Poor User Experience Research Articles

Users' experiences with chatbots: findings from a questionnaire study

For chatbots to be broadly adopted by users, it is critical that they are experienced as useful and pleasurable. While there is an emerging body of research concerning user uptake and use of chatbots, there is a lack of theoretically grounded studies detailing what constitutes good or poor chatbot user experiences. In this paper, we present findings from a questionnaire study involving more than 200 chatbot users who reported on episodes of chatbot use that they found particularly satisfactory or frustrating. The user reports were analysed with basis in theory on user experience, with particular concern for pragmatic and hedonic attributes. We found that pragmatic attributes such as efficient assistance (positive) and problems with interpretation (negative) were important elements in user reports of satisfactory and frustrating episodes. Hedonic attributes such as entertainment value (positive) and strange and rude responses (negative) were also frequently mentioned. Older participants tended to report on pragmatic attributes more often, whereas younger participants tended to report on hedonic attributes more often. Drawing on the findings, we propose four high-level lessons learnt that may benefit chatbot service providers, and we suggest relevant future research.

Knowledge Sharing Platforms: An Empirical Study of the Factors Affecting Continued Use Intention

Under the background of the constant development of sharing economy and constant upgrade of Internet technologies, various knowledge sharing platforms have emerged in recent years. As a new form for sharing economy, knowledge sharing platforms have made great strides, thus, providing new channels of knowledge acquisition, knowledge sharing and individual knowledge storage for users. Nevertheless, knowledge sharing platforms are still faced with problems, such as uneven product quality (Content quality) and poor user experience. Therefore, how to maintain consumers’ continued use intention without impairing consumer satisfaction has obviously become an issue of great concern on the development path of knowledge sharing platforms. Based on the expectation confirmation theory (ECT) and information system (IS) success model, and integrating major characteristics of knowledge sharing platforms, the conceptual models and hypotheses influencing consumers’ continued use intention of knowledge sharing platforms are built to study primary factors affecting consumers’ continued use intention. Through online and offline questionnaires, 428 copies were collected online, and 150 copies were collected offline, with a total of 578 copies recovered. We deleted some questionnaires with untrue and contradictory answers, because these interviewees might not have carefully read. A questionnaire is conducted with 512 valid copies collected from respondents. At the same time, the structural equation model (SEM) is used to verify and analyze hypotheses. Results show that various variables have a direct or indirect influence on consumers’ continued our intention. Meanwhile, it is observed that perceived usefulness and satisfaction degree have a significant influence on continued use intention of knowledge sharing platforms. The higher the satisfaction degree is, the more likely the consumers will continue using knowledge sharing platforms. The expectation confirmation degree exerts an indirect influence on consumers’ continued use intention of knowledge sharing platforms via perceived usefulness. The IS success model can also significantly influence consumers’ satisfaction degree of knowledge sharing platforms. Additionally, perceived usefulness is observed to have the strongest influence on consumers’ continued use intention of knowledge sharing platforms. According to the above research findings, knowledge sharing platforms should strengthen their content quality control, ensure the consistency between service quality and payment, and improve the platform quality. To sum up, this research can provide theoretical bases for sustainable development and follow-up research of the knowledge sharing platform industry.

From Bits of Data to Bits of Knowledge-An On-Board Classification Framework for Wearable Sensing Systems.

Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

Research and application of non-invasive intelligent power system

Most of the traditional intelligent power systems collect data information through sensors installed on the user’s equipment side, and then conduct intelligent operation, resulting in poor user experience. With the increase of all kinds of electrical equipment, the number of sensors to be installed is also increasing, resulting in waste of resources. It is an important direction of customer side service upgrading to build ubiquitous power Internet of things and explore a new type of intelligent power consumption mode that is customer-centric and market-oriented. Based on the requirements of the current Ubiquitous Power Internet of Things, this paper proposes a new mode of intelligent power system based on non-invasive load monitoring and identification technology. This new mode realizes diversified value-added services for power consumption, also, this mode is applied to the customization of user’s personalized business, precise management and control of power equipment, so as to enhance the user’s power consumption experience and build a market-oriented customer side business application has important application value.

