New Set Of Heuristics Research Articles

Development of a new set of Heuristics for the evaluation of Human-Robot Interaction in industrial settings: Heuristics Robots Experience (HEUROBOX).

Humans and robots will increasingly have to work together in the new industrial context. Therefore, it is necessary to improve the User Experience, Technology Acceptance, and overall wellbeing to achieve a smoother and more satisfying interaction while obtaining the maximum performance possible out of it. For this reason, it is essential to analyze these interactions to enhance User Experience. The heuristic evaluation is an easy-to-use, low-cost method that can be applied at different stages of a design process in an iterative manner. Despite these advantages, there is rarely a list of heuristics in the current literature that evaluates Human-Robot interactions both from a User Experience, Technology Acceptance, and Human-Centered approach. Such an approach should integrate key aspects like safety, trust, and perceived safety, ergonomics and workload, inclusivity, and multimodality, as well as robot characteristics and functionalities. Therefore, a new set of heuristics, namely, the HEUROBOX tool, is presented in this work in the form of the HEUROBOX tool to help practitioners and researchers in the assessment of human-robot systems in industrial environments. The HEUROBOX tool clusters design guidelines and methodologies as a logic list of heuristics for human-robot interaction and comprises four categories: Safety, Ergonomics, Functionality, and Interfaces. They include 84 heuristics in the basic evaluation, while the advanced evaluation lists a total of 228 heuristics in order to adapt the tool to the evaluation of different industrial requirements. Finally, the set of new heuristics has been validated by experts using the System Usability Scale (SUS) questionnaire and the categories has been prioritized in order of their importance in the evaluation of Human-Robot Interaction through the Analytic Hierarchy Process (AHP).

A Cognitive Style Based Framework for Usability Evaluation of Online Lecturing Platforms – A Case Study on Zoom and Teams

Recently, Zoom and Microsoft Teams have emerged as the most common tools for lecturing activities in higher education institutions. The fact that these platforms were not originally developed for educational purposes resulted in a significant reduction in learning effectiveness, especially in STEM subjects that require a lot of practical knowledge. Despite the enhancements and updates made to these platforms, they have failed to meet the learning effectiveness criteria. A major hindering to learning effectiveness on such systems can be traced not only to the absence of learning-related features but also to their usability. In this research, we propose a framework for evaluating the usability of online lecturing platforms. The proposed framework introduces a new set of heuristics that are specifically designed for lecturing activities and considers VAK learning styles. Since students with different learning styles learn differently, we developed the first cognitive style-based usability heuristics that can reliably assess the usability of online learning environments for various types of learners. We present a case study that uses the suggested evaluation framework to assess the usability of Zoom and Teams in higher education lecturing activities. The findings of this study, however, can be applied to almost any online lecture platform.

SNUXH: A Set of Social Network User Experience Heuristics

With the growth and overcrowding of the internet, the use of online social networks has been increasing. Currently, social networks are used by a wide variety of users–with different objectives and in different contexts of use–, so it is essential to design intuitive and easy to use social network applications that generate a positive user experience (UX). The heuristic evaluation is a well-known evaluation method that allows detecting usability problems; a group of experts evaluates a product and/or system using a set of heuristics as a guide. Although the heuristic evaluation is oriented to evaluate the usability, it can be useful to evaluate other aspects related to the UX. Due to the specific features of social networks, it is necessary to have a specific set of heuristics to evaluate them. Sets of specific heuristics for social networks have been proposed, but they focus on evaluating the only usability. This article presents a set of heuristics that attend not only usability issues, but other UX factors as well, social network user experience heuristics (SNUXH). The new set of heuristics was developed, validated, and refined in four iterations. The results obtained in the experimental validation indicate that the SNUXH set is useful and more effective than generic heuristics (Nielsen’s heuristics) when evaluating social networks.

Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics.

