Reviewerly/exHarmony huggingface dataset:
Computational vector fiducial for deflectometry system alignment

One of deflectometry’s cardinal strengths is its ability to measure highly dynamically sloped optics without needing physical null references. Accurate surface measurements using deflectometry, however, require precise calibration processes. In this Letter, we introduce an alignment technique using a computational fiducial to align a deflectometry system without additional hardware equipment (i.e., algorithmic innovation). Using the ray tracing program, we build relationships between the plane of the screen and detector and algorithmically generate a fiducial pattern for the deflectometry configuration. Since the fiducial pattern is based on ideal system geometry, misalignment of the unit under test with its target position causes a discrepancy between the actual image on the camera detector and the ideal fiducial image. We leverage <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>G</mml:mi> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>C</mml:mi> </mml:math> vector polynomials to quantify misalignment and estimate the alignment status through a reverse optimization method. Simulation and experimental results demonstrate that the proposed algorithm can align the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>195</mml:mn> <mml:mspace width="thinmathspace" /> <mml:mspace width="thinmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mn>80</mml:mn> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> of a rectangular aperture freeform optic within 10 µm of peak-to-valley accuracy. The computational fiducial-based alignment algorithm is simple to apply and can be an essential procedure for conventional methods of deflectometry system alignment.
MultiScale Approach for Two-Dimensional Diffeomorphic Image Registration

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 2 December 2020Accepted: 02 August 2021Published online: 18 October 2021Keywordsmultiscale, diffeomorphism, multigrid, image registration, algorithmAMS Subject Headings65M12, 68U10, 94A08, 65K10Publication DataISSN (print): 1540-3459ISSN (online): 1540-3467Publisher: Society for Industrial and Applied MathematicsCODEN: mmsubt
Communication-Efficient Online Federated Learning Framework for Nonlinear Regression

Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced a framework for online FL (Online-Fed) wherein clients perform model learning on streaming data and communicate the model to the server; however, they do not address the associated communication overhead. As a solution, this paper presents a partial-sharing-based online federated learning framework (PSO-Fed) that enables clients to update their local models using continuous streaming data and share only portions of those updated models with the server. During a global iteration of PSO-Fed, non-participant clients have the privilege to update their local models with new data. Here, we consider a global task of kernel regression, where clients use a random Fourier features-based kernel LMS on their data for local learning. We examine the mean convergence of the PSO-Fed for kernel regression. Experimental results show that PSO-Fed can achieve competitive performance with a significantly lower communication overhead than Online-Fed.
On games and simulators as a platform for development of artificial intelligence for command and control

Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces. These characteristics have attracted the artificial intelligence (AI) community by supporting development of algorithms with complex benchmarks and the capability to rapidly iterate over new ideas. The success of AI algorithms in real-time strategy games such as StarCraft II has also attracted the attention of the military research community aiming to explore similar techniques in military counterpart scenarios. Aiming to bridge the connection between games and military applications, this work discusses past and current efforts on how games and simulators, together with the AI algorithms, have been adapted to simulate certain aspects of military missions and how they might impact the future battlefield. This paper also investigates how advances in virtual reality and visual augmentation systems open new possibilities in human interfaces with gaming platforms and their military parallels.
Unsupervised Vehicle Search in the Wild

In urban surveillance systems, finding a specific vehicle in video frames efficiently and accurately has always been an essential part of traffic supervision and criminal investigation. Existing studies focus on vehicle re-identification (re-ID), but vehicle search is still underexploited. These methods depend on the locations of many vehicles (bounding boxes) that are not available in most real-world applications. Therefore, the unsupervised joint study of vehicle location and identification for the observed scene is a pressing need. Inspired by person search, we conduct a study on the vehicle search while considering four main discrepancies among them, summarized as: 1) It is challenging to select the candidate regions for the observed vehicle due to the perspective differences (front or side); 2) The sides of the same type of vehicles are almost the same, resulting in smaller inter-class; 3) Lacking satisfied dataset for vehicle search to meet the practical scenarios; 4) Supervised search publishing methods rely on datasets with expensive annotations. To address these issues, we have established a new vehicle search dataset. We design an unsupervised framework on this benchmark dataset to generate pseudo labels for further training existing vehicle re-ID or person search models. Experimental results reveal that these methods turn less effective on vehicle search tasks. Therefore, the vehicle search task needs to be further developed, and this dataset can advance the research of vehicle search. Https://github.com/zsl1997/VSW.
Captioning Changes in Bi-Temporal Remote Sensing Images

