitrary, nonlinearly parameterized network such that the potential for future interference during learning is reduced. This is accomplished by the reduction of a biobjective cost function that combines the approximation error and a term that measures interference. An analysis of the algorithm's convergence properties shows that learning with this algorithm reduces future unlearning. The algorithm can be used either during online learning or can be used to condition a network to have immunity from interference during a future learning stage. A simple example demonstrates how interference manifests itself in a network and how less interference can lead to more efficient learning. Simulations demonstrate how this new learning algorithm speeds up the training in various situations due to the extra cost function term.     

Multi-label feature selection based on neighborhood mutual information

Multi-label learning deals with data associated with a set of labels simultaneously. Like traditional single-label learning, the high-dimensionality of data is a stumbling block for multi-label learning. In this paper, we first introduce the margin of instance to granulate all instances under different labels, and three different concepts of neighborhood are defined based on different cognitive viewpoints. Based on this, we generalize neighborhood information entropy to fit multi-label learning and propose three new measures of neighborhood mutual information. It is shown that these new measures are a natural extension from single-label learning to multi-label learning. Then, we present an optimization objective function to evaluate the quality of the candidate features, which can be solved by approximating the multi-label neighborhood mutual information. Finally, extensive experiments conducted on publicly available data sets verify the effectiveness of the proposed algorithm by comparing it with state-of-the-art methods.     

Using local-spin k-exclusion algorithms to improve wait-free object implementations

Summary.  We present the first shared-memory algorithms for k-exclusion in which all process blocking is achieved through the use of “local-spin” busy waiting. Such algorithms are designed to reduce interconnect traffic, which is important for good performance. Our k-exclusion algorithms are starvation-free, and are designed to be fast in the absence of contention, and to exhibit scalable performance as contention rises. In contrast, all previous starvation-free k-exclusion algorithms require unrealistic operations or generate excessive interconnect traffic under contention. We also show that efficient, starvation-free k-exclusion algorithms can be used to reduce the time and space overhead associated with existing wait-free shared object implementations, while still providing some resilience to delays and failures. The resulting “hybrid” object implementations combine the advantages of local-spin spin locks, which perform well in the absence of process delays (caused, for example, by preemptions), and wait-free algorithms, which effectively tolerate such delays. We present performance results that confirm that this k-exclusion-based technique can improve the performance of existing wait-free shared object implementations. These results also show that lock-based implementations can be susceptible to severe performance degradation under multiprogramming, while our hybrid implementations are not. Received: December 1995 / Accepted: February 1997     

Ear recognition based on local information fusion

Ears have rich structural features that are almost invariant with increasing age and facial expression variations. Therefore ear recognition has become an effective and appealing approach to non-contact biometric recognition. This paper gives an up-to date review of research works on ear recognition. Current 2D ear recognition approaches achieve good performance in constrained environments. However the recognition performance degrades severely under pose, lighting and occlusion. This paper proposes a 2D ear recognition approach based on local information fusion to deal with ear recognition under partial occlusion. Firstly, the whole 2D image is separated to sub-windows. Then, Neighborhood Preserving Embedding is used for feature extraction on each sub-window, and we select the most discriminative sub-windows according to the recognition rate. Each sub-window corresponds to a sub-classifier. Thirdly, a sub-classifier fusion approach is used for recognition with partially occluded images. Experimental results on the USTB ear dataset and UND dataset have illustrated that using only few sub-windows we can represent the most meaningful region of the ear, and the multi-classifier model gets higher recognition rate than using the whole image for recognition.     

Iterative sIB algorithm

Recent years have witnessed a growing interest in the information bottleneck theory. Among the relevant algorithms in the extant literature, the sequential Information Bottleneck (sIB) algorithm is recognized for its balance between accuracy and complexity. However, like many other optimization techniques, it still suffers from the problem of getting easily trapped in local optima. To that end, our study proposed an iterative sIB algorithm (isIB) based on mutation for the clustering problem. From initial solution vectors of cluster labels generated by a seeding the sIB algorithm, our algorithm randomly selects a subset of elements and mutates the cluster labels according to the optimal mutation rate. The results are iteratively optimized further using genetic algorithms. Finally, the experimental results on the benchmark data sets validate the advantage of our iterative sIB algorithm over the sIB algorithm in terms of both accuracy and efficiency.     

