Reviewerly/exHarmony huggingface dataset:
Co-phase state detection for segmented mirrors by dual-wavelength optical vortex phase-shifting interferometry

The application of large-aperture telescopes requires the support of co-phase measurement techniques for segmented mirrors. This paper proposes a novel method to detect the co-phase state of segmented mirrors by applying a dual-wavelength phase-shifting interferometer based on optical vortex. Theory and experiments indicate that the wrapped phase map edges obtained by phase-shifting interference of the vortex beam are distributed in the form of a Fermat spiral. The piston error of the segmented mirrors corresponds to the rotation of the standard Fermat spiral center. In contrast, the tip/tilt error corresponds to the alteration of the center position of the deformed Fermat spiral. The rotation angle and the center position of the spiral are obtained by curve fitting, and the co-phase errors can be inversely solved. The experiments achieved an accuracy of approximately 4.04 nm in the piston and 0.16″ in the tip/tilt. The method avoids using complex lens arrays and devices, has an extended measurement range, high accuracy, and allows the co-phase errors between all sub-mirrors to be obtained in real-time. This study provides a novel and general method for detecting co-phase errors in a segmented primary mirror.
IDPG: An Instance-Dependent Prompt Generation Method

Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage.It freezes the pre-trained language model and only optimizes a few taskspecific prompts.In this paper, we propose a conditional prompt generation method to generate prompts for each input instance, referred to as the Instance-Dependent Prompt Generation (IDPG).Unlike traditional prompt tuning methods that use a fixed prompt, IDPG introduces a lightweight and trainable component to generate prompts based on each input sentence.Extensive experiments on ten natural language understanding (NLU) tasks show that the proposed strategy consistently outperforms various prompt tuning baselines and is on par with other efficient transfer learning methods such as Compacter while tuning far fewer model parameters.
Clean the Ocean: An Immersive VR Experience Proposing New Modifications to Go-Go and WiM Techniques

In this paper we present our solution to the 2022 3DUI Contest challenge. We aim to provide an immersive VR experience to increase player&#x0027;s awareness of trash pollution in the ocean while improving the current interaction techniques in virtual environments. To achieve these objectives, we adapted two classic interaction techniques, Go-Go and World in Miniature (WiM), to provide an engaging minigame in which the user collects the trash in the ocean. To improve the precision and address occlusion issues in the traditional Go-Go technique we propose ReX Go-Go. We also propose an adaptation to WiM, referred to as Rabbit-Out-of-the-Hat to allow an exocentric interaction for easier object retrieval interaction.
Augmented Reality in Supporting Healthcare and Nursing Independent Learning

New advances in technology have brought challenges and opportunities for education and instructional methods. Compared with traditional education, the increased use of technology-enhanced blended learning in healthcare and nursing education requires students to take more responsibility for their learning. The use of advanced technology has resulted in independent learning skills becoming increasingly important. Many studies have reported a positive correlation between independent learning and success rates in an e-learning environment. This paper focuses on the potential contribution of augmented reality, which superimposes layers of virtual content on real physical objects. The paper initially presents a narrative literature review to identify augmented reality's strengths and challenges in facilitating independent learning and highlights several potential approaches for utilizing augmented reality in nursing education. However, it also reveals a lack of studies integrating augmented reality and independent learning theories such as self-regulated learning. The paper then addresses this gap by proposing a new learning approach to support independent learning.
Membership Inference via Backdooring

Recently issued data privacy regulations like GDPR (General Data Protection Regulation) grant individuals the right to be forgotten. In the context of machine learning, this requires a model to forget about a training data sample if requested by the data owner (i.e., machine unlearning). As an essential step prior to machine unlearning, it is still a challenge for a data owner to tell whether or not her data have been used by an unauthorized party to train a machine learning model. Membership inference is a recently emerging technique to identify whether a data sample was used to train a target model, and seems to be a promising solution to this challenge. However, straightforward adoption of existing membership inference approaches fails to address the challenge effectively due to being originally designed for attacking membership privacy and suffering from several severe limitations such as low inference accuracy on well-generalized models. In this paper, we propose a novel membership inference approach inspired by the backdoor technology to address the said challenge. Specifically, our approach of Membership Inference via Backdooring (MIB) leverages the key observation that a backdoored model behaves very differently from a clean model when predicting on deliberately marked samples created by a data owner. Appealingly, MIB requires data owners' marking a small number of samples for membership inference and only black-box access to the target model, with theoretical guarantees for inference results. We perform extensive experiments on various datasets and deep neural network architectures, and the results validate the efficacy of our approach, e.g., marking only 0.1% of the training dataset is practically sufficient for effective membership inference.
Visualizing Urban Accessibility: Investigating Multi-Stakeholder Perspectives through a Map-based Design Probe Study