Motivated by the good performance recorded for image captioning (IC) techniques in different remote sensing (RS) applications, we propose in this paper a change detection (CD) system based on IC. It aims to the creation of a user-friendly solution that describes, via human-like sentences, the changes detected comparing bi-temporal images acquired over the same geographical area. The model we propose, is based on a convolutional neural network (CNN), and a multimodal recurrent neural network (RNN). Our experiments have been performed combining a set of aerial images with semantic information that we generated to describe the changes observed for different types of objects. This work yielded to encouraging results, evaluated using the BLEU metric.
DreamLoc: A Deep Relevance Matching-Based Framework for bug Localization

To improve the software debugging efficiency, bug localization techniques have been developed to automatically locate buggy files based on bug reports. Traditional information retrieval-based bug localization cannot deal with the lexical mismatch, thus its performance is limited. In recent years, some deep learning models have been proposed to learn the semantics of bug reports and source files to bridge the lexical gap. However, their accuracy is still limited as building accurate semantic representations of bug reports and source files is very challenging. Recently, relevance matching was proposed to identify whether a document is relevant to a given query by considering both local matching and global matching. In this work, we propose a novel framework DreamLoc, which utilizes a relevance matching model to locate buggy files. Specifically, DreamLoc conducts the local matching by employing an attention-based mechanism to calculate the matching scores between bug report terms and code snippets. It also conducts the global matching by employing a gating mechanism to aggregate results of local matching and obtain the final matching score between a bug report and a source file. Since the local matching considers the relevance between each word and the global matching differentiates the importance of words, DreamLoc can effectively model the characteristics of bug reports and source files. Experimental results on five benchmark datasets show that DreamLoc outperforms five state-of-the-art models. For example, compared with DeepLoc, a recently proposed approach, the evaluation measures Accuracy@10, MAP, and MRR are improved by 6.4%, 7.4%, and 7.2%, respectively.
Streaming Transformer Transducer based Speech Recognition Using Non-Causal Convolution

This paper improves the streaming transformer transducer for speech recognition using non-causal convolution. Many works apply the causal convolution to improve streaming transformer ignoring the lookahead context. We propose to use non-causal convolution to process the center block and lookahead context separately. This method leverages the lookahead context in convolution and maintains similar training and decoding efficiency. Given the similar latency, using the non-causal convolution with lookahead context gives better accuracy than causal convolution, especially for open-domain dictation. Besides, this paper applies talking-head attention and a novel history context compression scheme to further improve the performance. The talking-head attention improves the multi-head self-attention by transferring information among different heads. The history context compression method introduces more extended history context compactly. On our in-house data, the proposed methods improve a small Emformer baseline with lookahead context by relative WERR 5.1%, 14.5%, 8.4% on open-domain dictation, assistant general scenarios, and assistant calling scenarios respectively.
Clinical Trial Information Extraction with BERT

Natural language processing (NLP) of clinical trial documents can be useful in new trial design. Here we identify entity types relevant to clinical trial design and propose a framework called CT-BERT for information extraction from clinical trial text. We trained named entity recognition (NER) models to extract eligibility criteria entities by fine-tuning a set of pre-trained BERT models. We then compared the performance of CT-BERT with recent baseline methods including attention-based BiLSTM and Criteria2Query. The results demonstrate the superiority of CT-BERT in clinical trial NLP.
Fingerprint-Based Data Deduplication Using a Mathematical Bounded Linear Hash Function

