Interval type-2 (IT2) fuzzy systems have become increasingly popular in the last 20 years. They have demonstrated superior performance in many applications. However, the operation of an IT2 fuzzy system is more complex than that of its type-1 counterpart. There are many questions to be answered in designing an IT2 fuzzy system: Should singleton or non-singleton fuzzifier be used? How many membership functions (MFs) should be used for each input? Should Gaussian or piecewise linear MFs be used? Should Mamdani or Takagi-Sugeno-Kang (TSK) inference be used? Should minimum or product $t$-norm be used? Should type-reduction be used or not? How to optimize the IT2 fuzzy system? These questions may look overwhelming and confusing to IT2 beginners. In this paper we recommend some representative starting choices for an IT2 fuzzy system design, which hopefully will make IT2 fuzzy systems more accessible to IT2 fuzzy system designers.
Faculty of Computer and Mathematical Science, Universiti Teknologi MARA（玛拉工艺大学计算机与数学科学学院）
Recommendation system or also known as a recommender system is a tool to help the user in providing a suggestion of a specific dilemma. Thus, recently, the interest in developing a recommendation system in many fields has increased. Fuzzy Logic system (FLSs) is one of the approaches that can be used to model the recommendation systems as it can deal with uncertainty and imprecise information. However, one of the fundamental issues in FLS is the problem of the curse of dimensionality. That is, the number of rules in FLSs is increasing exponentially with the number of input variables. One effect…
We developed an Android Smartophone application software for tourist information system. Especially, the agent system recommends the sightseeing spot and local hospitality corresponding to the current feelings. The system such as concierge can estimate user's emotion and mood by Emotion Generating Calculations and Mental State Transition Network. In this paper, the system decides the next candidates for spots and foods by the reasoning of fuzzy Petri Net in order to make more smooth communication between human and smartphone. The system was developed for Hiroshima Tourist Information and descr…
The population in Sweden is growing rapidly due to immigration. In this light, the issue of infrastructure upgrades to provide telecommunication services is of importance. New antennas can be installed at hot spots of user demand, which will require an investment, and/or the clientele expansion can be carried out in a planned manner to promote the exploitation of the infrastructure in the less loaded geographical zones. In this paper, we explore the second alternative. Informally speaking, the term Infrastructure-Stressing describes a user who stays in the zones of high demand, which are prone…
Harvard University（哈佛大学）；Facebook AI Research（脸书人工智能研究院）
Deep learning recommendation systems must provide high quality, personalized content under strict tail-latency targets and high system loads. This paper presents RecPipe, a system to jointly optimize recommendation quality and inference performance. Central to RecPipe is decomposing recommendation models into multi-stage pipelines to maintain quality while reducing compute complexity and exposing distinct parallelism opportunities. RecPipe implements an inference scheduler to map multi-stage recommendation engines onto commodity, heterogeneous platforms (e.g., CPUs, GPUs).While the hardware-aw…
University of Sfax（斯法克斯大学）；National Engineering School of Sfax（斯法克斯国家工程学院）；University of Las Palmas de Gran Canaria（大加那利岛拉斯帕尔马斯大学）
Urban Traffic Networks are characterized by high dynamics of traffic flow and increased travel time, including waiting times. This leads to more complex road traffic management. The present research paper suggests an innovative advanced traffic management system based on Hierarchical Interval Type-2 Fuzzy Logic model optimized by the Particle Swarm Optimization (PSO) method. The aim of designing this system is to perform dynamic route assignment to relieve traffic congestion and limit the unexpected fluctuation effects on traffic flow. The suggested system is executed and simulated using SUMO,…
Institute of Software Technology Graz University of Technology（格拉茨工业大学软件技术研究所）；Web Information Systems TU Delft（代尔夫特理工大学网络信息系统系）
Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific \emph{goals} such as increasing the transparency of a recommendation or increasing a user's trust in the recommender s…
While designing machine learning based text analytics applications, often, NLP data scientists manually determine which NLP features to use based upon their knowledge and experience with related problems. This results in increased efforts during feature engineering process and renders automated reuse of features across semantically related applications inherently difficult. In this paper, we argue for standardization in feature specification by outlining structure of a language for specifying NLP features and present an approach for their reuse across applications to increase likelihood of ide…
A VLSI Design and Implementation for a Real-Time Approximate Reasoning
Rockwell International Science Center（罗克韦尔国际科学中心）
在基于规则的专家系统中，不确定性推理的作用变得越来越重要，因为人类专家给出的知识通常是不确定的或不精确的。我们已经成功地设计了一个超大规模集成电路芯片，它可以执行一个完整的推理过程的基础上模糊逻辑。VLSI模糊推理机的设计强调简单性、可扩展性和效率（运算速度和布局面积）。它采用2.5 um CMOS工艺制造。推理机由三个主要部分组成：规则集存储器、推理处理器和控制器。在该实现中，规则集存储器由只读存储器（ROM）实现。控制器由两个计数器组成。在推理处理器中，每个规则都有一条数据路径。推理规则的数量可以增加，从而向推理处理器添加更多的数据路径。所有规则都是并行执行的，但每个规则都是串行处理的。本文提出的模糊推理的逻辑结构很好地映射到VLSI结构上。使用两相非重叠时钟方案。时序测试表明，推理机可以工作在约20.8 MHz。这转化为每秒大约80，000个模糊逻辑推理（FLIPS）的执行速度，并且表明推理引擎适合于要求实时的【形】应用。潜在的应用包括智能机器人系统的指挥和控制、过程控制、导弹和飞机制导以及其他高性能机器的决策。
DRN e-Commerce（DRN电子商务公司）；University of Western Ontario（西安大略大学）；NFA Estimation Inc.（NFA估算公司）
Accurate software development effort estimation is critical to the success of software projects. Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software development effort prediction is still a challenging endeavor in the field of software engineering, especially in handling uncertain and imprecise inputs and collinear characteristics. In this paper, a hybrid in-telligent model combining a neural network model integrated with fuzzy model (neuro-fuzzy model) has been used to improve the accuracy of estimating software cost. The per…
Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users. Justifying recommendations, i.e., explaining why a user might like the recommended item, has been shown to improve user satisfaction and persuasiveness of the recommendation. In this paper, we develop a method for generating post-hoc justifications that can be applied to the output of any recommendation algorithm. Existing post-hoc methods are often limited in providing diverse justifications, as they either use o…
EB-NeRD: A Large-Scale Dataset for News Recommendation
JP/Politikens Media Group（JP/Politikens媒体集团）；Technical University of Denmark（丹麦技术大学）；Copenhagen Business School（哥本哈根商学院）；ETH Zürich（苏黎世联邦理工学院）；University of Bari Aldo Moro（巴里阿尔多·莫罗大学）；Politecnico di Bari（巴里理工大学）；Indian Institute of Management Visakhapatnam（维沙卡帕特南印度管理学院）
