Enhanced device-based 3D object manipulation technique for handheld mobile augmented reality

3D object manipulation is one of the most important tasks for handheld mobile Augmented Reality (AR) towards its practical potential, especially for realworld assembly support. In this context, techniques used to manipulate 3D object is an important research area. Therefore, this study developed an improved device based interaction technique within handheld mobile AR interfaces to solve the large range 3D object rotation problem as well as issues related to 3D object position and orientation deviations in manipulating 3D object. The research firstly enhanced the existing device-based 3D object rotation technique with an innovative control structure that utilizes the handheld mobile device tilting and skewing amplitudes to determine the rotation axes and directions of the 3D object. Whenever the device is tilted or skewed exceeding the threshold values of the amplitudes, the 3D object rotation will start continuously with a pre-defined angular speed per second to prevent over-rotation of the handheld mobile device. This over-rotation is a common occurrence when using the existing technique to perform large-range 3D object rotations. The problem of over-rotation of the handheld mobile device needs to be solved since it causes a 3D object registration error and a 3D object display issue where the 3D object does not appear consistent within the user’s range of view. Secondly, restructuring the existing device-based 3D object manipulation technique was done by separating the degrees of freedom (DOF) of the 3D object translation and rotation to prevent the 3D object position and orientation deviations caused by the DOF integration that utilizes the same control structure for both tasks. Next, an improved device-based interaction technique, with better performance on task completion time for 3D object rotation unilaterally and 3D object manipulation comprehensively within handheld mobile AR interfaces was developed. A pilot test was carried out before other main tests to determine several pre-defined values designed in the control structure of the proposed 3D object rotation technique. A series of 3D object rotation and manipulation tasks was designed and developed as separate experimental tasks to benchmark both the proposed 3D object rotation and manipulation techniques with existing ones on task completion time (s). Two different groups of participants aged 19-24 years old were selected for both experiments, with each group consisting sixteen participants. Each participant had to complete twelve trials, which came to a total 192 trials per experiment for all the participants. Repeated measure analysis was used to analyze the data. The results obtained have statistically proven that the developed 3D object rotation technique markedly outpaced existing technique with significant shorter task completion times of 2.04s shorter on easy tasks and 3.09s shorter on hard tasks after comparing the mean times upon all successful trials. On the other hand, for the failed trials, the 3D object rotation technique was 4.99% more accurate on easy tasks and 1.78% more accurate on hard tasks in comparison to the existing technique. Similar results were also extended to 3D object manipulation tasks with an overall 9.529s significant shorter task completion time of the proposed manipulation technique as compared to the existing technique. Based on the findings, an improved device-based interaction technique has been successfully developed to address the insufficient functionalities of the current technique

HasTEE: Programming Trusted Execution Environments with Haskell

Trusted Execution Environments (TEEs) are hardware-enforced memory isolation units, emerging as a pivotal security solution for security-critical applications. TEEs, like Intel SGX and ARM TrustZone, allow the isolation of confidential code and data within an untrusted host environment, such as the cloud and IoT. Despite strong security guarantees, TEE adoption has been hindered by an awkward programming model. This model requires manual application partitioning and the use of error-prone, memory-unsafe, and potentially information-leaking low-level C/C++ libraries. We address the above with \textit{HasTEE}, a domain-specific language (DSL) embedded in Haskell for programming TEE applications. HasTEE includes a port of the GHC runtime for the Intel-SGX TEE. HasTEE uses Haskell's type system to automatically partition an application and to enforce \textit{Information Flow Control} on confidential data. The DSL, being embedded in Haskell, allows for the usage of higher-order functions, monads, and a restricted set of I/O operations to write any standard Haskell application. Contrary to previous work, HasTEE is lightweight, simple, and is provided as a \emph{simple security library}; thus avoiding any GHC modifications. We show the applicability of HasTEE by implementing case studies on federated learning, an encrypted password wallet, and a differentially-private data clean room.Comment: To appear in Haskell Symposium 202

MUSCLE : automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis:Automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis

Summary: Developing liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses of (bio)chemicals is both time consuming and challenging, largely because of the large number of LC and MS instrument parameters that need to be optimized. This bottleneck significantly impedes our ability to establish new (bio)analytical methods in fields such as pharmacology, metabolomics and pesticide research. We report the development of a multi-platform, user-friendly software tool MUSCLE (multi-platform unbiased optimization of spectrometry via closed-loop experimentation) for the robust and fully automated multi-objective optimization of targeted LC-MS/MS analysis. MUSCLE shortened the analysis times and increased the analytical sensitivities of targeted metabolite analysis, which was demonstrated on two different manufacturer’s LC-MS/MS instruments. Availability and implementation: Available at http://www.muscleproject.org. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Personalizing the web: A tool for empowering end-users to customize the web through browser-side modification

167 p.Web applications delegate to the browser the final rendering of their pages. Thispermits browser-based transcoding (a.k.a. Web Augmentation) that can be ultimately singularized for eachbrowser installation. This creates an opportunity for Web consumers to customize their Web experiences.This vision requires provisioning adequate tooling that makes Web Augmentation affordable to laymen.We consider this a special class of End-User Development, integrating Web Augmentation paradigms.The dominant paradigm in End-User Development is scripting languages through visual languages.This thesis advocates for a Google Chrome browser extension for Web Augmentation. This is carried outthrough WebMakeup, a visual DSL programming tool for end-users to customize their own websites.WebMakeup removes, moves and adds web nodes from different web pages in order to avoid tabswitching, scrolling, the number of clicks and cutting and pasting. Moreover, Web Augmentationextensions has difficulties in finding web elements after a website updating. As a consequence, browserextensions give up working and users might stop using these extensions. This is why two differentlocators have been implemented with the aim of improving web locator robustness

OmniFill: Domain-Agnostic Form Filling Suggestions Using Multi-Faceted Context

Predictive suggestion systems offer contextually-relevant text entry completions. Existing approaches, like autofill, often excel in narrowly-defined domains but fail to generalize to arbitrary workflows. We introduce a conceptual framework to analyze the compound demands of a particular suggestion context, yielding unique opportunities for large language models (LLMs) to infer suggestions for a wide range of domain-agnostic form-filling tasks that were out of reach with prior approaches. We explore these opportunities in OmniFill, a prototype that collects multi-faceted context including browsing and text entry activity to construct an LLM prompt that offers suggestions in situ for arbitrary structured text entry interfaces. Through a user study with 18 participants, we found that OmniFill offered valuable suggestions and we identified four themes that characterize users' behavior and attitudes: an "opportunistic scrapbooking" approach; a trust placed in the system; value in partial success; and a need for visibility into prompt context.Comment: 14 pages, 5 figure

Feature extraction and selection for myoelectric control based on wearable EMG sensors

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly (p < 0.05) from 2% to 56% depending on the evaluated features when using the lower sampling rate, and especially for transradial amputee subjects. Importantly, for these subjects, no number of existing features can be combined to compensate for this loss in higher-frequency content. From these results, we identify two new sets of recommended EMG features (along with a novel feature, L-scale) that provide better performance for these emerging low-sampling rate systems