Due to the quick increase in digital data, especially in mobile usage and social media, data deduplication has become a vital and cost-effective approach for removing redundant data segments, reducing the pressure imposed by enormous volumes of data that must be kept. As part of the data deduplication process, fingerprints are employed to represent and identify identical data blocks. However, when the amount of data increases, the number of fingerprints grows as well, and due to the restricted memory size, the speed of data deduplication suffers dramatically. Various deduplication solutions show a bottleneck in the form of matching lookups and chunk fingerprint calculations, for which we pay in the form of storage and processors needed for storing hashes. Utilizing a fast hash algorithm to improve the fingerprint lookup performance is an appealing challenge. Thus, this study is focused on enhancing the deduplication system by suggesting a novel and effective mathematical bounded linear hashing algorithm that decreases the hashing time by more than two times compared to MD5 and SHA-1 and reduces the size of the hash index table by 50%. Due to the enormous number of chunk hash values, looking up and comparing hash values takes longer for large datasets; this work offers a hierarchal fingerprint lookup strategy to minimize the hash judgement comparison time by up to 78%. Our suggested system reduces the high latency imposed by deduplication procedures, primarily the hashing and matching phases. The symmetry of our work is based on the balance between the proposed hashing algorithm performance and its reflection on the system efficiency, as well as evaluating the approximate symmetries of the hashing and lookup phases compared to the other deduplication systems.
Keyword Search Using Attention-Based End-to-End ASR and Frame-Synchronous Phoneme Alignments

Attention-based end-to-end (E2E) automatic speech recognition (ASR) architectures are now the state-of-the-art in terms of recognition performance. However, despite their effectiveness, they have not been widely applied in keyword search (KWS) tasks yet. In this paper, we propose the Att-E2E-KWS architecture, an attention-based E2E ASR framework for KWS that can afford accurate and reliable keyword retrieval results. First, we design a basic framework to carry out KWS based on attention-based E2E ASR. We adopt the connectionist temporal classification and attention (CTC/Att) joint E2E ASR architecture and exploit the spike posterior property of CTC to provide the keywords time stamps. Second, we introduce the frame-synchronous phonemes modeling and use the dynamic programming (DP) algorithm to provide alignments between E2E grapheme outputs and phoneme outputs. We call this alignment procedure dynamic time alignment (DTA), which can provide the proposed Att-E2E-KWS system with more accurate time stamps and reliable confidence scores. Third, we use the Transformer, a self-attention-based encoder-decoder neural network, in place of conventional recurrent neural networks in order to yield more parallelizable models and increased training speed. We conduct comprehensive experiments on English and Mandarin Chinese. To the best of our knowledge, this is the first practical Att-E2E-KWS framework, and experimental results on Switchboard and HKUST corpora show that our proposed Att-E2E-KWS systems significantly outperform the CTC E2E ASR based KWS baselines.
RTSNet: Deep Learning Aided Kalman Smoothing

The smoothing task is the core of many signal processing applications. It deals with the recovery of a sequence of hidden state variables from a sequence of noisy observations in a one-shot manner. In this work we propose RTSNet, a highly efficient model-based and data-driven smoothing algorithm. RTSNet integrates dedicated trainable models into the flow of the classical Rauch-Tung-Striebel (RTS) smoother, and is able to outperform it when operating under model mismatch and non-linearities while retaining its efficiency and interpretability. Our numerical study demonstrates that although RTSNet is based on more compact neural networks, which leads to faster training and inference times, it outperforms the state-of-the-art, data-driven smoother in a non-linear use case.
ION

Visual object navigation is a fundamental task in Embodied AI. Previous works focus on the category-wise navigation, in which navigating to any possible instance of target object category is considered a success. Those methods may be effective to find the general objects. However, it may be more practical to navigate to the specific instance in our real life, since our particular requirements are usually satisfied with specific instances rather than all instances of one category. How to navigate to the specific instance has been rarely researched before and is typically challenging to current works. In this paper, we introduce a new task of Instance Object Navigation (ION), where instance-level descriptions of targets are provided and instance-level navigation is required. In particular, multiple types of attributes such as colors, materials and object references are involved in the instance-level descriptions of the targets. In order to allow the agent to maintain the ability of instance navigation, we propose a cascade framework with Instance-Relation Graph (IRG) based navigator and instance grounding module. To specify the different instances of the same object categories, we construct instance-level graph instead of category-level one, where instances are regarded as nodes, encoded with the representation of colors, materials and locations (bounding boxes). During navigation, the detected instances can activate corresponding nodes in IRG, which are updated with graph convolutional neural network (GCNN). The final instance prediction is obtained with the grounding module by selecting the candidates (instances) with maximum probability (a joint probability of category, color and material, obtained by corresponding regressors with softmax). For the task evaluation, we build a benchmark for instance-level object navigation on AI2-Thor simulator, where over 27,735 object instance descriptions and navigation groundtruth are automatically obtained through the interaction with the simulator. The proposed model outperforms the baseline in instance-level metrics, showing that our proposed graph model can guide instance object navigation, as well as leaving promising room for further improvement. The project is available at https://github.com/LWJ312/ION.
On Secure E-Voting over Blockchain