Students' Awareness on Good and Poor User Experiences

Students' Awareness on Good and Poor User Experiences

Resource-constrained O2O service recommended strategy research

With the development of internet, the problem of information overload is becoming more and more serious. Recommendation technology can screen out information that is useful to people. Therefore, many scholars pay attention to it. There are two types of internet services: online and offline. At present, recommendation technology has become more and more mature in purely online applications, including news recommendation and commodity recommendation. However, O2O service recommendation needs the support of offline resources. Owing to the constraints of limited resources, many users adopt recommendation result at the same time, which often leads to crowded service points, useless recommendations and poor user experience. How to improve the effectiveness of O2O service recommendation under condition of resource constraints is a crucial issue. This paper proposes a group of O2O service recommendation strategies from the prospect of supply and demand matching to solve the problem step by step. Furthermore, we utilise computational experiment to perform performance comparison analysis for these service strategies. The results show that the adaptive adjustment mechanism based on current supply and demand conditions is conductive to improving effectiveness of O2O service recommendation so as to increase profit of the merchant and improve user experience.

Resource-constrained O2O service recommended strategy research

With the development of internet, the problem of information overload is becoming more and more serious. Recommendation technology can screen out information that is useful to people. Therefore, many scholars pay attention to it. There are two types of internet services: online and offline. At present, recommendation technology has become more and more mature in purely online applications, including news recommendation and commodity recommendation. However, O2O service recommendation needs the support of offline resources. Owing to the constraints of limited resources, many users adopt recommendation result at the same time, which often leads to crowded service points, useless recommendations and poor user experience. How to improve the effectiveness of O2O service recommendation under condition of resource constraints is a crucial issue. This paper proposes a group of O2O service recommendation strategies from the prospect of supply and demand matching to solve the problem step by step. Furthermore, we utilise computational experiment to perform performance comparison analysis for these service strategies. The results show that the adaptive adjustment mechanism based on current supply and demand conditions is conductive to improving effectiveness of O2O service recommendation so as to increase profit of the merchant and improve user experience.

TRUS: Towards the Real-Time Route Update Scheduling in SDN for Data Centers

Periodically data plane update is a necessary task in Software Defined Networking(SDN). Most prior approaches update many flows without considering which flows should be updated. Hence, most of them get long update delay. However, due to the dramatic increase of data arriving in data centers in the coming 5G networks, flows to be updated should be carefully chosen. Otherwise, unsatisfactory update delay may occur, which will make the route after the reconfiguration invalid for most flows and even bring about a poor user experience. In this paper, we present a Timely Route Update Scheme(TRUS). Different from previously proposed update approaches, TRUS not only meets the real-time route update demand for data centers, but also guarantees bandwidth requirements of delay-sensitive flows. Further, it can avoid the congestion on “hot” links in the update process. Simulation results verify the effectiveness of our scheme and demonstrate it can largely cut down the route update latency while obtaining a good network performance when comparing with some former approaches.

Introducing an Unsupervised Automated Solution for Root Cause Diagnosis in Mobile Networks

Today’s network operators strive to create self-healing cellular networks that have a fully automated troubleshooting management process. To this end, the network monitoring system should be capable of detecting issues, diagnosing them, and triggering the adequate recovery action. In this paper, we propose an unsupervised solution to diagnose the root causes of network issues. As monitoring systems collect a large number of logs from the different devices in their networks, it is possible to determine which connections resulted in a poor user experience and apply a failed/successful label. Our solution, Automatic Root Cause Diagnosis (ARCD), analyzes labeled connection logs to identify the major contributors to the network inefficiency (e.g., a faulty core device) as well as the incompatibilities between different elements (e.g., make and model of a phone not being able to access a service). We evaluate the effectiveness of our solution by using logs from three different real cellular networks. In each case, ARCD was able to identify the major contributors and the most widespread incompatibilities. In the three cases, the precision (detection accuracy) and the recall (detection rate) are higher than 90%.