Effective local search algorithms for high school timetabling problems

This paper addresses the high school timetabling problem. The problem consists in building weekly timetables for meetings between classes and teachers with the goal of minimizing violations of specific requirements. In the last decades, several mixed-integer programs have been proposed and tested for this family of problems. However, medium and large size instances are still not effectively solved by these programs using state-of-the-art solvers and the scientific community has given special attention to the devising of alternative soft computing algorithms. In this paper, we propose a soft computing approach based on Iterated Local Search and Variable Neighborhood Search metaheuristic frameworks. Our algorithms incorporate new neighborhood structures and local search routines to perform an effective search. We validated the proposed algorithms on variants of the problem using seven public instances and a new dataset with 34 real-world instances including large cases. The results demonstrate that the proposed algorithms outperform the state-of-the-art approaches in both cases, finding the best solutions in 38 out of the 41 tested instances.     

From local neural networks to granular neural networks: A study in information granulation

In this study, we are concerned with a construction of granular neural networks (GNNs)—architectures formed as a direct result reconciliation of results produced by a collection of local neural networks constructed on a basis of individual data sets. Being cognizant of the diversity of the results produced by the collection of networks, we arrive at the concept of granular neural network, producing results in the form of information granules (rather than plain numeric entities) that become reflective of the diversity of the results generated by the contributing networks. The design of a granular neural network exploits the concept of justifiable granularity. Introduced is a performance index quantifying the quality of information granules generated by the granular neural network. This study is illustrated with the aid of machine learning data sets. The experimental results provide a detailed insight into the developed granular neural networks.     

Human-centric wireless sensor networks to improve information availability during urban search and rescue activities

When a natural disaster hits an urban area, the first 72 h after are the most critical. After that period the probability of finding survivors falls dramatically, therefore the search and rescue activities in that area must be conducted as quickly and effectively as possible. These activities are often improvised by first responders, stemming from the lack of communication and information support needed for making decisions in the field. Unfortunately, improvisations reduce the effectiveness and efficiency of the activities, in turn, affecting the number of people that can be rescued. To address this challenge, this article introduces the concept of a human-centric wireless sensor network, as an infrastructure that supports the capture and delivery of shared information in the field. These networks help increase the information availability, and therefore, the efficiency and effectiveness of the emergency response process. The use of these networks, which is complimentary to the currently used VHF/UHF radio systems, was evaluated using a simulated scenario and also through the feedback provided by an expert in urban search and rescue. The obtained results are highly encouraging.     

快速核密度估计算法研究进展

作为当前最先进有效的密度估计算法,核密度估计（KDE）得到了广泛的研究。但是其二次的计算复杂度严重阻碍了KDE在具有海量高维数据的实际问题中的应用。为了排除算法计算性能上的障碍,研究者从不同角度提出了多种解决方案。在简要介绍KDE基本算法的基础上,简要分析了近年来提出的一些KDE的快速计算和逼近算法,以便为进一步的研究提供一定的支持与帮助。     

巧用信息技术实现学科整合

课堂教学效率影响着学生的学习效果和知识能力掌握.在实际教学过程中,由于教学方法不适宜等多种原因,我国技校课堂教学效率提升需要综合考虑教材内容和学生学习特点,本文分析了我国技校教学现状,同时剖析了技校教学效率低的根源,有针对性地提出了应用信息技术,提升技校教学效率举措.     