BackgroundAn established and well-known method for usability assessment of various human-computer interaction technologies is called heuristic evaluation (HE). HE has been adopted for evaluations in a wide variety of specialized contexts and with objectives that go beyond usability. A set of heuristics to evaluate how health information technologies (HITs) incorporate features that enable effective patient use of person-generated health data (PGHD) is needed in an era where there is a growing demand and variety of PGHD-enabled technologies in health care and where a number of remote patient-monitoring technologies do not yet enable patient use of PGHD. Such a set of heuristics would improve the likelihood of positive effects from patients’ use of PGHD and lower the risk of negative effects.ObjectiveThis study aims to describe the development of a set of heuristics for the design and evaluation of how well remote patient therapeutic technologies enable patients to use PGHD (PGHD enablement). We used the case of Kinect-based stroke rehabilitation systems (K-SRS) in this study.MethodsThe development of a set of heuristics to enable better use of PGHD was primarily guided by the R3C methodology. Closer inspection of the methodology reveals that neither its development nor its application to a case study were described in detail. Thus, where relevant, each step was grounded through best practice activities in the literature and by using Nielsen’s heuristics as a basis for determining the new set of heuristics. As such, this study builds on the R3C methodology, and the implementation of a mixed process is intended to result in a robust and credible set of heuristics.ResultsA total of 8 new heuristics for PGHD enablement in K-SRS were created. A systematic and detailed process was applied in each step of heuristic development, which bridged the gaps described earlier. It is hoped that this would aid future developers of specialized heuristics, who could apply the detailed process of heuristic development for other domains of technology, and additionally for the case of PGHD enablement for other health conditions. The R3C methodology was also augmented through the use of qualitative studies with target users and domain experts, and it is intended to result in a robust and credible set of heuristics, before validation and refinement.ConclusionsThis study is the first to develop a new set of specialized heuristics to evaluate how HITs incorporate features that enable effective patient use of PGHD, with K-SRS as a key case study. In addition, it is the first to describe how the identification of initial HIT features and concepts to enable PGHD could lead to the development of a specialized set of heuristics.

Applying a methodology to develop user eXperience heuristics

A heuristic evaluation method allows the evaluation of the usability of application domains. To evaluate applications that have specific domain features, researchers can use sets of specific usability heuristics in addition to the well-known (usually Nielsen's) heuristics. Heuristics can also focus on the User eXperience (UX) aspects other than the usability. In a previous work, we proposed a formal methodology for establishing usability/UX heuristics. The methodology has 8 stages including activities to formulate, specify, validate and refine a new set of heuristics for a specific application domain. The methodology was validated through expert opinion and several case studies. Although when specifying the methodology, we explained each of its stages in detail, some activities can be difficult to perform without a guide that helps the researcher determine how the stages should be carried out. This article presents a detailed explanation regarding how to apply each stage of the methodology to create a new set of heuristics for a specific domain. Additionally, this paper explains how to iterate the methodology's stages and when to stop the process of developing new heuristics.

Optimizing Answer Set Computation via Heuristic-Based Decomposition

AbstractAnswer Set Programming (ASP) is a purely declarative formalism developed in the field of logic programming and non-monotonic reasoning: computational problems are encoded by logic programs whose answer sets, corresponding to solutions, are computed by an ASP system. Different, semantically equivalent, programs can be defined for the same problem; however, performance of systems evaluating them might significantly vary. We propose an approach for automatically transforming an input logic program into an equivalent one that can be evaluated more efficiently. One can make use of existing tree-decomposition techniques for rewriting selected rules into a set of multiple ones; the idea is to guide and adaptively apply them on the basis of proper new heuristics, to obtain a smart rewriting algorithm to be integrated into an ASP system. The method is rather general: it can be adapted to any system and implement different preference policies. Furthermore, we define a set of new heuristics tailored at optimizing grounding, one of the main phases of the ASP computation; we use them in order to implement the approach into the ASP systemDLV, in particular into its grounding subsystemℐ-DLV, and carry out an extensive experimental activity for assessing the impact of the proposal.

Using a new set of heuristics in evaluating Arabic interfaces

The Information Technology Sector has been growing rapidly in the Arab world. As a result, Arabic interfaces are now being widely used and developed in the region. However, little existing work has been done that focused on evaluating said systems. In this paper, we propose a new set of heuristics for evaluating the usability of systems that use Arabic interfaces. We focused on formal heuristic evaluation as the usability inspection method for identifying issues in these systems. In order to test the proposed set, we compared the findings of heuristics set by Nielsen with ones reported using the proposed heuristics on three Arabic interfaces. Experimental results show that using our heuristics, evaluators were able to find more severe problems than those who used Nielsen’s. Therefore, we believe that heuristic evaluation, when conducted using our proposed heuristics, can be more effective in finding usability issues in Arabic interfaces.

Heuristic Evaluation for Virtual Reality Systems

The advent of Virtual Reality (VR) has created new user-interaction paradigms that VR designers need to attend to in order to avoid usability issues. Currently, there are few formal methods for evaluating the usability of VR interfaces. In this paper, we introduce a new set of heuristics that can be used to carry out usability inspections of VR systems via the Heuristic Evaluation method. The heuristics were developed to identify usability problems in both developing and currently-existing VR hardware and software. The heuristics reported here were developed by surveying VR users, and then using their data to identify nine classes of usability problems common to VR systems. The result is a new resource for UXRs who are seeking to use Heuristic Review to assess VR products.

