Non-technical Users Research Articles

Interacting with personal fabrication devices

AbstractPersonal fabrication tools, such as 3D printers, are on the way of enabling a future in which non-technical users will be able to create custom objects. While the hardware is now affordable and the number of people who own a 3D printer is increasing, only few create new 3D models. Most users download models from a 3D model database and after downloading fabricate them on their 3D printers. At most, users adjust a few parameters of the model, such as changing its color or browsing between predetermined shape options.I argue that personal fabrication has the potential for more: Instead of only consuming existing content, I envision a future in which non-technical users will create objects only trained experts can create today. While there are many open challenges for human-computer interaction, such as abstracting away the necessarydomainandmachineknowledge, I focus on improving theinteraction modelunderlying current personal fabrication devices.In this article, I illustrate why today’s interaction model for personal fabrication tools is not suitable for non-technical users. For this, I draw an analogy to the development of the user interface in personal computing and show how solutions developed by human-computer interaction researchers over the last decades can be applied to this new domain. I analyze the challenges when creating interactive systems for personal fabrication and describe six research prototypes I built to overcome these challenges. I discuss the limitations of these systems and conclude with an overview of recent advancements in personal fabrication that will allow us to go beyond what is possible today.

Building a Digital Twin: Testing the Effectiveness of Telecommunication Policies in a Virtual World

Infrastructure decision-making is challenging due to high levels of future uncertainty. Indeed, when considering broadband coverage, we are also faced with a situation where operators are reluctant to share data, there are few existing open-source evaluation models, and most available tools cannot be used by non-technical users. Transparency is sometimes low. Often disagreements arise between operators, regulators and other actors, with little independent assessment of key issues. Consequently, this paper proposes the development of a Digital Twin as a virtual test-bed for evaluating telecommunication policies. The concept of a Digital Twin has been common for several years in aerospace engineering, since first proposed by NASA in 2010. The vision defined in this paper is for a Digital Twin to be a virtual engineering-economic representation of a real-world telecommunication network, whereby simulation techniques allow exploration of potential future states under different policy conditions. A Digital Twin is developed for the British incumbent’s fixed broadband network, spanning 30 million premises and over 4.3 million geospatial telecommunication network assets. Using a network subset for Cambridgeshire, England the rollout of Fibre-To-The-Premises and Fibre-To-The-Distribution-Point upgrade options are then tested. Under different demand scenarios, market-based rollout is compared to a subsidised rollout strategy. Independently testing broadband deployment strategies in a virtual market and evaluating their effectiveness can provide greater transparency for decision-making processes. Over the long-term, a Digital Twin could help to generate new knowledge by testing experimental policy options, fostering greater innovation in how we tackle both perennial and emerging digital divide issues.

Towards of a modular framework for semi-autonomous driving assistance systems

Road traffic accidents are a leading cause of deaths globally and represent the main cause of death among 15–29 years olds (World Health Organization in Global status report on road safety 2015, World Health Organization, Geneva, 2015). Efforts have been made to develop and improve advanced driver assistance systems as to provide better quality of driving and reduce the number of traffic accidents. However, most of the current solutions focus on developing systems for fully autonomous cars. This study investigates a modular architecture that could potentially assist drivers in situations such as traffic jams, autonomous parking, and detection of obstacles such as pedestrians. The aim is to develop a low-cost driver assistance system that could be acquired and easily incorporated by non-technical users. It is becoming increasingly feasible to build a powerful and low-cost system because of the low cost of big-sized cameras, electronics, and processing cards. As a proof of the concept, a hardware and software solution had been developed for autonomous steering-wheel actuation that only utilizes a stereoscopic camera sensor.

