Development of an Intelligent Wheelchair Using Computer Graphics Animation and Simulation

A new robot simulator JC-1 is used as a control software development tool in a project in progress where an intelligent wheelchair for a blind user is being developed. The intelligent wheelchair is planned to be able to fulfill simple symbolic commands like "follow wall" or "follow object" and using the JC-1 simulator an evaluation team which includes e.g. the user, a rehabilitation engineer and a software engineer, can check control algorithms and user interface routines before constructing a real wheelchair prototype. The JC-1 simulator models the environment using simplified boundary- representation where objects, robot sensors and actuators are presented as symbolic objects in the graphics data-base of the simulator. In the JC-1 simulator a robot controller under development controls the motion of the graphical model of the robot while simulator commands or other robot controllers can be used to control the movement of disturbing obstacles. Computer graphics animation and simulation help to find fundamental design errors at an early design stage and as this paper suggests, enable the user of the final product to take part in to the designing process of the robot controller. Benefits and difficulties of using computer graphics simulation in the wheelchair development process are discussed.     

Thomas: building Bayesian statistical expert systems to aid in clinical decision making.

Knowledge-based system for classical statistical analysis must separate the task of analyzing data from that of using the results of the analysis. In contrast, a Bayesian framework for building biostatistical expert system allows for the integration of the data-analytic and decision-making tasks. The architecture of such a framework entails enabling the system (1) to make its recommendations on decision-analytic grounds; (2) to construct statistical models dynamically; (3) to update a statistical model based on the user's prior beliefs and on data from, the methodological concerns evinced by, the study. This architecture permits the knowledge engineer to represent a variety of types of statistical and domain knowledge. Construction of such systems requires that the knowledge engineer reinterpret traditional statistical concerns, such as by replacing the notion of statistical significance with that of a pragmatic clinical threshold. The clinical user of such a system can interact with the system at a semantic level appropriate to her fund of methodological knowledge, rather than at the level of statistical details. We demonstrate these issues with a prototype system called THOMAS which helps a physician decision maker interpret the results of a published randomized clinical trial.     

Perceptions of overuse,underuse, and change of use of a social media site: definition,measurement instrument,and their managerial impacts

Drawing on the person-environment fit model, this study investigates a user’s perceptions towards his/her online contacts’ overuse, underuse, and change of use of a social media site (SMS). A new instrument for measuring these perceptions is introduced. Using depersonalisation and continuance as an example, we test how the relationship between a user and an SMS can be impacted by his/her online contacts’ peculiar usage of the SMS. A survey-based methodology is used to collect data from Facebook users. The data analysis shows that a user is likely to feel estranged from an SMS, if he/she perceives that the website is being overused, underused, or used for non-social purposes by his/her online contacts. The depersonalisation can then have a strong negative effect on the user’s SMS continuance. This study takes the interpersonal influences between users into consideration when investigating a user’s relationship with an SMS. The findings can provide several practical implications for an SMS user retention management.     

Finding interesting patterns using user expectations

One of the major problems in the field of knowledge discovery (or data mining) is the interestingness problem. Past research and applications have found that, in practice, it is all too easy to discover a huge number of patterns in a database. Most of these patterns are actually useless or uninteresting to the user. But due to the huge number of patterns, it is difficult for the user to comprehend them and to identify those interesting to him/her. To prevent the user from being overwhelmed by the large number of patterns, techniques are needed to rank them according to their interestingness. In this paper, we propose such a technique, called the user-expectation method. In this technique, the user is first asked to provide his/her expected patterns according to his/her past knowledge or intuitive feelings. Given these expectations, the system uses a fuzzy matching technique to match the discovered patterns against the user's expectations, and then rank the discovered patterns according to the matching results. A variety of rankings can be performed for different purposes, such as to confirm the user's knowledge and to identify unexpected patterns, which are by definition interesting. The proposed technique is general and interactive     

Capital equipment justification: A spreadsheet application template

In many organizations the industrial engineering group has the responsibility of preparing the request for capital funds for new product introduction, new equipment purchase or expansion of existing plant facilities. Much of the time required to develop the capital equipment request is devoted to gathering background data on product costs, market share, machinery/equipment costs, and expenses associated with launching the project. Once the background data has been gathered, the industrial engineer is then faced with the difficult task of sorting, arranging and performing multitudes of calculations on the data in order that the end product will resemble a snapshot of the financial impact that the acceptance of the project will have on the firm.

This process can take hours or days, depending on the level of detail required. Once the final results have been obtained, a management review of the analysis might indicate that mistakes in the raw data have been made, product sales estimates were too high or cost estimates for products were too low. This causes the industrial engineer to essentially recompile the data set in order to obtain accurate results.

