Intelligent Interface Research Articles

An evolving museum metaphor applied to cultural heritage for personalized content delivery

The aim of this article concerns adaptive and personalized navigation in a cultural heritage database. The theoretical grounding of the proposition relies on cognitive science, particularly constructivism and enaction. The navigation is conducted via an intelligent interface through a 3D “living” museum metaphor. The purpose of this interface is to recommend dynamic cultural heritage objects according to a user profile that is computed online from the interactions that a user has with these objects. To this end, objects are linked to semantic structures that represent relations between cultural heritage concepts. The user profile is described in terms of cultural heritage interests. A prototype of this principle is used to evaluate some of the basic hypotheses of this proposition.

An Intelligent and Interactive Interface to Support Symmetrical Collaborative Educational Writing among Visually Impaired and Sighted Users

It is often uncomfortable for disabled individuals, especially those with vision impairment, to conduct educational activities in collaboration with people that have perfect vision. This can be because of the former’s lack of confidence, vision capability, and acceptance. Information and communications technology (ICT) has played a vital role in giving support to people with visual impairments so that they can overcome their issues. This study proposes innovative solutions that address the challenges faced by partially or completely visually impaired people. It provides an interactive and intelligent interface, which they may use to perform educational activities, such as editing, writing, or reviewing documents, in collaboration with people without visual impairments. The system provides high-quality awareness features by sending them instant voice notifications about the actions and events occurring in the shared environment. A speech-recognition engine has been integrated into the system to allow users to interact with the application through voice commands. The system is evaluated through experiments, where people with visual impairment and people without visual impairment were engaged in collaborative writing. The obtained results are encouraging. The users showed curiosity in the system and were able to focus on the productive task instead of their disability.

Converness: Ontology‐driven conversational awareness and context understanding in multimodal dialogue systems

AbstractDialogue‐based systems often consist of several components, such as communication analysis, dialogue management, domain reasoning, and language generation. In this paper, we present Converness, an ontology‐driven, rule‐based framework to facilitate domain reasoning for conversational awareness in multimodal dialogue‐based agents. Converness uses Web Ontology Language 2 (OWL 2) ontologies to capture and combine the conversational modalities of the domain, for example, deictic gestures and spoken utterances, fuelling conversational topic understanding, and interpretation using description logics and rules. At the same time, defeasible rules are used to couple domain and user‐centred knowledge to further assist the interaction with end users, facilitating advanced conflict resolution and personalised context disambiguation. We illustrate the capabilities of the framework through its integration into a multimodal dialogue‐based agent that serves as an intelligent interface between users (elderly, caregivers, and health experts) and an ambient assistive living platform in real home settings.

Application of Eye Tracking in Intelligent User Interface

Aiming at the requirement of intelligent user interface state awareness and intention prediction, a method of interactive state classification and intention prediction based on eye tracking is proposed. 5 states of interaction are defined, which are monitoring state, tracking state, decision state, burst state and off-loop state, design induced experiments were conducted to collect eye movement data in each state, a single factor analysis of variance shows that the 7 eye movements in the 5 interactive states has a significant difference. The prediction accuracy of the SVM classification model under category 5 conditions is 77.2%, while the accuracy rate of the classification under category 4 conditions is 85.9%, individual differences also have important effects on prediction accuracy, the accuracy rate of single subjects under category 5 conditions are more than 84%, while under category 4 conditions up to 90%. The research results are of reference value to the design and application of intelligent user interface.

Wireless Network Instabilities in the Wild: Measurement, Applications (Non)Resilience, and OS Remedy

While the bandwidth and latency improvement of both WiFi and cellular data networks in the past decades are plenty evident, the extent of signal strength fluctuation and network disruptions (unexpected switching or disconnections) experienced by mobile users in today’s network deployment remains less clear. This paper makes three contributions. First, we conduct the first extensive measurement of network disruptions and significant signal strength fluctuations (together denoted as network instabilities) experienced by 2000 smartphones in the wild. Our results show that network disruptions and signal strength fluctuations remains prevalent as we moved into the 4G era. Second, we study how well popular mobile apps today handle such network instabilities. Our results show that even some of the most popular mobile apps do not implement any disruption-tolerant mechanisms. Third, we present Janus, an intelligent interface management framework that exploits the multiple interfaces on a handset to transparently handle network disruptions and satisfy apps’ performance requirement. We have implemented a prototype of Janus and our evaluation using a set of popular apps shows that Janus can: 1) transparently and efficiently handle network disruptions; 2) reduce video stalls by 2.9 times and increase 31% of the time of good voice quality; 3) reduce traffic size by 26.4% and energy consumption by 16.3% compared to naive solutions.