GNC–app: A new Cytoscape app to rate gene networks biological coherence using gene–gene indirect relationships

MotivationGene networks are currently considered a powerful tool to model biological processes in the Bioinformatics field. A number of approaches to infer gene networks and various software tools to handle them in a visual simplified way have been developed recently. However, there is still a need to assess the inferred networks in order to prove their relevance. ResultsIn this paper, we present the new GNC-app for Cytoscape. GNC-app implements the GNC methodology for assessing the biological coherence of gene association networks and integrates it into Cytoscape. Implemented de novo, GNC-app significantly improves the performance of the original algorithm in order to be able to analyse large gene networks more efficiently. It has also been integrated in Cytoscape to increase the tool accessibility for non-technical users and facilitate the visual analysis of the results. This integration allows the user to analyse not only the global biological coherence of the network, but also the biological coherence at the gene–gene relationship level. It also allows the user to leverage Cytoscape capabilities as well as its rich ecosystem of apps to perform further analyses and visualizations of the network using such data. AvailabilityThe GNC-app is freely available at the official Cytoscape app store: http://apps.cytoscape.org/apps/gnc.

A process-oriented modeling approach for graphical development of mobile business apps

Mobile app development is an activity predominantly performed by software developers. Domain experts and future users are merely considered in early development phases as source of requirements or consulted for evaluating the resulting product. In the domain of business apps, many cross-platform programming frameworks exist but approaches also targeted at non-technical users are rare. Existing graphical notations for describing apps either lack the simplicity to be understandable by domain experts or are not expressive enough to support automated processing. The MAML framework is proposed as model-driven approach for describing mobile apps in a platform-agnostic fashion not only for software developers but also for process modelers and domain experts. Data, views, business logic, and user interactions are jointly modeled from a process perspective using a graphical domain-specific language. To aggregate multiple use cases and provide advanced modeling support, an inference mechanism is utilized to deduce a global data model. Through model transformations, native apps are then automatically generated for multiple platforms without manual programming. Our approach is compared to the IFML notation in an observational study, with promising results regarding readability and usability.

ATR-Vis

The worldwide adoption of Twitter turned it into one of the most popular platforms for content analysis as it serves as a gauge of the public’s feeling and opinion on a variety of topics. This is particularly true of political discussions and lawmakers’ actions and initiatives. Yet, one common but unrealistic assumption is that the data of interest for analysis is readily available in a comprehensive and accurate form. Data need to be retrieved, but due to the brevity and noisy nature of Twitter content, it is difficult to formulate user queries that match relevant posts that use different terminology without introducing a considerable volume of unwanted content. This problem is aggravated when the analysis must contemplate multiple and related topics of interest, for which comments are being concurrently posted. This article presents Active Tweet Retrieval Visualization (ATR-Vis), a user-driven visual approach for the retrieval of Twitter content applicable to this scenario. The method proposes a set of active retrieval strategies to involve an analyst in such a way that a major improvement in retrieval coverage and precision is attained with minimal user effort. ATR-Vis enables non-technical users to benefit from the aforementioned active learning strategies by providing visual aids to facilitate the requested supervision. This supports the exploration of the space of potentially relevant tweets, and affords a better understanding of the retrieval results. We evaluate our approach in scenarios in which the task is to retrieve tweets related to multiple parliamentary debates within a specific time span. We collected two Twitter datasets, one associated with debates in the Canadian House of Commons during a particular week in May 2014, and another associated with debates in the Brazilian Federal Senate during a selected week in May 2015. The two use cases illustrate the effectiveness of ATR-Vis for the retrieval of relevant tweets, while quantitative results show that our approach achieves high retrieval quality with a modest amount of supervision. Finally, we evaluated our tool with three external users who perform searching in social media as part of their professional work.

A novel mechanism for user-friendly and self-activated microdroplet generation capable of programmable control.

Unlike conventional approaches that require bulky and expensive pumping equipment, herein, we present a simple method for self-activated microdroplet generation and transport inside a long microchannel. The high gas-pressure in the syringes is used to provide the built-in power of self-priming so that the continuous phase and the dispersed phase are sequentially automated into the generator junction to produce stabilized droplets. The volume ratio between the aqueous and oil phases can be adjusted in a flexible way by accurately controlling the volume of the compressed air in the two syringes, and a novel self-activated micropumping mechanism is introduced to explain this phenomenon. Through the flow rate test inside the microchannels under different conditions, it is found that the flow rate of microdroplets inside the Teflon microchannel is highly stable. As a proof of concept, this novel micropump is applied for a 3D spiral chip for flow through PCR. It is demonstrated that this self-activated micropump is acceptable for droplet-based continuous flow microfluidic PCR with the thermal-cycle controlled by a single thermostatic heater, while the real-time (RT) fluorescence signal is comparable to a commercial qPCR cycler. This self-activated, portable, and controllable droplet generator would extend the droplet-based applications to in-field analysis and facilitate the exploitation of droplet microfluidics by non-technical users.

