Reviewerly/exHarmony huggingface dataset:
Progressive Parsing and Commonality Distillation for Few-Shot Remote Sensing Segmentation

In recent years, few-shot segmentation (FSS) has received widespread attention from scholars by virtue of its superiority in low-data regimes. Most existing research focuses on natural image processing, and very few studies are dedicated to the practical but challenging topic of remote sensing image understanding. Related experimental results show that directly transferring the previously proposed framework to the current domain is prone to produce unsatisfactory results with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incomplete objects</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irrelevant distractors</i> . Such phenomena can be attributed to the lack of modules specifically designed for the complex characteristics of remote sensing images, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., great intra-class diversity and low target-background contrast. In this paper, we propose a conceptually simple and easy-to-implement framework to tackle the aforementioned problems. Specifically, our innovative design embodies two main aspects: i) the support mask is progressively parsed into multiple valuable sub-regions that can be further exploited to compute local descriptors with segmentation cues about intractable parts; ii) the base-class memories stored in the meta-training phase are replayed and leveraged for the distillation of novel-class prototypes, where the commonalities between classes are adequately explored, more in line with the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning to learn</i> . These two components, i.e., the progressive parsing module and commonality distillation module, contribute to each other and together constitute the proposed PCNet. We conduct extensive experiments on the standard benchmark to evaluate segmentation performance in few-shot settings. Quantitative and qualitative results illustrate that our PCNet distinctly outperforms previous FSS approaches and sets a new state-of-the-art.
Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR

Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a path planning and tracking control framework for autonomous vehicles based on an upgraded RRT* and Particle Swarm Optimization Linear Quadratic Regulator (PSO-LQR) to address the abovementioned issues. Firstly, according to the driving characteristics of autonomous vehicles, a variable sampling area is used to limit the generation of random sampling points, significantly reducing the number of iterations. At the same time, an improved Artificial Potential Field (APF) method was introduced into the RRT* algorithm, which improved the convergence speed of the algorithm. Utilizing path pruning based on the maximum steering angle constraint of the vehicle and the cubic B-spline algorithm to achieve path optimization, a continuous curvature path that conforms to the precise tracking of the vehicle was obtained. In addition, optimizing the weight matrix of LQR using POS improved path-tracking accuracy. Finally, this article’s improved RRT* algorithm was simulated and compared with the RRT*, target bias RRT*, and P-RRT*. At the same time, on the Simulink–Carsim joint simulation platform, the PSO-LQR is used to track the planned path at different vehicle speeds. The results show that the improved RRT* algorithm optimizes the path search speed by 34.40% and the iteration number by 33.97%, respectively, and the generated paths are curvature continuous. The tracking accuracy of the PSO-LQR was improved by about 59% compared to LQR, and its stability was higher. The position error and heading error were controlled within 0.06 m and 0.05 rad, respectively, verifying the effectiveness and feasibility of the proposed path planning and tracking control framework.
Chip-scale simulations in a quantum-correlated synthetic space

An efficient simulator for quantum systems is one of the original goals for the efforts to develop a quantum computer. In recent years, synthetic dimensions in photonics have emerged as a potentially powerful approach for simulation that is free from the constraint of geometric dimensionality. Here we demonstrate a quantum-correlated synthetic crystal that is based on a coherently controlled broadband quantum frequency comb produced in a chip-scale, dynamically modulated lithium niobate microresonator. The time–frequency entanglement inherent with the comb modes greatly extends the dimensionality of the synthetic space, creating a massive, nearly 400 × 400 synthetic lattice with electrically controlled tunability. With such a system, we are able to utilize the evolution of quantum correlations between entangled photons to perform a series of simulations, demonstrating quantum random walks, Bloch oscillations and multilevel Rabi oscillations in the time and frequency correlation space (demonstrated in a 5 × 5 mode subspace). The device combines the simplicity of monolithic nanophotonic architecture, high dimensionality of a quantum-correlated synthetic space and on-chip coherent control, which opens up an avenue towards chip-scale implementation of large-scale analogue quantum simulation and computation in the time–frequency domain. A special-purpose quantum simulator, based on a coherently controlled broadband quantum frequency comb produced in a chip-scale dynamically modulated monolithic lithium niobate microresonator, is demonstrated, opening paths for chip-scale implementation of large-scale analogue quantum simulation and computation in the time–frequency domain.
Demonstration of quantum-digital payments