Urban accessibility assessments are challenging: they involve varied stakeholders across decision-making contexts while serving a diverse population of people with disabilities. To better support urban accessibility assessment using data visualizations, we conducted a three-part interview study with 25 participants across five stakeholder groups using map visualization probes. We present a multi-stakeholder analysis of visualization needs and sensemaking processes to explore how interactive visualizations can support stakeholder decision making. In particular, we elaborate how stakeholders’ varying levels of familiarity with accessibility, geospatial analysis, and specific geographic locations influences their sensemaking needs. We then contribute 10 design considerations for geovisual analytic tools for urban accessibility communication, planning, policymaking, and advocacy.
T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification

Time series classification is a popular and important topic in machine learning, and it suffers from the class imbalance problem in many real-world applications. In this paper, to address the class imbalance problem, we propose a novel and practical oversampling method named T-SMOTE, which can make full use of the temporal information of time-series data. In particular, for each sample of minority class, T-SMOTE generates multiple samples that are close to class border. Then, based on those samples near class border, T-SMOTE synthesizes more samples. Finally, a weighted sampling method is called on both generated samples near class border and synthetic samples. Extensive experiments on a diverse set of both univariate and multivariate time-series datasets demonstrate that T-SMOTE consistently outperforms the current state-of-the-art methods on imbalanced time series classification. More encouragingly, our empirical evaluations show that T-SMOTE performs better in the scenario of early prediction, an important application scenario in industry, which indicates that T-SMOTE could bring benefits in practice.
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation

Self-Supervised Learning (SSL) models have been successfully applied in various deep learning-based speech tasks, particularly those with a limited amount of data. However, the quality of SSL representations depends highly on the relatedness between the SSL training domain(s) and the target data domain. On the contrary, spectral feature (SF) extractors such as log Mel-filterbanks are hand-crafted non-learnable components, and could be more robust to domain shifts. The present work examines the assumption that combining non-learnable SF extractors to SSL models is an effective approach to low resource speech tasks. We propose a learnable and interpretable framework to combine SF and SSL representations. The proposed framework outperforms significantly both baseline and SSL models on Automatic Speech Recognition (ASR) and Speech Translation (ST) tasks on three low resource datasets. We additionally design a mixture of experts based combination model. This last model reveals that the relative contribution of SSL models over conventional SF extractors is very small in case of domain mismatch between SSL training set and the target language data.
Guided Filter Network for Semantic Image Segmentation

The existing publicly available datasets with pixel-level labels contain limited categories, and it is difficult to generalize to the real world containing thousands of categories. In this paper, we propose an approach to generate object masks with detailed pixel-level structures/boundaries automatically to enable semantic image segmentation of thousands of targets in the real world without manually labelling. A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from an existed dataset, and such GFN then transfers the learned segmentation knowledge to generate initial coarse object masks for the target images. These coarse object masks are treated as pseudo labels to self-optimize the GFN iteratively in the target images. Our experiments on six image sets have demonstrated that our proposed approach can generate object masks with detailed pixel-level structures/boundaries, whose quality is comparable to the manually-labelled ones. Our proposed approach also achieves better performance on semantic image segmentation than most existing weakly-supervised, semi-supervised, and domain adaptation approaches under the same experimental conditions.
Untangling parasitic reflection in phase measuring deflectometry by multi-frequency phase-shifting

Phase measuring deflectometry (PMD) is a technique that reconstructs the three-dimensional (3D) profiles of specular surfaces. When the object under test is transparent, its bottom surface creates a parasitic reflection that superimposes with the primary reflection created by the top surface. The superimposed reflections cause phase error in decoding of the fringe patterns and reduce the reconstruction accuracy. To accurately reconstruct the 3D profile of transparent objects, the superimposed reflections must first be untangled. In this paper, a multi-frequency phase-shifting approach is proposed to untangle the superimposed signals. Based on the principle of phase-shifting, a mathematical model is developed. The unknown phase angles in the mathematical model are solved by an optimization technique with input data obtained from fringe patterns at different spatial frequencies. A procedure is also developed to obtain the set of initial conditions for the optimization process. Both simulation and experiments were conducted to verify the proposed method. The results show that the proposed method can accurately untangle the phase angles corresponding to primary and parasitic reflections. The surface reconstruction result was compared to a reference measurement given by an interferometer, and a root-mean-square error of 32.95 nm was recorded. The accuracy achieved by the proposed method is compatible with another existing multi-frequency approach, while using roughly eight times fewer images. With the proposed method, better time efficiency can be achieved, and the computer's memory requirement can be lowered.
Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review

