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This research investigates whether the interaction methods of XR headsets can be improved by using haptic feedback. As a first and most common technique, indirect interactions are considered. Indirect interactions correspond to manipulations of virtual objects from a virtual distance using pre-defined hand gestures. As a second interaction technique, direct interaction (namely DIM) has been implemented where the user manipulates objects by virtually touching these with their hands. A third interaction method extends the previous one with haptic feedback (namely HEDIM). These 3 methods are compared with each other based on objective and subjective user tests, also taking into account financial considerations. This research concludes that the DIM improves upon the standard indirect method. Additionally, it has been observed that haptic feedback could enhance the DIM in specific situations. Nevertheless, when considering the current financial cost, our subjects were not convinced of the small improvements haptic feedback brings.

The A* algorithm is commonly used to solve NP-hard combinatorial optimization problems. When provided with a completely informed heuristic function, A* solves many NP-hard minimum-cost path problems in time polynomial in the branching factor and the number of edges in a minimum-cost path. Thus, approximating their completely informed heuristic functions with high precision is NP-hard. We therefore examine recent publications that propose the use of neural networks for this purpose. We support our claim that these approaches do not scale to large instance sizes both theoretically and experimentally. Our first experimental results for three representative NP-hard minimum-cost path problems suggest that using neural networks to approximate completely informed heuristic functions with high precision might result in network sizes that scale exponentially in the instance sizes. The research community might thus benefit from investigating other ways of integrating heuristic search with machine learning.

Presenting dynamic scenes without incurring motion artifacts visible to observers requires sustained effort from the display industry. A tool that predicts motion artifacts and simulates artifact elimination through optimizing the display configuration is highly desired to guide the design and manufacture of modern displays. Despite the popular demands, there is no such tool available in the market. In this study, we deliver an interactive toolkit, Binocular Perceived Motion Artifact Predictor (BiPMAP), as an executable file with GPU acceleration. BiPMAP accounts for an extensive collection of user-defined parameters and directly visualizes a variety of motion artifacts by presenting the perceived continuous and sampled moving stimuli side-by-side. For accurate artifact predictions, BiPMAP utilizes a novel model of the human contrast sensitivity function to effectively imitate the frequency modulation of the human visual system. In addition, BiPMAP is capable of deriving various in-plane motion artifacts for 2D displays and depth distortion in 3D stereoscopic displays.

Missing data can lead to inefficiencies and biases in analyses, in particular when data are missing not at random (MNAR). It is thus vital to understand and correctly identify the missing data mechanism. Recovering missing values through a follow up sample allows researchers to conduct hypothesis tests for MNAR, which are not possible when using only the original incomplete data. Investigating how properties of these tests are affected by the follow up sample design is little explored in the literature. Our results provide comprehensive insight into the properties of one such test, based on the commonly used selection model framework. We determine conditions for recovery samples that allow the test to be applied appropriately and effectively, i.e. with known Type I error rates and optimized with respect to power. We thus provide an integrated framework for testing for the presence of MNAR and designing follow up samples in an efficient cost-effective way. The performance of our methodology is evaluated through simulation studies as well as on a real data sample.

Ultra-reliable and low-latency communication (URLLC) is one of three major application scenarios of the 5G new radio, which has strict latency and reliability requirements. Contention-based grant-free (GF) access protocols, such as Reactive, K-Repetition, and Proactive, have been proposed for uplink URLLC service. In the GF access, user equipment (UE) resends packet immediately after an unsuccessful transmission such that the latency requirement can be satisfied. Taking Reactive as an example, this paper studies the impact of 1- persistent retransmission (1-pR) on the distribution of user-plane delay. We define the number of UEs that try to send packets in each mini-slot as attempt rate. We show that the 1-pR makes the attempt rate seen by the packet in retransmission larger than that seen by the packet in the first transmission. As a result, the successful probability of retransmission is lower than that of the first transmission. Based on this observation, we derive the distribution of user-plane delay, which also takes into account the delay incurred by queueing process. We demonstrate that whether to include the effect of 1-pR and queueing process in the analysis would have a significant impact on the prediction accuracy of delay distribution.