Until the advent of microcomputers, preparation of a detailed capital equipment justification request for introducing new products could take much of the industrial engineers time. However, with the evolution of microcomputer spreadsheet programs, the industrial engineer has found a powerful tool which can manage the vast volume of data often present in capital investment analysis, by eliminating the drudgery of performing hundreds of hand calculations.     

专家系统外壳开发环境ESSDE

本文描述一个专家系统外壳开发环境ＥＳＳＤＥ，该环境支持用户选择不同的管理用户的基于规则的知识库机制来构造特殊的专家系统外壳。ＥＳＳＤＥ由若干ｍｅｔａ－ｉｎｔｅｒｐｒｅｔｅｒｓ组成，ｍｅｔａ－ｉｎｔｅｒｐｒｅｔｅｒｓ执行面向目标的推理机，若干解释机制，规则追踪和精确及不精确推理等不同的功能，ＥＳＳＤ还提供了“向用户提问”的界面和机制以及存储中间结论和重新评价结构的功能。     

A simply constructable user-oriented computer language using fortran

One important task of the industrial engineer is to be an interface person—between operations and planning, between workers and their physical environment, between bench level tasks and systems level management, and with growing need, between the computer and the user. The latter has been caused in part by the programming requirement to express how a task is to be done, rather than what is to be done. The IEs role here is to provide the interface to ask the “what” questions. This paper addresses one strategy for constructing the computer-user interface.Using a core set of sixteen short FORTRAN subprograms, a simple procedure for constructing user oriented conversational computer languages has been developed (seven subprograms are directed at the conversational language and nine are list handling routines). This core set has been used successfully to develop user packages for medical doctors doing cell kinetics simulation in cancer research, for energy policy makers to access and manipulate time series energy data, for undergraduates to solve statistical and mathematical programming problems. The procedure is machine independent, only requiring a FORTRAN compiler, and can be used by the IE to bridge the gap between the user with a question, and the solution power of the computer.     

Cloud-oriented emotion feedback-based Exergames framework

Exergames platform can be more appealing to the users if they can interact with emotion. Therefore, in this paper, we propose an automatic emotion recognition system from speech to be embedded in the Exergames platform. While playing and doing exercise, the user expresses his or her feeling by uttering some phrases. The speech is recorded by an omnidirectional mic and transmitted to an emotion recognition server in a cloud environment, where the emotion (e.g. happy, sad or neutral) is recognized. For the recognition, we use MPEG-7 low-level audio features and a Gaussian mixture model based classifier. A tactile vibration is generated based on the emotion and feedback to the user for a real feeling. The user can thus have an instantaneous vibrational feeling based on his or her satisfaction. The recognized emotion can also be used as the user’s satisfaction of the framework on the fly without the need of a survey session. The experimental study and performance comparison show that the proposed framework has positive effects on the perception of physical activities.     

The design of a user friendly engineering economy analysis package for a microcomputer

The design and features of a user friendly engineering economy analysis software package for the Apple II microcomputer is described. The software package is written in Applesoft Basic. An Apple II with 64k RAM, one or two disk drives and a line printer is required to use the software package. The package consists of five complementary programs: (1) Selection of the better of two alternatives using present-worth evaluations. (2) Conventional rate-of-return computations for a single project. (3) Selection from mutually exclusive alternatives using rate-of-return analysis. (4) Optimal capital allocation among several investment opportunities under risk. (5) Probabilistic after-tax economic analysis for a single project. All programs but (4) are based on the time value of money. Program (4) is a chance-constrained optimization model utilizing the Lagrange-multiplier technique. Program (5) implements depreciation, debt/equity financing, and taxes and performs a sequential after-tax Monte Carlo simulation over a specified life-span. The use of this software package does not require any previous computer or programming experience. A menu format is used. The software package allows the user to specify the shapes of the interest and inflation probability distributions on a year by year basis. The user has the option to specify the reinvestment rates. The package presents the results in tables and graphs. User acceptance has been excellent with this software package and this package is well suited for the use in an engineering economy class or for the practicing industrial engineer in industry.     

Enhancing cultural recommendations through social and linked open data

In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) sources. Concretely, the proposed RS (1) extracts information from Facebook by analyzing content generated by users and their friends; (2) performs disambiguation tasks through LOD tools; (3) profiles the active user as a social graph; (4) provides her with personalized suggestions of artistic and cultural resources in the surroundings of the user’s current location. The last point is performed by integrating collaborative filtering algorithms with semantic technologies in order to leverage LOD sources such as DBpedia and Europeana. Based on the recommended points of cultural interest, the proposed system is also able to suggest to the active user itineraries among them, which meet her preferences and needs and are sensitive to her physical and social contexts as well. Experimental results on real users showed the effectiveness of the different modules of the proposed recommender.