Systems using passive infrared sensors with a low resolution were recently proposed to answer the dilemma effectiveness-ethical considerations for human fall detection by Information and Communication Technologies (ICTs) in older adults. How effective is this type of system? We performed a systematic review to identify studies that investigated the metrological qualities of passive infrared sensors with a maximum resolution of 16×16 pixels to identify falls. The search was conducted on PubMed, ScienceDirect, SpringerLink, IEEE Xplore Digital Library, and MDPI until November 26-28, 2020. We focused on studies testing only these types of sensor. Thirteen articles were "conference papers", five were "original articles" and one was a found in arXiv.org (an open access repository of scientific research). Since four authors "duplicated" their study in two different journals, our review finally analyzed 15 studies. The studies were very heterogeneous with regard to experimental procedures and detection methods, which made it difficult to draw formal conclusions. All studies tested their systems in controlled conditions, mostly in empty rooms. Except for two studies, the overall performance reported for the detection of falls exceeded 85-90% of accuracy, precision, sensitivity or specificity. Systems using two or more sensors and particular detection methods (eg, 3D CNN, CNN with 10-fold cross-validation, LSTM with CNN, LSTM and Voting algorithms) seemed to give the highest levels of performance (> 90%). Future studies should test more this type of system in real-life conditions.
Detection of Faulty Nodes in Distributed Environment using Machine Learning

Faulty nodes are expected to have a significant effect on the performance of Big Data systems. The cause of discharges, on the other hand, is a complex issue. The previous work has mostly focused on identifying Stragglers, optimizing the quality of programming, and root cause investigation. These methods can't offer useful information unless they can assist users automate their services. We are developing a framework that determines when there will be stragglers and must identify gaps to prevent those circumstances. This research proposed a dynamic log collection of heterogeneous virtual machine data and detect the faulty node based on execution performance using various parameters. We introduce multi-task learning mechanisms in this paper, which exchange knowledge between the different versions of nodes. The system first collects log data from available data nodes and applies an unsupervised machine learning technique to detect the straggler. Moreover, once a straggler has been detected by the learning algorithm, it tries mitigating such nodes instead of eliminating those nodes. The major benefit of this research is that the system can handle massive data with minimum data nodes or even system efficiently work with already heated nodes in a distributed environment. Our compositions absorb the shared context in our data, enhancing sweeping statement efficiency on minimal data, unlike naive inter learning frameworks.
Improved Correlation Power Analysis on Bitslice Block Ciphers

Bitslice block ciphers have the advantage of allowing parallel computation using bitwise logical operations, and Boolean masking can be applied efficiently.Thus, various bitslice block ciphers, such as Robin, Fantomas, RECTANGLE, RoadRunneR, PRIDE, and CRAFT, have been proposed previously.Additionally, a bitslice implementation for AES, National Institute of Standards and Technology (NIST) standard block cipher, has been proposed.These ciphers construct an S-Box using only bitwise logical operators.They perform operations by storing the i-th bits of each S-Box input/output value in one register, i.e., they have a feature that each bit of an S-Box output is stored in a different register.Because of this feature, in correlation power analysis (CPA) for bitslice block ciphers, a single-bit of the S-Box output should be selected as an intermediate value.Moreover, depending on which bit is selected as the intermediate value, there are differences in analysis performance.Consequently, we propose an algorithm that predicts the CPA performance of each single-bit and we describe the theoretical basis of this algorithm.The effectiveness of the proposed algorithm is verified experimentally by comparing actual CPA results and predicted results on various bitslice block ciphers.
Optical injection locking based local oscillator regeneration for continuous variable quantum key distribution