Abstract Digital payments have replaced physical banknotes in many aspects of our daily lives. Similarly to banknotes, they should be easy to use, unique, tamper-resistant and untraceable, but additionally withstand digital attackers and data breaches. Current technology substitutes customers’ sensitive data by randomized tokens, and secures the payment’s uniqueness with a cryptographic function, called a cryptogram. However, computationally powerful attacks violate the security of these functions. Quantum technology comes with the potential to protect even against infinite computational power. Here, we show how quantum light can secure daily digital payments by generating inherently unforgeable quantum cryptograms. We implement the scheme over an urban optical fiber link, and show its robustness to noise and loss-dependent attacks. Unlike previously proposed protocols, our solution does not depend on long-term quantum storage or trusted agents and authenticated channels. It is practical with near-term technology and may herald an era of quantum-enabled security.
ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI.
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding

Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding.However, most existing methods lack the systematic mining and utilization of layout-centered knowledge, leading to sub-optimal performances.In this paper, we propose ERNIE-Layout, a novel document pre-training solution with layout knowledge enhancement in the whole workflow, to learn better representations that combine the features from text, layout, and image.Specifically, we first rearrange input sequences in the serialization stage, and then present a correlative pre-training task, reading order prediction, to learn the proper reading order of documents.To improve the layout awareness of the model, we integrate a spatial-aware disentangled attention into the multi-modal transformer and a replaced regions prediction task into the pretraining phase.Experimental results show that ERNIE-Layout achieves superior performance on various downstream tasks, setting new stateof-the-art on key information extraction, document image classification, and document question answering datasets.The code and models are publicly available at PaddleNLP 1 .
Anaconda: a non-adaptive conditional sampling algorithm for distribution testing

We investigate distribution testing with access to non-adaptive conditional samples. In the conditional sampling model, the algorithm is given the following access to a distribution: it submits a query set S to an oracle, which returns a sample from the distribution conditioned on being from S. In the non-adaptive setting, all query sets must be specified in advance of viewing the outcomes.Our main result is the first polylogarithmic-query algorithm for equivalence testing, deciding whether two unknown distributions are equal to or far from each other. This is an exponential improvement over the previous best upper bound, and demonstrates that the complexity of the problem in this model is intermediate to the the complexity of the problem in the standard sampling model and the adaptive conditional sampling model. We also significantly improve the sample complexity for the easier problems of uniformity and identity testing. For the former, our algorithm requires only O(log n) queries, matching the information-theoretic lower bound up to a O(log log n)-factor.Our algorithm works by reducing the problem from e1-testing to e∞-testing, which enjoys a much cheaper sample complexity. Necessitated by the limited power of the non-adaptive model, our algorithm is very simple to state. However, there are significant challenges in the analysis, due to the complex structure of how two arbitrary distributions may differ.
Building a bibliographic hierarchy for manga through the aggregation of institutional and hobbyist descriptions

