Guidelines for audio-enhancement of graphical user Interface widgets

Audio feedback remains little used in most graphical user interfaces despite its potential to greatly enhance interaction. Not only does sonic enhancement of interfaces permit more natural human-computer communication but it also allows users to employ an appropriate sense to solve a problem rather than having to rely solely on vision. Research shows that designers do not typically know how to use sound effectively; subsequently, their ad hoc use of sound often leads to audio feedback being considered an annoying distraction. Unlike the design of purely graphical user interfaces for which guidelines are common, the audio-enhancement of graphical user interfaces has (until now) been plagued by a lack of suitable guidance. This paper presents a series of empirically substantiated guidelines for the design and use of audio-enhanced graphical user interface widgets.

Selecting the 'invisible' user interface development tool

Developers of interactive software are confronted by an increasing variety of software tools to help engineer the interactive aspects of software applications. Typically resorting to ad hoc means of tool selection, developers are often dissatisfied with their chosen tool on account of the fact that the tool lacks required functionality or does not fit seamlessly within the context in which it is to be used. This paper describes a system for evaluating the suitability of user interface development tools for use in software development organisations and projects such that the selected tool appears ‘invisible’ within its anticipated context of use. The paper also outlines and presents the results of an informal empirical study and a series of observational case studies of the system.

Intermediation for technology diffusion and user innovation in a developing rural economy:a social learning perspective

Technology intermediaries are seen as potent vehicles for addressing perennial problems in transferring technology from university to industry in developed and developing countries. This paper examines what constitutes effective user-end intermediation in a low-technology, developing economy context, which is an under-researched topic. The social learning in technological innovation framework is extended using situated learning theory in a longitudinal instrumental case study of an exemplar technology intermediation programme. The paper documents the role that academic-related research and advisory centres can play as intermediaries in brokering, facilitating and configuring technology, against the backdrop of a group of small-scale pisciculture businesses in a rural area of Colombia. In doing so, it demonstrates how technology intermediation activities can be optimized in the domestication and innofusion of technology amongst end-users. The design components featured in this instrumental case of intermediation can inform policy making and practice relating to technology transfer from university to rural industry. Future research on this subject should consider the intermediation components put forward, as well as the impact of such interventions, in different countries and industrial sectors. Such research would allow for theoretical replication and help improve technology domestication and innofusion in different contexts, especially in less-developed countries.

Intelligent Model of User Behavior in Distributed Systems

We present a complex neural network model of user behavior in distributed systems. The model reflects both dynamical and statistical features of user behavior and consists of three components: on-line and off-line models and change detection module. On-line model reflects dynamical features by predicting user actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of users of Space Research Institute of NASU-NSAU.

A Method of Estimating Usability of a User Interface Based on its Model

The article presents a new method to estimating usability of a user interface based on its model. The principal features of the method are: creation of an expandable knowledge base of usability defects, detection defects based on the interface model, within the design phase, and information to the developer not only about existence of defects but also advice on their elimination.

The Application of Graph Model for Automation of the User Interface Construction

The ability of automatic graphic user interface construction is described. It is based on the building of user interface as reflection of the data domain logical definition. The submitted approach to development of the information system user interface enables dynamic adaptation of the system during their operation. This approach is used for creation of information systems based on CASE-system METAS.

From an Ontology-Oriented Approach Conception to User Interface Development

* The presented work has discussed on the KDS-2003. It has corrected in compliance with remarks and requests of participants.

Multi-Agent Security System based on Neural Network Model of User's Behavior

It is proposed an agent approach for creation of intelligent intrusion detection system. The system allows detecting known type of attacks and anomalies in user activity and computer system behavior. The system includes different types of intelligent agents. The most important one is user agent based on neural network model of user behavior. Proposed approach is verified by experiments in real Intranet of Institute of Physics and Technologies of National Technical University of Ukraine "Kiev Polytechnic Institute”.

Comparing interaction techniques for serious games through brain-computer interfaces:a user perception evaluation study

This paper examines the application of commercial and non-invasive electroencephalography (EEG)-based brain-computer (BCIs) interfaces with serious games. Two different EEG-based BCI devices were used to fully control the same serious game. The first device (NeuroSky MindSet) uses only a single dry electrode and requires no calibration. The second device (Emotiv EPOC) uses 14 wet sensors requiring additional training of a classifier. User testing was performed on both devices with sixty-two participants measuring the player experience as well as key aspects of serious games, primarily learnability, satisfaction, performance and effort. Recorded feedback indicates that the current state of BCIs can be used in the future as alternative game interfaces after familiarisation and in some cases calibration. Comparative analysis showed significant differences between the two devices. The first device provides more satisfaction to the players whereas the second device is more effective in terms of adaptation and interaction with the serious game.