Research on Multi-task Scheduling Algorithm for Integrated Energy Metering Acquisition System

With the development of the energy Internet and the construction of a new energy measurement system, it is particularly important to promote the remote automatic collection and copying of water, gas, heat and electricity. Based on the electricity information collection system, this paper designs an integrated acquisition technology architecture system for electricity, water, gas and thermal energy measurement, and clarifies the technical route of the energy measurement system. Then, through the analysis of the system task classification, a multi-task adaptive real-time scheduling method based on Spark cluster is proposed, which solves the problem that the collection system has uneven task allocation, long overall task completion time and high priority based on the existing scheduling mechanism of the Spark cluster. The task timeout response causes problems such as poor user experience. Finally, the experimental results show that the total number of 2 million collection tasks are divided into six categories, and the default scheduling methods in the text and Spark are used for comparison. The time-based and priority-critical tasks are completed earlier than the default method. The response speed greatly improves the system user experience and effectively improves the level of social public services.

Creating high fidelity 360° virtual reality with high dynamic range spherical panorama images

Abstract This research explores the development of a novel method and apparatus for creating spherical panoramas enhanced with high dynamic range (HDR) for high fidelity Virtual Reality 360 degree (VR360) user experiences. A VR360 interactive panorama presentation using spherical panoramas can provide virtual interactivity and wider viewing coverage; with three degrees of freedom, users can look around in multiple directions within the VR360 experiences, gaining the sense of being in control of their own engagement. This degree of freedom is facilitated by the use of mobile displays or head-mount-devices. However, in terms of image reproduction, the exposure range can be a major difficulty in reproducing a high contrast real-world scene. Imaging variables caused by difficulties and obstacles can occur during the production process of spherical panorama facilitated with HDR. This may result in inaccurate image reproduction for location-based subjects, which will in turn result in a poor VR360 user experience. In this article we describe a HDR spherical panorama reproduction approach (workflow and best practice) which can shorten the production processes, and reduce imaging variables, and technical obstacles and issues to a minimum. This leads to improved photographic image reproduction with fewer visual abnormalities for VR360 experiences, which can be adaptable into a wide range of interactive design applications. We describe the process in detail and also report on a user study that shows the proposed approach creates images which viewers prefer, on the whole, to those created using more complicated HDR methods, or to those created without the use of HDR at all.

Requirement Model of Conference Management System: A Mobile App for Creating and Managing Conference Paper

Technology have changed the way we work. This have increased the productivity in the workplace. The purpose of having conference paper s app is to aid researcher to improve the effectiveness and efficiency on management of conference paper.Without a proper management of these matters, people might suffer from theinconvenient by using desktop system to complete certain tasks and monitor them efficiently. However, based on investigating existing applications, a few shortcomings have been identified. Certain shortcoming leads us to develop a new mobile application to fix the identified shortcomings. Some others include complex navigation of user interfaces this cause a poor user experience for user. Hence, this project proposes a development of a mobile application that is called CPMA to serve as a better alternative of tool to allow users managing and monitoring any related conference paper matters in more efficient way.This app specifically targeted for researcher in higher learning institutions who are needed to proceed with conference paper. Software development methodology named prototyping will be adapted which consisted of phases such as planning, repeated phases (analysis, design, implementation and system prototype), implementation and final system. The essential significant of this project is to provide a platform for researcher to manage and monitor conference paper in a savvy manner. By the time of completing this project, users are expected to access this application without any costs and increase the efficiency and effectiveness of managing conference paper.to ensure the security is presented in a tabular form for each online marketplace.