This article discusses secure methods to conduct e-voting over a blockchain in three different settings: decentralized voting, centralized remote voting, and centralized polling station voting. These settings cover almost all voting scenarios that occur in practice. A proof-of-concept implementation for decentralized voting over Ethereum’s blockchain is presented. This work demonstrates the suitable use of a blockchain not just as a public bulletin board but, more importantly, as a trustworthy computing platform that enforces the correct execution of the voting protocol in a publicly verifiable manner. We also discuss scaling up a blockchain-based voting application for national elections. We show that for national-scale elections the major verifiability problems can be addressed without having to depend on any blockchain. However, a blockchain remains a viable option to realize a public bulletin board, which has the advantage of being a “preventive” measure to stop retrospective changes on previously published records as opposed to a “detective” measure like the use of mirror websites. CCS Concepts: • Security and privacy ;
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty

This paper studies the problem of autonomous exploration under localization uncertainty for a mobile robot with 3D range sensing. We present a framework for self-learning a high-performance exploration policy in a single simulation environment, and transferring it to other environments, which may be physical or virtual. Recent work in transfer learning achieves encouraging performance by domain adaptation and domain randomization to expose an agent to scenarios that fill the inherent gaps in sim2sim and sim2real approaches. However, it is inefficient to train an agent in environments with randomized conditions to learn the important features of its current state. An agent can use domain knowledge provided by human experts to learn efficiently. We propose a novel approach that uses graph neural networks in conjunction with deep reinforcement learning, enabling decision-making over graphs containing relevant exploration information provided by human experts to predict a robot's optimal sensing action in belief space. The policy, which is trained only in a single simulation environment, offers a real-time, scalable, and transferable decision-making strategy, resulting in zero-shot transfer to other simulation environments and even real-world environments.
Efficient and Secure Digital Signature Scheme for Post Quantum Epoch

It is expected the massive release of quantum computers in the near future. Quantum computers can easily break the crypto schemes, which are used in practice. Therefore, classical encryption systems have become vulnerable to quantum computer-based attacks. This involves the research efforts that look for encryption schemes that are immune to quantum computer-based attacks. This paper describes the digital signature schemes, which are safe against quantum computer attacks, but these schemes have different efficiency problems. The signature size of the scheme is very large and one-way function are used many time during the signature process. The paper offers the ways of reducing the signature size and acceleration the process of using one-way functions. It is offered to integrate the quantum key distribution algorithms into the scheme. It is also offered to use Blake family hash function as the one-way function.
Move your virtual body: differences and similarities in brain activation patterns during hand movements in real world and virtual reality

Abstract Virtual reality (VR) is a promising tool for neurological rehabilitation, especially for motor rehabilitation. In the present study, we investigate whether brain activation patterns that are evoked by active movements are comparable when these movements are carried out in reality and in VR. Therefore, 40 healthy adults (20 men, mean age 25.31 years) performed hand movements and viewed these movements in a first-person view in reality, a VR scene showing realistic virtual hands, and a VR scene showing abstract virtual hands, in a randomized order. The VR conditions were presented via an immersive 3D head-mounted display system. EEG activity was assessed over the hand motor areas during and after movement execution. All three conditions led to typical EEG activation patterns over the motor cortex. Hence, brain activation patterns were largely comparable between conditions. However, the VR conditions, especially the abstract VR condition, led to a weaker hemispheric lateralization effect compared to the real-world condition. This indicates that hand models in VR should be realistic to be able to evoke activation patterns in the motor cortex comparable to real-world scenarios.
An overview of skew distributions in model-based clustering

The literature on non-normal model-based clustering has continued to grow in recent years. The non-normal models often take the form of a mixture of component densities that offer a high degree of flexibility in distributional shapes. They handle skewness in different ways, most typically by introducing latent 'skewing' variable(s), while some other consider marginal transformations of the original variable(s). We provide a selective overview of the main types of skew distributions used in the area, based on their characterization of skewness, and discuss different skew shapes they can produce. For brevity, we focus on the more commonly-used families of distributions.