Purpose Multiple studies have illustrated that the needs of various users seeking descriptive bibliographic data for pop culture resources (e.g. manga, anime, video games) have not been properly met by cultural heritage institutions and traditional models. With a focus on manga as the central resource, the purpose of this paper is to address these issues to better meet user needs. Design/methodology/approach Based on an analysis of existing bibliographic metadata, this paper proposes a unique bibliographic hierarchy for manga that is also extendable to other pop culture sources. To better meet user requirements of descriptive data, an aggregation-based approach relying on the Object Reuse and Exchange-Open Archives Initiative (OAI-ORE) model utilized existing, fan-created data on the web. Findings The proposed hierarchy is better able to portray multiple entities of manga as they exist across data providers compared to existing models, while the utilization of OAI-ORE-based aggregation to build and provide bibliographic metadata for said hierarchy resulted in levels of description that more adequately meet user demands. Originality/value Though studies have proposed alternative models for resources like games or comics, manga has remained unexamined. As manga is a major component of many popular multimedia franchises, a focus here with the intention while building the model to support other resource types provides a foundation for future work seeking to incorporate these resources.
A construction of V-sequences and its application to binary asymmetric error-correcting codes

Binary asymmetric error-correcting codes (AECC) play an important role in communication systems modeled by the binary symmetric channel (Z-channel). Varshamov proposed a general construction of binary AECC via V h -sequences and obtained many good binary AECC. In this paper, we give a general construction of V h -sequences in ray class groups of algebraic function fields. Our construction induces new lower bounds for binary AECC which improves the results in [18] .
Neural CRF Transducers for Sequence Labeling

Conditional random fields (CRFs) have been shown to be one of the most successful approaches to sequence labeling. Various linear-chain neural CRFs (NCRFs) are developed to implement the non-linear node potentials in CRFs, but still keeping the linear-chain hidden structure. In this paper, we propose NCRF transducers, which consists of two RNNs, one extracting features from observations and the other capturing (theoretically infinite) long-range dependencies between labels. Different sequence labeling methods are evaluated over POS tagging, chunking and NER (English, Dutch). Experiment results show that NCRF transducers achieve consistent improvements over linear-chain NCRFs and RNN transducers across all the four tasks, and can improve state-of-the-art results.
Bounds on entanglement catalysts

Given a finite-dimensional pure state transformation restricted by entanglement-assisted local operations and classical communication (ELOCC), we derive minimum and maximum bounds on the entanglement of an ancillary catalyst that allows that transformation. These bounds are nontrivial even when the Schmidt number of both the original and ancillary states becomes large. We identify a lower bound for the dimension of a catalyst allowing a particular ELOCC transformation. Along with these bounds, we present further constraints on ELOCC transformations by identifying restrictions on the Schmidt coefficients of the target state. In addition, an example showing the existence of qubit ELOCC transformations with multiple ranges of potential ancillary states is provided. This example reveals some additional difficulty in finding strict bounds on ELOCC transformations, even in the qubit case. Finally, a comparison of the bounds in this paper with previously discovered bounds is presented.
Token Model and Interpretation Function for Blockchain-Based FinTech Applications

Financial Technology (FinTech) is considered a taxonomy that describes a wide range of ICT (information and communications technology) associated with financial transactions and related operations. Improvement of service quality is the main issue addressed in this taxonomy, and there are a large number of emerging technologies including blockchain-based cryptocurrencies and smart contracts. Due to its innovative nature in accounting, blockchain can also be used in lots of other FinTech contexts where token models play an important role for financial engineering. This paper revisits some of the key concepts accumulated behind this trend, and shows a generalized understanding of the technology using an adapted stochastic process. With a focus on financial instruments using blockchain, research directions toward stable applications are identified with the help of a newly proposed stabilizer: interpretation function of token valuation. The idea of adapted stochastic process is essential for the stabilizer, too.
Call-by-push-value in Coq: operational, equational, and denotational theory

Call-by-push-value (CBPV) is an idealised calculus for functional and imperative programming, introduced as a subsuming paradigm for both call-by-value (CBV) and call-by-name (CBN). We formalise weak and strong operational semantics for (effect-free) CBPV, define its equational theory, and verify adequacy for the standard set/algebra denotational semantics. Furthermore, we prove normalisation of the standard reduction, confluence of strong reduction, strong normalisation using Kripke logical relations, and soundness of the equational theory using logical equivalence. We adapt and verify the known translations from CBV and CBN into CBPV for strong reduction. This yields, for instance, proofs of strong normalisation and confluence for the full λ-calculus with sums and products. Thanks to the automation provided by Coq and the Autosubst 2 framework, there is little formalisation overhead compared to detailed paper proofs.
Requirements Engineering (RE) for Social Good: RE Cares [Requirements]