Inverted pattern approach to improve image quality of information hiding by LSB substitution

Capacity and invisibility are two targets of the methods for information hiding. Because these two targets contradict each other, to hide large messages into the cover image and remain invisible is an interesting challenge. The simple least-significant-bit (LSB) substitution approach, which embeds secret messages into the LSB of pixels in cover images, usually embeds huge secret messages. After a large message is embedded, the quality of the stego-image will be significantly degraded. In this paper, a new LSB-based method, called the inverted pattern (IP) LSB substitution approach, is proposed to improve the quality of the stego-image. Each section of secret images is determined to be inverted or not inverted before it is embedded. The decisions are recorded by an IP for the purpose of extracting data and the pattern can be seen as a secret key or an extra data to be re-embedded. The experimental results show that our proposed method runs fast and has better results than that of previous works.     

A meaning based information theory - informalogical space: Basic concepts and convergence of information sequences

This paper presents an information theory that is based on meanings and relationships between information. It first introduces the foundation of our approach, a binary relation contain between two pieces of information, based on inference between the two pieces of information. Then, based on the contain relation, it introduces two basic operations union and intersection on a collection (i.e., set) of information.This paper lays the foundation of our approach by introducing the core concept, informalogical space. An informalogical space is a collection of information that satisfies certain conditions represented in terms of the contain relation, and the union and intersection operations. An informalogical space is similar to a topological space in a symbolic sense, but is different in nature.This paper also introduces an information net in an informalogical space. An information net is a generalization of information sequence, just as a net is a generalization of sequence in general topology. This paper builds a convergence theory for information nets that is similar in a symbolic sense to the Moore-Smith convergence theory in general topology. Then, this paper applies the results for information nets to information sequences.     

Reactive Search strategies using Reinforcement Learning,local search algorithms and Variable Neighborhood Search

Optimization techniques known as metaheuristics have been applied successfully to solve different problems, in which their development is characterized by the appropriate selection of parameters (values) for its execution. Where the adjustment of a parameter is required, this parameter will be tested until viable results are obtained. Normally, such adjustments are made by the developer deploying the metaheuristic. The quality of the results of a test instance [The term instance is used to refer to the assignment of values to the input variables of a problem.] will not be transferred to the instances that were not tested yet and its feedback may require a slow process of “trial and error” where the algorithm has to be adjusted for a specific application. Within this context of metaheuristics the Reactive Search emerged defending the integration of machine learning within heuristic searches for solving complex optimization problems. Based in the integration that the Reactive Search proposes between machine learning and metaheuristics, emerged the idea of putting Reinforcement Learning, more specifically the Q-learning algorithm with a reactive behavior, to select which local search is the most appropriate in a given time of a search, to succeed another local search that can not improve the current solution in the VNS metaheuristic. In this work we propose a reactive implementation using Reinforcement Learning for the self-tuning of the implemented algorithm, applied to the Symmetric Travelling Salesman Problem.     

互联网个人信息挖掘模型设计

互联网个人信息挖掘技术是指综合利用搜索引擎、博客、网络社交平台等一系列互联网公开资源挖掘某个人物的个人信息。通过从互联网上海量信息中提取线索,寻找关联,最终获取所需信息。本文设计了一种互联网个人信息挖掘模型并详细介绍了其工作原理与应用,利用该模型所提供的方法可以有效进行互联网个人信息挖掘。     

运用跳端口技术进行信息隐藏

针对Internet的信息安全问题,介绍了一种新的信息隐藏技术--跳端口技术,它利用扩频通信中的跳频思想,通过变更数据包接收端口实现数据信息的隐藏,可以广泛地应用在民用和军事领域中.分析了这种技术的原理和实现方法,并指出需要进一步解决的问题.     

关于信息管理与信息系统专业《信息存储与检索》课程的研究

本文从信息管理与信息系统的专业目标开始分析,确立《信息存储与检索》课程在该专业中的地位,然后阐述《信息存储与检索》课程的跨多学科的特点,分析当前大学的主要教材,选择最适合信息管理与信息系统专业的教材,针对所选教材阐述了该课程的教学内容及教学方式、方法。