On the efficiency of user identification: a system-based approach

In the Internet era, users’ fundamental privacy and anonymity rights have received significant research and regulatory attention. This is not only a result of the exponential growth of data that users generate when accomplishing their daily task by means of computing devices with advanced capabilities, but also because of inherent data properties that allow them to be linked with a real or soft identity. Service providers exploit these facts for user monitoring and identification, albeit impacting users’ anonymity, based mainly on personal identifiable information or on sensors that generate unique data to provide personalized services. In this paper, we report on the feasibility of user identification using general system features like memory, CPU and network data, as provided by the underlying operating system. We provide a general framework based on supervised machine learning algorithms both for distinguishing users and informing them about their anonymity exposure. We conduct a series of experiments to collect trial datasets for users’ engagement on a shared computing platform. We evaluate various well-known classifiers in terms of their effectiveness in distinguishing users, and we perform a sensitivity analysis of their configuration setup to discover optimal settings under diverse conditions. Furthermore, we examine the bounds of sampling data to eliminate the chances of user identification and thus promote anonymity. Overall results show that under certain configurations users’ anonymity can be preserved, while in other cases users’ identification can be inferred with high accuracy, without relying on personal identifiable information.     

The um toolkit for cooperative user modelling

This paper gives an overview of the um toolkit: the philosophy underlying its design, examples of its use and discussion of the way it deals with some major issues in creating user modelling shells. The um toolkit has been developed to provide support for a variety of cooperative agents. An important element of its cooperativeness is due to its capacity to give users an understanding of their own user models. This paper describes two substantial but very different uses of the toolkit. The first involves a collection of coaching systems that help users learn more about their text editor. Experimental results suggest that the user model is associated with users learning more. The second is a movie advisor that uses a range of tools to construct and refine the user model and to filter a database of movies. Both these systems are built from combining tools in um. The paper describes several of the tools for constructing and refining user models. In addition it describes the user-model viewing tools and the way that these help users ensure their user models are correct. The paper also discusses the two central themes of the um work, the application of a tools approach to the design of a user modelling toolkit and the implications of making the user model accessible to its owner, the person modelled.     

User preferences for adaptive user interfaces in health information systems

An adaptive user interface requires identification of user requirements. Interface designers and engineers must understand end-user interaction with the system to improve user interface design. A combination of interviews and observations is applied for user requirement analysis in health information systems (HIS). Then, user preferences are categorized in this paper as either data entry, language and vocabulary, information presentation, or help, warning and feedback. The user preferences in these categories were evaluated using the focus group method. Focus group sessions with different types of HIS users comprising medical staff (with and without computer skills) and system administrators identified each user group’s preference for the initial adaptation of the HIS user interface. User needs and requirements must be identified to adapt the interface to users during data entry into the system. System designers must understand user interactions with the system to identify their needs and preferences. Without this, interface design cannot be adapted to users and users will not be comfortable using the system and eventually abandon its use.     

Exploring interactive style and user experience design for social web of things of Chinese users: A case study in Beijing

This study designed an interactive IoT (Internet of Things) service on mobile devices based upon the concept of Social Web of Things (SWoT), with which users can interact with IoT in the same way they use the social network services. In order to integrate IoT into Chinese daily use, this study also investigated how Chinese users interact with things and IoT technologies. A four-phase study among users living in Beijing was conducted with a lifecycle of user-centered design. Results revealed that SWoT could activate users׳ intuitive understanding of social network services, and make the interaction with SWoT natural in their own ways. Users living in Beijing tended to be utility-oriented and highly emphasized efficiency; they were distinctive in uncertainty avoidance and preference of hierarchical way of managing things. Our results suggested implications for the design of user-centered IoT systems in China, and shed light on improvements for the performance of IoT systems and balance of requirements between users and system design.     

基于Linux的超算系统中Windows应用程序运行环境探究

超算系统大多是基于Linux操作系统搭建的,限制了基于Windows操作系统的应用软件使用。此外,超算系统操作的高门槛使不熟悉Linux操作系统的用户望而却步,造成超算系统用户流失。基于Linux超算系统环境,探索兼顾超算系统运维管理便利性的Windows应用程序使用方法。研究通过X11转发、Wine和虚拟化等技术,为用户提供兼容超算作业调度系统Windows应用程序运行环境,同时提供安全、稳定的用户个人文件访问方法。所采用的配置方法与实例,可为具有类似需求的超算中心提供解决方案,从而拓宽用户软件应用范围,提高用户满意度。     

User-centered design of a QoS-based web service selection system

In most of quality of service (QoS)-based web service selection systems, inexperienced end users are not the focal point of the design. Most systems assume that users could formulate their QoS requirements easily and are accurately using the provided query languages. However, oftentimes it is not true. To tackle this issue and emphasize more on the user-centered design of the service selection system, in this paper, we come up with a more expressive and flexible way for non-expert users to define their QoS queries, together with the user support on formulating queries and understanding services in the registry. Then, we propose an enhanced selection model that could handle both exact and fuzzy requirements, return two categories of matching offers—super-exact and partial matches, and rank them using a two-level ranking algorithm. A prototype system is implemented, and experiments are done using a real QoS dataset to show the system performance.     