Detecting Bug Inducing Commit (BIC) or Just in Time (JIT) defect prediction using Machine Learning (ML) based models requires tabulated feature values extracted from the source code or historical maintenance data of a software system. Existing studies have utilized meta-data from source code repositories (we named them GitHub Statistics or GS), n-gram-based source code text processing, and developer's information (e.g., the experience of a developer) as the feature values in ML-based bug detection models. However, these feature values do not represent the source code syntax styles or patterns that a developer might prefer over available valid alternatives provided by programming languages. This investigation proposed a method to extract features from its source code syntax patterns to represent software commits and investigate whether they are helpful in detecting bug proneness in software systems. We utilize six manually and two automatically labeled datasets from eight open-source software projects written in Java, C++, and Python programming languages. Our datasets contain 642 manually labeled and 4,014 automatically labeled buggy and non-buggy commits from six and two subject systems, respectively. The subject systems contain a diverse number of revisions, and they are from various application domains. Our investigation shows the inclusion of the proposed features increases the performance of detecting buggy and non-buggy software commits using five different machine learning classification models. Our proposed features also perform better in detecting buggy commits using the Deep Belief Network generated features and classification model. This investigation also implemented a state-of-the-art tool to compare the explainability of predicted buggy commits using our proposed and traditional features and found that our proposed features provide better reasoning about buggy.....

This paper considers the problem of kernel regression and classification with possibly unobservable response variables in the data, where the mechanism that causes the absence of information is unknown and can depend on both predictors and the response variables. Our proposed approach involves two steps: In the first step, we construct a family of models (possibly infinite dimensional) indexed by the unknown parameter of the missing probability mechanism. In the second step, a search is carried out to find the empirically optimal member of an appropriate cover (or subclass) of the underlying family in the sense of minimizing the mean squared prediction error. The main focus of the paper is to look into the theoretical properties of these estimators. The issue of identifiability is also addressed. Our methods use a data-splitting approach which is quite easy to implement. We also derive exponential bounds on the performance of the resulting estimators in terms of their deviations from the true regression curve in general Lp norms, where we also allow the size of the cover or subclass to diverge as the sample size n increases. These bounds immediately yield various strong convergence results for the proposed estimators. As an application of our findings, we consider the problem of statistical classification based on the proposed regression estimators and also look into their rates of convergence under different settings. Although this work is mainly stated for kernel-type estimators, they can also be extended to other popular local-averaging methods such as nearest-neighbor estimators, and histogram estimators.

As machine translation (MT) metrics improve their correlation with human judgement every year, it is crucial to understand the limitations of such metrics at the segment level. Specifically, it is important to investigate metric behaviour when facing accuracy errors in MT because these can have dangerous consequences in certain contexts (e.g., legal, medical). We curate ACES, a translation accuracy challenge set, consisting of 68 phenomena ranging from simple perturbations at the word/character level to more complex errors based on discourse and real-world knowledge. We use ACES to evaluate a wide range of MT metrics including the submissions to the WMT 2022 metrics shared task and perform several analyses leading to general recommendations for metric developers. We recommend: a) combining metrics with different strengths, b) developing metrics that give more weight to the source and less to surface-level overlap with the reference and c) explicitly modelling additional language-specific information beyond what is available via multilingual embeddings.

Blockchain is an emerging decentralized data collection, sharing and storage technology, which have provided abundant transparent, secure, tamper-proof, secure and robust ledger services for various real-world use cases. Recent years have witnessed notable developments of blockchain technology itself as well as blockchain-adopting applications. Most existing surveys limit the scopes on several particular issues of blockchain or applications, which are hard to depict the general picture of current giant blockchain ecosystem. In this paper, we investigate recent advances of both blockchain technology and its most active research topics in real-world applications. We first review the recent developments of consensus mechanisms and storage mechanisms in general blockchain systems. Then extensive literature is conducted on blockchain enabled IoT, edge computing, federated learning and several emerging applications including healthcare, COVID-19 pandemic, social network and supply chain, where detailed specific research topics are discussed in each. Finally, we discuss the future directions, challenges and opportunities in both academia and industry.

We describe ACE0, a lightweight platform for evaluating the suitability and viability of AI methods for behaviour discovery in multiagent simulations. Specifically, ACE0 was designed to explore AI methods for multi-agent simulations used in operations research studies related to new technologies such as autonomous aircraft. Simulation environments used in production are often high-fidelity, complex, require significant domain knowledge and as a result have high R&D costs. Minimal and lightweight simulation environments can help researchers and engineers evaluate the viability of new AI technologies for behaviour discovery in a more agile and potentially cost effective manner. In this paper we describe the motivation for the development of ACE0.We provide a technical overview of the system architecture, describe a case study of behaviour discovery in the aerospace domain, and provide a qualitative evaluation of the system. The evaluation includes a brief description of collaborative research projects with academic partners, exploring different AI behaviour discovery methods.