Quantitative approaches for optimization of user experience based on network resilience for wireless service provider networks

Since the 1980′s and in particular 1996, telecom operators and recently mobile operators have been facing increasingly fierce competition, combined with flat subscriber growth and increased data usage resulting in tremendous downward pressures on profitability, forcing operators to differentiate themselves by trying to offer network services with better customer experience at lower operational costs. Wireless operators are challenged with measuring user experience which in itself is subjective, in a manner that accurately reflects the functional and emotional aspects of perceived quality and linking to Network Resiliency which characterizes the network behavior as it responds to disruptions. Current network faults and alarms only consider device failures and do not consider actual impact to user experience. For instance a failed router may not impact the users experience due to built in redundancies in the network. Studies to date, have proposed methods and models that focus on specific aspects of user experience in wired and cellular networks. However, to the best of our knowledge, there is currently very little research that connects linking poor user network experience to root cause. Previous recent work in this area focus on identifying what and where measurements to gage subscriber OoE, modeling and high level concepts, but do not address realistic challenges and approaches that can be automated to materially impact improved customer experiences at lower operational expenses. There is a gap on how operators can automatically associate poor user experience, relevant network metrics and root causes with a suitable model that can be analyzed and optimized. We propose a general framework for a solution that links these entities together, with a quantified approach to optimize user network experience by optimizing network resilience using a model that can be analyzed and optimized using machine learning methods to improve resilience and hence user experience. Results of directly applying existing machine learning algorithms for identifying root causes to network telemetry data have proven to be ineffective in practice due to the fact that existing machine learning algorithms are designed for prediction, classification and ranking not for identifying causal relationships and further complicated by the fact that these algorithms have assumptions on the data and in reality the network data distributions vary wildly during network disturbances. The proposed general framework combines existing methods for anomaly detection and machine learning algorithms, however the novel contribution centers on improving the accuracy of finding associated root causes by dynamically selecting the optimal machine learning algorithm based on the network telemetry data features that are recomputed before, during and after network disturbances. The proposed approach then allows us to automate the time consuming manual tasks of network engineers that proactively monitor key performance metrics for anomalies, correlate with other data sources to ultimately determine actionable insights to maintain a certain acceptable level of user experience by dynamically selecting the appropriate machine learning algorithm for the given data characteristics or features. We describe an example case study specific to wireless provider environment, illustrating the potential viability with results from actual wireless(approx 8 million monthly subscribers) operations data showing promising results by applying the proposed approach. The prototype implementation was able to programmatically detect anomalies, identify potential root causes using different algorithms suitable for the given data and time frame, which dramatically increased the accuracy and efficiency of the small network engineering team, and hence improved the user experience by improving network resiliency.

Having a Smarter City through Digital Urban Interfaces: An Evaluation Method

This paper appraises a tool developed to evaluate user experiences of urban digital interfaces. The authors propose an evaluation method that uses 14 guidelines to analyze questions pertaining to efficiency, assistance and instructions, content structure, resemblance to reality, feedback interface, visual design, cognitive processes, internationalization, and perceptive access. The proposed tool serves to identify obstacles that once identified can then be tackled and resolved in the design phase. Addressing obstacles in the design phase serves to prevent the creation of inefficient interfaces that would lead to poor user experiences, or, likewise, the rejection of these interfaces by users. To verify the effectiveness of the proposed guidelines in a real-world environment a field study has been conducted in which eight urban interfaces located in different cities and countries were observed. The study reveals the issues typically encountered by users that prevent them from having satisfactory or enjoyable experiences when using digital urban interfaces. The paper concludes by identifying and discussing areas of opportunity for further research and improvements to the proposed guidelines.

An Intuitive Way to Unmask In-Browser Cryptojacking in Network Level using Support Vector Machine (SVM) in Machine Learning

cryptocurrency mining is skyrocketed in the recent times. The hackers are turning their heads towards this type of attacks mainly through browser-based mining which makes them undetected. This leads to the increase in Electricity bills and heavy load on the computer processing. This can be avoided by disabling the java script in the device. The disabling Java Script leads to poor user experience of the websites as the websites in recent times are powered with Java Script frameworks. This paper discusses about a live network filtering tool which detects the illicit In-browser miningwithin the range of a network of devices.

Evaluating Recommender System Stability with Influence-Guided Fuzzing

Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender’s stability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset. We find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches.