As researchers and teachers and practitioners, we “software types” excel at multitasking. This, in part, led us to ask the question: Can one attend a software engineering conference and do something good for society? We found the answer to be a resounding yes. In this article, we present our first experience of running RE Cares, a conference collocated event. This event included a workshop, conference sessions, and a hackathon for developing an application to support emergency field activity for Mutual Aid Alberta, a nonprofit organization coordinating natural disaster responses in the Canadian province.
A framework to define the relationship between cyber security and cloud performance

Cloud computing has become one of the most critical components of businesses and corporate organisations worldwide. It is a technology that has applications and services running on distributed networks and pooled virtualised resources that small, medium and large organisations can utilise to host their applications, processes and portals online via datacentres to provide access over the Internet to end users, customers and employees. In today's dynamic business world, traditional IT systems cannot keep up with ever-changing market demands and complex environments. Cloud computing has become one of the most critical components of enterprises worldwide. In today's dynamic business world, traditional IT systems cannot keep up with ever-changing market demands and complex environments. Many organisations are seeking an all-purpose cloud deployment to achieve increased service quality, which translates into better security and higher performance. In such cases, it becomes critical to concentrate on metrics for security and performance. Akashdeep Bhardwaj and Sam Goundar propose a framework for examining those two crucial elements.
Cost-aware scheduling for ensuring software performance and reliability under heterogeneous workloads of hybrid cloud

Cloud computing is a rapidly growing paradigm in software engineering that offers different services. The hybrid cloud is the best choice for the enterprise to benefit by taking resources on lease from the public cloud only if private cloud resources are not sufficient. However, the key is how to provide better cloud services and improve software performance in the hybrid cloud for software engineers. In this paper, the efficient job scheduling method in the private cloud is proposed by considering the heterogeneity of hybrid cloud resources to guarantee the software performance and reliability. The experimental results show that the efficient job scheduling method can effectively reduce the average job response time and improve the system throughput. Moreover, the task scheduling method based on BP neural network in the hybrid cloud is proposed by considering both the cost and deadline constraints to ensure the quality of service (QoS) for software. The experimental results show that the task scheduling method can improve the QoS, maximize the resources utilization of private cloud and minimize the cost of hybrid cloud resources.
Security analysis of Game Changer Password System

In the paper we present the results of security analysis of the Game Changer Password System, proposed by McLennan et al.—a mnemonic variant of password security that uses game positions as passwords. The idea is that several different games are graphically presented on the screen, allowing users to select the game first and then to enter his or her password in the game selected in the form of putting pieces on a board. For example, a user first selects chess, then puts four chess figures on the chessboard. A password is represented by the fact that chess was used and figures’ locations. The first issue with the proposed system is a small search space—the number of possible combinations of passwords, enabling relatively simple and quite inexpensive brute force attacks. The second issue is that users prefer specific locations and figures over others, further reducing the search space and thus enabling attackers to speed up the attacks with high probabilities of success. Based on the issue of non-uniformity of locations and figures attackers can build special dictionaries to launch dictionary-based attacks. We elaborate on the weaknesses and propose a solution that produces stronger passwords. However, the tradeoff between memorability and attack resilience must be taken into the account.
Errata to “RingText: Dwell-Free and Hands-Free Text Entry for Mobile Head-Mounted Displays Using Head Motions” [May 19 1991-2001]

Presents corrections to author information for the paper, “RingText: Dwell-Free and Hands-Free Text Entry for Mobile Head-Mounted Displays Using Head Motions,” (Xu, W., et al), IEEE Trans. Vis. Comput. Graph., vol. 25, no. 5, pp. 1991–2001, May 2019.