Privacy and the City:user identification and location semantics in location-based social networks

With the advent of GPS enabled smartphones, an increasing number of users is actively sharing their location through a variety of applications and services. Along with the continuing growth of Location-Based Social Networks (LBSNs), security experts have increasingly warned the public of the dangers of exposing sensitive information such as personal location data. Most importantly, in addition to the geographical coordinates of the user’s location, LBSNs allow easy access to an additional set of characteristics of that location, such as the venue type or popularity. In this paper, we investigate the role of location semantics in the identification of LBSN users. We simulate a scenario in which the attacker’s goal is to reveal the identity of a set of LBSN users by observing their check-in activity. We then propose to answer the following question: what are the types of venues that a malicious user has to monitor to maximize the probability of success? Conversely, when should a user decide whether to make his/her check-in to a location public or not? We perform our study on more than 1 million check-ins distributed over 17 urban regions of the United States. Our analysis shows that different types of venues display different discriminative power in terms of user identity, with most of the venues in the “Residence” category providing the highest re-identification success across the urban regions. Interestingly, we also find that users with a high entropy of their check-ins distribution are not necessarily the hardest to identify, suggesting that it is the collective behaviour of the users’ population that determines the complexity of the identification task, rather than the individual behaviour.

Spatio-temporal techniques for user identification by means of GPS mobility data

One of the greatest concerns related to the popularity of GPS-enabled devices and applications is the increasing availability of the personal location information generated by them and shared with application and service providers. Moreover, people tend to have regular routines and be characterized by a set of “significant places”, thus making it possible to identify a user from his/her mobility data. In this paper we present a series of techniques for identifying individuals from their GPS movements. More specifically, we study the uniqueness of GPS information for three popular datasets, and we provide a detailed analysis of the discriminatory power of speed, direction and distance of travel. Most importantly, we present a simple yet effective technique for the identification of users from location information that are not included in the original dataset used for training, thus raising important privacy concerns for the management of location datasets.

The uncertainty of identity toolset:analysing digital traces for user profiling

People manage a spectrum of identities in cyber domains. Profiling individuals and assigning them to distinct groups or classes have potential applications in targeted services, online fraud detection, extensive social sorting, and cyber-security. This paper presents the Uncertainty of Identity Toolset, a framework for the identification and profiling of users from their social media accounts and e-mail addresses. More specifically, in this paper we discuss the design and implementation of two tools of the framework. The Twitter Geographic Profiler tool builds a map of the ethno-cultural communities of a person's friends on Twitter social media service. The E-mail Address Profiler tool identifies the probable identities of individuals from their e-mail addresses and maps their geographical distribution across the UK. To this end, this paper presents a framework for profiling the digital traces of individuals.

Integrating service user and practitioner expertise within a web-based system for collaborative mental-health risk and safety management

Objectives: To develop a decision support system (DSS), myGRaCE, that integrates service user (SU) and practitioner expertise about mental health and associated risks of suicide, self-harm, harm to others, self-neglect, and vulnerability. The intention is to help SUs assess and manage their own mental health collaboratively with practitioners. Methods: An iterative process involving interviews, focus groups, and agile software development with 115 SUs, to elicit and implement myGRaCE requirements. Results: Findings highlight shared understanding of mental health risk between SUs and practitioners that can be integrated within a single model. However, important differences were revealed in SUs' preferred process of assessing risks and safety, which are reflected in the distinctive interface, navigation, tool functionality and language developed for myGRaCE. A challenge was how to provide flexible access without overwhelming and confusing users. Conclusion: The methods show that practitioner expertise can be reformulated in a format that simultaneously captures SU expertise, to provide a tool highly valued by SUs. A stepped process adds necessary structure to the assessment, each step with its own feedback and guidance. Practice Implications: The GRiST web-based DSS (www.egrist.org) links and integrates myGRaCE self-assessments with GRiST practitioner assessments for supporting collaborative and self-managed healthcare.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.