New intelligent interface study based on K-means gaze tracking

User interface (UI) is an interaction and information exchange medium between systems and users, which is convenient to communicate and enable users to operate hardware easily and effectively to achieve the bidirectional interaction. Traditional UI has difficulty in satisfying requirements of users. Therefore, this paper proposes a new intelligence interface scheme based on K-means gaze tracking. First, it uniformly describes the user, interface and system in intelligent interface interaction framework based on visual attention selection mechanism. Second, it uses K-means method to calculate attention degree value of interface on the basis of mapping relation of user, interface and system. Third, it adopts visual attention allocation strategy to predict users' interest degree on interface. Finally, experiments are conducted to verify the performance of our new scheme. The results show that the accuracy of intelligent interface predicting user decision intention is very high. This new method is a kind of selectable solution for marking intelligent interface of user interest goal automatically. What's more, intelligence interface scheme based on K-means gaze tracking can effectively improve the quality of gaze tracking.

New intelligent interface study based on K-means gaze tracking

User interface (UI) is an interaction and information exchange medium between systems and users, which is convenient to communicate and enable users to operate hardware easily and effectively to achieve the bidirectional interaction. Traditional UI has difficulty in satisfying requirements of users. Therefore, this paper proposes a new intelligence interface scheme based on K-means gaze tracking. First, it uniformly describes the user, interface and system in intelligent interface interaction framework based on visual attention selection mechanism. Second, it uses K-means method to calculate attention degree value of interface on the basis of mapping relation of user, interface and system. Third, it adopts visual attention allocation strategy to predict users' interest degree on interface. Finally, experiments are conducted to verify the performance of our new scheme. The results show that the accuracy of intelligent interface predicting user decision intention is very high. This new method is a kind of selectable solution for marking intelligent interface of user interest goal automatically. What's more, intelligence interface scheme based on K-means gaze tracking can effectively improve the quality of gaze tracking.

An independent-domain natural language interface for multimodel databases

Databases are gaining prime importance in the world of modern computing. Retrieving information stored in databases required the knowledge of the database query languages such as structured query language (SQL). However, learning this language can be difficult for non-expert users. Hence, the using of natural language is a very easy and convenient method that can provide powerful improvements to the use of data stored in databases. In this paper, we present the architecture of an intelligent natural language interface for a multimodel database. This interface functions independently of database domain, language and model. The using of machine learning approach helps our system to improve automatically its knowledge base through experience.

Context-aware network selection in heterogeneous wireless networks

Today, most handheld devices including smartphones, tablets and notebooks are equipped with Wi-Fi, Bluetooth, LTE/4G radio interfaces that can allow connections to the Internet and other devices. However, given the properties changes of the radio environment and user mobility, an open question still lies in how to select the best access network interface, considering that the availability and characteristics of an access network will change in time. This paper describes a new intelligent interface selection mechanism based on analysis of contextual information obtained from the user, network environment, and device. To select the best available wireless network, the mechanism, named as CANS (Context-Aware Network Selection), allows for dynamic decision-making during the operation of a mobile device, taking into account different metrics such as price, local network capabilities, user preferences, and the mobile terminal capabilities. The evaluation of the proposed mechanism in a real environment shows that the adoption of selection strategies enables the best exploitation of the advantages on multiple access technologies, achieving a reduction in device power consumption and financial cost.

Designing the intelligent user interface for electronic education support systems

The research proposes a technology for designing an intelligent user interface for e-learning support systems. A general analysis of characteristic features of the intelligent user interface is conducted. The content and structures of the didactic interface of e-learning systems are substantiated. Particular attention is given to the question of adjusting the brightness of the site mapping. The scheme of construction of the adaptive component of the intelligent user interface of the e-learning system is designed