Toward Direct Manipulation for Personal Fabrication

Paralleling the evolution of more user-friendly interfaces in personal computing, emerging personal fabrication tools will soon enable nontechnical users to create objects that only trained experts can create today.

A novel approach for phishing emails real time classifica-tion using k-means algorithm

The dangers phishing becomes considerably bigger problem in online networking, for example, Facebook, twitter and Google+. The phishing is normally completed by email mocking or texting and it frequently guides client to enter points of interest at a phony sites whose look and feel are practically indistinguishable to the honest to goodness. Non-technical user resists learning of anti-phishing technic. Also not permanently remember phishing learning. Software solutions such as authentication and security warnings are still depending on end user action.In this paper we are mainly focus on a novel approach of real time phishing email classification using K-means algorithm. For this we uses 160 emails of last year computer engineering students. we get True positive of legitimate and phishing as 67% and 80% and true negative is 30 % and 20%.,which is very high so we ask same users reasons which I mainly categories into three categories ,look and feel of email, email technical parameters, and email structure.

MAGPIE: Simplifying access and execution of computational models in the life sciences.

Over the past decades, quantitative methods linking theory and observation became increasingly important in many areas of life science. Subsequently, a large number of mathematical and computational models has been developed. The BioModels database alone lists more than 140,000 Systems Biology Markup Language (SBML) models. However, while the exchange within specific model classes has been supported by standardisation and database efforts, the generic application and especially the re-use of models is still limited by practical issues such as easy and straight forward model execution. MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation, closes this gap by providing a software platform for both, publishing and executing computational models without restrictions on the programming language, thereby combining a maximum on flexibility for programmers with easy handling for non-technical users. MAGPIE goes beyond classical SBML platforms by including all models, independent of the underlying programming language, ranging from simple script models to complex data integration and computations. We demonstrate the versatility of MAGPIE using four prototypic example cases. We also outline the potential of MAGPIE to improve transparency and reproducibility of computational models in life sciences. A demo server is available at magpie.imb.medizin.tu-dresden.de.

Development of a research-oriented system for collecting mechanical ventilator waveform data.

Lack of access to high-frequency, high-volume patient-derived data, such as mechanical ventilator waveform data, has limited the secondary use of these data for research, quality improvement, and decision support. Existing methods for collecting these data are obtrusive, require high levels of technical expertise, and are often cost-prohibitive, limiting their use and scalability for research applications. We describe here the development of an unobtrusive, open-source, scalable, and user-friendly architecture for collecting, transmitting, and storing mechanical ventilator waveform data that is generalizable to other patient care devices. The system implements a software framework that automates and enforces end-to-end data collection and transmission. A web-based data management application facilitates nontechnical end users' abilities to manage data acquisition devices, mitigates data loss and misattribution, and automates data storage. Using this integrated system, we have been able to collect ventilator waveform data from >450 patients as part of an ongoing clinical study.

SIMPLIFIED GRAPHICAL DOMAIN-SPECIFIC LANGUAGES FOR THE MOBILE DOMAIN – PERSPECTIVES OF LEARNABILITY BY NONTECHNICAL USERS

Increasing number of technologically advanced mobile devices causes the need for seeking methods of software development that would involve persons without or with highly limited programming skills. They could participate as domain experts or individual creators of personal applications. Methods based on models might be the right answer, thus the author conducted workshops and surveys concerning perspectives of graphical modeling languages for the mobile domain. Research revealed that nontechnical users declared high learnability of simplified ones as well as the majority of them correctly read models in such languages.