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.     

User-adapted travel planning system for personalized schedule recommendation

Recently, the Internet has made a lot of services and products appear online provided by many tourism sectors. By this way, many information such as timetables, routes, accommodations, and restaurants are easily available to help travelers plan their travels. However, how to plan the most appropriate travel schedule under simultaneously considering several factors such as tourist attractions visiting, local hotels selecting, and travel budget calculation is a challenge. This gives rise to our interest in exploring the recommendation systems with relation to schedule recommendation. Additionally, the personalized concept is not implemented completely in most of travel recommendation systems. One notable problem is that they simply recommended the most popular travel routes or projects, and cannot plan the travel schedule. Moreover, the existing travel planning systems have limits in their capabilities to adapt to the changes based on users’ requirements and planning results. To tackle these problems, we develop a personalized travel planning system that simultaneously considers all categories of user requirements and provides users with a travel schedule planning service that approximates automation. A novel travel schedule planning algorithm is embedded to plan travel schedules based on users’ need. Through the user-adapted interface and adjustable results design, users can replace any unsatisfied travel unit to specific one. The feedback mechanism provides a better accuracy rate for next travel schedule to new users. An experiment was conducted to examine the satisfaction and use intention of the system. The results showed that participants who used the system with schedule planning have statistical significant on user satisfaction and use intention. We also analyzed the validity of applying the proposed algorithm to a user preference travel schedule through a number of practical system tests. In addition, comparing with other travel recommendation systems, our system had better performance on the schedule adjustment, personalization, and feedback giving.     

基于移动Agent的个性化信息服务系统的设计与实现

本文从现有信息服务系统的不足出发,提出了一种基于移动Agent技术的全新信息服务模式,较好地满足了用户个性化方面的需求。文中详细阐述了系统的体系结构和设计思想,并对实验结果进行了分析。     

Android平台上的多用户无线投影控制系统设计

运用WIFI无线通信技术、ARM Cortex A8处理器嵌入式系统、TCP/IP协议以及无线局域网监控等技术设计并制作了一个Android平台的多用户无线投影控制系统。通过WIFI无线通信技术建立用户端与无线投影网关的无线通信,解决了数据连线对投影仪使用时的限制,极大地方便了有线投影仪用户使用无线投影功能。本系统通过无线局域网实现多用户、多平台的无线投影,采用一主多从的方式进行切换,适用于PC机和Android手持移动设备。系统经测试,达到使用要求。本文还详细描述了无线投影网关设计和无线投影管理软件设计。     

浅谈统一用户管理系统的建设方案

随着信息技术的迅猛发展,企业用户在日常办公中使用的应用系统越来越多,由于受建设年代、开发技术、设计方法等因素的制约,应用系统之间往往互相孤立,各自形成了独立的用户信息库和用户认证体系,包括组织机构、角色定义、身份认证等等互相独立,信息不一致。如此不仅使用户在使用应用系统时需要记录多个密码,增加了出错的可能性,而且由于不同系统间用户认证的安全性不一,安全风险非常突出。针对这种情况,建立一套统一用户管理系统成为企业用户的选择．而这个系统为企业统一的工作流管理、统一数据交换、统一信息等功能提供的基础的服务。     

On the implications of user variability in open systems An overview of the little we know and of the lot we have to find out

As the use of computers increases, the cost of educating computer users rises. Therefore the need for extensive education of users should be avoided as much as possible. Instead computer systems should be adapted to the requirements and characteristics of computer users. Hereby the need to identify user characteristics arises. The less computer educated the users, the more their requirements of the systems will differ. Where user characteristics are difficult to change, it will be worthwhile to attend to user differences in the design of systems. The implications of user differences may vary depending upon the type of task to be performed. The present paper presents an analysis of different user characteristics and their possible interactions with different task requirements. Hereby some recommendations regarding the need to educate users or adapt systems to the user were derived.     

On the implications of user variability in open systems An overview of the little we know and of the lot we have to find out

Abstract

As the use of computers increases, the cost of educating computer users rises. Therefore the need for extensive education of users should be avoided as much as possible. Instead computer systems should be adapted to the requirements and characteristics of computer users. Hereby the need to identify user characteristics arises. The less computer educated the users, the more their requirements of the systems will differ. Where user characteristics are difficult to change, it will be worthwhile to attend to user differences in the design of systems. The implications of user differences may vary depending upon the type of task to be performed. The present paper presents an analysis of different user characteristics and their possible interactions with different task requirements. Hereby some recommendations regarding the need to educate users or adapt systems to the user were derived.     

Design and evaluation of a gamified system for ERP training

Enterprise Resource Planning Systems (ERP) facilitate the flow of information within a company by storing data in common databases. These systems offer a holistic view of the organization because they reduce information redundancy, offer information in real time, help with process standardization, and improve information flow and communication among employees. Nevertheless, the benefits attributed to an ERP implementation can be lost without an effective user training. Previous studies have observed that common training mechanisms don't provide meaningful learning to users, and that user satisfaction rates range from neutral to low. Therefore, in this paper we study the effects that gamification has on ERP training concerning user learning and user satisfaction. Gamification is the use of game elements and game design techniques in non-gaming environments. Several applications prove that gamified systems increase user engagement and performance. Our hypotheses were that a gamified system for ERP training improve user learning and user satisfaction levels during the training period. To test our hypotheses, we designed and evaluated a gamified system. The results showed that users trained using a gamified system performed better than those trained using a conventional, non-gamified, training mechanism.     

