Machine learning: A tool to support usability?

One problem facing designers of interactive systems is catering to the wide range of users who will use a particular application. Understanding the user is critical to designing a usable interface. There are a number of ways of addressing this problem, including improved design methodologies using ''intuitive'' interface styles, adaptive interfaces, and better training and user support materials. In this article, we argue that each of these solutions involves pattern recognition in one form or another and that machine learning can therefore aid designers of interactive systems in these areas. We report on experiments that demonstrate the potential of machine learning to user modeling that has application to two of these areas in particular: adaptive systems and design methodologies.     

User Modeling for Adaptive News Access

We present a framework for adaptive news access, based on machine learning techniques specifically designed for this task. First, we focus on the system's general functionality and system architecture. We then describe the interface and design of two deployed news agents that are part of the described architecture. While the first agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) and cell phones. Based on implicit and explicit user feedback, our agents use a machine learning algorithm to induce individual user models. Motivated by general shortcomings of other user modeling systems for Information Retrieval applications, as well as the specific requirements of news classification, we propose the induction of hybrid user models that consist of separate models for short-term and long-term interests. Furthermore, we illustrate how the described algorithm can be used to address an important issue that has thus far received little attention in the Information Retrieval community: a user's information need changes as a direct result of interaction with information. We empirically evaluate the system's performance based on data collected from regular system users. The goal of the evaluation is not only to understand the performance contributions of the algorithm's individual components, but also to assess the overall utility of the proposed user modeling techniques from a user perspective. Our results provide empirical evidence for the utility of the hybrid user model, and suggest that effective personalization can be achieved without requiring any extra effort from the user.     

基于SVM用户建模的核函数选择研究

用户建模是从用户偏好数据中建立用户偏好模型的过程,用户偏好数据具有系统运行初期的稀疏性和非线形的特点。支持向量机(Support Vector Machine,简称SVM)具有小样本学习、非线形处理的能力,是合适的用户建模工具。SVM的非线形处理能力主要依赖于核函数,采用不同的核函数进行建模对模型的预测效果有重大影响。本文重点研究核函数的选择对基于SVM建模方法的影响,从中选取了表现较优的小波核函数,构建性能突出的SVM进行用户建模。实验证明该建模方法可以有效地从小样本数据中学习用户偏好信息,建立反映用户真实偏好的用户模型。     

QUESTO: Interactive Construction of Objective Functions for Classification Tasks

Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta-information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain-specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.     

Efficient and non-parametric reasoning over user preferences

We consider the problem of modeling and reasoning about statements of ordinal preferences expressed by a user, such as monadic statement like “X is good,” dyadic statements like “X is better than Y,” etc. Such qualitative statements may be explicitly expressed by the user, or may be inferred from observable user behavior. This paper presents a novel technique for efficient reasoning about sets of such preference statements in a semantically rigorous manner. Specifically, we propose a novel approach for generating an ordinal utility function from a set of qualitative preference statements, drawing upon techniques from knowledge representation and machine learning. We provide theoretical evidence that the new method provides an efficient and expressive tool for reasoning about ordinal user preferences. Empirical results further confirm that the new method is effective on real-world data, making it promising for a wide spectrum of applications that require modeling and reasoning about user preferences.     

Architecture and implementation of a smart-pregnancy monitoring system using web-based application

Today, people use web-based technologies to meet their information needs, socialise, communicate, and deal with formal and informal processes. At the same time, mobile versions of these applications provide people with great convenience in daily life. These applications include blood-pressure monitors, blood-glucose monitors, body-analysis scales, pulse oximeters, and activity and sleep trackers. Many of these products sync directly with a free mobile app that makes monitoring, viewing, storing, and sharing of health vitals simple and comprehensive. The data collected from the user is stored in a cloud-based application, then trained by intelligent algorithms that use machine learning for health aims so that the user can instantly see his or her status and development. In this study, the aim was to construct a cloud-based application specific to women for monitoring pregnancy. In the web-based application working with membership logic, members can access machine learning assisted calculators of the baby percentile, period tracker, pregnancy calendar, and baby vaccination schedule. Moreover, they can access augmented/virtual-reality-assisted visual training.     