Архітектурне рішення для побудови інтелектуальних інтерфейсів користувача

Розглянуто проблему побудови інтелектуальних інтерфейсів користувача (IUI). Хоча дослідження в галузі IUI проводять із часів перших розробок штучного інтелекту, на сучасному етапі розвитку інженерії програмного забезпечення існує небагато успішних рішень. Проаналізовано найвідоміші приклади реалізації IUI – проекти компанії Microsoft: The Office Assistant та Lumiere Project. Узагальнено недоліки цих реалізацій. Актуальним є розроблення концепції програмного забезпечення на підстаі підходу "мінімальної взаємодії", який охоплює використання інтелектуальних інтерфейсів. Важливою проблемою IUI, яка насамперед потребує вимагає вирішення, є порушення встановлених Usability-принципів для систем із класичним інтерфейсом. Допоміжним методом "пом'якшення" наслідків від порушення принципів до побудови інтерфейсів є чітке розділення адаптивного інтерфейсу і класичного. Як рішення запропоновано використовувати штучний інтелект у вигляді віддалених баєсових мереж; хмарних технологій для використання баєсових мереж у локальному ПЗ наживо та поповнення мережі знаннями, отриманих від окремих локальних користувачів; спілкування між мережею та локальним ПЗ за допомогою API. Метою розробленого програмного продукту є створення гнучкої та спрощеної системи високого рівня для імплементації аспектів IUI в ПЗ інших розробників. Цю мету реалізовано у вигляді REST API сервісу, який знаходиться на віддаленому сервері та призначений для побудови баєсових мереж. Перспективою розвитку представленого методу є врахування інших особливостей користувачів уже на рівні більш глибокого штучного інтелекту і передбачення можливих дій з погляду не тільки статистики, але й логіки.

Enhancing Trust in Autonomous Vehicles through Intelligent User Interfaces That Mimic Human Behavior

Autonomous vehicles use sensors and artificial intelligence to drive themselves. Surveys indicate that people are fascinated by the idea of autonomous driving, but are hesitant to relinquish control of the vehicle. Lack of trust seems to be the core reason for these concerns. In order to address this, an intelligent agent approach was implemented, as it has been argued that human traits increase trust in interfaces. Where other approaches mainly use anthropomorphism to shape appearances, the current approach uses anthropomorphism to shape the interaction, applying Gricean maxims (i.e., guidelines for effective conversation). The contribution of this approach was tested in a simulator that employed both a graphical and a conversational user interface, which were rated on likability, perceived intelligence, trust, and anthropomorphism. Results show that the conversational interface was trusted, liked, and anthropomorphized more, and was perceived as more intelligent, than the graphical user interface. Additionally, an interface that was portrayed as more confident in making decisions scored higher on all four constructs than one that was portrayed as having low confidence. These results together indicate that equipping autonomous vehicles with interfaces that mimic human behavior may help increasing people’s trust in, and, consequently, their acceptance of them.

A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms

Online handwritten Urdu character recognition is one of the key technologies for intelligent interface on smart phones and touch screens. It is a challenging research topic as Urdu script has many similar character groups. A novel similar character discrimination method for online handwritten Urdu character recognition is proposed in this paper which includes pre-classification, feature extraction and fine classification process. The pre-classifier enables the discrimination of similar characters by putting them in distinct smaller subsets according to stroke number and diacritics. Then structural features and wavelet features are extracted. Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Recurrent Neural Network (RNN) classifiers are compared for fine classification within subsets. Results of RNN classifier without using the proposed pre-classifier and features have also been obtained to check the end-to-end capability of the RNN classifier. Experimental results show that the proposed method is efficient and achieves an overall accuracy of 96% on a large-scale self-collected dataset. It is feasible to extend this method for other Arabic scripts.

MEMS Seismic Sensor with FPAA Based Interface Circuit for Frequency-Drift Compensation using ANN

<p>Electrochemical MEMS seismic sensor is limited by its non-ideality of frequency dependent characteristics hence interface circuits for compensation is necessary. The conventional compensation circuits are limited by high power consumption, bulky external hardware circuitry. In these methods digital circuits are also limited by inherent analog to digital conversion and vice versa which consumes significant power, acquires more size and limits speed. A Field programmable analog array (FPAA) overcomes these limitations and gives fast, simple and user friendly development platform with less development speed comparable to ASIC. Recently FPAA becoming popular for rapid prototyping. The proposed system presents FPAA (Anadigm AN231E04) based hardware implementation of ANN model. Using this FPAA based compensation circuit, the error in frequency drift have been minimized in the range of 3.68% to about 0.64% as compared to ANN simulated results in the range of 23.07% to 0.99 %. This single neuron consumes of power of 206.62 mW. and has minimum block wise resource utilization. The proposed hardware uses all analog blocks which remove the requirement of ADC and DAC reducing significant power and size of interface circuit. This work gives the SMART MEMS seismic sensor with reliable output and ANN based intelligent interface circuit implemented in FPAA hardware.<strong></strong></p>

Agent-Based Intelligent Interface for Wheelchair Movement Control.

People who suffer from any kind of motor difficulty face serious complications to autonomously move in their daily lives. However, a growing number research projects which propose different powered wheelchairs control systems are arising. Despite of the interest of the research community in the area, there is no platform that allows an easy integration of various control methods that make use of heterogeneous sensors and computationally demanding algorithms. In this work, an architecture based on virtual organizations of agents is proposed that makes use of a flexible and scalable communication protocol that allows the deployment of embedded agents in computationally limited devices. In order to validate the proper functioning of the proposed system, it has been integrated into a conventional wheelchair and a set of alternative control interfaces have been developed and deployed, including a portable electroencephalography system, a voice interface or as specifically designed smartphone application. A set of tests were conducted to test both the platform adequacy and the accuracy and ease of use of the proposed control systems yielding positive results that can be useful in further wheelchair interfaces design and implementation.