We develop an optical injection locking (OIL) based local oscillator (LO) regeneration for continuous variable quantum key distribution (CVQKD) by sending a weak polarization multiplexed pilot carrier from the transmitter. The OIL at the receiver has superior performance in terms of minimum input power and noise level at offset frequencies to the erbium-doped fiber amplifier (EDFA)-based scheme. The weak pilot carrier is recovered both in power and phase via the OIL while incurring little excess noise to the CVQKD system. The phase-locked LO enables heterodyne detection of a Gaussian modulated quantum signal with a simple data-aided phase recovery without pilot tone. The obtained parameters are compatible with a raw key rate of 0.83 Mbit/s in the asymptotic regime over a 22-km fiber transmission. The technique is expected to be used in more phase-sensitive quantum optical applications.
Creating a Knowledge Graph for Ireland’s Lost History: Knowledge Engineering and Curation in the Beyond 2022 Project

The Beyond 2022 project aims to create a virtual archive by digitally reconstructing and digitizing historical records lost in a catastrophic fire which consumed items in the Public Record Office of Ireland in 1922. The project is developing a knowledge graph (KG) to facilitate information retrieval and discovery over the reconstructed items. The project decided to adopt Semantic Web technologies to support its distributed KG and reasoning. In this article, we present our approach to KG generation and management. We elaborate on how we help historians contribute to the KG (via a suite of spreadsheets) and its ontology. We furthermore demonstrate how we use named graphs to store different versions of factoids and their provenance information and how these are serviced in two different endpoints. Modeling data in this manner allows us to acknowledge that history is, to some extent, subjective and different perspectives can exist in parallel. The construction of the KG is driven by competency questions elicited from subject matter experts within the consortium. We avail of CIDOC-CRM as our KG’s foundation, though we needed to extend this ontology with various qualifiers (types) and relations to support the competency questions. We illustrate how one can explore the KG to gain insights and answer questions. We conclude that CIDOC-CRM provides an adequate, albeit complex, foundation for the KG and that named graphs and Linked Data principles are a suitable mechanism to manage sets of factoids and their provenance.
A Dynamic Multi-Protocol Entanglement Distribution Quantum Network

We implement a six-user quantum communication network utilising a quantum-enabled ROADM for flexible and on-demand allocation of entanglement across different users. This allows dynamic networking for multiple quantum protocols.
Pride: Prioritizing Documentation Effort Based on a PageRank-Like Algorithm and Simple Filtering Rules

Code documentation can be helpful in many software quality assurance tasks. However, due to resource constraints (e.g., time, human resources, and budget), programmers often cannot document their work completely and timely. In the literature, two approaches (one is supervised and the other is unsupervised) have been proposed to prioritize documentation effort to ensure the most important classes to be documented first. However, both of them contain several limitations. The supervised approach overly relies on a difficult-to-obtain labeled data set and has high computation cost. The unsupervised one depends on a graph representation of the software structure, which is inaccurate since it neglects many important couplings between classes. In this paper, we propose an improved approach, named Pride, to prioritize documentation effort. First, Pride uses a weighted directed class coupling network to precisely describe classes and their couplings. Second, we propose a PageRank-like algorithm to quantify the importance of classes in the whole class coupling network. Third, we use a set of software metrics to quantify source code complexity and further propose a simple but easy-to-operate filtering rule. Fourth, we sort all the classes according to their importance in descending order and use the filtering rule to filter out unimportant classes. Finally, a threshold <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is utilized, and the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> % ranked classes are the identified important classes to be documented first. Empirical results on a set of nine software systems show that, according to the average ranking of the Friedman test, Pride is superior to the existing approaches in the whole data set.
MAFFNet: real-time multi-level attention feature fusion network with RGB-D semantic segmentation for autonomous driving

Compared with RGB semantic segmentation, RGB-D semantic segmentation can combine the geometric depth information to effectively improve the segmentation accuracy. Considering the application of RGB-D semantic segmentation in autonomous driving, we design a real-time semantic segmentation network, that is, MAFFNet, which can effectively extract depth features and combine the complementary information in RGB and depth. We also design a multi-level attention feature fusion module that can excavate the available context information of RGB and depth features. At the same time, its inference speed can also meet the demands of autonomous driving. Experiments show that our network achieves excellent performance of 74.4% mIoU and an inference speed of 15.9 Hz at a full resolution of 2048×1024 on the cityscapes dataset. Using multi-source learning, we mixed the cityscapes and lost and found as the multi-dataset. Our network is also superior to previous algorithms in using the multi-dataset to detect small obstacles outside the road.