Heuristic Evaluation for Novice Programming Systems

The past few years has seen a proliferation of novice programming tools. The availability of a large number of systems has made it difficult for many users to choose among them. Even for education researchers, comparing the relative quality of these tools, or judging their respective suitability for a given context, is hard in many instances. For designers of such systems, assessing the respective quality of competing design decisions can be equally difficult. Heuristic evaluation provides a practical method of assessing the quality of alternatives in these situations and of identifying potential problems with existing systems for a given target group or context. Existing sets of heuristics, however, are not specific to the domain of novice programming and thus do not evaluate all aspects of interest to us in this specialised application domain. In this article, we propose a set of heuristics to be used in heuristic evaluations of novice programming systems. These heuristics have the potential to allow a useful assessment of the quality of a given system with lower cost than full formal user studies and greater precision than the use of existing sets of heuristics. The heuristics are described and discussed in detail. We present an evaluation of the effectiveness of the heuristics that suggests that the new set of heuristics provides additional useful information to designers not obtained with existing heuristics sets.

A Framework for Generating Domain-Specific Heuristics for Evaluating Online Educational Websites

Websites are growing in use worldwide but need to bcontinuously evaluated and monitored to measure their efficiency, effectiveness and user sa tisfaction, and ultimately to improve quality. For this purpose, heuristic evaluation methodologies, such as Nielsen 's Heuristics, have become the accepted means for t he usability evaluation of user interface designs; how ever, they are general, and unlikely to encompass a ll usability attributes for all website domains. The aim of this paper is to enhance one of the most-used usability evaluation methods by generating specific heuristics for the e ducational domain, and then to compare and contrast them against Nielsen's ten heuristics (as first validation stage for proposed framework) in terms of the nu mber and severity of problems found, and of a number of usab ility measurements. The result show that the propos ed framework succeeded in building a new set of heuris tics for online educational websites, which managed to discover uniquely 55 (69%) of the usability problem s in all chosen websites (80 problems in total), in comparison with Nielsen's heuristics, which discove red only 6 (8%). 19 problems (24%) were commonly discovered (overlapping or sharing) by both sets of heuristics. The time taken using Nielsen's heurist ics was less than the time taken using the newly developed Educa tional Heuristics but this is because Nielsen's heu ristics do not cover all the issues related to educational web sites. It appears that the framework for generating context- specific heuristics did in fact produce an efficaci ous set of Educational Heuristics that covered the issues well in this domain.

Detecting Linkedin Spammers and its Spam Nets

Spam is one of the main problems of the WWW. Many studies exist about characterising and detecting several types of Spam (mainly Web Spam, Email Spam, Forum/Blob Spam and Social Networking Spam). Nevertheless, to the best of our knowledge, there are no studies about the detection of Spam in Linkedin. In this article, we propose a method for detecting Spammers and Spam nets in the Linkedin social network. As there are no public or private Linkedin datasets in the state of the art, we have manually built a dataset of real Linkedin users, classifying them as Spammers or legitimate users. The proposed method for detecting Linkedin Spammers consists of a set of new heuristics and their combinations using a kNN classifier. Moreover, we proposed a method for detecting Spam nets (fake companies) in Linkedin, based on the idea that the profiles of these companies share content similarities. We have found that the proposed methods were very effective. We achieved an F-Measure of 0.971 and an AUC close to 1 in the detection of Spammer profiles, and in the detection of Spam nets, we have obtained an F-Measure of 1.

Resource aware mapping on coarse grained reconfigurable arrays

Coarse grain reconfigurable array architectures have become increasingly popular due to their flexibility, scalability and performance. However, the mapping of programs on these architectures is characterized by huge complexity. This work presents a new mapping methodology for effectively mapping applications on coarse grained reconfigurable arrays. The core of this methodology comprises of the scheduling and register allocation phases performed, for the first time in the case of CGRAs, in a single step. Additionally, modulo scheduling with backtracking capability is incorporated in this scheme. The main contribution of this work includes a novel technique for minimizing the memory bandwidth bottleneck, a new priority scheme and a new set of heuristics which target on the maximization of the Instruction Level Parallelism by efficiently managing the architecture’s resources. The overall approach is retargetable with respect to a parametric architecture template modelling a large number of architecture alternatives and it has been automated with a prototype tool which permits experimental exploration. The experimental results showed that the achieved performance figures are very close to the most effective ones derived from the theoretical study on the architecture’s resources and the applications requirements. Moreover, the application of the bandwidth optimization technique lead to a 20–130% increase on operation parallelism. Finally, the experiments quantified the benefit from applying the new priority scheme and heuristics.