An Arduino Family Controller and its Interactions via an Intelligent Interface

An Arduino Family Controller and its Interactions via an Intelligent Interface

A system to transform natural language queries into SQL queries

In the present scenario, every user is not familiar with the use of Structured Query Language (SQL). So, user is not able to understand or write complex queries in SQL. An enhanced application with intelligent interface is needed to improve communication between the naive user and the databases application. The database is an efficient structure for handling data. To understand the structure of the database, a user has to learn SQL. The non-expert users who are not familiar with the use of SQL, need a system by which the user can interact with the database in their natural language. The system must have the ability to understand the natural language and interact with the database accordingly. In this research, an improved system with three-tier architecture is developed. Pattern matching and semantic matching techniques are used to develop the system which transforms natural language into SQL queries. The SQL query is generated with the production rules and the predefined data dictionary. The predefined data dictionary contains semantics for attributes and relation among attributes. The input given by the user is transformed by the system into SQL query by passing through steps like tokenization, escape word removal, classification of elements and query formation. Finally, the output is in the form of a SQL query. The results are compared with the existing system. The results given by the proposed system are better than the existing system. The proposed system has better recall value, accuracy, and precision in comparison to existing systems.

Summary of Research Progress of Brain Machine Integration

Objective: Brain Machine Integration refers to a hybrid intelligent system that integrates biological intelligence and machine intelligencethrough brain computer interface technology, which is considered one of the most important frontier science fields in the 21st century. Asan important part of brain integration, machine intelligence has strong storage and computing power. Compared with machine intelligence,the advantage of bio-intelligence lies in its high efficiency and low power consumption perception and logical reasoning ability. It is a keyproblem and challenge to combine the advantages of the two and build a more powerful new intelligent form.Methods: The research work related to brain-computer fusion system was analyzed with literature review and compared with the research.Results: This paper summarizes the research work related to migratory brain fusion system, and analyzes the main problems and challengesin the study of migratory brain-computer fusion system.

MEMS Seismic Sensor with FPAA-Based Interface Circuit for Frequency-Drift Compensation Using ANN

Abstract Electrochemical micro-electro mechanical systems (MEMS) seismic sensor is limited by its nonideality of frequency dependent characteristics hence interface circuits for compensation is necessary. The conventional compensation circuits are limited by high power consumption, bulky external hardware. These digital circuits are limited by inherent analog to digital conversions which consumes significant power, acquires more size and limits processing speed. This system presents field programmable analog array (FPAA) (Anadigm AN231E04) based hardware implementation of artificial neural network (ANN) model with minimized error in frequency drift in the range of 3.68% to about 0.64% as compared to ANN simulated results in the range of 23.07% to 0.99%. Single neuron consumes power of 206.62 mW with minimum block wise resource utilization. The proposed hardware uses all analog blocks removing the requirement of analog to digital converter and digital to analog converter, reducing significant power and size of interface circuit and enhancing processing speed. It gives the reliable, SMART MEMS seismic sensor with ANN-based intelligent interface circuit implemented in FPAA hardware.

Economic Feasibility Analysis for Renewable Energy Project Using an Integrated TFN–AHP–DEA Approach on the Basis of Consumer Utility

A renewable energy (RE) project has been brought into focus in recent years. Although there is quite a lot of research to assist investors in assessing the economic feasibility of the project, because of the lack of consideration of consumer utility, the existing approaches may still cause a biased result. In order to promote further development, this study focuses on the economic feasibility analysis of the RE project on the basis of consumer utility in the whole life cycle. Therefore, an integrated approach is proposed, which consists of triangular fuzzy numbers (TFNs), an analytic hierarchy process (AHP) and data envelopment analysis (DEA). The first step is to determine the comprehensive cost index weights of DEA by TFN–AHP. Secondly, to solve the problem, the first DEA model, which is proposed by A. Charnes, W. W. Cooper and E. Rhodes (C2R), is established to calculate the DEA effectiveness. Then, the third task involves designing a computer-based intelligent interface (CBII) to simplify realistic application and ensure performance efficiency. Finally, a solar water heater case study is demonstrated to validate the effectiveness of the entire method’s system. The study shows that this could make investors’ lives easier by using the CBII scientifically, reasonably and conveniently. Moreover, the research results could be easily extended to more complex real-world applications.