     

Dynamic adaptation of numerical attributes in a user profile

Recommender systems try to help users in their decisions by analyzing and ranking the available alternatives according to their preferences and interests, modeled in user profiles. The discovery and dynamic update of the users’ preferences are key issues in the development of these systems. In this work we propose to use the information provided by a user during his/her interaction with a recommender system to infer his/her preferences over the criteria used to define the decision alternatives. More specifically, this paper pays special attention on how to learn the user’s preferred value in the case of numerical attributes. A methodology to adapt the user profile in a dynamic and automatic way is presented. The adaptations in the profile are performed after each interaction of the user with the system and/or after the system has gathered enough information from several user selections. We have developed a framework for the automatic evaluation of the performance of the adaptation algorithm that permits to analyze the influence of different parameters. The obtained results show that the adaptation algorithm is able to learn a very accurate model of the user preferences after a certain amount of interactions with him/her, even if the preferences change dynamically over time.     

面向微博用户标签推荐的关系约束主题模型

社会化标签系统允许用户使用个性化的词汇对网络中的资源进行标注而被用户广泛接受。在微博网络中,用户可以为自己加注标签以推广自己或者方便别人找到自己。深入分析了微博用户数据,总结了微博用户标签的特点,针对LDA（latent Dirichlet allocation）主题模型在处理短文本时存在的不足,提出了一种基于好友关系约束主题模型。在此基础上对微博用户标签进行主题分析,计算用户的主题分布,对标签词进行聚类,并最终为用户推荐标签。通过对比实验证明了该方法可以提高标签推荐的准确度。     

An autonomous multistrategy theorem proving system using knowledge-based techniques

Most general-purpose theorem-proving systems have weak search control. There is no alternative to the use of a large number of heuristics or strategies for search guidance. Choosing appropriate strategies for solving a given problem may require the knowledge of different strategies and may involve a lot of painstaking trial-and-error. To encourage the widespread use of computer reasoning systems, it is important that a theorem prover be usable by those with little knowledge of problem-solving strategies, and that a theorem prover be able to select good strategies for the user. An autonomous multistrategy theorem-proving system is developed, using knowledge-based techniques, to entirely free the user from the necessity of understanding the system or the merits of different strategies. All the user has to do is input his or her problem in first-order logic, and the system solves the problem efficiently for him or her without any manual intervention. The system embodies much of expert knowledge about how to solve problems. The knowledge is represented as metarules in knowledge base which guide a hyperlinking theorem prover to solve problems automatically and efficiently.     

Assisted Navigation for Persons with Reduced Mobility: Path Recognition Through Particle Filtering (Condensation Algorithm)

In the few past decades, several international researchers have worked to develop intelligent wheelchairs for the people with reduced mobility. For many of these projects, the structured set of commands is based on a sensor-based command. Many types of commands are available but the final decision is to be made by the user. A former work established a behaviour-based multi-agent form of control ensuring that the user selects the best option for him/her in relation to his/her preferences or requirements. This type of command aims at “merging” this user and his/her machine—a kind of symbiotic relationship making the machine more amenable and the command more effective. In this contribution, the approach is based on a curve matching procedure to provide comprehensive assistance to the user. This new agent, using a modelization of the paths that are most frequently used, assists the user during navigation by proposing the direction to be taken when the path has been recognized. This approach will spare the user the effort of determining a new direction—which might be a major benefit in the case of severe disabilities. The approach considered uses particle filtering to implement the recognition of the most frequent paths according to a topological map of the environment.     

搜索引擎结果的重排序方法

当前Web搜索引擎返回的搜索结果一般是按“超链分析”进行排序的。采用词频统计、词分布特征量等方法对Web搜索引擎的搜索结果的关键词相关度进行计算,并重新对搜索结果排序,可以使得搜索结果中有关的页面文集更加集中。从而方便了信息的使用,特别是在对于特定内容的信息搜索时。     

