Planning and decision making are closely interconnected processes that often occur in tandem, influence and informing each other. Planning usually precedes decision making in the chronological sequence, and it can be viewed as a strategy to make decisions. A comprehensive planning or decision strategy can facilitate effective decisions. Thus, understanding and learning human decision-making strategies has drawn intensive attention from the AI community. For example, applying planning algorithms into reinforcement leaning (RL) can simulate the consequence of different actions and select optimal decisions based on learned models, while inverse reinforcement learning (IRL) learns a reward function and policy from expert demonstration and applies them into new scenarios. Most of these methods work based on learning human decision strategies by using modeling of a Markovian decision-making process (MDP). In this paper, we argue that the property of MDP is not fit for human decision-making processes in the real-world and it is insufficient to capture human decision strategies. To tackle this challenge, we propose a new approach to identify the characteristics of human decision-making processes as a decision map, where the decision strategy is defined by the probability distribution of human decisions that are adaptive to the dynamic changes in the environment. The proposed approach was inspired by imitation learning (IL) but with fundamental differences: (a) Instead of aiming to learn an optimal policy based on expert’s demonstrations, we aimed to estimate the distribution of decisions of any group of people. (b) Instead of modeling the environment by an MDP, we used an ambiguity probability model to consider the uncertainty of each decision. (c) The participant trajectory was obtained by categorizing each decision of a participant to a certain cluster based on the commonness in the distribution of decisions. The result shows a feasible way to capture human long-term decision dependency, which provides a complement to the existing machine learning methods for understanding and learning human decision strategies. © 2024 by the authors.

The integration of outdoor smartphone applications with fitness trackers has introduced new opportunities and challenges for user interaction, particularly in mobility-driven activities. While these innovations offer significant benefits, they also pose challenges due to the many factors influencing the user’s quality of experience. Traditional methods of assessing user experiences, such as offline surveys and static questionnaires, often fail to capture the dynamic nature of outdoor activities. This research proposes a novel Quality of Experience (QoE) methodology for mobile applications to enhance the assessment of user experiences in cycling. Focusing on a use case in Blekinge, Sweden, where residents were encouraged to adopt cycling for daily transportation, we extracted land cover data and developed a server-side workflow for bicycle path segmentation. By incorporating dynamic surveys that adapt to users’ real-time experiences, we aim to generate a more accurate and context-aware dataset. This study makes several key contributions: First, it presents a scalable method for bicycle path segmentation; second, it demonstrates the utility and benefits of land cover data extraction; and finally, it evaluates the effectiveness of QoE influence factors through user surveys based on real-world cyclist feedback. This approach is expected to enhance the planning and development of cycling infrastructure by providing urban planners and stakeholders with valuable user insights using adaptable surveys based on route segmentation.

Additive manufacturing encompasses technologies that produce three-dimensional computer-aided design (CAD) models through a layer-by-layer production process. Compared to traditional manufacturing methods, additive manufacturing technologies offer significant advantages in producing intricate components with minimal energy consumption, reduced raw material waste, and shortened production timelines. AM methods based on shielded gas welding have recently piqued the interest of researchers due to their high efficiency and cost-effectiveness in manufacturing critical components. However, one of the most formidable challenges in additive manufacturing methods based on shielded gas welding lies in the irregularity of weld bead height at different points, compromising the precision of components produced using these techniques. In this current research, we aimed to achieve uniform weld heights along the welding path by considering the most influential parameters on weld bead geometry and conducting experimental tests. Input parameters of the process, including nozzle angle, welding speed, wire speed, and voltage, were considered. Simultaneously, image processing and wavelet transform were employed to assess the uniformity of weld bead height. These parameters were applied to produce intricate parts after identifying optimal parameters that yielded the smoothest weld lines. According to the results, the appropriate bead for manufacturing the part was extracted. The results show that the smoothest bead line is achieved in 27 V as the highest level of voltage, at a 90° nozzle position and the maximum wire feed rate. Parts manufactured using this method across different layers exhibited no distortions, and the repeatability of production substantiated the high reliability of this approach for component manufacturing. © 2024 by the authors.

Mobile gaming accounts for more than 50% of global online gaming revenue, surpassing console and browser-based gaming. The success of mobile gaming titles depends on optimizing applications for the specific hardware constraints of mobile devices, such as smaller displays and lower computational power, to maximize battery life. Additionally, these applications must dynamically adapt to the variations in network speed inherent in mobile environments. Ultimately, user engagement and satisfaction are critical, necessitating a favorable comparison to browser and console-based gaming experiences. While Quality of Experience (QoE) subjective evaluations through user surveys are the most reliable method for assessing user perception, various factors, termed influence factors (IFs), can affect user ratings of stimulus quality. This study examines human influence factors in mobile gaming, specifically analyzing the impact of user delight towards displayed content and the effect of gaze tracking. Using Pupil Core eye-tracking hardware, we captured user interactions with mobile devices and measured visual attention. Video stimuli from eight popular games were selected, with resolutions of 720p and 1080p and frame rates of 30 and 60 fps. Our results indicate a statistically significant impact of user delight on the MOS for most video stimuli across all games. Additionally, a trend favoring higher frame rates over screen resolution emerged in user ratings. These findings underscore the significance of optimizing mobile gaming experiences by incorporating models that estimate human influence factors to enhance user satisfaction and engagement.