Semi-supervised learning with an imperfect supervisor

Real-life applications may involve huge data sets with misclassified or partially classified training data. Semi-supervised learning and learning in the presence of label noise have recently emerged as new paradigms in the machine learning community to cope with this kind of problems. This paper describes a new discriminant algorithm for semi-supervised learning. This algorithm optimizes the classification maximum likelihood (CML) of a set of labeled–unlabeled data, using a discriminant extension of the Classification Expectation Maximization algorithm. We further propose to extend this algorithm by modeling imperfections in the estimated class labels for unlabeled data. The parameters of this label-error model are learned together with the semi-supervised classifier parameters. We demonstrate the effectiveness of the approach using extensive experiments on different datasets. Massih R. Amini is currently assistant professor in the University of Pierre and Marie Curie (Paris 6). He received an engineering degree in computer science from the Ecole Supérieure d'Informatique (Computer science engineering school) in Paris in 1995. He then accomplished his master thesis in science in artificial intelligence in 1997 and obtained his PhD in 2001 at University of Pierre and Marie Curie. His research interests include Statistical Learning and Text-Mining. Patrick Gallinari is currently professor in the University of Pierre and Marie Curie (Paris 6) and head of the Computer Science laboratory (LIP6). His main research activity has been in the field of statistical machine learning for the last 15 years. He has also contributed in developing machine learning techniques for different application domains like information retrieval and text mining, user modelling, man–machine interaction and pen interfaces.     

Enhancing touch behavioral authentication via cost-based intelligent mechanism on smartphones

Due to the popularity of smartphones, there is a great need to deploy appropriate authentication mechanisms to safeguard users’ sensitive data. Touch dynamics-based authentication has been developed to verify smartphone users and detect imposters. These schemes usually employ machine learning techniques to detect behavioral anomalies by comparing current behavioral actions with the stored normal model. However, we notice that machine learning classifiers often have an unstable performance, which would greatly reduce the system usability, i.e., causing a high false rejection. In this work, we are motivated by this challenge and design a cost-based intelligent mechanism that can choose a less costly algorithm for user authentication. In the evaluation, we conduct a user study with a total of 60 users to investigate the performance of our mechanism with a lightweight touch gesture-based scheme on smartphones. Experimental results demonstrate that our approach can help achieve a relatively higher and more stable authentication accuracy, as compared to the use of a sole classifier.     

布料仿真建模研究进展

布料仿真一直是计算机动画中的研究热点与难点,对提高计算机动画质量以及用户体验具有重要意义,布料是一种非常经典的柔性材料物体,遍布于人们的日常生活中。虚拟世界中虚拟角色强烈的视觉真实感主要来源于逼真的虚拟人物的服装动画,这在很大程度上可以增强用户的体验感,在游戏娱乐、电影电视和动画制作等领域有着十分广泛的应用前景。布料仿真的质量与速度直接决定了计算机动画的整体水平,而布料的模拟水平则起着至关重要的作用。随着计算机软硬件的不断发展和计算机动画市场需求的提高,对布料仿真建模方法的研究受到越来越多的关注,布料仿真建模方法也因此有了较大发展。本文通过回顾布料仿真建模方法的相关工作,对国内外方法的研究进展进行综述,从布料仿真中数值积分方法的改进、多分辨率网格的改进和机器学习方法的使用等方面对布料仿真方法的发展进行简要阐述,并根据不同方法在布料模拟应用上的特性,对几大类改进方法进行了相应的总结与展望。同时选取几种算法进行对比,并给出建议。     

Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

Cloud computing is a high network infrastructure where users, owners, third users, authorized users, and customers can access and store their information quickly. The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently. This cloud is nowadays highly affected by internal threats of the user. Sensitive applications such as banking, hospital, and business are more likely affected by real user threats. An intruder is presented as a user and set as a member of the network. After becoming an insider in the network, they will try to attack or steal sensitive data during information sharing or conversation. The major issue in today's technological development is identifying the insider threat in the cloud network. When data are lost, compromising cloud users is difficult. Privacy and security are not ensured, and then, the usage of the cloud is not trusted. Several solutions are available for the external security of the cloud network. However, insider or internal threats need to be addressed. In this research work, we focus on a solution for identifying an insider attack using the artificial intelligence technique. An insider attack is possible by using nodes of weak users’ systems. They will log in using a weak user id, connect to a network, and pretend to be a trusted node. Then, they can easily attack and hack information as an insider, and identifying them is very difficult. These types of attacks need intelligent solutions. A machine learning approach is widely used for security issues. To date, the existing lags can classify the attackers accurately. This information hijacking process is very absurd, which motivates young researchers to provide a solution for internal threats. In our proposed work, we track the attackers using a user interaction behavior pattern and deep learning technique. The usage of mouse movements and clicks and keystrokes of the real user is stored in a database. The deep belief neural network is designed using a restricted Boltzmann machine (RBM) so that the layer of RBM communicates with the previous and subsequent layers. The result is evaluated using a Cooja simulator based on the cloud environment. The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.