A personalized query expansion approach for engineering document retrieval

Engineers create engineering documents with their own terminologies, and want to search existing engineering documents quickly and accurately during a product development process. Keyword-based search methods have been widely used due to their ease of use, but their search accuracy has been often problematic because of the semantic ambiguity of terminologies in engineering documents and queries. The semantic ambiguity can be alleviated by using a domain ontology. Also, if queries are expanded to incorporate the engineer’s personalized information needs, the accuracy of the search result would be improved. Therefore, we propose a framework to search engineering documents with less semantic ambiguity and more focus on each engineer’s personalized information needs. The framework includes four processes: (1) developing a domain ontology, (2) indexing engineering documents, (3) learning user profiles, and (4) performing personalized query expansion and retrieval. A domain ontology is developed based on product structure information and engineering documents. Using the domain ontology, terminologies in documents are disambiguated and indexed. Also, a user profile is generated from the domain ontology. By user profile learning, user’s interests are captured from the relevant documents. During a personalized query expansion process, the learned user profile is used to reflect user’s interests. Simultaneously, user’s searching intent, which is implicitly inferred from the user’s task context, is also considered. To retrieve relevant documents, an expanded query in which both user’s interests and intents are reflected is then matched against the document collection. The experimental results show that the proposed approach can substantially outperform both the keyword-based approach and the existing query expansion method in retrieving engineering documents. Reflecting a user’s information needs precisely has been identified to be the most important factor underlying this notable improvement.     

Discovering relationship types between users using profiles and shared photos in a social network

In this paper, we propose a new approach to discover the relationship types between a user and her contacts in a social network. This is of key importance for many applications in the domain of photo sharing, privacy protection, information enriching, etc. Our approach is based, on one hand, on information extracted from users’ profiles and their shared photos, and, on the other hand, on a set of predefined rules validated by the main user before being mined and derived according to her preferences and social network content. The contribution of our method is twofold: 1) it is user-based enabling the user to set her preferences and give her feedbacks on the derived rules and results, and 2) it is multi-criteria that exploits and combines several attributes and features from user profiles and shared photos respectively. It also allows the user to define new relationship types. We conducted a set of experiments to validate our approach. The obtained results show the accuracy of our approach in different scenarios.     

Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System

It is important that systems that exhibit proactive behaviour do so in a way that does not surprise or frustrate the user. Consequently, it is desirable for such systems to be both personalised and designed in such a way as to enable the user to scrutinise her user model (part of which should hold the rules describing the behaviour of the system). This article describes on-going work to investigate the design of a prototype system that can learn a given user’s behaviour in an office environment in order to use the inferred rules to populate a user model and support appropriate proactive behaviour (e.g. turning on the user’s fan under appropriate conditions). We explore the tension between user control and proactive services and consider issues related to the design of appropriate transparency with a view to supporting user comprehensibility of system behaviour. To this end, our system enables the user to scrutinise and possibly over-ride the ‘IF-THEN’ rules held in her user model. The system infers these rules from the context history (effectively a data set generated using a variety of sensors) associated with the user by using a fuzzy-decision-tree-based algorithm that can provide a confidence level for each rule in the user model. The evolution of the system has been guided by feedback from a number of real-life users in a university department. A questionnaire study has yielded supplementary results concerning the extent to which the approach taken meets users’ expectations and requirements.     

User-private information retrieval based on a peer-to-peer community

Private information retrieval (PIR) is normally modeled as a game between two players: a user and a database. The user wants to retrieve some item from the database without the latter learning which item is retrieved. Most current PIR protocols are ill-suited to provide PIR from a search engine or large database: (i) their computational complexity is linear in the size of the database; (ii) they assume active cooperation by the database server in the PIR protocol. If the database cannot be assumed to cooperate, a peer-to-peer (P2P) user community is a natural alternative to achieve some query anonymity: a user gets her queries submitted on her behalf by other users in the P2P community. In this way, the database still learns which item is being retrieved, but it cannot obtain the real query histories of users, which become diffused among the peer users. We name this relaxation of PIR user-private information retrieval (UPIR). A peer-to-peer UPIR system is described in this paper which relies on an underlying combinatorial structure to reduce the required key material and increase availability. Extensive simulation results are reported and a distributed key management version of the system is described.     

Inferences,suppositions and explanatory extensions in argument interpretation

We describe a probabilistic approach for the interpretation of user arguments that integrates three aspects of an interpretation: inferences, suppositions and explanatory extensions. Inferences fill in information that connects the propositions in a user’s argument, suppositions postulate new information that is likely believed by the user and is necessary to make sense of his or her argument, and explanatory extensions postulate information the user may have implicitly considered when constructing his or her argument. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation—a Bayesian network. Our evaluations show that suppositions and explanatory extensions are necessary components of interpretations, and that users consider appropriate the suppositions and explanatory extensions postulated by our system. This article integrates and extends research described in George et al., 2004; Zukerman et al., 2004; Zukerman and George, 2005; George et al., 2005. The research described in this article was conducted while Sarah George was employed at Monash University and was supported in part by the ARC Centre for Perceptive and Intelligent Machines in Complex Environments.