A capstone design project for teaching cybersecurity to nontechnical users

This paper presents a multi-year undergraduate computing capstone project that holistically contributes to the development of cybersecurity knowledge and skills in non-computing high school and college students. We describe the student-built Vulnerable Web Server application, which is a system that packages instructional materials and pre-built virtual machines to provide lessons on cybersecurity to non-technical students. The Vulnerable Web Server learning materials have been piloted at several high schools and are now integrated into multiple security lessons in an intermediate, general education information technology course at the United States Military Academy. Our paper interweaves a description of the Vulnerable Web Server materials with the senior capstone design process that allowed it to be built by undergraduate information technology and computer science students, resulting in a valuable capstone learning experience. Throughout the paper, a call is made for greater emphasis on educating the nontechnical user.

End-user composition of interactive applications through actionable UI components

Developing interactive systems to access and manipulate data is a very tough task. In particular, the development of user interfaces (UIs) is one of the most time-consuming activities in the software lifecycle. This is even more demanding when data have to be retrieved by accessing flexibly different online resources. Indeed, software development is moving more and more toward composite applications that aggregate on the fly specific Web services and APIs. In this article, we present a mashup model that describes the integration, at the presentation layer, of UI components. The goal is to allow non-technical end users to visualize and manipulate (i.e., to perform actions on) the data displayed by the components, which thus become actionable UI components. This article shows how the model has guided the development of a mashup platform through which non-technical end users can create component-based interactive workspaces via the aggregation and manipulation of data fetched from distributed online resources. Due to the abundance of online data sources, facilitating the creation of such interactive workspaces is a very relevant need that emerges in different contexts. A utilization study has been performed in order to assess the benefits of the proposed model and of the Actionable UI Components; participants were required to perform real tasks using the mashup platform. The study results are reported and discussed.

MISNIS: An intelligent platform for twitter topic mining

Twitter has become a major tool for spreading news, for dissemination of positions and ideas, and for the commenting and analysis of current world events. However, with more than 500 million tweets flowing per day, it is necessary to find efficient ways of collecting, storing, managing, mining and visualizing all this information. This is especially relevant if one considers that Twitter has no ways of indexing tweet contents, and that the only available categorization “mechanism” is the #hashtag, which is totally dependent of a user's will to use it. This paper presents an intelligent platform and framework, named MISNIS - Intelligent Mining of Public Social Networks’ Influence in Society - that facilitates these issues and allows a non-technical user to easily mine a given topic from a very large tweet's corpus and obtain relevant contents and indicators such as user influence or sentiment analysis. When compared to other existent similar platforms, MISNIS is an expert system that includes specifically developed intelligent techniques that: (1) Circumvent the Twitter API restrictions that limit access to 1% of all flowing tweets. The platform has been able to collect more than 80% of all flowing portuguese language tweets in Portugal when online; (2) Intelligently retrieve most tweets related to a given topic even when the tweets do not contain the topic #hashtag or user indicated keywords. A 40% increase in the number of retrieved relevant tweets has been reported in real world case studies. The platform is currently focused on Portuguese language tweets posted in Portugal. However, most developed technologies are language independent (e.g. intelligent retrieval, sentiment analysis, etc.), and technically MISNIS can be easily expanded to cover other languages and locations.

A new approach for managing Android permissions: learning users\u2019 preferences

Today, permissions management solutions on mobile devices employ Identity Based Access Control (IBAC) models. If this approach was suitable when people had only a few games (like Snake or Tetris) installed on their mobile phones, the current situation is different. A survey from Google in 2013 showed that, on average, french users have installed 32 applications on their Android smartphones. As a result, these users must manage hundreds of permissions to protect their privacy. Scalability of IBAC is a well-known issue and many more advanced access control models have introduced abstractions to cope with this problem. However, such models are more complex to handle by non-technical users. Thus, we present a permission management system for Android devices that (1) learns users’ privacy preferences with a novel learning algorithm, (2) proposes them abstract authorization rules, and (3) provides advanced features to manage these high-level rules. Our learning algorithm is compared to two other well-known approaches to show its efficiency. Finally, we prove this whole approach is more efficient than current permission management system by comparing it to Privacy Guard Manager.