Reproducing performance bug reports in server applications: The researchers’ experiences

Performance is one of the key aspects of non-functional qualities as performance bugs can cause significant performance degradation and lead to poor user experiences. While bug reports are intended to help developers to understand and fix bugs, they are also extensively used by researchers for finding benchmarks to evaluate their testing and debugging approaches. Although researchers spend a considerable amount of time and effort in finding usable performance bugs from bug repositories, they often get only a few. Reproducing performance bugs is difficult even for performance bugs that are confirmed by developers with domain knowledge. The amount of information disclosed in a bug report may not always be sufficient to reproduce the performance bug for researchers, and thus hinders the usability of bug repository as the resource for finding benchmarks. In this paper, we study the characteristics of confirmed performance bugs by reproducing them using only informations available from the bug report to examine the challenges of bug reproduction from the perspective of researchers. We spent more than 800 h over the course of six months to study and to try to reproduce 93 confirmed performance bugs, which are randomly sampled from two large-scale open-source server applications. We (1) studied the characteristics of the reproduced performance bug reports; (2) summarized the causes of failed-to-reproduce performance bug reports from the perspective of researchers by reproducing bugs that have been solved in bug reports; (3) shared our experience on suggesting workarounds to improve the bug reproduction success rate; (4) delivered a virtual machine image that contains a set of 17 ready-to-execute performance bug benchmarks. The findings of our study provide guidance and a set of suggestions to help researchers to understand, evaluate, and successfully replicate performance bugs.

Design and implementation of Surakarta game based on iOS

Aiming at the present situation that Surakarta game has a narrow audience and poor user interface and user experience, this paper designs and implements a Surakarta game system based on iOS platform. A chessboard data structure based on iOS platform is designed, which is suitable for different screen sizes of mobile phones. The animation representation of game process, especially the eating process, is realized. This paper studies and attempts to use GCD-based multi-threaded search to solve the problem of limited mobile phone resource performance. Experiments show that the Surakarta game system based on iOS platform can play man-machine game on portable devices and has good performance.

Design of two-wheel self-balancing vehicle based on visual identification

Poor user experience is caused by the current unstable operation of the two-wheel self-balancing vehicle control system, the high failure rate, and the poor accuracy and sensitivity of the control system (prone to deviation). Therefore, the work studied the two-wheel self-balancing vehicle system with the visual recognition of vehicle attitude. Kalman filtering and PID control algorithm were adopted to reduce the high frequency interference of the accelerometer and the low frequency error of the gyroscope, improve the response speed and control precision of motor to error, and ensure vertical control. The road images were acquired by a charge-coupled device vision sensor with the edge information extracted by the Laplacian operator, which ensures autonomous navigation control of a balance car. Based on the calculation of the distance between road axis and the existence detection of forward obstacles, the early warning mechanism was established through the calculation of the group agent to improve the safety performance of a balance vehicle. Experiments showed the improved control system has good stability, fast walking speed, strong anti-interference, and high security.

SYSTEMATIC REVIEW OF VISUALLY-IMPAIRED AND BLIND USER EXPERIENCE OF WEB TRENDS

For visually impaired people, interaction with the Web can be a challenging task; this is due to poor accessibility, usability, and User eXperience (UX) of websites. Therefore, it is necessary to conduct a thorough investigation on the UX of websites, especially considering new trends on the Web. In this study, we identified, classified, and analyzed 34 906 scientific papers published in the last eight years to show the originality and relevance of investigating the UX of visually impaired and blind users. We considered new Web trends, focusing on Flat Design and Responsive Web Design. We performed a systematic review and selected 1 015 scientific papers that addressed the importance of UX or other issues relevant to UX. In this literature, we found only five articles addressing issues faced by blind or visually impaired users. None of the studies compared the experiences of users who are blind or visually impaired with those of sighted users. Considering the lack of scientific studies that address the impact of new web trends on UX and studies that examine the importance of UX, we concluded that it is original and relevant to investigate the UX of visually impaired, blind, and sighted users on the Web. From our research, it was possible to identify recurrent subjects of research relevant to UX; among them were UX methods applied to mobile, Approach to Agile UX, Usability techniques UX, and Centered Design to User.