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The mobile and wearable nature of smartwatches poses challenges in evaluating their usability. This paper presents a study employing customized heuristic evaluation and use of the system usability scale (SUS) on four smartwatches, along with their mobile applications. A total of 11 heuristics were developed and validated by experts by combining Nielsen’s heuristic and Motti and Caines’ heuristics. In this study, 20 participants used the watches and participated in the SUS survey. A total of 307 usability issues were reported by the evaluators. The results of this study show that the Galaxy Watch 5 scored highest in terms of efficiency, ease of use, features, and battery life compared to the other three smartwatches and has fewer usability issues. The results indicate that ease of use, features, and flexibility are important usability attributes for future smartwatches. The Galaxy Watch 5 received the highest SUS score of 87.375. Both evaluation methods showed no significant differences in results, and customized heuristics were found to be useful for smartwatch evaluation.

User interface is an essential element of an information system from the user perspective. The use of text in the user interface presents a challenge to some users, such as illiterate, elderly, non-local language speakers, or users with any disabilities. Numerous studies have proposed text-free user interfaces based on non-textual aids such as pictograms, audio, video, or specialized hardware. Such rich elements in the user interface have implications for an information system’s storage and processing requirements. We propose using concept hierarchies to present a dynamic interface to a user based upon user characteristics. A drill-down approach may present more specific interface elements to a user, and a roll-up scheme can identify more generalized interface elements. The proposed scheme can be used to manage internal data of large user interfaces with a rich set of features more effectively as it is computationally inexpensive than sequential processing techniques. We also explore various physical and psychological characteristics of elements and evaluate user preference for them. The results of a survey of 200 respondents show that users consider the use of various colors, alignments, mental models, and look & feel to be essential criteria in interface design. We also conclude that size of interface elements and the use of characters for expressing emotions are significantly important characteristics in interface design. The future work will apply the proposed scheme to a full spectrum of applications ranging from enterprise systems to mobile applications.

Social media has become common as a means to convey opinions and express the extent of satisfaction and dissatisfaction with a service or product. In the Kingdom of Saudi Arabia specifically, most social media users share positive and negative opinions about a service or product, especially regarding communication services, which is one of the most important services for citizens who use it to communicate with the world. This research aimed to analyse and measure user satisfaction with the services provided by the Saudi Telecom Company (STC), Mobily, and Zain. This type of sentiment analysis is an important measure and is used to make important business decisions to succeed in increasing customer loyalty and satisfaction. In this study, the authors developed advanced methods based on deep learning (DL) to analyse and reveal the percentage of customer satisfaction using the publicly available dataset AraCust. Several DL models have been utilised in this study, including long short-term memory (LSTM), gated recurrent unit (GRU), and BiLSTM, on the AraCust dataset. The LSTM model achieved the highest performance in text classification, demonstrating a 98.04% training accuracy and a 97.03% test score. The study addressed the biggest challenge that telecommunications companies face: that the company’s services influence customers’ decisions due to their dissatisfaction with the provided services.

Abstract Social media postings are increasingly being used in modern days disaster management. Along with the textual information, the contexts and cues inherent in the images posted on social media play an important role in identifying appropriate emergency responses to a particular disaster. In this paper, we proposed a disaster taxonomy of emergency response and used the same taxonomy with an emergency response pipeline together with deep-learning-based image classification and object identification algorithms to automate the emergency response decision-making process. We used the card sorting method to validate the completeness and correctness of the disaster taxonomy. We also used VGG-16 and You Only Look Once (YOLO) algorithms to analyze disaster-related images and identify disaster types and relevant cues (such as objects that appeared in those images). Furthermore, using decision tables and applied analytic hierarchy processes (AHP), we aligned the intermediate outputs to map a disaster-related image into the disaster taxonomy and determine an appropriate type of emergency response for a given disaster. The proposed approach has been validated using Earthquake, Hurricane, and Typhoon as use cases. The results show that 96% of images were categorized correctly on disaster taxonomy using YOLOv4. The accuracy can be further improved using an incremental training approach. Due to the use of cloud-based deep learning algorithms in image analysis, our approach can potentially be useful to real-time crisis management. The algorithms along with the proposed emergency response pipeline can be further enhanced with other spatiotemporal features extracted from multimedia information posted on social media.

Enterprise Resource Planning (ERP) is a frequently used system among organizations to automate their workflows, and companies’ performances are highly dependent on the ERP system. The usability issues of ERP systems may cause performance degradation, resulting in the company’s loss in terms of cost. Previously, several studies reported many usability problems of ERP systems. It can be helpful for the developers and designers of ERP systems to use design recommendations as a quick reference to avoid recurrent usability problems of ERP systems. Currently, this area lacks effective consolidation of the previously reported usability problems data. This paper presents a unique approach to developing a precise checklist of ERP usability problems using the topic modeling technique. Our analysis found six different usability problem-related topics that can be generalized for various ERP systems. We have successfully validated our checklist in three different usability studies of ERP systems. The most found usability problems are “difficulty searching and finding desired item/information in interface and error handling” and “missing data and information”. The outcome of our paper is the provision of recommendations to avoid the usability problems of ERP systems and help organizations efficiently prevent frequent issues during the development and maintenance of ERP systems.

The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users’ location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users’ tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities.

Natural disasters, like pandemics and earthquakes, are some of the main causes of distress and casualties. Governmental crisis management processes are crucial when dealing with these types of problems. Social media platforms are among the main sources of information regarding current events and public opinion. So, they have been used extensively to aid disaster detection and prevention efforts. Therefore, there is always a need for better automatic systems that can detect and classify disaster data of social media. In this work, we propose enhanced Arabic disaster data classification models. The suggested models utilize domain adaptation to provide state-of-the-art accuracy. We used a standard dataset of Arabic disaster data collected from Twitter for testing the proposed models. Experimental results show that the provided models significantly outperform the previous state-of-the-art results.