Selecting users for participation in IT projects: Trading a representative sample for advocates and champions?

The selection of users for participation in IT projects involves trade-offs between multiple criteria, one of which is selecting a representative cross-section of users. This criterion is basic because trading it for other criteria means basing designs on information biased toward some user groups at the expense of others. Based on interviews in development and customer organizations we find that their criteria for user selection favor persons who can contribute to the progress of the IT project over persons who are representative of the full range of users. A highly valued contribution from participating users is the ability to advocate a vision for the system and champion its organizational implementation. A survey in one customer organization shows that respondents’ personal traits explain up to 31% of the variation in their experience of aspects of the usability of a recently introduced system. Thus, unless participating users are representative as to these personal traits, IT projects may, inadvertently, bring about systems that will fail to satisfy many users.     

Mood modeling: accuracy depends on active logging and reflection

Current behavior change systems often demand extremely advanced sensemaking skills, requiring users to interpret personal datasets in order to understand and change behavior. We describe EmotiCal, a system to help people better manage their emotions, that finesses such complex sensemaking by directly recommending specific mood-boosting behaviors to users. This paper first describes how we develop the accurate mood models that underlie these mood-boosting recommendations. We go on to analyze what types of information contribute most to the predictive power of such models, and how we might design systems to reliably collect such predictive information. Our results show that we can derive very accurate mood models with relatively small samples of just 70 users. These models explain 61% of variance by combining: (a) user reflection about the effects of different activities on mood, (b) user explanations of how different activities affect mood, and (c) individual differences. We discuss the implications of these findings for the design of behavior change systems, as well as for theory and practice. Contrary to many recent approaches, our findings argue for the importance of active user reflection rather than passive sensing.     

TADEUS: seamless development of task-based and user-orientedinterfaces

Task-based and user-oriented user interfaces utilize knowledge about user tasks and user characteristics to the utmost extent. They support users throughout their work flows, and must be constructed by a development process that avoids loss of application context and involves user feedback, from requirements specification to code generation. The concepts behind the task analysis/design/end users systems (TADEUS) approach to enable seamless task-based development are a semantically rich representation scheme, a model-driven development procedure, a diagrammatic notation and unifying specification scheme. Thus, interactive applications can be developed seamlessly. Specifications comprise problem domain knowledge, work processes, user roles and personal profiles, as well as interaction modalities (required for task accomplishment). For user-interface prototyping the TADEUS environment contains a model interpreter that executes structure and behavior specifications. This way, early feedback on task-based portals can be provided by users. In this paper we detail the latest developments in the TADEUS project when implementing a work-process based usability life cycle. We review the underlying methodology and the features of the TADEUS environment, in order to demonstrate the benefits for developers and users resulting of smooth transition support for and between the different stages of development     

Facebook single and cross domain data for recommendation systems

The emergence of social networks and the vast amount of data that they contain about their users make them a valuable source for personal information about users for recommender systems. In this paper we investigate the feasibility and effectiveness of utilizing existing available data from social networks for the recommendation process, specifically from Facebook. The data may replace or enrich explicit user ratings. We extract from Facebook content published by users on their personal pages about their favorite items and preferences in the domain of recommendation, and data about preferences related to other domains to allow cross-domain recommendation. We study several methods for integrating Facebook data with the recommendation process and compare the performance of these methods with that of traditional collaborative filtering that utilizes user ratings. In a field study that we conducted, recommendations obtained using Facebook data were tested and compared for 95 subjects and their crawled Facebook friends. Encouraging results show that when data is sparse or not available for a new user, recommendation results relying solely on Facebook data are at least equally as accurate as results obtained from user ratings. The experimental study also indicates that enriching sparse rating data by adding Facebook data can significantly improve results. Moreover, our findings highlight the benefits of utilizing cross domain Facebook data to achieve improvement in recommendation performance.     

A middleware architecture for privacy protection

The issue of user privacy is constantly in spotlight since an ever increasing number of online services collects and processes personal information from users, in the context of personalized service provision. In fact, recent advances in mobile communications, location and sensing technologies and data processing are boosting the deployment of context-aware personalized services and the creation of smart environments; but at the same time, they pose a serious risk on individuals’ privacy rights. Although technology makes the collection of data easy, its protection against abuse is left to data protection legislation. However, the privacy requirements, other than being general and abstract terms to be regarded as legislature issues, should be brought down in technological reality and carefully accounted for in devising technical solutions. In order to limit the disclosure and avoid the misuse of personal data, this paper discusses an architectural proposal for a middleware system that will enforce protection of user privacy through technical means. The proposed architecture mediates between the users, the service providers and the law, constituting a middleware shield for individuals’ personal data.