Integrating UIMA annotators in a web-based text processing framework.

The Unstructured Information Management Architecture (UIMA) [1] framework is a growing platform for natural language processing (NLP) applications. However, such applications may be difficult for non-technical users deploy. This project presents a web-based framework that wraps UIMA-based annotator systems into a graphical user interface for researchers and clinicians, and a web service for developers. An annotator that extracts data elements from lung cancer radiology reports is presented to illustrate the use of the system. Annotation results from the web system can be exported to multiple formats for users to utilize in other aspects of their research and workflow. This project demonstrates the benefits of a lay-user interface for complex NLP applications. Efforts such as this can lead to increased interest and support for NLP work in the clinical domain.

Social interaction for efficient agent learning from human reward

Learning from rewards generated by a human trainer observing an agent in action has been proven to be a powerful method for teaching autonomous agents to perform challenging tasks, especially for those non-technical users. Since the efficacy of this approach depends critically on the reward the trainer provides, we consider how the interaction between the trainer and the agent should be designed so as to increase the efficiency of the training process. This article investigates the influence of the agent’s socio-competitive feedback on the human trainer’s training behavior and the agent’s learning. The results of our user study with 85 participants suggest that the agent’s passive socio-competitive feedback—showing performance and score of agents trained by trainers in a leaderboard—substantially increases the engagement of the participants in the game task and improves the agents’ performance, even though the participants do not directly play the game but instead train the agent to do so. Moreover, making this feedback active—sending the trainer her agent’s performance relative to others—further induces more participants to train agents longer and improves the agent’s learning. Our further analysis shows that agents trained by trainers affected by both the passive and active social feedback could obtain a higher performance under a score mechanism that could be optimized from the trainer’s perspective and the agent’s additional active social feedback can keep participants to further train agents to learn policies that can obtain a higher performance under such a score mechanism.

ExSIDE: Component Based Object Oriented Expert System’s Integrated Development Environment

This paper describes the design and development of a component-based object oriented Expert System's Integrated Development Environment (ExSIDE). It is integrated with (i) a user-friendly manual and automated knowledge acquisition and management tool (ExSIDE_KAMT);(ii) an independent and customizable runtime module (ExSIDE_RTM); (iii) an object-oriented in-process Component Object Model (COM)-based inference engine (ExSIDE_IE); (iv) an object-oriented out-of-process COM-based inference engine (ExSIDE_IESvr); (v) and a PHP based inference engine (ExSIDE_PHP). ExSIDE_RTM can function independently as an Expert System Shell (ESS) and helps user to develop Expert Systems rapidly. ExSIDE_IE and ExSIDE_IES can be integrated with COM-supporting general purpose and scientific application development tools such as variants of C/C++/C#, BASIC (Visual BASIC®, REALbasic®), Java, MATLAB®, LabVIEW®, and Mathematica® to develop more advanced Expert Systems. Finally, ExSIDE_IE and ExSIDE_PHP can be used with Active Server Pages (ASP) and PHP technologies to generate web based Expert Systems. The unique framework of the ExSIDE enables rapid development of Expert Systems' on PC and web for technical and non-technical users. The overall system was developed successfully, and its usability was demonstrated via five unique Expert Systems case studies discussed in this paper.

Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy

The public buildings sector represents one of the most intensive items of EU energy consumption; the application of retrofit solutions in existing buildings is a crucial way to reduce its impact. To facilitate the knowledge of the energy performance of existing non-residential buildings and the choice of the more adequate actions, Public Administrations (PA) should have the availability of proper tools. Within the Italian project “POI 2007-13”, a database and a decision support tool, for easy use, even to a non-technical user, have been developed. A large set of data, obtained from the energy audits of 151 existing public buildings located in four regions of South Italy have been analysed, elaborated, and organised in a database. This was used to identify the best architectures of two ANNs and to train them. The first ANN provides the actual energy performance of any building; the second ANN assesses key economic indicators. A decision support tool, based on the use of these ANNs is conceived for a fast prediction of the energy performance of buildings and for a first selection of energy retrofit actions that can be applied.