The advancements of high resolution display technologies support applications ranging from mobile multimedia streaming to automotive cockpits and infotainment systems. Given the limited transmission and processing resources in a mobile communication context, a large number of upscaling methods for restoring high-resolution visual content from low-resolution representations have been proposed and assessed from a theoretical point of view. This paper, therefore, deals with the practical implementation of state-of-the-art upscaling methods on development boards. The proposed tool chain and solutions may be applied as a general framework for the implementation of current and future upscaling methods on resource limited devices. 

Today, we capture and store images in a way that has never been possible. However, huge numbers of degraded and blurred images are captured unintentionally or by mistake. In this paper, we propose a geometrical hypothesis stating that blurring occurs by shifting or scaling the depth of field (DOF). The validity of the hypothesis is proved by an independent method based on depth estimation from a single image. The image depth is modeled regarding its edges to extract amplitude comparison ratios between the generated blurred images and the sharp/blurred images. Blurred images are generated by a stepwise variation in the standard deviation of the Gaussian filter estimate in the improved model. This process acts as virtual image recording used to mimic the recording of several image instances. A historical documentation database is used to validate the hypothesis and classify sharp images from blurred ones and different blur types. The experimental results show that distinguishing unintentionally blurred images from non-blurred ones by a comparison of their depth of field is applicable.

While current video quality assessment research predominantly revolves around resolutions of 4 K and beyond, targeted at ultra high-definition (UHD) displays, effective video quality for mobile video streaming remains primarily within the range of 480 p to 1080 p. In this study, we conducted a comparative analysis of the quality of experience (QoE) for widely implemented video codecs on mobile devices, specifically Advanced Video Coding (AVC), its successor High-Efficiency Video Coding (HEVC), and Google’s VP9. Our choice of 720 p video sequences from a newly developed database, all with identical bitrates, aimed to maintain a manageable subjective assessment duration, capped at 35–40 min. To mimic real-time network conditions, we generated stimuli by streaming original video clips over a controlled emulated setup, subjecting them to eight different packet-loss scenarios. We evaluated the quality and structural similarity of the distorted video clips using objective metrics, including the Video Quality Metric (VQM), Peak Signal-to-Noise Ratio (PSNR), Video Multi-Method Assessment Fusion (VMAF), and Multi-Scale Structural Similarity Index (MS-SSIM). Subsequently, we collected subjective ratings through a custom mobile application developed for Android devices. Our findings revealed that VMAF accurately represented the degradation in video quality compared to other metrics. Moreover, in most cases, HEVC exhibited an advantage over both AVC and VP9 under low packet-loss scenarios. However, it is noteworthy that in our test cases, AVC outperformed HEVC and VP9 in scenarios with high packet loss, based on both subjective and objective assessments. Our observations further indicate that user preferences for the presented content contributed to video quality ratings, emphasizing the importance of additional factors that influence the perceived video quality of end users. © 2024 by the authors.

Generating precise saliency maps from eye tracker fixation points is a challenging task influenced by environmen-tal factors and the choice of evaluation metrics. This paper presents a novel, sustainable, scale-invariant, and sampling-independent method for converting fixation points into saliency maps. Leveraging the inherent predictability of human behavior, the proposed method ensures the highest compatibility with the chosen evaluation metric. Moreover, it introduces a mechanism to calculate the maximum achievable similarity score for each conversion. In addition, it offers crucial insights for both saliency map evaluation and the training of machine learning systems dedicated to saliency map generation. Experimental results demonstrate the method's efficacy in producing saliency maps that align seamlessly with diverse evaluation metrics, showcasing its adaptability and predictive capabilities. This approach con-tributes not only to the refinement of saliency map generation but also to the broader understanding of the intricacies involved in converting eye tracker data into meaningful ground truths. © 2024 IEEE.

This paper studies a header syntax repair approach for quality enhancement of digital video broadcasting (DMB) videos. A sensitivity analysis of the impact of transmission errors in different parts of the header of the underlying MPEG-2 transport stream packets on DMB video quality is provided. On this basis, a header syntax repair approach is presented using header masks and bounded minimum distance decoding of the headers of the received DMB packets. Objective and subjective quality assessment show a tremendous improvement in the quality of the header syntax repaired DMB video clips. The concepts of the header syntax repair approach proposed for packets of DMB video streams may also be adapted to the syntax of packet formats of other digital video standards. © 2022 IEEE.

People around the globe are immensely trying to connect using light as a carrier due to its low power consumption and high data transfer rates. Sound and microwaves are examples of other carriers that can be used, although they are not nearly as efficient as light. Using light under the water, refractive index is an important parameter in optical communication. Lights propagation quality depends on the temperature, pressure, and salinity. The aim of this work is to show a new approach to simulating the underwater environment, including depth as one of the parameters. In this paper, the proposed model examines the ultraviolet wavelengths between 200–400 nm wider than previous studies in light traveling. We verify the usefulness of our model by conducting a pilot study in a virtual environment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.